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New approaches to the optimized maintenance of embryonic stem cells in culture Glover, Clive Hamilton 2007

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NEW APPROACHES TO THE OPTIMIZED MAINTENANCE OF EMBRYONIC STEM CELLS IN CULTURE by  Clive Hamilton Glover  B . S c , M c G i l l University 1999 M . S c . , University o f British Columbia 2001  A THESIS S U B M I T T E D IN P A R T I A L F U L F I L L M E N T OF T H E R E Q U I R E M E N T S FOR T H E D E G R E E OF  D O C T O R OF PHILOSOPHY  in  The Faculty O f Graduate Studies  (Genetics)  T H E UNIVERSITY OF BRITISH C O L U M B I A  March 2007  © Clive Hamilton Glover, 2007  Abstract The realization o f many stem cell-based therapies w i l l rely on the development o f methods for the expansion and controlled differentiation o f stem cells and the development o f assays to rapidly detect the results o f culture manipulation. Mouse embryonic stem cells ( E S C ) provide a relatively abundant and high purity model system to investigate environmental cues that influence stem cell fate.  The dynamics o f loss o f pluripotency following the removal o f  leukemia inhibitory factor (LIF) were investigated using three functional assays (chimeric mouse formation, embryoid body generation and colony forming ability).  A rapid loss (>70%) o f  pluripotent cells was detected within 24 hours, with very l o w residual activity by all criteria within 72 hours. Surprisingly, functional endpoints o f pluripotency correlated poorly with two commonly used markers, expression o f Oct4 and S S E A - 1 . The embryoid body assay was then used in factorial and central composite design experiments to define optimized levels o f ascorbic acid ( A A ) , chondroitin sulphate and PD98059 (PD), three factors that enhance undifferentiated E S C maintenance. These experiments identified an unexpected negative interaction between A A and P D and allowed the development o f a cocktail o f these three factors that increased E S C yield 3-fold over untreated cultures.  To identify more rapid endpoints for the detection o f  undifferentiated E S C , global gene expression profiling was performed on E S C induced to differentiate by removal o f L I F , addition o f retinoic acid and addition o f D M S O . Gene expression profiles were determined using the Affymetrix platform and a new meta-analysis methodology was developed and applied to the resulting gene expression data sets. This analysis revealed that the expression o f the stem cell factor receptor, c-kit, expressed on the cell surface,  ii  correlated with the frequency o f undifferentiated E S C making it an ideal marker for further studies. Furthermore, the expression changes o f seven genes - 103728_at, 8430410A17Rik, Mf2, nrObl, sox2, tell and zfp42 - was able to predict undifferentiated E S C frequencies i n both differentiation and maintenance cultures. These experiments should provide a useful framework for making further improvements in the culture o f undifferentiated E S C .  in  Table of Contents Abstract  ii  Table o f Contents  iv  List o f Tables  ix  List o f Figures  xi  List o f Abbreviations  xiii  Acknowledgements  xiv  Co-Authorship Statement  1.  xv  Introduction 1.1.  1  Embryonic Stem Cells  2  1.1.1.  Differences Between Mouse and Human Embryonic Stem Cells  1.1.2.  Advantages o f Mouse Embryonic Stem Cells  1.2.  5 ;  Culture and Characterization o f Embryonic Stem Cells  1.2.1.  6 7  Culture Conditions for the Propagation o f Undifferentiated Embryonic  Stem Cells  7  1.2.2.  Molecular Control o f Self-Renewal  9  1.2.3.  Differentiation o f Mouse Embryonic Stem Cells  11  1.2.4.  Functional Assays to Detect Undifferentiated Embryonic Stem Cells  12  1.2.5.  Phenotypic Markers o f Undifferentiated Embryonic Stem Cells  14  1.2.6.  Characterization o f Mouse Embryonic Stem C e l l Responses  15  iv  1.3.  Challenges in Embryonic Stem Cell Culture  1.3.1.  '.  H i g h Throughput Screening to Identify Factors Supporting the Maintenance o f  Embryonic Stem Cells 1.3.2.  2.  16  16  Statistical Design o f Experiments to Understand Factor Interactions and  Determine Non-Linear Responses  19  1.3.3.  20  Culture Systems Suitable for H i g h Throughput Screening  1.4.  Thesis Objectives  21  1.5.  References  25  Correlation o f Mouse Embryonic Stem Cell Gene Expression Profiles with Functional  Measures o f Pluripotency  36  2.1.  Introduction  36  2.2.  Materials and Methods  39  2.2.1.  Culture o f Mouse Embryonic Stem Cells and Embryonic Fibroblasts  39  2.2.2.  Preparation o f Embryonic Stem Cells for Differentiation  40  2.2.3.  Blastocyst Injection  41  2.2.4.  Embryoid Body Formation Assays  42  2.2.5.  Colony Forming Cell Assay  42  2.2.6.  R N A Extraction and Array Hybridization  43  2.2.7.  Data Analysis  43  2.2.8.  Quantitative R T - P C R  44  2.2.9.  F l o w Cytometry  45  2.2.10.  Comparison with Previously Published Gene Expression Array Data  46  2.3.  3.  Results  47  2.3.1.  Pluripotency During Early Differentiation  47  2.3.2.  Array Analysis  50  2.3.3.  Gene Expression Validation  52  2.3.4.  Quantitative R T - P C R validation  55  2.3.5.  Functional Classification o f Differentially Expressed Genes  56  2.3.6.  Molecular Markers of Pluripotency  59  2.3.7.  Comparison with Previously Published Gene Expression Array Data  60  2.4.  Discussion  64  2.5.  References  83  Increased Capacity of Mouse Embryonic Stem Cells to Form Embryoid Bodies F o l l o w i n g  Optimization o f Ascorbic A c i d , PD98059 A n d Chondroitin Sulphate Levels  91  3.1.  Introduction  91  3.2.  Materials and Methods  94  3.2.1.  Embryonic Stem Cell Maintenance Cultures  3.2.2.  Embryonic Stem C e l l Experimental Cultures  94  3.2.3.  Design o f Experiments  95  3.2.4.  F l o w Cytometry  96  3.2.5.  R N A extraction and Quantitative R T - P C R  97  3.3.  ;  Results  94  97  3.3.1.  Dose Response o f Ascorbic A c i d , Chondroitin Sulphate or PD98059  97  3.3.2.  Interactions between Ascorbic A c i d , Chondroitin Sulphate and PD98059  98  vi  3.3.3.  Central Composite Design Experiments to Determine Non-Linear Responses....99  3.3.4.  Verification o f Cocktail to Maximize Expansion o f Embryoid  Body-Forming Cells 3.3.5.  4.  100  Influence o f Ascorbic A c i d , Chondroitin Sulphate and PD98059 on Self-Renewal  and Apoptosis  101  3.3.6.  103  Differentiation Ability o f Treated Cells  3.4.  Discussion  104  3.5.  References  ...119  Meta-Analysis o f Differentiating Mouse Embryonic Stem Cell Gene Expression Kinetics  Reveals Early Change o f a Small Gene Set  121  4.1.  Introduction  121  4.2.  Materials and Methods  124  4.2.1.  Embryonic Stem Cell Maintenance Cultures  124  4.2.2.  Embryonic Stem Cell Experimental Cultures  124  4.2.3.  Embryoid Body and Colony Forming Cell assays  124  4.2.4.  R N A Extraction and Array Hybridization  124  4.2.5.  Gene Expression Data Sets  124  4.2.6.  Gene Expression Analysis  126  4.2.7.  Defining the Embryonic Stem Cell Signature Change in Terms o f Gene  Expression  127  4.2.8.  Confidence Values  129  4.2.9.  Comparison o f M O E 4 3 0 and M G _ U 7 4 v 2 Chips  130  vii  4.2.10.  Gene Ontology  130  4.2.11.  Quantitative R T - P C R  131  4.3.  5.  Results  131  4.3.1.  Functional Assay Analysis  131  4.3.2.  Gene Expression Analysis  132  4.3.3.  Identification o f a Robust Set o f Early Gene Expression Changes that Indicate  Decreased Frequency o f Undifferentiated Embryonic Stem Cells  133  4.3.4.  Confidence Values  134  4.3.5.  Meta-Analysis via Pareto Optimization  135  4.3.6.  Q - R T - P C R Verification o f Array Results  136  4.4.  Discussion  139  4.5.  References  157  Conclusions and Future Directions  163  5.1.  Conclusions  163  5.2.  Future Directions  167  5.3.  References  170  Appendix A  172  Appendix B  174  Appendix C  201  viii  List of Tables Table 1.1 Similarities and differences between human and mouse embryonic stem cells  23  Table 2.1 Expression change of genes previously reported as enriched in undifferentiated embryonic stem cells or to be markers of embryonic stem cell differentiation  76  Table 2.2 Comparison of relative expression levels obtained by quantitative R T - P C R in the R l , J1, and E F C embryonic stem cell lines during differentiation  77  Table 2.3 Genes decreased during embryonic stem cell differentiation and most strongly correlated with the loss of pluripotency  78  Table 2.4 Differentially expressed genes with plasma membrane localization  80  Table 2.5 Summary of published gene expression studies in mouse and human embryonic stem cells used for comparison  82  Table 3.1 Design matrix for a two-level, three-factor factorial design experiment  116  Table 3.2 Design matrix for a two-factor central composite design experiment  117  Table 3.3 Factor concentrations used in the factorial design and central composite design experiments  118  Table 4.1 Summary of all microarray experiments used in this study  150  Table 4.2 Thresholds used in the definition of the embryonic stem cell signature change  151  Table 4.3 Genes identified on the first five Pareto fronts  152  Table A 1 Primers used for quantitative R T - P C R throughout this study  172  Table B l Genes decreased between 0 and 18 hours after leukemia inhibitory factor removal  174  ix  Table B 2 Genes increased between 0 and 18 hours after leukemia inhibitory factor removal  176  Table B3 Genes decreased between 18 and 72 hours after leukemia inhibitory factor removal  177  Table B 4 Genes increased between 18 and 72 hours after leukemia inhibitory factor removal  181  Table B5 Genes decreased between 0 and 72 hours after leukemia inhibitory factor removal  183  Table B 6 Genes increased between 0 and 72 hours after leukemia inhibitory factor removal  188  Table B7 Comparison of changed genes to previously published data sets  192  Table C I Function of the 88 genes on the first five Pareto fronts  201  Table C 2 Comparison of genes on the first five Pareto Fronts with previously published data sets 202  x  List of Figures Figure 1.1 Graphical representation of factorial and central composite design experiments for 3 factors  24  Figure 2.1 Embryonic stem cell morphology during leukemia inhibitory factor removal  68  Figure 2.2 Assays of embryonic stem cell pluripotency following removal of leukemia inhibitory factor  69  Figure 2.3 Hierarchical clustering of array samples  70  Figure 2.4 Correlation between array and quantitative R T - P C R results for multiple genes in multiple cells lines  71  Figure 2.5 Annotation of differentially expressed genes  72  Figure 2.6 Flow cytometry profile to test for cell surface expression of c-kit following removal of leukemia inhibitory factor  74  Figure 2.7 Correlation between c-kit expression and embryoid body formation ability  75  Figure 3.1 Dose response of embryonic stem cells to ascorbic acid  109  Figure 3.2 Dose response of embryonic stem cells to PD98059  110  Figure 3.3 Parameter estimates derived from factorial design experiments  Ill  Figure 3.4 Parameter estimates describing yield of embryoid body-forming cells in two central composite design experiments  112  Figure 3.5 Verification of factor combination that maximizes embryoid body-forming cell output compared to other factor combinations and untreated cells  xi  113  Figure 3.6 Effect of ascorbic acid, chondroitin sulphate and PD98059 on self-renewal and apoptosis  114  Figure 3.7 Affect of differentiation by either leukemia inhibitory factor removal or retinoic acid addition on lineage marker genes of ascorbic acid, chondroitin sulphate and PD98059treated cells  115  Figure 4.1 The effect of leukemia inhibitory factor removal with or without addition of D M S O or retinoic acid on the maintenance and differentiation of embryonic stem cells  145  Figure 4.2 Transcriptome plots of estimated expression changes, based on fitting models to each data set  146  Figure 4.3 Comparison of differently measured changes in gene expression within and between two embryonic stem cell lines grown for 0, 2 4 , 7 2 and 96 hours in + L I F ± R A or - L I F ± DMSO  147  Figure 4.4 Quantitative R T - P C R profiles of transcript levels for seven genes that showed rapid decrease in embryonic stem cells subjected to several differentiating conditions  148  Figure 4.5 Comparison of biological and molecular changes in embryonic stem cells stimulated to differentiate by exposure to ascorbic acid  xii  149  List of Abbreviations AA AP CCD CFC CoVar, CS CV DMSO DMEM EB ESC FD FBS FSC GFP HSC HF ICM IMDM LIF MAS MIAME M-LR MTG PBS PBSFBS PD PE PEF PF PFA Q-RT-PCR Rl-LR RA SCF SSC T/E  ascorbic acid alkaline phosphatase central composite design colony forming cell coefficient o f variation chondroitin sulphate confidence value dimethylsulfoxide Dulbecco's Modified Eagles Medium embryoid body embryonic stem cell factorial design fetal bovine serum forward scatter green fluorescent protein hematopoietic stem cell Hanks Buffered Saline Solution + 2% F B S inner cell mass Iscove's Modified Dulbecco's Medium leukemia inhibitory factor M i c r o A r r a y Suite minimal information about a mieroarray experiment multiple cell line L I F removal monothioglycerol phosphate buffered saline phosphate buffered saline + 2% F B S PD98059 phycoerythrin primary embryonic fibroblast Pareto front Pareto front analysis quantitative real time P C R R l L I F removal retinoic acid stem cell factor side scatter Trypsin-EDTA  xiii  Acknowledgements I would like to acknowledge my supervisors Dr. James Piret and D r . Connie Eaves for their support and guidance through this thesis. Without their energy and enthusiasm this thesis would not have been completed.  '  Thanks are also due to the many collaborators that I have worked with during the course o f this thesis, in particular D r . Jenny Bryan and Dr. Cheryl Helgason. M y education is the better for having the opportunity to work with both o f them.  I would also like to thank Dr. Arshad  Chaudhry, Dr. Lars Palmqvist, Dr. M i n L u , L i e n Hsu, Rewa Grewal, Bolette Bossen, Dr. Keith Humphries, Michael M a r i n , Dr. Nicolas Caron, Dr. Fabio Rossi and D r Leah Keshet for advice and support at various stages during this thesis.  Finally Dave Thomson has spent many evenings and weekends as a "science widower." Words cannot express how thankful I am for all the love and support that y o u have given me and the patience that you have shown over the course of this work.  This work was funded by the Natural Sciences and Engineering Research Council, Canadian Institutes o f Health Research, M i c h a e l Smith Foundation for Health Research, Genome Canada, the Stem C e l l Network and the Mathematics o f Information Technology and Complex Systems Network. I personally have been supported by studentships from the Stem C e l l Network and the Canadian Institutes o f Health Research.  xiv  Co-Authorship Statement Chapter 2 was co-authored with Lars Palmqvist, Bolette Bossen, L i e n H s u , M i n L u , James Piret, Keith Humphries and Cheryl Helgason. I conceived, designed and performed the experiments leading to Figures 2.1, 2.2, 2.6 and 2.7, performed the analysis to generate Table 2.5 and assisted in analysis o f mieroarray data and preparation o f the manuscript.  Chapter 3 is co-authored with Connie Eaves and James Piret.  I conceived, designed and  performed all experiments and prepared the manuscript with assistance from both co-authors.  Chapter 4 is co-authored with Michael Marin, Connie Eaves, Cheryl Helgason, James Piret and Jenny Bryan.  I conceived, designed and performed all the experiments and developed and  implemented the analysis techniques in collaboration with M i c h a e l M a r i n and Jenny Bryan. I prepared the manuscript in collaboration with Jenny Bryan with input from the other authors.  xv  1. Introduction Stem cells are defined by their ability to differentiate into multiple, specialized cell types, the function o f w h i c h depends on the tissue o f origin o f the stem cell, and to self-renew this property. In vivo, stem cells are now recognized as being responsible for the generation of many tissues and organs during development and for their maintenance and repair into adulthood. To date, stem cells have been isolated from the early blastocyst (Evans and Kaufman 1981; Kunath et al. 2005; M a r t i n 1981; Tanaka et al. 1998; Thomson et al. 1998), several adult tissues including hematopoietic ( T i l l and M c C u l l o c h 1961), central nervous system (Reynolds and Weiss 1992), the epidermis (Lavker and Sun 2000), the retina (Tropepe et al. 2000) and the breast (Shackleton et al. 2006; Stingl et al. 2006); as w e l l as several cancers such as acute myeloid leukemia (Bonnet and D i c k 1997), breast cancer (Al-Hajj et al. 2003) and brain tumours (Singh et al. 2003).  Because o f their regenerative potential, normal stem cells have been  proposed as sources o f cells useful for the replacement of defective tissue which otherwise does not have a renewal capacity (Zandstra and Nagy 2001).  Application o f clinical-scale stem cell based therapies requires a thorough understanding o f the environmental clues that determine stem cell fate choices (i.e. proliferation or quiescence, selfrenewal or differentiation and survival or apoptosis). Understanding the ways in which the cell integrates the multiple, and possibly conflicting, external signals that they receive w i l l help determine protocols for the expansion o f undifferentiated cells or for the differentiation o f cells towards specific clinically-relevant lineages.  1  1.1.  Embryonic Stem Cells  In mice, the blastocyst develops at embryonic day 3.5 and contains cells from three different lineages. The trophoectoderm surrounds a cavity called the blastocoel, at one end o f which lies the inner cell mass ( I C M ) . The I C M consists o f cells from the epiblast and primitive endoderm lineages. M a n y studies have demonstrated that cells i n the epiblast give rise to the embryo proper as w e l l as several extraembryonic tissues, while the progeny o f trophoectoderm and primitive endoderm are restricted to the trophoblast layers o f the placenta and the visceral endoderm respectively ( L u et al. 2001).  Mouse embryonic stem cells ( E S C ) can be derived from the epiblast o f preimplantation embryos by culturing either intact blastocysts or immunosurgically-isolated epiblast (Solter and Knowles 1975) on a layer o f mitotically inactivated primary embryonic fibroblasts (Abbondanzo et al. 1993). F o l l o w i n g several days o f culture, colonies o f cells expand, some o f which retain an undifferentiated morphology. These undifferentiated colonies can be further passaged until a continuous cell line is established that retains the ability to contribute to all tissues o f chimeric mice including the germ cells, when injected back into recipient blastocysts.  Subsequent  application o f similar methods to the human system resulted in the derivation o f human E S C (Thomson et al. 1998). While their ability to contribute to all tissues in a developing embryo has not been assessed for ethical reasons, in vitro and in vivo testing indicates that they retain multilineage differentiation capacity (Reubinoff et al. 2000).  2  Techniques have been developed to differentiate E S C into a variety o f adult cell types including hematopoietic (Palacios et al. 1995; Wang et al. 2006b), cardiac (Kattman et al. 2006; Moretti et al. 2006; W u et al. 2006), neural (Conti et al. 2005), osteogenic (zur Nieden et al. 2003) and germ cells (Geijsen et al. 2004; Hubner et al. 2003; Nayernia et al. 2006;.Toyooka et al. 2003). Because o f this differentiation potential, it is hoped that these cells could form the basis o f cellular therapies i n many diseases where tissue damage or malfunction is severe and irreversible.  W h i l e clinical-scale application o f E S C based therapies is likely several years  away, initial reports from animal studies suggest that E S C - d e r i v e d cells have the ability to contribute to the repair o f injury and disease.  For example, transplant o f E S C - d e r i v e d  oligodendrocyte progenitor cells into a rat model o f spinal chord injury was able to significantly improve remyelenation and locomotor recovery (Keirstead et al. 2005) and it is hoped that human clinical trials based on this result w i l l begin i n 2007 (Geron website). Cardiovascular diseases, type-1 diabetes and Parkinson's disease are other examples o f diseases where it is hoped that ESC-based therapies w i l l provide a cure.  A number o f obstacles remain before the clinical-scale application o f ESC-based therapies can be realized. A t present, only limited testing of ESC-derived cells has been performed to ensure full functionality o f differentiated cells. A l l o f these tests have been performed i n laboratory animals and have had mixed results with respect to efficacy o f treatment (Menard et al. 2005). Furthermore, protocols for the differentiation o f E S C to functionally relevant progeny are currently inefficient, resulting in low differentiated cell yields and contamination as a result o f aberrant differentiation.  O f greater concern is the p o s s i b i l i t y o f contamination o f  3  undifferentiated E S C i n transplanted populations which may form tumors known as teratomas (Bjorklund et al. 2002; Fujikawa et al. 2005). To counteract this, the molecular regulators o f the tumorogenic potential o f E S C are being explored (Takahashi et al. 2003a) with the hope that increased understanding o f this may lead to methods o f eliminating this possibility.  Further concerns surround the possibility o f immune rejection o f transplanted cells either due to the expression o f different major histocompatability complex antigens on donor cells (Swijnenburg et al. 2005) or from the expression o f foreign antigens as a result o f culturing E S C in animal products (Martin et al. 2005). The possibility o f the derivation o f patient-specific E S C through somatic cell nuclear transfer would eliminate the possibility o f immune rejection. However this process is currently very inefficient as well as difficult due to the requirement o f a donor human egg (Gurdon et al. 2003). Although the possibility o f rejection due to expression o f foreign animal antigens is controversial (Cerdan et al. 2006; Martin et al. 2006), scientists are devoting much effort to determining xeno-free culture conditions for the expansion o f E S C (Ellerstrom et al. 2006).  Because o f their tissue o f origin, it is also hypothesized that differentiating E S C recapitulate many aspects o f development and thus can be used as a model system to  understand  developmental biology. Traditionally, this has been difficult to study in vivo due to the small number o f cells available early in development and the ethical questions surrounding scientific access to early embryos.  Recently, three groups reported the discovery o f a common  cardiovascular progenitor which gives rise to cardiomyocytes and endothelial and smooth muscle  4  cells (Kattman et al. 2006; Moretti et al. 2006; W u et al. 2006).  A l l three studies initially  identified these progenitors i n populations o f differentiated E S C and used these findings to identify similar cell types in vivo. Conti et al. used mouse E S C to establish conditions for the expansion o f a purified population o f neural stem cells derived from E S C (Conti et al. 2005). These conditions were readily applied to the derivation o f neural stem cell lines from the fetal mouse and human brains demonstrating the usefulness o f this in vitro system for understanding developmental cues present in vivo.  1.1.1.  Differences Between Mouse and Human Embryonic Stem Cells  H u m a n and mouse E S C share many fundamental characteristics including the ability to proliferate as an undifferentiated, untransformed population for many passages in culture and to differentiate into all germ layers both in culture and upon formation o f a teratoma when injected into recipient mice. In addition, they have high telomerase and alkaline phosphatase ( A P ) activity (Kirschstein and Skirboll 2001).  Despite these similarities, several differences do exist. Reports have indicated that human E S C have the ability to differentiate into trophoblast (Odorico et al. 2001; Thomson et al. 1998), a lineage that mouse E S C have never been reported to generate ( N i w a et al. 2000; Rossant 2001). Furthermore, while mouse E S C pluripotency is optimally retained by signaling through leukemia inhibitory factor ( L I F ) / g p l 3 0 / S T A T 3 pathway (Matsuda et al. 1999; N i w a et al. 1998; Smith et al. 1988), concurrent with B M P 4 / S m a d signaling ( Y i n g et al. 2003), S T A T 3 is dispensable for human E S C self-renewal (Sumi et al. 2004). Instead, optimal maintenance o f human E S C occurs in the presence o f b F G F (Levenstein et al. 2006) and TGFpVactivin/nodal (Beattie et al. 2005;  5  L u d w i g et al. 2006; Vallier et al. 2004) or noggin ( X u et al. 2005). Differences also exist i n commonly used cell surface marker expression. Undifferentiated mouse E S C express high levels of the cell surface marker S S E A - 1 and low levels o f S S E A - 3 (Kirschstein and Skirboll 2001). In contrast, human E S C express high levels o f S S E A - 3 and no S S E A - 1 (Draper et al. 2002).  Transcriptional analysis o f mouse and human E S C has revealed further molecular differences. Ginis et al., studied 400 genes that were expressed by both human and mouse E S C and found that at least one quarter o f them were differentially expressed between the two cell types (Ginis et al. 2004). A larger scale comparison revealed only a small set o f commonly expressed genes and revealed large differences i n expression o f genes associated with the L I F , TGF(3, Wnt and F G F signaling pathways further confirming findings o f differences in signaling between the two cell types. Interestingly the conserved genes were enriched with those previously identified as being involved in maintenance o f E S C (Wei et al. 2005).  1.1.2.  Advantages of Mouse Embryonic Stem Cells  Mouse E S C provide a well-established system for investigation o f stem cell properties due to their ease o f culture and rapid growth rate. They can be readily differentiated into multiple cell types providing closely related progeny that have lost their stem cell potential.  Furthermore,  much is known about the specific factors that govern the self-renewal o f these cells. They are also able to grow clonally allowing many biological questions to be asked at the single cell level. Experimental access to their tissue o f origin and the ability to test their pluripotency by blastocyst injection are also relatively easy, thus providing rigorous assays o f their stem cell activity.  6  1.2. 1.2.1.  Culture and Characterization of Embryonic Stem Cells Culture Conditions for the Propagation of Undifferentiated Embryonic Stem Cells  Optimal culture conditions for mouse E S C requires growth on a feeder layer o f mitogenically inactivated P E F and medium supplemented with fetal bovine serum ( F B S ) and L I F . Defined medium for the growth o f E S C have been reported i n which P E F are replaced with gelatin (Smith et al. 1988) and B M P addition replaces F B S ( Y i n g et al. 2003). It has, however, been noted that growth o f E S C on gelatin (Glover et al. 2006) or replacement o f serum w i t h a commercially available replacement (Chaudhry 2006) reduces the output o f functionally defined E S C indicating that there are more factors that are responsible for their maintenance.'  L I F is the primary mediator o f self-renewal although it appears to have multiple effects on E S C . In addition to a role in inhibiting differentiation o f undifferentiated cells, it also appears to confer a survival and growth advantage over differentiated  cells present i n the same culture  (Viswanathan et al. 2003a). L I F binds to a heterodimeric receptor complex consisting o f the L I F receptor and g p l 3 0 (Davis et al. 1993). Binding o f L I F causes a cascade o f signaling events mediated by associated Janus-associated  tyrosine kinases, culminating predominantly i n  phosphorylation o f S T A T 3 ( N i w a et a l . T 9 9 8 ) and, to a lesser extent, activation o f the E R K pathway (Burdon et al. 1999). Interestingly these two pathways have opposite effects on E S C self-renewal, S T A T 3 being necessary and sufficient for self-renewal (Matsuda et al. 1999) and E R K activation being inhibitory to self-renewal (Burdon et al. 1999).  7  In addition to the  involvement o f these pathways i n E S C self-renewal, the P I 3 K pathway is involved i n E S C propogation (Burdon et al. 2002). Evidence also suggests the involvement o f the Wnt pathway in E S C self-renewal (Ding et al. 2003).  In addition to soluble factors, there is also evidence that insoluble factors, such as those contained in the extracellular matrix secreted by P E F , play a role in modulating E S C fate. The extracellular matrix consists o f a large variety o f proteins, proteoglycans (such as chondroitin sulphate (CS) and heparin sulphate) and hyaluronic acid and can mediate cell fate both through direct interaction with cells as w e l l as by sequestering soluble growth factors. Replacement o f P E F with collagen in the form o f gelatin is possible although, as noted above, self-renewal o f E S C is compromised in this setting. Immunocytochemical characterization o f mouse embryonic fibroblasts revealed a large amount o f collagen I V and laminin as well as vimentin but very low levels o f fibronectin. It has been reported that laminin, with addition o f P E F conditioned media, can replace the requirement for P E F in human E S C culture for short periods, although P E F are more commonly replaced with matrigel, a complex o f multiple extracellular matrix proteins ( X u et al. 2001).  Inactivation o f (31 integrin extracellular matrix receptor severely inhibited  association with P E F (Fassler and M e y e r 1995). (31 integrin expression is essential for the growth o f teratomas (Bloch et al. 1997) although E S C lacking (31 integrin expression showed equal ability to integrate into the I C M (Fassler and M e y e r 1995).  Disruption o f talin, an  intracellular mediators o f integrin signaling, leads to impaired interaction response to attachment in undifferentiated cells only (Priddle et al. 1998). Downstream signaling pathways activated by integrin receptors include P I 3 K and M A P K , the same pathways known to be activated by L I F .  8  Ultimately, cell fate decisions are mediated by the integration o f multiple signals stimulated by both soluble and insoluble factors (Prudhomme et al. 2004).  1.2.2.  Molecular Control of Self-Renewal  Oct4 (Nichols et al. 1998), sox2 ( A v i l i o n et al. 2003) and nanog (Chambers et al. 2003; Mitsui et al. 2003) are thought to be central to the specification o f stem cell identity due to their unique expression patterns and their essential roles in early development.  M u c h effort has been dedicated to understanding the transcriptional state o f undifferentiated stem cells using a variety o f techniques (Rao and Stice 2004). Earlier studies focused on cataloguing the transcripts that were unique to undifferentiated cells b y comparing the genes expressed to those o f differentiated progeny (Ivanova et al. 2002; Ramalho-Santos et al. 2002). These initial studies were o f limited value due to the large number o f genes identified and small amount o f agreement between the studies (Fortunel et al. 2003).  Subsequent studies investigated more  detailed time courses o f differentiation (Sharov et al. 2003) or used multiple differentiation pathways to induce loss o f pluripotency (Brandenberger et al. 2004; Rao et al. 2004). Overall there has been some consensus on a small set o f genes that is expressed i n undifferentiated cells (including oct4, nanog and sox2), but many differences exist between the studies indicating the sensitivity o f the expression o f genes to specific culture conditions used to treat the cells (Skottman et al. 2005).  More recently, gene expression studies have focused on finding the genes downstream o f oct4, sox2 and nanog (Boyer et al. 2005; L o h et al. 2006). These studies identified binding sites for  9  these transcription factors i n many active and inactive genes. Since many o f the inactive genes are developmental transcription factors, oct4, nanog and sox2 may be involved i n blocking differentiation.  Computational analysis o f the network formed by these three transcription  factors also supports the hypothesis that they are centrally involved i n maintaining pluripotency of E S C (Chickarmane et al. 2006). Further functional analysis o f these downstream target genes has also started to identify a series o f pathways w h i c h are involved i n making specific developmental decisions (Boyer et al. 2006).  Studies o f nanog and its associated proteins have allowed determination o f a putative protein interaction network which governs stem cell pluripotency (Wang et al, 2006a).  This study  identifies a specific interaction between nanog and oct4 as well as connections with several other proteins which may be anticipated from gene expression studies including zfp42 (Rogers et al. 1991), sall4 (Zhang et al. 2006), daxl (Swain et al. 1998) and rifl (Adams and M c L a r e n 2004). In addition, several new.interactions are determined including the involvement o f several proteins known to function as transcriptional repressors.  Another intriguing study showed that overexpression o f 24 carefully selected genes was able to recover a E S C - l i k e state i n fibroblasts. U p o n subsequent investigation, the authors determined that only 4 o f these factors - oct4, sox2, c-myc and klf4 - were absolutely required to produce this effect. Interestingly, nanog was not required indicating that it acts downstream o f the required factors (Takahashi and Yamanaka 2006).  10  1.2.3.  Differentiation of Mouse Embryonic Stem Cells  Differentiation o f mouse E S C can be easily induced by removal o f L I F and P E F either when cultured i n a monolayer or by aggregation i n suspension culture where the cells form a heterogeneous m i x o f differentiated cells called an embryoid body ( E B , Doetschman et al. 1985). In either o f these formats, a variety o f differentiated cell types develop that express specific lineage markers. It is possible to bias the type o f differentiated cell formed by adding certain factors, for example retinoic acid (Rohwedel et al. 1999), however, inevitably a mixture o f differentiated cell types w i l l be formed. Furthermore, although ESC-derived cell types express key lineage markers for a variety o f cell types, true functional ability o f these cells remain to be established for many lineages (Smith 2001).  Ultimately, efficient generation o f differentiated cell types requires knowledge o f the molecular cues that drive formation o f specific lineages. For example, constitutive activation o f the notch pathway forces E S C towards a neural fate (Lowell et al. 2006). However, at present, little is known about these early cues and many protocols instead rely on the enrichment o f desired cell types by selection. One selection strategy involves coupling lineage-specific markers to either a drug resistance or fluorescence gene and then consequent selection by the appropriate method. For example, cardiac cells can be selected from a mixed population o f differentiated cells by incorporation o f a drug resistance cassette coupled to a cardiac lineage-specific marker (Zandstra et al. 2003). Alternatively a survival advantage can be conferred upon desired cell types with appropriate culture conditions. For example, hematopoietic cells can readily be generated by culture in semi-solid with appropriate growth factors (Wiles and Keller 1991).  11  Several factors have been identified which promote differentiation towards specific cell types although at present it is not known whether these work by driving differentiation towards a specific lineage or by allowing the survival o f the differentiated cells!  D M S O treatment  promotes differentiation towards skeletal myoblasts (Dinsmore et al. 1996). Retinoic acid ( R A ) causes differentiation primarily towards neural cells (Dinsmore et a l . 1996) although other lineages have also been reported (Dani et al. 1997). Ascorbic acid ( A A ) was identified i n a screen o f small molecules which caused differentiation towards cardiomyocytes (Takahashi et al. 2003b) although it has also been reported to enhance differentiation towards several lineages i n conjunction with other compounds (Lee et al. 2000; Tsuneto et al. 2005; zur Nieden et al. 2003). A l s o , increasing intracellular reactive oxygen species concentration is hypothesized to increase differentiation o f E S C towards cardiomyocytes (Sauer et al. 2000).  1.2.4.  Functional Assays to Detect Undifferentiated Embryonic Stem Cells  The most rigorous in vivo assay to establish functionality o f cultured mouse E S C requires their contribution to a l l adult tissue, including germ cells, i n chimeric mice established from the injection o f E S C into pre-implantation mouse blastocysts. Here a defined number o f test cells (~15) are injected into 3.5-day blastocysts before reintroduction into a pseudo-pregnant female. The blastocyst is allowed to come to term and the degree o f chimerism is assessed i n the newborn pup. Karyotypically normal, multipotent E S C are typically able to contribute to the germ line. In order to test this property, chimeric mice must be bred and a genetic contribution from the injected test cells must be observed i n the resulting offspring. While this assay shows rigorous functional ability, it does so i n a non-quantitative fashion and is time consuming and  12  difficult to perform. A second in vivo assay involves the transplantation o f undifferentiated cells into recipient mice. Undifferentiated cells retain the ability to form teratocarcinomas containing cell types from  each o f the 3 germ layers.  W h i l e this assay  clearly  determines  multidifferentiation potential, it is again non quantitative and analysis o f all differentiated tissues formed is time-consuming and difficult.  Two in vitro assays, the E B formation and colony-forming cell ( C F C ) assays, lend themselves to more quantitative analysis. The E B formation assay (for review see (Keller 1995) relies on the assumption that only undifferentiated E S C are able to differentiate into multicellular, multilineage, three dimensional cellular clusters called embryoid bodies i n optimal conditions. Defined numbers o f test cells are plated i n semi-solid methylcellulose in the absence o f L I F and the resulting number o f E B counted several days later. The C F C assay relies on the assumption that the ability to attach and generate colonies o f undifferentiated E S C on gelatin-coated tissue culture dishes is restricted to undifferentiated stem cells. In this assay, a defined number o f test culture output cells are plated on gelatin-coated dishes containing E S C maintenance media. Since it is known that undifferentiated E S C have high A P activity (Robertson et al. 1993), the number o f colonies w i t h high activity can be determined after several days.  This assay  specifically tests E S C self-renewal. Both o f these assays rely on a specific definition o f what constitutes positive colonies in each assay. This is done by defining a minimum colony size and optimal colony shape.  13  Little is known about the stringency o f these in vitro assays although a significant correlation was found between cell-specific S S E A - 1 and A P expression and the ability o f cells to clonally form E B (Zandstra et al. 2000).  1.2.5.  Phenotypic Markers of Undifferentiated Embryonic Stem Cells  In addition to the functional assays mentioned above, E S C can be identified by a variety o f phenotypic and gene expression markers. Oct4 has been shown to be an important player i n the regulation o f pluripotency i n mouse E S C and it decreases i n expression upon differentiation (Viswanathan et al. 2003b). A s a consequence, it is one o f the most commonly used phenotypic markers o f undifferentiated stem cells (see for example (Davey and Zandstra 2006; Viswanathan et al. 2003a)). However, unless it is specifically linked to a fluorescent marker such as G F P (Viswanathan et al. 2003a), its intracellular location reduces its value as a marker. Several other cell surface markers have been used to identify mouse E S C . The most prevalent o f these is S S E A - 1 , a specific glycosylation modification o f a surface marker o f undifferentiated cells that is lost upon differentiation (Solter and K n o w l e s 1978).  Other cell surface markers whose  expression on undifferentiated E S C is k n o w n to distinguish them from their differentiated progeny are 5T4 (Ward et al. 2003), P E C A M - 1 ( L i et al. 2005), C D 9 (Oka et al. 2002) and I C A M - 1 (Tian etal. 1997).  Another potential assay for undifferentiated cells involves the assessment o f changes in signal transduction pathways. Davey et al. have demonstrated concurrent radial organization o f Oct4 and phosphorylated S T A T 3 ( p S T A T 3 ) indicating that the level o f p S T A T 3 may be used to distinguish between differentiated and undifferentiated E S C (Davey and Zandstra 2006).  14  However, more work is required to determine i f this difference reflects a reversible or irreversible commitment o f cells towards a differentiated fate.  1.2.6.  Characterization of Mouse Embryonic Stem Cell Responses  Verification o f the performance o f the different assays o f undifferentiated cells described above requires conditions that are known to alter the developmental potential o f E S C . B y comparing assay readouts i n cells that have been cultured i n these conditions, more can be understood about their relative performance.  Several factors have been found to increase the maintenance and proliferation rates o f undifferentiated E S C i n the presence o f L I F . Growth o f E S C on P E F increases the frequency o f EB-forming cells (Glover et al. 2006). Inhibition o f the E R K pathway, which is activated by L I F signaling, increases E S C self-renewal (Burdon et al. 1999). Furthermore, several factors have been shown to either increase the proliferation or self-renewal rate o f E S C i n culture (Han et al. 2006; Heo and Han 2006; Heo et al. 2006; K i m et al. 2006b).  E S C cultured in the absence o f L I F rapidly lose their pluripotent state. When E S C are cultured in liquid suspension or i n semi-solid methylcelluose-based media in the absence o f L I F , they form spheroid aggregates o f proliferating cells k n o w n as E B .  These colonies contain  differentiated cells from all germ layers and some reports have advocated that these structures recapitulate in vivo development (Doetschman et al. 1985).  15  1.3.  Challenges in Embryonic Stem Cell Culture  A t present, optimal expansion o f E S C requires the use o f undefined factors such as F B S and PEF;  This is o f concern due to batch-to-batch variability as well as the increased potential for  rejection o f ESC-derived tissues following transplantatation due to the expression o f foreign antigens (Martin et al. 2005). Furthermore, current methods o f culturing human E S C result i n excessive loss o f desirable cells through spontaneous differentiation i n culture and during standard passaging despite relatively rapid growth rates (Stewart et al. 2006).  1.3.1.  High Throughput Screening to Identify Factors Supporting the Maintenance of Embryonic Stem Cells  A variety o f screening technologies are now being used to gain further insight into molecular mechanisms that govern E S C fate determination as well as to discover new factors that are able to direct stem cells to predetermined fates.  Several gene screens o f both overexpression and R N A i mediated knockdown have begun to determine the molecular machinery that maintains E S C in the undifferentiated state.  Gene  expression based studies (summarized above) have been performed that have identified candidate genes. Subsequent experiments have identified several o f these that are specifically involved in E S C maintenance. Using an R N A i based approach, Ivanova identified 5 genes in addition to oct4, sox2 and nanog which were involved in self-renewal o f E S C (Ivanova et al. 2006). B y performing further gene expression profiling o f cells with these genes knocked down they were also able to propose a provisional control hierarchy map for these genes. In a related  16  study, the same group overexpressed a c D N A library in E S C and then by observing the effect on cell cycle identified clones that increased or decreased self-renewal. These identified 11 genes that were able to partially rescue E S C self-renewal in the absence o f L I F (Pritsker et al. 2006).  While the screens described above identify molecular pathways involved in E S C fate control, these have not yet helped to determine optimal culture conditions for the expansion o f undifferentiated E S C . Other studies have focused on identifying small molecules and adherent factors that have an effect on E S C fate, from the perspective o f both self-renewal and differentiation. Chen et al. used an Oct4-GFP reporter i n E S C unable to sustain self-renewal in PEF-free conditions to screen a library o f 50,000 small molecules (Chen et al. 2006). After 6 days o f culture i n the absence o f feeders, they identified 28 molecules that supported E S C selfrenewal. Further investigation o f these factors revealed that 17 were able to maintain expression o f S S E A - 1 , Oct4 and A P . . B y investigating the specific chemical structure o f these 17 compounds, they were able to determine a basic structure upon which chemical modifications had different levels o f effect on E S C self-renewal. A similar screen determined that an inhibitor o f GSK-3(3 increased self-renewal i n E S C revealing a role for the Wnt pathway i n E S C maintenance (Ding et al. 2003). Identification o f non-soluble factors that support E S C selfrenewal is also the subject o f research. Anderson and colleagues designed a mieroarray which allows the screening o f multiple factors for the attachment o f E S C (Anderson et al. 2004). Similarly Flaim and colleagues present a similar device for investigating combinations o f various extracellular matrix proteins on differentiation o f human E S C (Flaim et al. 2005). While these  17  devices have only been applied to differentiation at present, this approach could be extended to self-renewal.  Several limitations exist at present with these approaches. First a specific challenge remains the identification o f a suitable assay for the evaluation of the effect o f the screened compounds. The assays used need to give a rapid readout and to be amenable to scale up to the level where tens of thousands o f variables are screened simultaneously. Existing functional assays are not suitable for this due to their long lead-time and labour-intensive nature. Fluorescent reporters are more amenable to high-throughput screening, but careful choices must be made to ensure that these give appropriate readouts. For example, it has been shown that Oct4 is a slow indicator o f loss of pluripotency (Palmqvist et al. 2005) indicating that more rapid markers should be used. Another problem lies i n determining effective concentration ranges at which to test chemical screens.  In a l l reported screening experiments, compounds are tested at one specific  concentration.  However, effective concentrations o f these chemicals w i l l probably differ  considerably and non-linear concentration responses are known to occur (Audet et al. 2002)  Once compounds and substrates are identified, consideration must be given to interactions between multiple molecules when added simultaneously to culture. Complex interactions such as synergy and redundancy may take place between multiple factors when added simultaneously. Thus systematic methods are required to identify non-linear responses as w e l l as factor interactions.  18  1.3.2.  Statistical Design of Experiments to Understand Factor Interactions and Determine Non-Linear Responses  Statistical design o f experiments, specifically factorial design (FD) and central composite design ( C C D ) , provide rigorous statistical approaches to efficiently determine individual and multifactor responses. They make use o f geometric principles for statistical sampling and minimize the variance o f estimated coefficients obtained by regression. In addition, the designs are able to minimize the impact o f unimportant factors (e.g. reagent batch, errors i n cell counts, volume addition etc.) by grouping experiments into homogeneous blocks and by inducing replication i n treatment (Figure 1.1). First-order designs such as F D can thus be useful as screening tools to identify a few critical factors, including interactions, for subsequent study. This type o f study can then be followed up by more in-depth investigation by CCD-type experiments to determine non-linear responses and to obtain a complete mathematical description o f responses. This can then be used to determine factor levels that maximize the response o f interest.  Statistical design o f experiments have previously been used to enhance stem cell expansion and differentiation for a variety o f stem cell types. Prudhomme et al. used F D experiments to gain insight into the signaling responses o f E S C to both soluble and insoluble factors (Prudhomme et al. 2004). Audet et al. used a F D experiment.to screen multiple factors for their influence on H S C expansion, and followed this with a C C D to optimize the level o f 3 o f these factors for maximal stem cell expansion (Audet et al. 2002). Statistical design o f experiments has also been used i n the context o f differentiation.  Chang et al., investigated the effect o f 5 factors on  endoderm differentiation from E S C (Chang and Zandstra 2004). Experiments o f this nature w i l l  19  become increasingly important as a clinical need for stem cell based therapies develops. Clinical application o f these therapies w i l l require the development o f xeno-free methods o f culturing stem cells to avoid subsequent tissue rejection and immunological reaction. A s replacements for xeno-derived components o f cell culture medium are identified, significant effort w i l l have to be made to incorporate these into cell culture medium and to ensure that there are no negative consequences o f addition as a result o f factor interactions.  1.3.3.  Culture Systems Suitable for High Throughput Screening  Specialized culture systems are required for efficient large-scale and multifactorial screening. Due to the large number o f cultures that must be run i n parallel, small volume, automatic control culture systems are ideal. Technology derived from the field o f microfluidics provides the basis for culture systems o f this type (Balagadde et al. 2005).  Application o f these technologies to E S C is just beginning. Recently K i m et al. demonstrated the growth and maintenance o f mouse E S C i n a microfluidic device with laminar flow ( K i m et al. 2006a).  Further advantages o f this technology are the other analytical tools that can be  incorporated.  For example, Warren and colleagues were able to monitor the expression o f  specific transcription factors in individual hematopoietic stem cells using microfluidic devices (Warren et al. 2006). Combinations o f these technologies w i l l provide incredible power and high-throughput ability to allow the optimization o f stem cell expansion as well as an increased understanding o f the control o f stem cell fate.  20  1.4.  Thesis Objectives  C l i n i c a l scale application o f ESC-based therapies requires development o f protocols for the expansion o f undifferentiated E S C and their subsequent differentiation into relevant cell types in large numbers.  Despite recent reports o f expansion o f undifferentiated cells i n large-scale  bioreactors (Cormier et al. 2006; Fong et al. 2005; O h et al. 2005), more work is needed to understand the essential components o f undifferentiated E S C as well as their tolerance o f varying culture conditions. Understanding o f these factors w i l l allow the development o f protocols for the optimal expansion o f undifferentiated E S C .  The overall goal o f this thesis was to develop tools to allow the optimal expansion o f E S C . The expectation was that investigation and comparison o f existing assays for undifferentiated E S C would allow the development o f novel assays for E S C based o f gene expression. Furthermore, the use o f these methods would allow the optimization o f E S C expansion.  In Chapter 2, the goal was to compare existing assays and markers o f E S C during differentiation to evaluate their relative ability to monitor loss o f E S C as w e l l as to select appropriate time points for gene expression analysis. changes  It was hypothesized that analysis o f gene expression  during differentiation w o u l d  a l l o w the  identification o f n o v e l markers  of  undifferentiated E S C as well as provide candidate genes that are involved i n the maintenance and differentiation o f E S C .  21  In Chapter 3, the goal was to optimize the level o f 3 different factors ascorbic acid ( A A ) , chondroitin sulphate (CS) and an inhibitor o f E R K 1 / 2 phosphorylation, PD98059 (PD), with respect to the expansion o f undifferentiated E S C . A A and P D have previously been shown to increase the self-renewal o f E S C (Burdon et al. 1999; Glover et al. 2006). C S was hypothesized to have an influence on E S C fate due to its inclusion in the extracellular matrix. Expansion o f E S C was measured using clonal E B formation as a measure o f pluripotency. F D and C C D analysis was used to identify optimal culture conditions for the expansion o f E S C as well as identify any factor interactions.  The E B assay provides a reliable indicator o f pluripotency within a population but is labourintensive and does not lend itself easily to high-throughput type experiments that would facilitate a greater understanding o f the influence o f the cell culture environment on E S C fate.  Thus a  further series o f experiments, described in Chapter 4, was undertaken to identify reliable gene expression indicators o f E S C fate. Since the gene expression changes useful for monitoring E S C fate, should be independent o f the type o f lineage that E S C were differentiating towards, gene expression profiling was carried out on E S C induced to differentiate by multiple stimuli. Desired gene expression changes should be able to identify changes i n the developmental potential o f E S C regardless o f the culture conditions.  22  Table 1.1 Similarities and differences between human and mouse embryonic stem cells Adapted from Kirschstein and Skirball, 2001. Property SSEA-1 SSEA-3 AP Oct4 Telomerase Feeder-cell dependent Self-renewal factors Teratoma formation Chimera formation Tissues generated  Human -  M o u s e ESC rf  + + + No LIF, BMP4 + + Mesoderm, endoderm, ectoderm  ESC  + + + Yes FGF2, TGFp\ noggin + + (with mouse) Mesoderm, endoderm, ectoderm, trophectoderm  Abbreviations: E S C - embryonic stem cell, A P - alkaline phosphatase, L I F - leukemia inhibitory factor.  23  Figure 1.1 Graphical representation o f factorial and central composite design experiments for 3 factors Factorial design experiments are represented by white points and central composite design experiments are represented by both white and yellow points. Figure provided by Julie Audet.  (1.63, 0,0)  1 . 1 . 1 ) (1,-1,1)  (-1,-1,1)  (-1,1,-1 (-1.63, 0,0)  24  1.5.  References  Abbondanzo SJ, Gadi I, Stewart C L . 1993. Derivation o f embryonic stem cell lines. Methods Enzymol 225:803-23. Adams IR, M c L a r e n A . 2004. 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Introduction  Embryonic stem cells ( E S C ) are characterized by their ability to both self-renew, as well as differentiate (Smith 2001; Suda et al. 1987).  However, the molecular mechanisms, which  regulate the decision between these two processes, are poorly understood.  Mouse E S C were  originally isolated from the inner cell mass ( I C M ) o f pre-implantation blastocysts (Evans and Kaufman 1981; Martin 1981) and can be maintained i n cell culture indefinitely without loss o f their broad pluripotent differentiation capacity as determined by their ability to give rise to all three germ layers both in vitro and in vivo (Suda et al. 1987). The more recent establishment o f human E S C lines (Thomson et al. 1998) has further increased interest i n E S C , since they raise hope for an unlimited source o f cells for tissue engineering and cell therapies i n the future. However, realization o f this potential requires an increased knowledge o f the molecular mechanisms governing self-renewal and pluripotency to guide the development o f processes that control expansion and differentiation o f stem cells ex vivo.  Unlike hematopoietic stem cells ( H S C ) for which quantitative assays o f stem cell potential have been defined and validated, no such assays currently exist for E S C . In the murine system self-  A version o f this chapter has been published as Palmqvist, L , Glover, C H , H s u , L , L u , M , Bossen, B , Piret, J M , Humphries, R K , Helgason, C D . Correlation o f murine embryonic stem cell gene expression profiles with functional measures o f pluripotency. Stem Cells 23(5): 663 680,2005. 1  36  renewal is measured by the ability o f mouse E S C to continuously proliferate i n culture while maintaining an undifferentiated colony morphology (Abbondanzo et a l . 1993).  The most  rigorous in vivo assay to establish functionality o f cultured mouse E S C is blastocyst injection and measurement o f their ability to give rise to chimeric mice, since it requires E S C contribution to all adult tissue, including germ cells (Smith 2001). However, injection o f 10 - 15 E S C into a single blastocyst does not provide a quantitative measure^of stem cell potential at the single cell level. T w o in vitro assays have been used extensively as surrogates for chimera formation when testing culture reagents or examining the consequences o f genetic manipulation. The colony forming cell ( C F C ) assay is used to determine the plating efficiency o f E S C populations under various conditions and thus may be considered indicative o f self-renewal potential. Formation o f embryoid bodies ( E B ) can be performed at a clonal level in vitro and reflects multi-lineage differentiation potential (Keller et al. 1993). The correlation between these in vitro assays and chimera generation has not been determined. Assessment o f pluripotency has also relied on the expression o f selected molecular markers.  F o r murine E S C these have included alkaline  phosphatase ( A P ) , S S E A - 1 , and Oct4 (Kirschstein and Skirboll 2001). However, the correlation between marker expression and the various functional assays has not been extensively studied. Knowledge o f the intricate mechanisms regulating E S C pluripotency and differentiation potential is currently limited to a few signaling pathways (i.e. leukemia inhibitory factor - L I F ) and regulatory factors (i.e. Oct4 and Nanog). Thus very little is known about the tolerance limits o f different culture condition for maintaining stem cell function during expansion or how these relate to altered gene expression patterns i n E S C . Identification o f molecular markers that  37  correlate with pluripotency would be invaluable to enrich for the desired cells, as well as monitor their maintenance during expansion protocols.  A c h i e v i n g the goal o f defining the core stem cell regulatory network requires more precise characterization o f the functional capacities o f the cells for which the transcriptional profile is described. In this study we established gene expression profiles during early differentiation o f the well-defined R l E S C line (Nagy et al. 1993) and correlated gene expression changes with both phenotypic and functional assessment o f the same cells. Cellular markers used included S S E A - 1 and Oct4, while blastocyst injections for chimeric mouse formation, E B assays and C F C counts were used to determine functional capacity.  Undifferentiated E S C and E S C cultured  without L I F for 18 hours and 72 hours were chosen for array analysis. W e identified 473 unique genes as significantly differentially expressed  during early E S C differentiation, and  approximately one third o f these have unknown biological function.  A m o n g the 275 genes  whose expression decreased with E S C differentiation were several factors previously identified as important for, or as markers of, E S C pluripotency including statS, zfp42, sox2, gbx2, and bmp4. A significant number o f the decreased genes also overlapped with previously published mouse and human E S C data.  Reverse-transcription-polymerase  chain reaction ( R T - P C R )  validation showed high correlation with the array data, and several genes were also shown to have similar changes after L I F removal i n two other murine E S C lines.  Expression o f the  commonly used E S C markers Oct4 and S S E A - 1 was also examined i n parallel w i t h the functional assays.  However, a close correlation was not observed.  38  Interestingly, among a  subset o f 48 decreased genes that showed the closest correlation with the functional assays was the stem cell factor (SCF) receptor c-kit that can be a useful marker o f undifferentiated E S C .  2.2. 2.2.1.  Materials and Methods Culture of Mouse Embryonic Stem Cells and Embryonic Fibroblasts  R l (Nagy et al. 1993), J l ( L i et al. 1992) and E F C (Nichols et al. 1998) E S C were routinely maintained at 3 7 ° C humidified air with 5% CO2 on a-layer o f irradiated primary embryonic fibroblasts (PEF) and fed daily with a complete change o f E S C maintenance media consisting o f high glucose D M E M (all reagents obtained from Stem C e l l Technologies Inc., Vancouver, B C unless otherwise indicated) supplemented with 15% ESC-tested fetal bovine serum ( F B S ) , 0.1 m M non-essential amino acids, 2 m M glutamine, lOOOU/ml L I F , 100 U / m l penicillin, 100 pg/ml streptomycin, and 100 p M monothioglycerol ( M T G ; Sigma, Oakville, O N ) . For gene expression profiling R l E S C were from passage 14 and had been frozen at 10 cells per vial. Cells were 6  passaged every second day i n maintenance cultures. To passage cells, a single cell suspension was generated by treatment w i t h 0.25% T r y p s i n -  1 m M E D T A ( T / E ; Invitrogen L i f e  Technologies, Burlington, O N ) for 5 minutes until cells detached from the culture vessel surface. T / E activity was then quenched w i t h D M E M supplemented w i t h 10% F B S . Cells were centrifuged at 1200 rpm for 7 minutes and resuspended in E S C maintenance media. Viable cells were plated at 1 x 10 /100 m m dish on irradiated P E F and cultured for a further 48 hours at 6  37°C, 5% CO2 prior to harvest for R N A isolation, differentiation, and/or functional assessment. In subsequent experiments all cells used were within 5 passages o f initial thawing.  39  P E F were maintained at 37°C humidified air with 5% CO2 i n D M E M supplemented with 10% F B S and 100 u.M M T G . Cells to be irradiated were trypsinized, resuspended i n 2 m L o f media, and exposed to 60 G y from an X - r a y source prior to replating for use as feeder cells. Alternatively, P E F were pelleted and placed in Trizol reagent for R N A isolation (Invitrogen).  2.2.2.  Preparation of Embryonic Stem Cells for Differentiation  E S C were thawed and cultured for two passages over 96 hours as described above. To prepare cells for differentiation cells were harvested and washed as described above, resuspended in E S cell maintenance medium, and pre-plated on tissue culture plates (Falcon) for one hour at 37°C, 5% CO2 to deplete contaminating P E F . A t the end o f this pre-plating step, non-adherent E S C were discarded and the loosely adherent E S C were collected by gently washing the surface o f the tissue culture plate.  Cells were pelleted by centrifugation and viable cell numbers were  determined. Contaminating P E F in the undifferentiated (day 0) E S C samples was estimated to be less than 0.2% based on cell size during counting.  A portion o f pre-plated E S C were suspended at a density o f 1-2 x 10 cells per 50 m l in liquid 7  differentiation medium consisting o f Iscove's modified Dulbecco's medium ( I M D M ) , 15% F B S selected for its ability to support E S C differentiation, 2 m M glutamine, 150 u.M M T G , and 40 ng/ml murine S C F and plated into 4 x 100 m m Petri style culture dishes (Falcon). Cells were cultured overnight (18 hours) at 37°C, 5% CO2. The following day, embryoid bodies (EB), both in suspension and loosely attached, were harvested and allowed to settle to the bottom o f a 50 m l conical tube for approximately 10 minutes. The supernatant, containing mainly single cells, was  40  removed and the spontaneously pelleting E B fraction was collected by centrifugation at 1200 rpm for 7 minutes. E B were disrupted by incubation in T / E for 3 minutes at room temperature followed by passage through a 21-gauge needle to obtain single cell suspensions.  Cells were  washed with 10% D M E M and suspended in 2 m l o f I M D M to count.  The remainder o f the pre-plated E S C were plated at a density o f 10 cells per 35 m m low4  adherence Petri dish i n I M D M - b a s e d E S C differentiation methylcellulose consisting o f 0.9% methylcellulose, 15% F B S , 2 m M glutamine, 150 p M M T G , and 40 ng/ml murine S C F . Cultures were grown for 3 days and all E B i n each dish were harvested by carefully flooding the dish with I M D M and collecting the methylcellulose/EB solution. E B were washed twice i n D M E M + 10% F B S to remove the residual methylcellulose and were then pooled and disrupted as described above.  2.2.3.  Blastocyst Injection  C57B1/6J mice (used as blastocyst donors) and B 6 C 3 F l females (used as pseudopregnant blastocyst recipients) were purchased from the in-house breeding program at the B C Cancer Agency A n i m a l Resource Center. A l l mice were maintained with sterilized food, water and bedding.  A l l protocols were conducted according to guidelines set forth by the Canadian  Council for A n i m a l Care and approved by the A n i m a l Care Committee at. the University o f British Columbia. R l E S C were thawed and maintained for two passages on irradiated P E F with daily feeding o f maintenance media.  After the second passage, cells were harvested and  subjected to 1 hour o f pre-plating on plastic to deplete remaining P E F . Cells were then plated onto gelatin coated tissue culture dishes and fed with maintenance media without L I F for the  41  indicated lengths o f time. To produce chimeras, 15 test cells (either E S C or differentiated) were injected into 3.5 day blastocysts from C57B1/6 mice as described previously (Helgason et al. 1998) and implanted back into pseudopregnant recipient females and allowed to gestate normally. Coat color was used to identify chimerism o f the resulting pups.  2.2.4.  Embryoid Body Formation Assays  Single cell suspensions were collected on day 0 or prepared, as outlined above, during differentiation.  Defined numbers o f cells (500 - 20,000 depending on time following L I F  removal) were plated i n 35 m m petri-style dishes i n the E S C differentiation methylcellulose medium described above to determine the efficiency o f E B formation.  E B numbers were  determined microscopically following 5 - 6 days o f culture and colonies were qualitatively scored as large or small. E B formation efficiency was calculated by dividing the total number of E B formed by the number o f cells plated multiplied by 100.  2.2.5.  Colony Forming Cell Assay  Single cell suspensions collected oh day 0 or during differentiation were plated at various. \  .  .  .  densities (500 - 20,000 cells per gelatinized 60 m m gridded tissue culture dish) to determine E S C C F C plating efficiency. Colonies were microscopically enumerated following 5 - 6 days o f growth.  T o enable differential assessment o f the colonies the protocols outlined i n the A P  detection kit (Sigma; 86-R) were modified for staining i n 60 m m dishes. In brief, the media was removed from the colonies and 1 m L o f room temperature fixative (prepared as per the Sigma protocol) was added for 30 seconds. The fixative was removed and colonies were washed with 2 m L phosphate buffered saline (PBS). Next 1.5 m L o f alkaline dye mixture (prepared as per the  42  Sigma protocol) was added; the dish was incubated i n the dark at room temperature for 15 minutes. Finally, the dye mixture was removed and the colonies were covered with 2 m L P B S for microscopic evaluation.  The numbers o f stained (undifferentiated) vs. unstained  (differentiated) colonies were determined. C F C plating efficiency was calculated by dividing the total number o f A P positive colonies by the number o f cells plated multiplied by 100.  2.2.6.  RNA Extraction and Array Hybridization  Single cell suspensions o f test cells were prepared as described above and resuspended i n Trizol (Invitrogen, Burlington, O N ) at a density o f 10 cells/mL. 7  R N A was extracted following the  manufacturer's instructions. Standard Affymetrix amplification protocols were used to prepare probe R N A for Affymetrix arrays with 5 p g o f starting total R N A .  Biotin-labeled amplified  R N A was fragmented and hybridization cocktails were prepared according to the Affymetrix protocol.  The mouse GeneChip M G _ U 7 4 v 2 chips were hybridized on a GeneChip System  (Affymetrix) at the Genome Science Centre, B C Cancer A g e n c y (Vancouver, Canada), according to the manufacturer's instructions. A l l experiments were performed i n triplicate with the exception o f P E F , which were analyzed i n duplicate. The M I A M E (minimal information about a mieroarray experiment) guidelines were followed for the presentation o f the data (Brazma et al. 2001).  2.2.7.  1  Data Analysis  The Affymetrix M i c r o Array Suite ( M A S ) 5.0 software was used to generate absolute expression estimates (Absence/Presence calls).  Software default thresholds were used to determine the  present or absent calls (cti=0.04, ci2 0.06 and x=0.015). R a w data (CEL-files) obtained from =  43  M A S was then normalized and analyzed in GeneSpring (Silicon Genetics, Redwood City, C A ) . Data were normalized as follows: values below 0.01 were set to 0.01 and then each measurement was divided by the 50th percentile o f all measurements in that sample. Each gene was divided by the median o f its measurements i n all samples. If the median o f the raw values was below 10 then each measurement for that gene was divided by 10. We judged genes to be differentially expressed during E S C differentiation only when 1) the difference i n expression between two time points was at least 2-fold; 2) the gene was identified as present in two out o f three replicates or present or. marginal i n a l l three replicates by M A S i n at least one o f the time points. Additionally, genes scored as decreasing were removed that were scored as absent i n the denominator o f the fold change, as well as genes scored as increasing but absent in the numerator of the fold change; and 3) the extent o f difference i n expression was statistically significant (p<0.05, i n parametric Welsh t-test). Classification o f genes into functional categories was done by collecting annotations and keywords with the Onto-Express T o o l (Draghici et al. 2003), Affymetrix NetAffx and the Simplified Gene Ontology.Tool i n GeneSpring. The GenMapp 2.0 software tool was used to analyze signaling pathways (Dahlquist et al. 2002).  2.2.8.  Quantitative RT-PCR  R N A was isolated using Trizol and the samples were then treated with DNase I (amplification grade) prior to the R T - P C R according to the manufacturer's instructions (Invitrogen, Burlington, ON).  c D N A was generated by reverse transcription ( R T ) w i t h random primers and the  Superscript II enzyme and RNase inhibitor (Invitrogen Life Technologies, Burlington, O N ) . The R T reaction was incubated at 42°C for 50 m i n followed by 15 m i n at 70°C. c D N A was stored at -20°C for subsequent Q - P C R analysis. Gene transcripts were quantified by real-time P C R using  44  the iCycler apparatus ( B i o - R a d Inc., Hercules, C A ) and were detected.with S Y B R Green as flurochrome (IQ S Y B R Green Supermix, B i o R a d Inc.). Gene sequences for primer design were obtained from the N C B I Reference Sequences database. Primers were chosen using the Primer3 software (Rozen and Skaletsky 2000).  Primer sequences are provided i n the Appendix A .  Synthesis o f the primers for this study was performed at Invitrogen. B L A S T searches were conducted to confirm gene specificity o f the P C R . Relative expression changes were determined with the 2 "  a a C T  method (Livak and Schmittgen 2001) and the gapdh gene transcript was used to  normalize the results. P C R efficiency was tested for each primer pair by dilution series o f c D N A to make sure that the efficiency was appropriate for the 2 "  AACT  method (i.e. 9 5 % or above). To  identify amplification o f any contaminating genomic D N A and ensure the specificity and the integrity o f the P C R product, melt curve analyses were performed on a l l P C R products. N o products were obtained with real-time P C R from R N A samples when reverse transcription was omitted. Samples without template were included for each primer pair to identify contamination. Pearson correlation and D e m i n g regression analysis were used to determine correlation and agreement respectively between the microarray and Q - R T - P C R results.  2.2.9.  Flow Cytometry  Antibodies used for phenotype analysis included phycoerythrin (PE)-conjugated anti-CD 117 (ckit, clone A C K 4 5 ; B D Pharmingen, San Diego, C A ) , as w e l l as purified anti-SSEA-1 (Clone M C - 4 8 0 ; C h e m i c o n International Inc., Temecula, C A ) that was detected using a F I T C conjugated anti-mouse I g M antibody ( B D Pharmingen). Single cell suspensions collected on day 0 or prepared from differentiating E S C were blocked for 10 minutes on ice with 5 pg/ml anti  45  mouse C D 1 6 / C D 3 2 (Fc Block, B D Pharmingen) in P B S + 2% Fetal Bovine Serum ( P B S F B S ) . Cells were washed once with P B S F B S and then incubated on ice for 20 minutes with the primary antibody.  Cells were then washed once, incubated with the secondary antibody i f needed,  washed again, and then analysed by flow cytometry using a F A C S C a l i b u r flow cytometer and C E L L Q u e s t software ( B D Pharmingen). The forward scatter ( F S C ) versus side scatter (SSC) profile was used to gate on viable cells and an unstained sample was used to determine appropriate gating for quantification o f expression.  C e l l s to be stained for Oct4 were  resuspended in 100 p i o f Hanks Buffered Saline Solution + 2% F B S (HF) and fixed with 100 p i of IntraPrep Permeabilization Reagent 1 (Immunotech, Westbrook, M E ) for 15 minutes at room temperature.  C e l l s were then washed w i t h H F and p e r m e a b i l i z e d w i t h  IntraPrep  Permeabilization Reagent 2 for 5 minutes before incubation with a 1:100 dilution o f mouse antimouse Oct3/4 monoclonal antibody (Transduction Laboratories, Lexington, K Y ) for 15 minutes at room temperature.  Cells were washed with H F before staining with A P C labeled anti-mouse  IgGi ( B D Pharmingen). Samples were analyzed by flow cytometry as outlined above.  2.2.10.  Comparison with Previously Published Gene Expression Array Data  Genes in Table B l (decreased between 0 and 18 hours), B 3 (decreased between 0 and 72 hours) and B 5 (decreased between 18 and 72 hours) (Tables in Appendix B ) were compared with the following data tables: (Bhattacharya et al. 2004), supplementary online Table 2; (Brandenberger et al. 2004), supplementary online Table 2; (Ivanova et al. 2002), supplementary online Table 1 (genes marked as either I or D ) ; (Ramalho-Santos et al. 2002), database S I ; (Sato et al. 2003), database 1; (Sharov et al. 2003), dataset S7; (Sperger et al. 2003), supporting supplementary online Table 6; (Kelly and Rizzino 2000), supplementary online Table 1; and (Ginis et al. 2004),  46  supplementary online Table 3 A . Comparison between (Ivanova et al. 2002) and (RamalhoSantos et al. 2002) was performed on the basis o f Affymetrix gene IDs. In (Ramalho-Santos et al. 2002), supplementary online Table 1 was refiltered as outlined i n the original publication. Comparisons with human E S data sets (Bhattacharya et al. 2004; Brandenberger et al. 2004; Sato et al. 2003; Sperger et al. 2003) was done by converting public ID references from the human data  sets  into  murine  Affymetrix  (http://www.ensembl.org/Multi/martview). converted  to  Unigene  codes  codes  using  EnsMart  For (Sharov et al. 2003), custom U codes were using  the  translation  tool  available  (http://lgsun.grc.nia.nih.gov/geneindex/). Groups F , I, L , and O were chosen because these were the lists that contained genes expressed i n E S C grown in L I F but not expressed i n E S C grown without L I F for 4 or 18 hours. For (Kelly and Rizzino 2000), Genbank codes were converted to Affymetrix codes using EnsMart. Following automated comparisons performed as described above, manual comparison between known genes was performed using gene names to ensure accurate comparison.  2.3. 2.3.1.  Results Pluripotency During Early Differentiation  We hypothesized that loss o f E S C pluripotency as defined by measurable functional readouts correlates with significant alterations in gene expression. Identification o f these gene expression changes would thus provide important insights into the genetic regulation o f E S C pluripotency and facilitate the identification o f new molecular markers o f the undifferentiated E S C state. W e relied on comparisons o f three measures o f E S C potential (chimera formation, E B formation, and  47  C F C assay) to select time points for analysis o f gene expression profiles. Undifferentiated E S C were cultured on P E F which supply a number o f as yet unidentified factors that enhance the plating efficiency o f the E S C , as well as assist in the maintenance o f the undifferentiated state. Pre-plating o f E S C removes the P E F and enriches for E S C with the capacity to contribute to the developing blastocyst. It has been suggested that those E S C capable o f loosely attaching to the feeder layer in a short (i.e. one hour) period o f time have the highest likelihood o f forming selfrenewing colonies and thus may also be the most competent at contributing to the germ-line following blastocyst injection (Stewart 1993). W e thus elected to use only pre-plated, loosely adherent mouse E S C for our analyses.  The baseline activities for undifferentiated E S C i n the three different assays were first determined. 100% o f the pups resulting from injection o f undifferentiated, pre-plated R l E S C into recipient blastocysts were chimeric (27 blastocysts injected; 6 born and analyzed), 6.90 ± 0.02 % o f plated cells differentiated into embryoid bodies i n the E B formation assay and 12.50 ± 0.04% o f plated cells gave rise to A P positive E S C colonies in the C F C assay. These results were within the normal range for R l E S C . E S C differentiation was then initiated by L I F removal and re-plating without P E F . The morphology o f the E S C before and after L I F removal is shown i n Figure 2.1. A t 18 and 24 hours after P E F and L I F removal, the E S C looked very similar to one another and did not exhibit any clear signs o f differentiation.  Morphological  differentiation was first, apparent at 48 hours and E B could be seen after 72 hours. Despite the lack o f appreciable phenotypic differentiation within the first 24 hours, there were significant changes i n the functional properties o f the E S C .  48  Cultured cells were harvested at subsequent  time points and tested i n the three different assays. The blastocyst injection assay showed a rapid decrease in the number o f chimeras obtained after initiation o f E S C differentiation (Figure 2.2A). Only 28% o f born pups (84 injected, 25 born and analyzed) were chimeras when E S C had differentiated for 24 hours and less than 5% after 72 hours o f differentiation (66 injected, 29 born, 3% chimeras).  The E B formation assay (Figure 2.2B) showed approximately 5.5% ±  1.0%, 3.7% ± 0.3% and 0.3% ± 0.1 % readout at 18, 24 and 72 hours after L I F removal respectively. In contrast, the frequency o f cells replating i n the C F C assay increased slightly during the first 24 hours but declined rapidly such that only 1.3% ± 0.3%> retained activity at 72 hours (Figure 2.2C). In summary, a l l three assays showed clearly that most differentiation potential and self-renewing capacity was gone after 72 hours o f differentiation. However, there was a high degree o f variation during the first 24 hours amongst the in vitro and in vivo assays, with the E B formation assay correlating most closely with chimeric mouse formation. For gene expression profiling, because both the E B formation and chimera assays showed a pronounced decrease within the first 18 - 24 hours, we elected to use the earlier time point, along with 72 hours o f differentiation, to compare against undifferentiated R l E S C .  In parallel with functional assay analysis, the expression patterns o f two markers commonly used to identify undifferentiated E S C , Oct4 and S S E A - 1 , were also analyzed to establish the level o f correspondence i n the readouts using each method.  Although Oct4 and S S E A - 1 have been  extensively used i n E S C research, their expression patterns during E S C differentiation have not been studied i n detail. Oct4 is used as a marker for E S C because o f its requirement i n E S C selfrenewal (Nichols et al. 1998).  The precise expression levels o f oct4 are important for  49  determining E S C fate, and repression o f oct4 induces loss o f pluripotency and differentiation into trophoectoderm ( N i w a et al. 2000). However, forced, constitutive expression o f oct4 cannot prevent  E S C differentiation, and a less than two-fold increase  differentiation into primitive endoderm and mesoderm.  i n expression causes  Thus, a critical amount o f oct4 is  required to sustain stem cell self-renewal but is not sufficient to prevent differentiation. S S E A - 1 is a glycoprotein expressed during early embryonic development and by undifferentiated E S C . However, the precise role o f S S E A - 1 i n pluripotency and self-renewal has not been defined. E S C selected for expression o f S S E A - 1 and P E C A M 1 are enriched for cells that differentiate predominantly into epiblast cells i n chimeric embryos (Furusawa et al. 2004). In the present study, protein expression o f both S S E A - 1 and Oct4 remained relatively unchanged throughout the first 72 hours o f differentiation (Figure 2.2D). A t 120 hours following L I F removal, 18.5 ± . 1.1% o f the cells still retained expression o f S S E A - 1 , while 40.6 ± 6.6% o f the cells continued to express Oct4. Thus there was no clear correlation between expression o f these two markers and the E S C functional assays.  2.3.2.  Array Analysis  R l E S C from matched passage numbers i n three separate experiments were collected at 0, 18, and 72 hours after removal o f L I F and analyzed using Affymetrix GeneChip M G _ U 7 4 v 2 arrays containing 36,767 probe sets. Duplicate samples o f P E F were also collected and analyzed to assess possible contamination i n the undifferentiated  E S C gene expression, samples.  Hybridization, scanning, and production o f raw data files were performed according to standard protocols. M A S software was used for initial scaling and expression analysis and data was then normalized and further analyzed in GeneSpring. To validate array reproducibility and the overall  50  variation o f the data, hierarchical clustering analysis was performed. The data was first filtered for genes expressed in at least one o f the three time points (present or marginal i n all three replicates) resulting in 13,002 different probe sets. The average number o f expressed genes for each time point was 12,818 (coefficient o f variation, CoVar, 6%) in undifferentiated E S , 11,806 (CoVar 4%) at 18 hours and 12,297 (CoVar 1%) at 72 hours after L I F removal. The number o f expressed genes at the different time points was not statistically significantly different. Hierarchical clustering was applied to the reduced gene set on individual array samples (three replicates for each time point) using Pearson correlation and average linkage clustering as implemented i n GeneSpring. Individual replicates clustered tightly together according to their respective time points as seen i n Figure 2.3 indicating that overall inter-experimental variation was low. Clustering results also reflected the temporal progression o f the E S C differentiation and, as expected, the first two time points (0 hours and 18 hours) clustered more closely together while the 72 hour time point showed a more distinct expression pattern as reflected in the larger distance from the other two time points. These observations are also consistent with functional data (Figure 2.2).  Genes that were differentially expressed during differentiation were determined using the following three criteria: 1) the difference in expression between two time points was at least 2fold; 2) the gene was expressed in all three replicates and i n at least one o f the time points as determined by the detection algorithm i n M A S 5.0; and 3) the extent o f difference i n expression was statistically significant (p<0.05 in parametric Welsh t-test). Additionally, decreased genes were removed that were scored as absent i n the denominator o f the fold change, as w e l l as  51  •J  increased genes scored as absent in the numerator o f the fold change. W i t h this approach 473 unique genes were identified as significantly differentially expressed (275 decreased, 194 increased and 4 both decreased and increased) during early E S C differentiation (Tables B l - B 6 , Appendix B ) . Unigene and RefSeq IDs were used i n the analysis to exclude redundant genes included in the array probe sets.  To exclude the possibility that contaminating P E F cells could distort the data, duplicate samples of P E F were analyzed for gene expression on the M G _ U 7 4 v 2 GeneChip. Data was scaled in M A S and normalized in GeneSpring (as for E S C data) before 1% o f the raw value (five times more than the maximum estimated contamination) obtained for the gene i n P E F was subtracted from the value o f the same gene i n undifferentiated R l E S C . The analysis steps to find differentially expressed genes were then performed as described above. Only genes detected as decreased during differentiation i n the original analysis were re-analyzed.  Genes whose  expression increased during differentiation would not be affected by P E F contamination in a way that, would give false positive results.  This indicated that none o f the genes that showed a  significant 2-fold or greater decrease in the initial analysis lost their significance after P E F subtraction (data not shown) and suggests that the levels o f P E F contamination were not sufficient to distort our E S C gene expression data.  2.3.3.  Gene Expression Validation  The expression patterns o f several genes previously implicated i n maintaining E S C pluripotency and self-renewal were analyzed to validate our data and the approach used to identify differentially expressed genes (Table 2.1).  L I F binds to the g p l 3 0 receptor that leads to  52  activation o f the transcription factor S T A T 3 (Smith 2001).  Stat3 was clearly detected i n  undifferentiated R l E S C and L I F removal decreased stat3 expression with the most pronounced effect during the first 18 hours (49% reduction). This was also seen for two other genes involved in the L I F / g p l 3 0 pathway, the Oncostatin M receptor, osmr, and the Interleukin 6 signal transducer, U6st. A known S T A T 3 target gene, piml (Shirogane et al. 1999), was also decreased significantly at the transcriptional level. Taken together, these observations confirm that the L I F / g p l 3 0 / S T A T 3 pathway was rapidly shut down following P E F and L I F removal. The bone morphogenetic proteins can act i n combination with L I F to sustain self-renewal and preserve multi-lineage differentiation, chimera colonization, and germline transmission properties (Ying et a l . 2003).  Bmp4 transcript levels were significantly decreased during differentiation.  However, the onset o f the decrease was later than for stat3 (between 18 and 72 hours, 80% reduction).  The same was seen for the zfp42 gene, k n o w n to be highly expressed i n  undifferentiated E S C and down regulated after retinoic acid induced differentiation (Hosier et al. 1989; Rogers et al. 1991). A 90% reduction in zfp42 expression was seen between 18 and 72 hours after L I F removal. Transcript abundance o f the akp2 gene coding for A P , commonly used as a marker for undifferentiated E S C , was also decreased significantly (55%) reduction between 18 and 72 hours).  Oct4 was highly expressed i n undifferentiated E S C but was not changed  significantly during the first 72 hours after L I F removal consistent with the protein expression data (Figure 2.2D). Transcript levels o f the embryonal stem cell-specific gene 1 (Esgl or developmental pluripotency associated gene 5, dppaS) did not change during differentiation. This is consistent with the observation that esgl is a downstream target o f oct4 and is downregulated slowly after oct4 suppression (Tanaka et al. 2002).  53  The abundance offoxd3, a  gene expressed early i n mouse embryonic development, also remained unchanged during the first 72 hours after L I F removal. The S R Y - b o x containing transcription factor sox2 may act to maintain E S C pluripotency and is expressed i n the I C M , epiblast and germ cells just like oct4 ( A v i l i o n et al. 2003). Sox2 was significant decreased (60% reduction between 18 and 72 hours, p=0.0136). Sox2, together with oct4, is involved i n the regulation o f Fibroblast growth factor 4 (fgf4), another factor confined to the I C M o f the blastocyst ( A v i l i o n et al. 2003). Although fgf4 expression was reduced, (52% reduction between 18h and 72h) the change was not statistically significant (p=0.09).  T h i s was also the case for the newly discovered marker o f E S C  pluripotency dppa3 (Bowles et al. 2003) which decreased 78% between 0-and 72 hours (p = 0.073). Similarly, the recently discovered regulatory factor Nanog (Chambers et al. 2003; Mitsui et al. 2003), which can rescue E S C from L I F / S T A T 3 dependence and maintain oct4 expression, did not show a significant 2-fold change during the first 72 hours o f differentiation (40% reduction, p=0.07). Several genes indicative o f E S C differentiation were also increased during differentiation. For example, the mesoderm marker brachyury (Wilkinson et al. 1990) increased 15-fold between 18 and 72 hours (p=0.003) and the ectoderm marker nestin (Wiese et al. 2004), increased approximately 3-fold between 18 and 72 hours (p=0.04).  The epithelial cell marker  promininl (Weigmann et al. 1997) increased 5-fold between 18 and 72 hours (p = 0.001).  In  conclusion most o f these changes are consistent with the loss o f E S C pluripotency as measured in all three assays and indicate that the thresholds used to define differentially expressed genes i n this study are reasonable.  54  2.3.4.  Quantitative RT-PCR validation  To further confirm the fidelity o f the microarray data, a set o f 28 genes was selected and their transcript levels were tested using Q - R T - P C R . The genes selected included some o f the genes mentioned above, as well as other genes increased, decreased, or unchanged after L I F removal according to the array results.  The fold change was calculated between 0 and 18 hours, 18 and  72 hours, and 0 and 72 hours respectively.  The analysis revealed a strong correlation between  the array data and the Q - R T - P C R results (Pearson's correlation, r=0.87, Figure 2.4A), with a tendency for Q - R T - P C R results to show greater changes than the array (proportional bias 1.4 i n Deming regression analysis).  To determine i f the gene expression changes observed i n the R l E S C are general and more broadly observed i n multiple E S C lines, the expression patterns o f 15 genes were followed for 72 hours after L I F removal in two other E S C lines, J l and E F C , and compared with the R l E S C line using Q - R T - P C R (Table 2.2). Overall, there was strong correlation between all three E S C lines (Figure 2.4 B - D ) . Specifically, Q - R T - P C R analysis was able to verify the expression changes o f genes such as lox, ankrdl and c-kit that showed significantly decreased transcript levels after 18 hours i n all three E S C lines tested.  Similarly, zfp42, sox2, leftb and mtf2 were all changed i n a  similar fashion i n the R l , J l and E F C cell lines. Q - R T - P C R results also confirmed that oct4 levels were not decreased significantly during the first 72 hours o f differentiation i n any o f the cell lines, i n agreement with array results and observed protein levels (Figure 2.2D). In contrast, although the decrease i n nanog was not significant i n R l E S C by array, the Q - R T - P C R results indicated a consistent reduction i n all cell lines. Only two o f the tested genes showed different  55  expression patterns amongst the cell lines. The mesoderm marker brachyury and the homeobox transcript factor pbx3 were both increased during differentiation i n the R l and J l E S C but not in the E F C cell line.  This observation suggests that the various E S C lines might follow slightly  altered differentiation pathways upon L I F removal.  These validation analyses demonstrated that the early changes during differentiation uncovered by the array analysis could be verified with an independent method and could in most cases be observed in multiple E S C lines. Taken together, they provide confidence i n our approach to identify differentially expressed genes with a high likelihood o f exhibiting true expression level changes during the loss o f E S C pluripotency.  2.3.5.  Functional Classification of Differentially Expressed Genes  We hypothesized that genes showing similar functional properties and expression patterns form interacting networks that contribute to the phenotypic and functional characteristics o f the cells of interest. Functional classification o f differentially expressed genes into appropriate biological processes was thus performed using NetAffx and GeneOntology Express as well as a simplified gene ontology tool i n GeneSpring. The differentially expressed genes were separated into 13 main categories (Figure 2.5A). M a n y differentially expressed genes were, as expected, classified as being involved i n development and differentiation (61 genes, 10%) or directly or indirectly involved in cell cycle control and cell proliferation (32 genes, 5%).  Furthermore, at least 81  genes (13%) were classified as being involved in intracellular signal transduction, cell-cell signaling and/or response to external stimuli.  56  A closer look at the signaling pathways affected i n E S C during differentiation revealed that the Yamaguchi sarcoma viral oncogene gene (yes) was decreased 60% between 0 and 72 hours. Yes has been shown to be regulated by L I F and to be important for E S C self-renewal (Anneren et al. 2004). Yes is a gene coding for a Src tyrosine kinase expressed i n both mouse and human E S C and is down-regulated when these cells differentiate (Anneren et al. 2004). The N o t c h ligand, jagged-1, previously implicated i n H S C self-renewal (Vas et al. 2004) exhibited a 55% decrease in expression between 0 and 18 hours.  Self-renewal o f E S C is influenced by the M A P K pathway where expression o f E R K and SHP-2 counteract the proliferative effects o f S T A T 3 and promote differentiation (Burdon et al. 1999). The E r k gene (mapkl or mapk3) and most other components i n this pathway were expressed i n undifferentiated E S C according to array results and their expression levels d i d not change significantly at the transcription level during the first 72 hours o f differentiation (data not shown).  Furthermore, two genes involved i n the S H P - 2 pathway, kras2 and mapkl2, were  increased significantly during differentiation indicating an activation o f this pathway. The gene coding for Grb2-associated binder 1 (gabl) was significantly decreased (58% reduction between 18 and 72 hours). G a b l binds to SHP-2 and is believed to suppress the M A P K pathway i n E S C . Increased synthesis o f G a b l together with Oct4 may suppress induction o f differentiation (Smith 2001). Although most o f the factors involved i n the M A P K pathway are primarily regulated at the post-transcriptional level, the fact that most o f the factors were detected i n undifferentiated E S C , at least at the transcriptional level, suggests that undifferentiated E S C already harbour the required components to quickly respond to signals that promote differentiation.  57  The W n t Signaling pathway is important for the maintenance o f pluripotency i n E S C and a recent study also showed that activation o f the Wnt pathway by pharmacological inhibition o f G S K - 3 was able to maintain pluripotency i n both human and mouse E S C (Sato et al. 2004). Most components o f the Wnt signaling pathway could be detected i n both undifferentiated and differentiated E S C and some factors were significantly changed during differentiation (e.g.fzd5, wnt3, cyclind3). However, overall there was no consistent change at the transcription level o f several factors belonging to this pathway during the first 72 hours o f differentiation (e.g. ficatenin, gsk-3, axin, data not shown).  The Hedgehog signaling pathway plays a critical role during development and has been extensively studied i n multiple species and tissues (Cohen 2003; Ogden et a l . 2004).  Four  components i n this pathway, two receptors, ptchl and ptch2, and two transcription factors, glil and gli2, were decreased significantly during E S C differentiation. Furthermore, bmp4, a putative downstream target o f this pathway was significantly decreased (discussed above) providing further evidence that this pathway might be involved i n maintaining E S C self-renewal and pluripotency.  Importantly, out o f the 473 differentially expressed genes, the largest group o f 173 genes had no known biological function. Further analysis o f these genes, especially those whose expression changes closely correlate with loss o f pluripotency might reveal novel mechanisms involved i n E S C maintenance.  58  O f the 275 genes significant decreased during E S C differentiation, 48 genes showed a more than twofold decrease i n transcript levels within the first 18 hour and continued to decrease or remained at a low level o f expression until 72 hours (Table 2.3). Changes i n the transcript levels for these genes correlated well with the functional assays (Figure 2.2). A t least half o f them have been identified as ESC-enriched or downregulated during differentiation i n previous studies (Brandenberger et al. 2004; Ivanova et al. 2002; K e l l y and Rizzino 2000; Ramalho-Santos et al. 2002; Sato et al. 2003; Sharov et al. 2003; Sperger et al. 2003) (see below), but several o f these genes have unknown biological function or have not previously been implicated i n E S C maintenance (e.g. lox, tnc and jagl; Table 2.5).  2.3.6.  Molecular Markers of Pluripotency  W e hypothesized that classification o f differentially expressed genes according to cellular component, i n combination with functional analyses, may lead to the identification o f new markers o f E S C potential (Figure 2.5B).  A t least 60 gene products (11%) were considered  localized to the plasma membrane and might therefore be good candidates as markers for E S C pluripotency. Both genes either increased or decreased during differentiation were included i n this list o f possible E S C markers since the former might be used to detect the onset o f differentiation and/or for negative selection strategies for isolating undifferentiated E S C . Protein expression o f some o f the genes has already been examined i n relation to E S C differentiation (i.e. C D 9 , C D 4 4 , Osmr) (Haegel et al. 1994; O k a et al. 2002; Rose et al. 1994; Wheatley and Isacke 1995). Additional gene products o f interest include the adhesion molecular V c a m l and the hedgehog signaling pathway receptors P t c h l and Ptch2.  59  One gene o f special interest  encodes the S C F receptor, c-kit. Array results indicated that expression o f the c-kit gene was one of the most significant changes during differentiation (54% reduction i n expression between 0 and 18 hours and 75% reduction between 0 and 72 hours, p=0.0016) and this was confirmed by Q - R T - P C R in multiple cell lines (Table 2.2). S C F was not added to the cell culture media used in these experiments and S C F was not expressed i n undifferentiated E S C according to array results. However, S C F was expressed at high levels in P E F (data not shown). F l o w cytometry revealed that expression o f c-kit on the cell surface o f differentiating E S C closely paralleled the loss o f functional potential as measured using the E B formation assay (Figure 2.6, Figure 2.7). A t later time points the correlation was not as close (i.e. E B formation capacity continued to decrease w h i l e protein expression remained unchanged)  suggesting the emergence o f  differentiated cell types expressing c-kit protein.  2.3.7.  Comparison with Previously Published Gene Expression Array Data  Gene expression profiling to determine regulatory factors and signaling pathways present in E S C has been performed extensively i n recent years (Bhattacharya et al. 2004; Brandenberger et al. 2004; Ginis et al. 2004; Ivanova et al. 2002; K e l l y and R i z z i n o 2000; Ramalho-Santos et al. 2002; Sato et al. 2003; Sharov et al. 2003; Sperger et al. 2003; Tanaka et al. 2002). It has been hypothesized that the undifferentiated state is conferred on various stem cell populations through the use o f similar molecular mechanisms.  Initial support for this hypothesis came from  experiments comparing the gene expression profiles o f multiple stem cell populations (Ivanova et al. 2002; Ramalho-Santos et al. 2002; Tanaka et al. 2002).  However,, further analysis o f  available data indicated minimal overlap between different published stem cell-associated gene sets (Evsikov and Solter 2003; Fortunel et al. 2003). The discrepancy observed amongst these  60  studies likely arises in large part from significant differences in the strategies used to identify the stem cell profile. However, true differences in stem cell biology probably also exist among stem cells taken from different tissues (e.g., E S C , neural stem cells, H S C ) . Some studies have relied on comparison o f E S C to terminally differentiated tissues (Kelly and Rizzino 2000; Sato et al. 2003; Tanaka et al. 2002) to establish a list o f genes enriched i n E S C . This strategy is likely to miss genes involved i n pluripotency that are transiently turned off early during the differentiation process but not uniquely expressed in the stem cell population. Another strategy used has been to compare stem cell populations arising from the same tissue source across species.  An  example is the comparison o f mouse with human E S C . Although mouse and human E S C are both derived from preimplantation blastocysts, they differ i n responsiveness to extrinsic signals and in expression o f surface markers; for example, L I F cannot sustain self-renewal o f human E S C , even i n the presence o f serum, suggesting the existence o f other signaling pathways essential for self-renewal in human E S C (Thomson et al. 1998). The evidence that human and mouse E S C share a common core molecular program is also somewhat conflicting (Bhattacharya et al. 2004; Ginis et al. 2004; Sato et al. 2003). However, the molecular mechanisms that confer pluripotency are likely evolutionary conserved, and thus comparison o f mouse and human E S C array data might still be informative.  The gene expression data reported here was compared with nine different array data sets, comprising four studies on murine E S C (Ivanova et al. 2002; K e l l y and Rizzino 2000; RamalhoSantos et al. 2002; Sharov et al. 2003), four studies on human E S C (Bhattacharya et al. 2004; Brandenberger et al. 2004; Sato et al. 2003; Sperger et al. 2003), and one study comparing  61  human and mouse E S C ( G i n i s et a l . 2004) (summarized i n Table 2 . 5 ) .  O n l y genes  downregulated during differentiation after L I F removal were used in the comparisons because most other studies only report ESC-enriched or ESC-specific genes. Complete gene lists derived from these comparisons can been found in Table B 7 , Appendix B .  Overall, there was a large overlap between the downregulated genes from our data and genes identified as ESC-enriched i n the other data sets, despite the wide variety o f experimental designs and cell lines used. O f the genes identified as significantly decreased after L I F removal in our study, 60% (164 o f 275) were found i n at least one other data set, and 28% (78 o f 275) were found in at least two other data sets. There was greater similarity with the murine data (129 of 275 or 47%o were found i n at least one other murine data set) than with the human data (75 o f 275 or 27%) were found in at least one other human data set).  Genes that we identified as  differentially expressed between 0 and 18 hours were the least represented in other published data (41%o between 0 and 18 hours compared with 67% between 18 and 72 hours and 63% between 0 and 72 hours), possibly because our approach to use early time points during differentiation for defining differential expression has not been widely used. Only two other studies included comparable early time points i n their study (Kelly and Rizzino 2000; Sharov et al. 2003). Within individual data sets, the degree o f overlap depended primarily on the number of genes i n the starting list (Table 2.5). The data set generated by K e l l y et al. (Kelly and Rizzino 2000) stood out because o f its high degree o f overlap (35%>), which can potentially be explained by the similarity i n the two studies i n terms o f experimental design and the similarity o f the genetic background o f the E S C used. However, this encompasses relatively few genes (6 out o f  62  17 genes). Furthermore, many genes that we report as decreased during the first 72 hours were reported as also being present i n human E S C by Sato et al. (Sato et al. 2003). However, they, were not identified as differentially expressed in that study. This discrepancy could possibly be due to the later time point (3 weeks) used for determining differential expression i n that study (data not shown).  N o single gene was found enriched i n every human and mouse E S C data set. Jadel, leftb, and smarcadl were the most commonly identified genes i n other data sets (i.e., i n six o f nine other data sets). Leftb is a TGF(3 family member, which is expressed on the left side o f developing mouse embryos and is implicated i n left-right determination (Meno et al. 1996). Jadel was identified as a gene involved i n anteroposterior axis development (Tzouanacou et al. 2003). Smarcadl has previously been identified as a marker o f preimplantation embryos (Schoor et al. 1993). The gene coding for tenascin was downregulated within the first 18 hours after L I F removal (Table 2.3) and was also commonly observed as differentially expressed i n other data sets (i.e., in four o f eight data sets). Tenascin, also known as hexabrachion and cytotactin3, is an extracellular matrix protein with a spatially and temporally restricted tissue distribution that is tightly regulated during embryonic development and i n adult tissue remodeling (Jones and Jones 2000).  Other commonly identified markers o f undifferentiated E S C were also found i n this  comparison. Sox2, zfp42, bmp4, and gbx2 were all observed i n at least three other data sets. This also included the gene lox, coding for lysyl oxidase, found i n three other murine data sets (Ivanova et al. 2002; Ramalho-Santos et al. 2002; Sharov et al. 2003). It was i n fact one o f the most pronounced and rapidly downregulated genes detected (Table 2.3).  63  A t least one study  identified c-kit as enriched i n human E S C (Sperger et al. 2003). Overall the comparison with previously published data indicated some intriguing similarities but also showed that most genes do not overlap in pairwise comparisons. This underlines the many differences that might arise when different sources o f cells, culture conditions, microarray technique platforms, and data analysis tools are used and emphasizes the need to correlate expression changes with rigorous measures o f E S C competence and differentiation potential.  2.4.  Discussion  The present study is distinct from previous studies i n two important aspects.  First and most  important, only a selected population o f germ line-competent E S C , grown under carefully controlled, optimized culture conditions, was used to establish the gene expression profiles. It is likely that substantial variations i n gene expression arise i n response to culture variables. Considering the multiplicity o f culture variables that can be important for the biological heterogeneity o f cell populations and their gene expression profiles, remarkably little information has been provided about the conditions used to generate the cells for the gene expression profiles reported thus far (Bhattacharya et al. 2004; Brandenberger et al. 2004; Ginis et al. 2004; Ivanova et al. 2002; K e l l y and Rizzino 2000; Ramalho-Santos et al. 2002; Sato et al. 2003; Sharov et al. 2003; Sperger et al. 2003; Tanaka et al. 2002). Second, few o f the previous studies addressing questions about a shared or common stem cell gene expression signature among different types of stem cells or different E S C lines, have involved correlative functional assays to assess the pluripotency and self-renewal capacity o f the cells o f interest.  B y combining the gene  expression profiling data with assays measuring E S C pluripotency and self-renewal, it should be  64  possible to more precisely define the genes critical for specifying these properties.  W e combined defined culture and differentiation conditions with various measures o f E S C pluripotency to determine optimal times for gene expression analysis using high-density oligonucleotide mieroarray.  The results showed that the functional capacity o f E S C declines  rapidly under differentiation conditions, with the most pronounced changes in function occurring during the first 18 - 24 hours o f differentiation.  Array results were subsequently used to find  genes whose expression correlated with loss o f pluripotency and that could explain loss o f this capacity during differentiation or serve as markers for pluripotent E S C . Analysis o f the array data revealed that 473 genes were differentially expressed during the first 72 hours o f E S C differentiation, suggesting they are potentially important for the maintenance o f E S C pluripotency and self-renewal.  Important roles i n the maintenance o f undifferentiated E S C have previously been demonstrated for several o f the downregulated factors such as stat3, zfp42, sox2, gbx2, and bmp4. Intriguingly, one third o f the differentially expressed genes have not been characterized i n terms o f involvement i n biological processes.  M o r e importantly, a refined list o f 48 genes whose  transcript levels closely correlated with the functional assays (i.e., with early and persistent decreases in transcript levels after L I F removal) contains several genes with unknown function or genes that have not previously been suggested to play a role in E S C maintenance (Table 2.3). These genes may be novel candidates that play critical roles in the regulation o f E S C pluripotency and self-renewal. Several o f the genes in this list have also been found in previous  65  gene expression studies in human or mouse E S C (e.g., tnc and lox; Table 2.3). Furthermore, several promising candidate markers for pluripotent E S C were identified from the array analysis (Table 2.4). The changes i n transcript levels observed for one gene, c-kit, were also verified at the protein level and showed good correlation with functional measures o f E S C pluripotency (Figure 2.7). This finding was verified in two other murine E S C lines (Table 2.2). T w o other commonly used E S C markers, Oct4 and S S E A - 1 , were also analyzed i n parallel with the functional assays used in this study but showed poor correlation with the outcome i n these (Figure 2.2D). Further studies are needed to determine i f c-kit is a more reliable marker o f undifferentiated E S C and, more importantly, whether it can be used to enrich for these cells. Additional work is also required to determine the functional significance o f these observations for E S C maintenance.  It is also important to note in the context o f these and other genes o f  interest that global gene expression profiling does not discriminate between changes arising at the level o f transcription versus m R N A stability. A l s o , changes in transcript levels in a subset o f cells may go undetected, and likewise subtle changes might arise from changes in just a small proportion o f the cells.  Transcriptional profiling o f various stem cell populations has been used to determine the types o f regulatory factors and signaling pathways present i n pluripotent cells, i n c l u d i n g E S C (Bhattacharya et al. 2004; Brandenberger et al. 2004; Ginis et al. 2004; Ivanova et al. 2002; K e l l y and R i z z i n o 2000; Ramalho-Santos et al. 2002; Sato et al. 2003; Sharov et al. 2003; Sperger et al. 2003; Tanaka et al. 2002). Comparing genes that were significantly decreased in our analysis with both murine and human data sets (Bhattacharya et al. 2004; Brandenberger et  66  al. 2004; Sato et al. 2003; Sperger et al. 2003) gave a large overlap with 176 o f 298 genes identified i n at least one o f these other studies. O f note, 139 o f these have been identified i n other murine E S C studies.  This analysis was not able to distinguish biological differences  between human and murine E S C because o f the way it was performed. The level o f overlap between our data and other murine studies was higher than the overlap with other human studies, but the difference could possibly be accounted for by the increased difficulty o f gene comparison between species. However, additional E S C gene expression profiling carried out in an similarly stringent manner (i.e., correlating measurable functional properties with transcript levels) is needed to accurately assess and determine the confidence i n the degree o f overlap in differentially expressed genes after E S C differentiation. Induction o f E S C differentiation by alternative methods or using E S C derived from mice o f different genetic backgrounds w i l l also be important to properly assess the generality o f these findings. Such analyses should be able to discriminate between treatment-specific or genetic-specific gene expression changes and those commonly observed under various conditions. These latter genes are more likely to play an important role in regulating E S C self-renewal and pluripotency.  67  Figure 2.1 Embryonic stem cell morphology during leukemia inhibitory factor removal Morphology o f the R l E S C grown on P E F with L I F is shown. E S C were followed for 72 hours after P E F and L I F removal, and pictures were taken at the indicated times.  ! ^* x200 >  i o  . °  x200  Q ,  (  >•  •;.  '  ,- -  tf100 Abbreviations: E S C - embryonic stem cells, P E F - primary embryonic fibroblasts, L I F leukemia inhibitory factor.  68  Figure 2.2, Assays o f embryonic stem cell pluripotency following removal o f leukemia inhibitory factor Comparison o f in vitro and in vivo functional measures o f E S C potential with expression o f the commonly used E S C markers Oct4 and S S E A - 1 . R l E S C were thawed, cultured and assayed as outlined i n Materials and Methods. The frequency o f cells capable o f ( A ) generating chimeric mice (two independent experiments), (B) giving rise to embryoid bodies (three independent experiments), or (C) giving rise to colonies i n the colony-forming cell assay (three independent experiments) is shown as a function o f time after L I F removal. (D) F l o w cytometry (three independent experiments) was used to determine the percentage o f cells expressing cell-surface S S E A - 1 and intracellular Oct4. Data are the mean ± standard error o f the mean for replicated experiments.  Time (hours)  Time (hours)  Abbreviations: E S C - embryonic stem cells  69  Figure 2.3 Hierarchical clustering o f array samples Pearson's correlation and average l i n k i n g clustering to determine reproducibility and interexperimental variability for array samples. Relative distance values are shown. 0.71  1 st experiment  84 1.07  2nd experiment  0 hours d  j  f  f  e  r  e  n  t  i  a  t  J  o  n  3rd experiment 1.1 0.92  3rd experiment  1.00 1 st experiment  18 hours differentiation  2nd experiment  0.51  1st experiment  0.82 3rd experiment  2nd experiment  70  72 hours differentiation  Figure 2.4 Correlation between array and quantitative R T - P C R results for multiple genes i n multiple cells lines (A) Pearson's correlation analysis was done on the log ratio values obtained from the Q - R T - P C R and the array fold changes (n = 84). ( B , C , D ) Comparison o f Q - R T - P C R results from the three different E S C lines are shown. Pearson's correlation analysis was done on the log ratios o f the values obtained with the 2 " method (n = 45 i n each comparison). a a C T  B  10 8 r = 0.94 6 4 2  ce  u CL  0 -2 -4 •6 -8 -10  •2  0  2  -2  Array log ratio '10  2  I 10  p  8  0  Rl - RT-PCR log ratio  8 -  r = 0.79  r = 0.83  6 • 6 4' 4 o  a.  8  2 2 0 0 -2  u  u.  -2 -4H  -4 -6 -6 -8 -8 -10 -10  -2  0  2  -8  Rl - RT-PCR log ratio  -4  -2  0  2  J1 - RT-PCR log ratio  Abbreviations: Q - R T - P C R - quantitative R T - P C R , E S C - embryonic stem cells  71  4  Figure 2.5 Annotation o f differentially expressed genes A l l genes differentially expressed during E S C differentiation were classified according to ( A ) biological process and (B) cellular component. Some genes are classified i n more than one category, resulting in the total number of genes indicated i n the figures being greater than the total number of differentially expressed genes. Development and differentiation, 61 10% Cell growth and maintenance, 32 5%  Unknown, 173 27%  Ceil proliferation, 32 5% Cell motility, 7 1% Cell adhesion, 17 3%  Cell death, 14 2%  Metabolism, 92 15%  Cell-cell signaling, 5 1% Signal transduction, 59 9% Response to external stimuli, 23 4%  Transcription, 84 13%  Transport, 32 5%  B  Plasma membrane, 60 11%  Unknown, 159 29%  Integral to membranes (other), 65 12%  Extracellular, 42 8%  intracellular unspecified 35 6%  Nucleus, 103 19%  Cytoplasm, 79 15%  72  Abbreviations: E S C - embryonic stem cells  73  Figure 2.6 F l o w cytometry profile to test for cell surface expression o f c-kit following removal of leukemia inhibitory factor Undifferentiated R l E S C (0 hour) and E S C differentiated 18 and 72 hours after L I F removal were analyzed with flow cytometry using c-kit antibody. Negative control was unstained cells.  0 hour  BsK'-'  64%  18 hours  K;..  29%  72 hours  p.' FSC  Negative control  •  1 8 %  c-kit  Abbreviations: E S C - embryonic stem cells, L I F - leukemia inhibitory factor, S S C - side scatter, F S C - forward scatter.  74  Figure 2.7 Correlation between c-kit expression and embryoid body formation ability Expression o f c-kit ( • ) was monitored at the indicated times during E S C differentiation using flow cytometry, and expression levels were plotted as percent gated cells versus time. E B - f o r m i n g cell frequency ( • ) was determined in parallel.  Time (hours) Abbreviations: E S C - embryonic stem cell, E B - embryoid body  75  Table 2.1 Expression change o f genes previously reported as enriched i n undifferentiated embryonic stem cells or to be markers of embryonic stem cell differentiation Expression relative to 0 hours after L I T removal Gene Symbol  Accession #  18 hours  72 hours  p-value  STAT3  N M 011486  0.51  0.41  0.0128 .  Osmr  N M 011019  0.4  0.18  0.0321  Il6st  N M 010560  0.62  0.4  0.0015  Piml  N M 008842  1.08  0.36  0.0106  Bmp4  N M 007554  0.99  0.4  0.0051  Zfp42  N M 009556  1.08  0.12  0.0005  Akp2  N M 007431  1.24  0.55  0.0168  Oct4  N M 013633  1.09  1.01  0.4823  Sox2  N M 011443  0.9  0.41  0.0136  Fgf4  N M 010202  1.39  0.67  0.0902  Nanog  X M 132755  0.82  0.59  0.069  FoxD3  N M 010425  1.74  0.95  0.549  Dppa3  N M 139218  1.06  0.24  0.0731  Dppa5  N M 025274  1.02  0.58  0.143  Brachyury  N M 009309  0.48  7.3  0.0034  Nestin  N M 016701  0.97  2.78  0.0384  Prominin 1  N M 008935  1.27  5.53  0.001  Relative expression levels compared with undifferentiated E S C are listed. The p values for the most significant fold change from Welsh t-tests are listed. Abbreviations: L I F - leukemia inhibitory factor.  76  Table 2.2 Comparison o f relative expression levels obtained by quantitative R T - P C R i n the J l , and E F C embryonic stem cell lines during differentiation Expression relative to 0 Hrs after LIF removal R l ESC line  Jl ESC line  E F C ESC line  Gene symbol  Accession #  18 hours  72 hours  18 hours  72 hours  18 hours  72 hours  Ankrdl  N M 013468  0.09  0.07  0.02  0.05  0.03  0.002  Lox  N M 010728  0.13  0.11  0.09  0.01  0.07  0.008  Kit  N M 021099  0.35  0.17  0.22  0.12  0.36  0.12  Leftb  N M 010094  0.58  0.41  1.84  0.51  0.69  0.27  Piml  N M 008842  0.81  0.48  0.66  0.39  0.48  0.22  Oct4  N M 013633  0.84  1.11  1.27  1.57  2 -  2.46  Nanog  X M 132755  0.91  0.53  0.62  0.29  0.59  0.23  Piml  N M 138606  0.87  4.14  1.87  9.19  1.32  17.15  Zfp42  N M 009556  0.9  0.05  1.07  0.01  0.97  0.005  Mtf2  N M 013827  1  0.3  0.93  0.13  0.68  0.1  Ptch  N M 008957  1.15  0.19  2  0.13  1.68  0.23  Sox2  N M 011443  2.07  0.35 .  1.15  0.11  1.04  0.17  Hck  N M 010407  2.3  0.05  1.11  0.01  1.19  0.03  Brachyury  N M 009309  0.54  23.4  0.07  14.9  0.15  0.59  Pbx3  N M 016768  2.93  3.73  1.04  2.38  0.24  0.55  '  Abbreviations: L I F - leukemia inhibitory factor, E S C - embryonic stem cell..  77  Rl,  Table 2.3  Genes decreased during embryonic stem cell differentiation and most strongly  correlated with the loss o f pluripotency Expression at 18 and 72 hours is relative to 0 hours. Gene symbol Vcaml Cd44 Cav Empl Csfl Osmr Abcal Jagl Kit Fzd5 Il6st Ly75 Icaml Gjb3 Cd9 Slc2a3 9430079M16Rik Ppap2a Gja7 Abcbla Enah Ptch Akp2. Slc29al Trfr Fgfr2 Utrn Ptch2 Akl Cdldl Adaml9 1600025H15Rik Ltb  18 hours Accession # Decreased  .72 Hours  p value 0.0344  N M  011693  0.08  0.08  N M  009851  0.08  0.19  0.043  N M  007616  0.2  0.23  0.0254  N M  010128  0.22  0.09  0.0302  N M  007778  0.36  0.49  0.0337  N M  011019  0.4  0.18  0.0321  N M  013454  0.42  0.05  0.006  N M  013822  0.45  0.47  0.0411  N M  021099  0.46  0.23  0.0016  N M  022721  0.47  0.31  0.0228  N M  010560  0.62  0.4  0.0058  N M  013825  0.8  0.31  0.035  N M  010493  0.81  0.31  0.0188  N M  008126  0.83  0.46  0.0423  N M  007657  0.99  0.45  ' 0.0345  N M  011401  1  0.44  0.0027  N M  175414  1  0.49  0.031  N M  008903  1.05  0.49  0.0041  N M  008122  1.1  0.22  0.0376  N M  011076  1.1  0.35  0.0201  N M  010135  1.21  0.31  0.0074 •  N M  008957  1.22  0.27  0.0012  .  '  N M  007431  1.24  0.55  0.0168  N M  022880  1.37 -  0.44  0.015  N M  011638  1.46  0.68  0.039  N M  010207  1.48  0.67  0.0293  N M  011682  1.49  0.59  0.0013  N M  008958  1.53  0.38  0.0054  N M  021515  1.53  0.66  0.0304  N M  007639  1.66  0.45  0.0291  N M  009616  1.82  0.45  0.002  N M  028064  1.88  0.83  0.0062  N M  008518  2.29  0.85  0.0202  9.13  0.0118  0.95  0.0079  Increased Enpp2 Dgka Cldn4 Cldn6 Btla Stl4  N M  015744  N M  016811  2.49 .  N M  009903  2.36  1.6  0.0472  N M  018777  2.14  3.4  0.0081  N M  177584  2.03  14.27  0.0034  N M  011176  1.73  2.45  0.0319  3.1  78  Gene symbol.. If&9 Podxl Itga8 Ddrl Cldn7 Spintl Met Rnfl28 Slc39a8 Tap! Proml Calcr Sfrp2 Tdpbp Perp Cd24a Dlkl Cask ' Gp38 Amot Lrp8  Accession # N M 033608 N M 013723 N M 001001309 N M 007584 N M 016887 N M 016907 N M 008591 N M 023270 N M 026228 N M 013683 N M 008935 N M 007588 N M 009144 N M 009318 N M 022032 N M 009846 N M 010052 N M 009806 N M 010329 N M 153319 N M 053073  18 hours 1.55 1.54 1.51 1.5 1.48 1.35 1.35 1.34 1.33 1.31 1.27 1.18 1.18 1.08 1.03 0.9 0.82 0.79 0.79 0.76 0.47  72 hours 2.22 2.54 2.81 2.39 5.97 2.5 2.18 2.51 2.32 2.73 5.53 6.05 2.28 2.21 2.39 2.73 1.77 1.91 1.63 4.1 2.26  p value 0.005 0.0019 0.0298 0.0264 0.00001 0.0138 0.0311 0.0453 0.0208 0.0074 0.001 0.0338 0.015 0.0202 0.0269 0 0.0291 0.0052 0.0323 0.0073 0.0255  p values for the most significant fold change from Welsh t-tests are listed.  79  Table 2.4 Differentially expressed genes with plasma membrane.localization  Gene symbol Collal Vcaml Thbsl •Acta2 Cav Empl S100a6 Akap2 Prss23 Dnajb9 Abcal Eif2s2 Ankrdl Fos Fosb Fosl2 A A 408868 Klf4 Bcl3 Tbx3 Aebp2 Egrl Mllt2h Cd44 Serpinel lnhba Csfl Ccl2 Apbblip Socs3 Osmr Mras Jagl Kit Fzd5 Lox  Expression relative Expression relative to 0 to 0 hours after LIF hours after LIF Accession # removal, 18 hours p value p value removal, 72 hours Cell adhesion N M 007742 0.05 0.00793 0.08 0.0278 N M 011693 0.08 0.03805 0.08 0.0344 N M 011580 0.11 0.01728 0.13 0.0203 Cell growth and maintenance N M 007392 0.13 0.0513 0.0472 o.i N M 007616 0.2 0.0254 0.0288 0.23 N M 010128 0.22 0.1 0.09 0.0381 N M 011313 0.38 0.113 . 0.16 0.0186 N M 009649 0.45 0.131 0.28 0.0159 Metabolism N M 029614 0.174 0.36 0.04408 0.46 0.0682 N M 013760 0.38 0.31 0.0216 N M 013454 0.42 0.149 0.05 0.006 N M 026030 0.43 0.00736 0.17' 0.017 Transcription 0.04 N M 013468 0.01611 0.07 0.0657 N M 010234 0.06 0.03985 0.11 0.0017 0.0094 N M 008036 0.25 0.02309 0.33 N M 008037 0.26 0.02917 0.31 0.283 N M 030612 0.27 . 0.5 0.01029 0.031 N M 010637 0.32 0.0021 0.00361 0.05 N M 033601 0.38 0.00266 0.38 0.157 0.41 NM_011535 0.01023 0.3 0.0214 N M 009637 0.44 0.00747 0.43 0.0002 N M 007913 0.49 0.02355 0.44 0.0258 N M 133919 0.5 0.283 0.32 0.0277 Cell development and differentiation N M 009851 0.08 0.00931 0.04301 0.19 N M 008871 0.11 0.0007 0.05 0.06 N M 008380 0.32 0.03052 0.29 0.02966 N M 007778 0.36 0.03371 0.49 0.0711 Signal transduction 0.04 N M 011333 0.0699 0.02 0.0279 N M - 019456 0.18 0.22 0.0664 0.04105 N M 007707 0.22 0.00055 0.15 0.0055 N M 011019 0.4 0.105 0.0321 0.18 N M 008624 0.43 0.04385 0.07 0.00001 N M 013822 0.45 0.04943 0.47 0.0411 N M 021099 0.46 0.0939 0.23 0.0016 N M 022721 0.47 0.0878 0.31 0.0228 Unknown biological process 0.02 N M 010728 0.00272 0.06 0.01  80  Gene symbol Fbln2 Tnc BB120430 Bgn C730049F20Rik - Sdpr Hnrphl Timp2 AI504685 C85523 All 15454 5730501N20Rik  Accession # N M 007992 N M 011607 AI847445 N M 007542 AI840339 N M 138741 N M 021510 N M 011594 AI504685 C85523 N M 175345 AI882080  Expression relative to 0 hours after L I F /removal, 18 hours. 0.14 0.14 0.14 0.15 0.16 0.18 0.33 0.41 0.44 0.45 0.48 0.49  Abbreviations: L I F - leukemia inhibitory factor.  81  p value 0.01975 0.02558 0.01359 0.01494 0.0534 0.123 0.04185 0.03587 0.01515 0.0718 0.0596 0.01931  Expression relative to 0 hours after L I F p Value removal, 72 hours 0.0054 0.09 0.09 0.0134 0.18 0.0224 0.1 0.0071 0.22 0.0361 0.11 0.0278 0.33 0.267 0.46 0.0298 0.34 • 0.0004 0.38 0.0449 0.31 0.0344 0.45 0.0068  Table 2.5 Summary o f published gene expression studies i n mouse and human embryonic stem cells used for comparison 1 ei liiinl<i»\ Kel'ereiiee  experimental <lesi«n  Undifferentiated ESC grown on PEF in the presence ol LIF, cultured for one passage on gelatin before R N A extraction; expression compared with differentiated (Ivanova et al. 2002) bone marrow and fetal liver cells Undifferentiated E S C cultured on gelatin in the (Kelly and Rizzino presence of LIF; expression compared with ESC differentiated for 96 hrs by treatment with 1 u M R A 2000) (Ramalho-Santos et Undifferentiated ESC grown on PEF for two passages al. 2002) before R N A isolation; expression compared with differentiated bone marrow and tissue from the lateral ventricles on the brain Expression profiles of 36 tissues from early development and adult stem cells, including undifferentiated ESC and ESC differentiated for 4 or 18 (Sharov et al. 2003) hrs in the absence of PEF and LIF  Cell line  philfni in  D3  Affymetrix M G U74v2 Atlas Mouse cDNA expression arrays  C57/B16  Affymetrix M G U74v2  CCE  cDNA library Rl construction Custom GE01,GE09, oligonucleotide BG01,BG02, Expression profiles of five E S C lines grown on PEF (Bhattacharya et al. glass arrays TE06 was compared with 8-day-old E B outgrowths 2004)  (if ne list used  Number of « e n e s (herkip  Supplementary online Table 1 (refiltered according to author's criteria)  2,270  90 (97)  Table 1  17  6  Database SI  1,787  82 (85)  Data set S7, groups F, I, L, and O, with comparable annotation  260  16(18)  Supplementary online Table 2  92  5  532  19(20)  918  39 (40)  1,760  45 (51)  13  2  Expression profiles of three undifferentiated ESC lines Massively compared with E B (8 days of differentiation), preparallel signature Supplementary online (Brandenberger et al. hepatocytes (induced by addition of DMSO), and preTable 2 H1,H7,H9 sequencing neural cells (induced by addition of retinoic acid) 2004) Affymetrix Undifferentiated E S C compared with E S C induced to Database 1 HGU133A HI (Sato et al. 2003) differentiated neurons for 3 wks Undifferentiated ESC compared with multiple cell lines, Supplementary online including germ cell tumor and other differentiated cell H1,H7, H9, Custom cDNA Table 6 arrays H13, H14 (Sperger et al. 2003) lines RT-PCR/focused functional Comparison between human and mouse undifferentiated Human H I , Table 3A mouse D3 microarrays (Ginis et al. 2004) ESC  2.5.  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M y e l o i d leukaemia inhibitory factor maintains the developmental potential o f embryonic stem cells. Nature 336(6200):684-7.  87  X u e Y , Gao X , Lindsell C E , Norton C R , Chang B , H i c k s C , Gendron-Maguire M , Rand E B , Weinmaster G , Gridley T. 1999. Embryonic lethality and vascular defects i n mice lacking the Notch ligand Jaggedl. H u m M o l Genet 8(5):723-30. Y i n g Q L , N i c h o l s J, Chambers I, Smith A . 2003. B M P induction o f Id proteins suppresses differentiation and sustains embryonic stem cell self-renewal i n collaboration with S T A T 3 . C e l l 115(3):281-92.  88  3. Increased Capacity of Mouse Embryonic Stem Cells to Form Embryoid Bodies Following Optimization of Ascorbic Acid, PD98059 And Chondroitin Sulphate Levels  2  3.1.  Introduction  The ability o f embryonic stem cells ( E S C ) to differentiate into all mature cell types represented has stimulated investigations to explore their use to replace tissues damaged through injury, disease or aging. However, clinical scale application o f many stem cell-based therapies w i l l require the development o f protocols for their expansion as an undifferentiated population prior, to the initiation o f differentiation.  Since regulation o f stem cell survival, proliferation and  differentiation are likely modulated by the integration o f multiple signaling pathways (Davey and Zandstra 2004), development o f these protocols requires systematic methods to investigate the influence o f multiple environmental cues simultaneously.  Mouse E S C (Evans and Kaufman 1981; Martin 1981) provide a useful model system to develop methods for the optimization o f stem cell cultures because o f their abundance, relative ease o f propagation and purity compared to adult stem cell populations.  In addition to leukemia  inhibitory factor (LIF), a number o f soluble and insoluble factors have been shown to influence E S C fate when added to the culture environment indicating the importance o f understanding the  A version of this chapter will be submitted for publication. Glover, C H , Eaves, C J , Piret, J M . Increased capacity of mouse embryonic stem cells to form embryoid bodies following the optimization of ascorbic acid, PD98059 and chondroitin sulphate levels.  2  91  influence o f multiple signalling pathways in directing cell fate (Prudhomme et al. 2004). For example, inhibition o f E R K 1 / 2 activation by the specific M A P K inhibitor PD98059 (PD) in the presence o f g p l 3 0 signaling leads to an increase i n E S C self-renewal (Burdon et al. 1999). Similarly, we have shown that ascorbic acid ( A A ) in the presence o f L I F increases the proportion o f EB-forming cells i n culture (Chapter 4). It appears that components o f the extracellular matrix also influence cell fate since E S C are maintained with higher efficiency when cultured on a layer o f inactivated feeders than when this is replaced w i t h gelatin (Glover et al. 2006) although the specific components have not been identified.  Chondroitin sulphate (CS) is a  component o f the extracellular matrix that is able to bind to growth factors and modulate their activity as w e l l as affect development and cell division (Sugahara et al. 2003).  Here, we  hypothesized that C S would have an influence oh cell fate and investigated its influence i n conjunction with P D and A A .  A s new factors with beneficial effects on stem cell maintenance and expansion are identified, their incorporation into existing culture medium can involve interactions, either negative or positive.  The large number o f factors present i n cell culture medium makes single factor  approaches impractical due to the number o f experiments that must be performed. In addition, single factor approaches are not well suited to uncover complex interactions that are seen when changes i n multiple factors are made.  Statistical design o f experiments, specifically factorial  design (FD) and central composite design ( C C D ) experiments, provide an efficient and rigorous statistical approach to efficiently determine  i n d i v i d u a l and multiple factor  responses  simultaneously. They make use o f geometric principles for statistical sampling and minimize the  92  variance o f estimated coefficients obtained by regression.  In addition, these designs can  minimize the impact o f unimportant factors (e.g. reagent batch, errors in cell counts, volume addition etc.) by grouping experiments into homogeneous blocks and by inducing replication. This is particularly useful when the magnitude but not the trend o f assay readouts is highly variable.  Statistical design o f experiments have previously been used to enhance stem cell  expansion and differentiation for a variety o f stem cell types. For example, a F D experiment was used to screen multiple factors for their influence on hematopoietic stem cell expansion, and this was followed by a C C D to optimize the level o f 3 o f these factors for maximal expansion (Audet et al. 2002). Statistical design o f experiments has also been used i n the context o f differentiation. Chang et al., investigated the effect o f 5 factors on endoderm differentiation from E S C (Chang and Zandstra 2004). Experiments o f this nature w i l l become increasingly important as clinical needs for stem cell based therapies develop. In particular, clinical application o f these therapies w i l l require the development o f xeno-free culture methods to avoid subsequent tissue rejection and immunological reaction (Martin et al. 2005). A s replacements for xeno-derived components of cell culture medium are discovered, significant effort w i l l have to be made to incorporate these into cell culture medium and to ensure that there are no negative consequences o f addition as a result o f factor interactions.  In the present study, we have determined the influence o f A A , C S and P D on E S C as measured by the subsequent ability o f treated E S C to form clonal E B . EB-forming cell frequencies were measured because this assay provides a relatively sensitive measure which correlates closely with the ability o f E S C - d e r i v e d cells to contribute to the formation o f embryos following  93  blastocyst injection (Chapter 2). Furthermore, the majority o f differentiation protocols require the formation o f E B prior to differentiation towards specific cell types. A statistical approach was used to determine factor interactions where a positive or negative interaction represents joint effects that are more or less than additive. Results from the interaction studies suggested specific molecular targets for A A and also allowed the determination o f a cocktail for the three factors that maximised E B output.  3.2.  3.2.1.  Materials and Methods  Embryonic Stem Cell Maintenance Cultures  R l (passage 17) E S C and primary embryonic fibroblasts ( P E F , StemCell Technologies) were maintained as described i n Chapter 2.  3.2.2.  Embryonic Stem Cell Experimental Cultures  E S C were thawed and maintained and prepared for experiments as described i n Chapter 2. Experimental cultures were performed on tissue culture dishes (Sarstedt) coated with 0.1% porcine gelatin (Sigma) with cells plated at a density o f 100,000 cells per 60 m m tissue culture dish. Cells were cultured for 72 hours with daily medium change before harvesting and testing in the EB-formation assay. Cells were counted using trypan-blue exclusion to test for viability. Differentiation media were based on maintenance medium with the following differences (a) L I F removal - maintenance medium minus L I F , (b) D M S O - maintenance medium minus L I F plus 1%> D M S O (Sigma), (c) R A - maintenance medium plus 2 u.M retinoic acid ( R A , Sigma) (d) A A + L I F - maintenance medium plus 50 u.g/mL A A (Sigma) and (e) A A - L I F - maintenance  94  medium without L I F plus 50 u.g/mL A A . Concentrated R A was prepared at a concentration o f 10 m M by dissolving 30 mg powder i n 10 m L 100% ethanol and stored at 4 ° C i n the dark. M e d i a was prepared by adding 10 p L o f thawed R A stock solution to 50 m L maintenance medium. Aliquots o f stock A A were prepared by dissolving in d F k O to a final concentration o f 10 mg/mL. P D was dissolved in D M S O to a final concentration o f 2 m M . Aliquots were frozen at -20°C for subsequent use. C S was dissolved in D M E M to a concentration o f 100 mg/mL and stored at 4 ° C for subsequent use. Cells were harvested daily for functional assay analysis ( C F C and E B assays - performed as described i n Chapter 2) or for R N A extraction.  3.2.3.  Design of Experiments  A two-level F D was used to screen initial factor levels and interactions (Table 3.1). H i g h levels used i n the F D were chosen based on the optimal level observed i n the single factor dose responses.  The F D experiment was repeated 3 times with no centre points included. Results  from these experiments were used to estimate parameter values for the following model:  Y-K  + P {AA) + P (CS) + P {PD) AA  +P s{AA AAIC  cs  m  • CS) + P {AA MIPD  • PD) + P  {CS  CSIPD  • PD)  where Y is either the net growth rate, the frequency o f EB-forming cells or the yield o f E B forming cells; K the intercept term; A A , C S and P D the coded factor terms (-1 or +1); p\ the main effect parameters and |3j/j the second-order interaction terms (i,j = A A , C S , P D ; i*j). Analysis o f variance was used to estimate the statistical significance o f the effects. Factor concentrations are varied on a log scale to satisfy the assumption that data are normally  95  distributed. Determination o f coefficients and statistical analysis was performed using J M P I N ( S A S Institute).  Further optimization o f factor levels was performed using 2-factor C C D to construct response surface maps o f net expansion, EB-forming cell frequency and yield o f EB-forming cells (Table 3.2, Table 3.3). Orthogonal, rotatable designs were used including 4 replicated centre points, 4 factorial points and 4 axial points. The data was analyzed to estimate parameter values for the following model: Y = K + fi {x ) + p {x ) 1  l  2  2  +A,(*i • * ) + Aj(*i) + ^2,2(4) 2  2  2  where x; are the log 10 (concentration o f factor i); Bi the main effect parameters, By the secondorder interaction terms and By the quadratic coefficients. Analysis o f variance was used to determine the significance o f the parameters.  Determination o f coefficients and statistical  analysis was performed using J M P I N ( S A S Institute).  3.2.4.  Flow Cytometry  Single cell suspensions were prepared and blocked for 10 minutes on ice with 5 p g / m l antimouse C D 1 6 / C D 3 2 (Fc B l o c k , B D Pharmingen) in phosphate buffered saline plus 2% F B S (PBSFBS).  Cells were washed once with P B S F B S and then incubated on ice for 20 minutes  with a phycoerythrin (PE)-conjugated anti-SSEA-1 antibody (clone M C - 4 8 0 , R & D Systems). Cells were then washed twice before subsequent preparation for annexin-V analysis. Annexin-V staining was performed using instructions provided i n the B D kit using 7 - A A D to exclude  96  nonviable cells. Samples were analyzed by flow cytometry using a F A C S C a l i b u r flow cytometer running C E L L Q u e s t software (BD). Data was analyzed using Flojo (Treestar).  3.2.5.  RNA extraction and Quantitative RT-PCR  These methods are described in Chapter 2. Primer sequences are shown in Appendix A .  3.3. 3.3.1.  Results Dose Response of Ascorbic Acid, Chondroitin Sulphate or PD98059  To define the dose response effects o f A A , C S and P D on the expansion o f E S C , R l E S C were cultured for 72 hours in adherent cultures on gelatin with daily medium exchanges i n the presence o f several concentrations o f A A , C S or P D . A d d i t i o n o f A A and P D significantly increased the yield o f EB-forming cells (p<0.05 for both) with 75 p g / m L A A and 20 p,M P D yielding the greatest expansion at 3.6 ± 0.1 and 1.9 ± 0.1-fold, respectively, over untreated cultures (Figure 3.1 A , Figure 3.2A). However, there was no significant difference between cells cultured at 50 or 75 u.g/mL A A .  Both factors decreased the average net cell growth rate (the  difference between cell proliferation and death) in a dose-dependent fashion (p<0.05, Figure 3 . I B , Figure 3.2B). The decreased net growth rate was compensated by an increased frequency of cells that were able to read out in the E B assay (p<0.05, Figure 3.1C, Figure 3.2C) leading to an overall expansion o f the number o f cells able to read out in the E B assay.  Addition o f C S between 0 and 2000 pg/mL led to a slight increase in the number o f EB-forming cells, but this change was not statistically significant (data not shown). This increase was due to  97  an increase i n the frequency o f EB-forming cells and there was no apparent trend i n the net growth rate.  3.3.2.  Interactions between Ascorbic Acid, Chondroitin Sulphate and PD98059  A 2-level, 3-way F D (Table 3.1, Table 3.3) was used to investigate the influence o f A A , P D and C S , individually and in combination, on the yield o f EB-forming cells, the net cell growth rate and the frequency o f EB-forming cells (Figure 3.3). In 8 experiments, a combination o f C S and P D consistently produced the largest number o f EB-forming cells, with a total yield 2.13 ± 0.27 times greater than untreated cultures. However, this was not significantly different from any o f the other combination treatments w h i c h yielded E B - f o r m i n g cell numbers as listed when compared to untreated cultures: 1.95 ± 0.08 (CS + A A ) , 1.65 ± 0.14 ( P D + A A ) and 1.73 ± 0.17 ( P D + C S + A A ) (numbers are fold-increase over untreated cultures).  The combination  treatments all yielded higher numbers o f EB-forming cells than the single factor experiments (1.30 ± 0.05 (CS), 1.34 ± 0.04 (PD) and 1.41 ± 0.05 ( A A ) ) .  Using analysis o f variance, these experiments were analyzed to estimate parameter values for equation 1. The magnitude o f each parameter and the p-value associated with them represent the influence o f each factor individually and i n combination on the particular culture output being modeled (yield o f EB-forming cells, net cell growth, and frequency o f EB-forming cells). Each individual component exhibited a significant positive effect on the yield o f EB-forming cells (p<0.05) including C S , which influenced yield by increasing the frequency o f EB-forming cells (p<0.05), but had no significant influence on the net cell growth. The F D revealed a significant negative interaction between P D and A A on the yield o f EB-forming cells (p<0.05) through a  98  reduction in the frequency o f EB-forming cells (p<0.1). Interestingly, there was a significant, positive interaction between these two factors with respect to total cell growth (p<0.1) but this was insufficient to counteract the negative effect on EB-forming cell potential.  3.3.3.  Central Composite Design Experiments to Determine Non-Linear Responses  Two C C D experiments were used to confirm factor interactions observed in the F D , to determine non-linear responses to these factors and to optimize concentrations for addition o f these factors in combination.  The first C C D experiment (Table 3.2, Table 3.3, Figure 3.4A) was performed with P D and C S in the absence o f A A since this was the combination from the F D that maximized EB-forming cell output.  Since a negative interaction was observed between A A and P D at the concentrations  tested in the F D , lower concentrations o f each o f these two factors were investigated in the C C D to explore this effect further.  The coded value o f +1 for C S and P D were fixed at the levels  tested in the F D experiment with C S being varied between 2 and 1250 p g / m L and P D between 0.08 and 50 p M around these points. The positive influence o f both C S and P D on the expansion of EB-forming cells was confirmed (p<0.05 and p<0.1 respectively) and a negative interaction between C S and P D revealed (p<0.1). Furthermore, there was a large, negative, quadratic term for P D ( L o g P D ) on the yield o f EB-forming cells (p<0.05) indicating that P D should be added 2  at lower concentrations to avoid high dose inhibition. This was consistent with the results seen in the single-factor dose response o f P D (Figure 3.2).  99  Since C S exhibited a large positive effect on EB-forming cell yield individually, its level was fixed at 500 p g / m L and the interaction between P D and A A was explored further i n a second C C D experiment (Table 3.2, Table 3.3, Figure 3.4B). The coded value o f +1 for A A was at 50 p g / m L as for the F D experiment with the corresponding range from 0.2 to 130 p g / m L .  The  range for P D was reduced because o f the negative interaction between it and the other two factors and the high dose inhibition that was observed i n the previous experiments. Thus, for P D the coded value o f +1 was at 10 p M with the corresponding range from 0.04 to 26 p M . Interestingly at the lower levels o f P D tested in this experiment (and contrary to the result observed i n the F D experiment, Figure 3.3), a positive interaction between A A and P D was observed (p<0.1).  However, the high dose inhibition o f P D was observed again (p<0.05).  Furthermore, A A exhibited a significant, positive quadratic term (p<0.1).  3.3.4.  Verification of Cocktail to Maximize Expansion of Embryoid Body-Forming Cells  A s a result o f the above experiments, a more favourable cocktail for the expansion o f E B forming cells was proposed to be: A A - 50 p.g/mL, C S - 500 p,g/mL and P D - 2 p i M . To confirm that this would promote the expansion o f EB-forming cells as expected this cocktail was compared to (1) the expansion o f untreated cells, (2) a combination which consisted o f all factors at concentrations determined from their isolated dose responses ( A A - 50 p g / m L , C S - 500 pg/mL and P D - 20 p M ) , (3) a two-way combination of A A and C S : A A - 50 pg/mL, C S - 500 pg/mL, and (4) a two-way combination o f P D and C S : P D - 20 p M , C S - 500 pg/mL (Figure 3.5).  100  A s expected, the optimized cocktail expanded E B - f o r m i n g cells 3.1 ± 0.2-fold more than untreated cells (p<0.05). The cocktail derived from the optimal concentrations during individual dose responses expanded E B - f o r m i n g cells almost 1.8 ± 0.2-fold over untreated cultures (p<0.05) but was less than both two way combinations as a result o f the high-dose inhibition o f P D and negative interactions between P D and A A .  3.3.5.  Influence of Ascorbic Acid, Chondroitin Sulphate and PD98059 on SelfRenewal and Apoptosis  A s indicated by the F D and C C D results, there was a significant negative interaction between A A and P D on the yield o f EB-forming cells at their optimal single factor concentrations but a positive interaction when P D was tested at lower concentrations. To gain further insight into the cellular kinetics responsible for this interaction, we investigated the activity o f these factors, as well as C S , on self-renewal and apoptosis. Cells were cultured i n maintenance medium in the presence o f each o f the single factors alone ( A A , P D or C S ) or i n a C S / P D combination for 72 hours. After this time, the C F C assay was used to determine the influence o f these components on self-renewal over this period. P D alone increased the yield o f C F C 2.1 ± 0.1-fold (p<0.05) while addition o f A A showed a marginal increase i n C F C (1.2 ± 0.1-fold increase, p>0.05, Figure 3.6A). Addition o f C S in combination with P D increased the yield o f C F C 4.8 ± 1.1-fold compared to untreated controls (p<0.05) while C S alone increased C F C yield 1.4 ± 0.4-fold (p>0.05, Figure 3.6B).  101  These results suggested that the addition o f P D either increased the proliferation rate o f undifferentiated cells or decreased their differentiation rate. T o distinguish between these two possibilities, the proportion o f colonies containing undifferentiated E S C was determined i n C F C assays using A P staining. Addition o f P D to C F C assays did not lead to a statistically significant increase i n the proproportion o f undifferentiated colonies i n culture (Figure 3.6C).  An  enhancement o f the self-renewal ability o f cells combined with the presence o f similar numbers o f differentiated and undifferentiated colonies suggested that addition o f P D increased the proliferation rate o f undifferentiated cells. The proportion o f undifferentiated colonies increased from 62% to 83%> when C S and P D were added i n combination and there was a concordant decrease i n the proportion o f differentiated colonies. This was i n contrast to the addition o f C S or P D alone where there was no significant change in the frequency o f both types o f colonies (Figure 3.6C).  The inability o f A A to increase the yield o f C F C implied that its primary action was through a change i n the relative apoptosis rates o f differentiated and undifferentiated cells. Consistent with this, the proportion o f AP-positive colonies i n C F C was indistinguishable from untreated cultures while the proportion o f AP-negative colonies was decreased (Figure 3.6C). To test specifically whether A A increased the apoptosis rate o f differentiated cells, cells were cultured for 48 hours in the presence o f A A with no medium change. Cells were harvested and stained for S S E A - 1 (to distinguish between differentiated and undifferentiated cells), A n n e x i n - V (to identify cells undergoing apoptosis) and 7 - A A D (to identify nonviable cells). There was no significant change in the apoptosis rate o f SSEA-1-positive cells under any o f the treatments. In contrast, A A  102  significantly increased the proportion o f SSEA-1-negative, 7-AAD-negative cells undergoing apoptosis relative to untreated cultures (3.4 ± 1.2-fold, p<0.05, Figure 3.6D).  3.3.6.  Differentiation Ability of Treated Cells  To verify that the ability o f E S C to differentiate to specific lineages was not compromised by addition o f A A , C S and P D , cells were grown i n medium with all three added at the optimal concentration ( A A - 50 pg/mL, C S - 500 p g / m L and P D - 2 p M ) for 48 hours with daily media exchange and then, to induce differentiation, replated into medium without L I F or into medium containing L I F and R A . A s a control, parallel cultures were maintained i n standard conditions for the same length o f time. The gene expression level o f 5 lineage marker genes was compared to untreated cells using Q - R T - P C R (Figure 3.7).  Removal o f L I F induced the increased expression o f genes associated with ectoderm, neural and mesoderm differentiation (fgf5, nestin, and brachyury  respectively), but had little effect on the  expression o f genes associated with endodermal differentiation (foxa2 and soxl 7). In contrast, treatment with R A strongly induced the markers o f neural and endodermal differentiation, but had no significant effect on the expression o f brachyury expression (ectodermal differentiation).  (mesodermal differentiation) and fgf5  Although the specific levels o f each o f these makers  was different, i n all cases differentiation towards the specific lineages appeared to be enhanced indicating that differentiation ability o f A A , C S or P D treated cells was not compromised.  103  3.4.  Discussion  This work has demonstrated that A A , C S and P D all have positive influences on the absolute y i e l d o f E B - f o r m i n g cells.  Furthermore, statistical design o f experiments allowed the  optimization o f the levels o f individual factors to maximize EB-forming cell yields.  These  experiments also revealed an unexpected negative interaction between P D and A A , suggesting that ascorbic acid acts through suppression o f the M A P K pathway.  A A , C S and P D each led to an increase i n the expansion o f EB-forming cells relative to untreated controls although the specific mechanism o f this enhancement appears to be different for each factor. While all three factors increased the frequency o f EB-forming cells i n culture, both A A and P D decreased the net growth rate whereas C S had no effect on the net growth rate. Nevertheless, P D increases the overall yield o f C F C indicating an enhancement o f E S C selfrenewal not obtained with A A . The fact that there was no change i n the proportion o f undifferentiated colonies obtained from cells in PD-treated cultures indicated that the increased yield o f C F C was due to an increased rate o f proliferation o f undifferentiated relative to differentiated cells ones.  The effect o f C S on culture output only became significant in the presence o f P D and A A , yet no factor interactions involving C S were revealed i n the F D experiment.  This suggests that C S  acted either through the stabilization o f P D and A A , both o f which are known to be labile, or by binding these compounds and making them more available to be taken up by the cells. However, when P D was tested at a lower concentration, as in the first C C D , a negative interaction between  104  C S and P D was revealed. This suggests that there is a critical level o f P D below which C S has an inhibitory effect suggesting some kind o f binding mechanism.  This observation is also  consistent with the observation i n the second C C D experiment, that P D alone has no significant effect since i n this experiment a constant level o f C S was maintained throughout.  C S has  previously been shown to stabilize growth factors and increase their functional availability (Sugahara et al. 2003). The combination o f P D and C S led to a significant reduction i n the rate o f spontaneous differentiation i n culture, as demonstrated by the increased proportion o f undifferentiated colonies i n culture.  C S is a glycosaminoglycan and is distributed widely  throughout the body primarily as a modification on many extracellular matrix proteins. C S has previously been shown to have functions i n C N S development, wound repair, growth factor signaling and cell division (Sugahara et al. 2003). Since optimal expansion o f E S C takes place in contact with irradiated P E F , addition o f C S to cultures that do not contain P E F may be necessary to replace this lost source. In development, an essential role for C S has been described in C . elegans where disruption o f a gene required for production o f C S resulted i n defects i n cytokinesis and early embryonic death (Mizuguchi et al. 2003).  The influence o f A A was minimal on the yield o f C F C indicating that it had little effect on selfrenewal. Rather, A A increased the apoptosis rate o f differentiated cells as demonstrated by an increased apoptosis rate among SSEA-1-negative cells as w e l l as a decreased number o f differentiated colonies in AA-treated cultures.  There have been no previous reports o f an  enhancement on E S C culture output by A A i n the presence o f L I F .  Some clues as to its  mechanism o f action are given by the negative interaction that was observed between A A and  105  P D in the F D experiment. Since it is known that P D inhibits the activation o f E R K 1 / 2 , it could be hypothesized that addition o f A A similarly inhibits this signaling pathway. However, given the differential response o f cells to the two factors in terms o f self-renewal, the mode o f action could also be through an effect on another step in the M A P K signal transduction pathway, or through a parallel pathway which influences the same molecular targets.  Surprisingly, a positive interaction was observed between A A and P D i n the second C C D experiment.  A possible explanation for this finding could be that there is a critical level o f  M A P K suppression that maximizes E S C output and above this, E S C output is decreased. Thus, when P D and A A are both present at lower levels as is the case in the second C C D experiment, a positive interaction is observed, but when they are at higher levels as seen i n the F D , a negative interaction is observed.  Support for this hypothesis is provided by the high dose inhibition  observed for P D (Figure 3.2, Figure 3.4) and the observation that a combination o f all three compounds, albeit with P D at a lower level, outperforms any tested two-way combination o f the factors (Figure 3.5). This is similar to the high dose inhibition seen i n many growth factor dose responses (Audet et al. 2002)  It is known that A A acts as an antioxidant i n many cellular systems including E S C ( C H G , unpublished observations) where it reduces the levels o f toxic reactive oxygen species such as hydrogen peroxide and the superoxide ion (McEligot et al. 2005). Hydrogen peroxide has been shown to cause the activation o f the M A P K pathway (Guyton et al. 1996), suggesting that  106  addition o f A A leads to a suppression o f the M A P K pathway through a reduction i n intracellular hydrogen peroxide.  Qualitatively there is no difference i n the ability o f the cells to differentiate towards specific lineages when treated with C S , P D or A A .  However, it appears that treatment increases the  proportion o f cells differentiating towards the mesodermal and endodermal lineages. It would be interesting to explore this further to see whether treatment i n specific ways can enhance differentiation towards specific lineages as has been previously observed for A A .  A A can be  used, i n the absence o f L I F , to drive differentiation to specific differentiated cell types including cardiomyocytes (Takahashi et al. 2003), neurons (Lee et al. 2000) and osteoblasts (Buttery et al. 2001).  It is unclear i n these protocols whether A A is i n fact directing cell fate or merely  enhancing the activity o f other compounds present i n the differentiation medium.  A n inherent problem with the functional assays used here is the shifting magnitude o f readout that is observed from experiment to experiment despite consistency within an experiment. It is important to note that despite this shift, trends remain consistent. The cause o f this change is unclear but it can provide a challenge i n process development and has caused us to propose surrogate assays for E S C developmental potential (Chapter 4).  In situations such as these,  statistical design o f experiments such as F D and C C D can be very useful tools to minimize the impact o f these unimportant sources o f variance. This is done by inducing replication within blocks o f experiments.  In this way statistically significant results can be obtained from a  minimal number o f experiments. This advantage is in addition to providing specific information  107  regarding factor interactions that would not be uncovered by single factor dose response type experiments.  In summary, we demonstrate here that A A and P D both increased E S C culture output when added i n the presence o f L I F . Furthermore, C S when added i n combination with either o f these factors also had a positive impact on E S C output. Statistical design o f experiments allowed the optimization o f E S C culture output w i t h respect to these three factors and uncovered unanticipated factor interactions. Further investigation into the mechanism by which each o f these factors act revealed that P D and C S each reduce the spontaneous differentiation observed in standard E S C culture while A A increase the loss o f differentiating cells.  Despite the  optimization methods shown here, the yields o f E S C are still not comparable to cultures with P E F , suggesting that identification o f more molecules is required to fully mimic these conditions. While signalling through LIF/gpl30/Stat3 and hence the action o f P D on human E S C is not relevant, experiments should be performed with A A and C S to determine their activity i n these cultures.  These experiments  should provide a useful framework for m a k i n g  improvements in the output o f undifferentiated E S C i n culture.  108  further  Figure 3.1 Dose response o f embryonic stem cells to ascorbic acid (A) Y i e l d o f E B - f o r m i n g cells (B) net growth rate and (C) frequency o f E B - f o r m i n g cells following treatment o f E S C cultures for 72 hours. B 18  o e  £8 CL X U •O  16  14  "o 12 o  u o 2  o  O  o.oo  a.  0  20  40  60  80  0  100  20  40  60  80  40  60  80  100  Ascorbic Acid concentration (ng/mL)  Ascorbic Acid concentration ((ig/mL)  0  20  100  Ascorbic Acid concentration (ng/mL)  Abbreviations: E B - embryoid body, E S C - embryonic stem cell.  109  Figure 3.2 Dose response of embryonic stem cells to PD98059 (A) Y i e l d of E B - f o r m i n g cells (B) net growth rate and (C) frequency o f E B - f o r m i n g cells following treatment of E S C cultures for 72 hours. B  10  20  30  10  40  20  30  30  40  PD98059 concentration <uM)  PD98059 concentration (uM)  10  20  40  a,  PD98059 concentration (\xM)  Abbreviations: E B - embryoid body, E S C - embryonic stem cell.  110  Figure 3.3 Parameter estimates derived from factorial design experiments Parameter estimates for the response o f (A) yield o f EB-forming cells, (B) net growth rate and (C) frequency o f EB-forming cells. Parameters derived from factorial design experiment. ** indicates p < 0.05, * indicated p < 0.1. A  B  PD x AA  PDx AA  CSxAA  CSxAA  CSxPD  CS x PD  AA  AA  PD  PI)  CS  CS  -0.2  -0.1 0.0 0.1 0.2 Parameter Estimates  —  - 2 - 1 0  0.3  Parameter Estimates  Parameter Estimates  Abbreviations: P D - PD98059, A A - ascorbic acid, C S - chondroitin sulphate  111  I  Figure 3.4 Parameter estimates describing yield of embryoid body-forming cells in two central composite design experiments (A) Experiment varying P D and C S (no A A ) . (B) Experiment varying A A and P D with C S held constant. See Table 3.3 for levels of factors i n these two experiments. ** indicates p < 0.05, * indicated p < 0.1. A  ^  ^  PD*PD  ^  ^  =  >  B  *  CS'CS CS'PD  ^  — —  —i  * *  PD  CS  ** i -0.03  1 1  1 1 1 1  i' -0.02  1 1 1  i ' r^- -0.01 0.00 1  1 1  0.01  Parameter Estimates  0.02  0.03  -0.04  -0.02  0.00  0.02  0.04  Parameter Estimates  Abbreviations: P D - PD98059, A A - ascorbic acid, C S - chondroitin sulphate  112  0.06  Figure 3.5 Verification o f factor combination that maximizes embryoid body-forming cell output compared to other factor combinations and untreated cells Numbers in column labels indicate concentrations. Units are \xg/mL for A A and C S , p M for P D . ** indicates p < 0.05.  1.6—1  untreated  500CS+20PD  500CS+50AA  500CS+20PD+50AA 500CS+50AA+2PD  Treatment  Abbreviations: P D - PD98059, A A - ascorbic acid, C S - chondroitin sulphate  113  Figure 3.6 apoptosis  Effect o f ascorbic acid, chondroitin sulphate and PD98059 on self-renewal and  (A) Y i e l d of CFC-forming cells following treatment with A A and P D (B) Y i e l d o f C F C forming cells following treatment with C S and P D , either individually or in combination (C) Proportion o f A P positive and A P negative cultures i n C F C containing A A , C S , P D and P D + C S . (D) Influence o f A A on apoptosis rates o f S S E A - 1 positive and negative cells. B 100000 80000  y 3  60000  C M  o 2  40000  '>> 20000 untreated  Ascorbic Acid  untreated  PD98059  Treatment  CS  PD  CS-PI)  Treatment  D 3 10  untreated  AA  CS  PD  CS+PD  >  6  B  2  a. o  Treatment  untreated  Ascorbic acid  PD98059  Treatment  Abbreviations: C F C - colony forming cell, P D - PD98059, A A - ascorbic acid, C S chondroitin sulphate, A P - alkaline phosphatase.  114  Figure 3.7 Affect o f differentiation by either leukemia inhibitory factor removal or retinoic acid addition on lineage marker genes of ascorbic acid, chondroitin sulphate and PD98059-treated cells Brachyury  -LIF  RA  Differentiation Treatment  -LIF  -LIF  RA  Differentiation Treatment  RA  Differentiation Treatment  Abbreviations: L I F - leukemia inhibitory factor, R A - retinoic acid  115  Table 3.1 Design matrix for a two-level, three-factor factorial design experiment Specific concentrations for each factor are shown i n Table 3.3.  Run 1 2 3 4 5 6 7 8  CS  .. A A -1 1 -1 -1 1 1 -1 1  -l -l l -l l -l l l  PP -1 -1 -1 1 -1 1 1 1  Abbreviations: P D - PD98059, A A - ascorbic acid, C S - chondroitin sulphate  116  Table 3.2 Design matrix for a two-factor central composite design experiment Specific concentrations for each factor are shown i n Table 3.3.  Run  Factor 1  Factor 2  1 2 3 4 5 6 7 8 9 10 11 12  -1.63 -1 -1 0 0 +1 +1 +1.63 0 0 0 0  0 -1 +1 -1.63 +1.63 -1 +1 0 0 0 0 0  117  Table 3.3 Factor concentrations used i n the factorial design and central composite design experiments Units for A A and CS are pg/mL and units for P D are p M .  Level  -1.63 -1 0 +1 +1.63  Factorial Design Experiment PD AA CS 0 50 -  0 500 -  0 20 -  1 Central Composite Desig n Experiment AA' ' PD CS st  -  2 5 50 500 1250  0.08 0.2 2 20 50  2  Central Composite Design Experiment PD ' A A • CS n d  0.2 0.5 5 50 130  500 500 500 500 500  Abbreviations: P D - PD98059, A A - ascorbic acid, CS - chondroitin sulphate  118  0.04 0.1 1 10 26  3.5.  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Introduction  Various types o f stem cells are now recognized as responsible both for the generation o f tissues and organs during embryonic development and also for the subsequent maintenance and repair o f these tissues and organs throughout adult life.  This has led to considerable interest i n the  potential o f these stem cell populations to be exploited as cellular therapies for medical conditions where tissue damage or malfunction is severe and irreversible.  The clinical  realization o f stem cell-based therapies w i l l , however, rely on the development o f robust, scalable methods for the ex v i v o expansion and controlled manipulation o f these cell populations. Development o f such protocols requires extensive testing o f multiple factors and culture conditions (Zandstra and Nagy 2001) but this is currently inhibited by the lack o f rapid endpoints o f stem cell frequency that can be used in high throughput assays.  The specific identification o f most stem cell types currently relies on the use o f functional assays to detect their developmental potential, either in vitro or in vivo (van Os et al. 2004).  Such  assays are thus, by their very nature, retrospective, protracted, cumbersome and labor-intensive. A version of this chapter has been published as Glover, C H , M a r i n , M , Eaves, C J , Helgason, C D , Piret, J M , B r y a n , J . Meta-analysis of differentiating mouse embryonic stem cell gene expression kinetics reveals early change of a small gene set. P L o S Computational Biology 2(11): e l 5 8 , 2 0 0 6 .  3  121  These features make such assays impractical for large-scale studies and/or rapid screening methodologies. Monitoring critical changes i n gene expression using either microarray or highthroughput quantitative R T - P C R ( Q - R T - P C R ) platforms offers a potentially attractive solution but depends on the identification o f a set o f genes whose expression changes predict decreased stem cell frequency with adequate precision and specificity.  Recently, several groups have described differences in the gene expression profiles o f several types o f stem cells and their differentiating progeny (Brandenberger et al. 2004; Ivanova et al. 2002; Ramalho-Santos et al. 2002; Sato et al. 2003; Sharov et al. 2003; Tanaka et al. 2002). Most o f these investigations have resulted in lists o f genes that are too large for comprehensive assessment o f their functional significance or specificity. Moreover, many have focused on the detection o f altered patterns o f gene expression that are more likely to be indicative o f emerging differentiated lineages than o f altered transcription o f genes responsible for sustaining the stem cell state. In many cases, the actual stem cell content o f the population was insufficient to infer any changes in the stem cell subset. Mouse embryonic stem cells ( E S C ) are less problematic i n this regard because o f their ease o f propagation i n culture as a predominantly undifferentiated population (Evans and Kaufman 1981; M a r t i n 1981) and the availability o f w e l l defined protocols for inducing their rapid differentiation into particular lineages. A few genes that are important to the maintenance o f the pluripotent status o f mouse E S C , like oct4 (Niwa et al. 2000) and nanog (Chambers et al. 2003), have been identified. However, recent studies o f the rate at which functional measures o f stem cell frequency o f mouse E S C are lost indicated that these occur well before changes in oct4 expression are initiated (Chapter 2). The goal o f this study  122  was to identify a robust set o f early gene expression changes that would be reliable indicators o f decreased pluripotent cell content i n mouse E S C cultures, regardless o f the differentiation stimulus applied. In the following, the E S C signature change is defined as a set o f gene expression changes that are indicative o f E S C loss from a population.  To identify candidates for inclusion in the E S C signature change, we sought genes that exhibited a pattern o f expression consistent with functional assay output i n 3 independently generated data sets from ESC-derived cell populations that had been treated for up to 96 hours in several ways to induce differentiation. This strategy required innovation i n statistical methodology since the E S C signature change is more complicated than simple differential expression. Here, we present a statistically rigorous approach where the probability that a gene exhibits a predetermined expression pattern is estimated using a semi-parametric bootstrap.  The definition o f the E S C  signature change was specific to each experimental context and, therefore, we obtained from each data set an objective summary o f the evidence that a gene is part o f the E S C signature change. Genes that exhibited the strongest evidence across all 3 data sets were then tested in other maintenance or differentiating conditions and shown to successfully predict functional assay readout, indicating their potential to be used as an assay i n high-throughput screening experiments.  123  4.2.  4.2.1.  Materials and Methods  Embryonic Stem Cell Maintenance Cultures  J l ( L i et al. 1992) (passage 14) and R l (Nagy et al. 1993) (passage 17) E S C lines and primary embryonic fibroblasts (PEF) were maintained as described i n Chapter 2  4.2.2.  Embryonic Stem Cell Experimental Cultures  Experiments were performed as described in Chapter 3.  4.2.3.  Embryoid Body and Colony Forming Cell assays  Assays were performed as described in Chapter 2.  4.2.4.  RNA Extraction and Array Hybridization  Cytoplasmic R N A was extracted using the RNeasy m i n i k i t (Qiagen, Mississauga, O N ) . Standard Affymetrix protocols (Affymetrix, Santa Clara, C A ) were used to generate R N A probes from 5 pg o f extracted R N A . Samples were hybridized to M O E 4 3 0 A & B chips on a Genechip system (Affymetrix) at the Ottawa Genome Sciences Centre (Ottawa, O N ) according to the manufacturer's instructions.  4.2.5.  Gene Expression Data Sets  Gene expression data was obtained at several time points i n 3 independent experiments i n which various differentiation induction protocols were applied to 3 mouse E S C lines. Summaries o f each o f the experiments are shown i n Table 4.1.  124  Dimethylsulfoxide ( D M S O ) and retinoic acid ( R A ) - i n d u c e d differentiation o f R l cells ( D M S O / R A data set):  Independent, duplicate cultures o f R l cells (Nagy et al. 1993) were  cultured for 96 hours with leukemia inhibitory factor (LIF) ± 2 p M R A or without L I F + 1% DMSO.  Samples were hybridized to Affymetrix M O E 4 3 0 A and B Genechips (Table 4.1,  ArrayExpress (Parkinson et al. 2005) accession number: E - M E X P - 4 1 2 ) .  Induction o f R l E S C differentiation by L I F removal ( R l - L R data set): These data have been described in detail in Chapter 2. Briefly, R l cells were cultured i n the absence o f L I F for 0, 18 and 72 hours. R N A for the 18-hour sample was generated from cells cultured i n suspension, while R N A for the 72 hour sample was generated from cells cultured i n mefhylcellulosecontaining medium.  Samples were generated independently i n triplicate and hybridized to  Affymetrix M G _ U 7 4 v 2 A , B and C Genechips (Table 4.1, ArrayExpress accession number: E MEXP-414).  Induction o f R l , J l and V 6 . 5 differentiation by L I F removal ( M - L R data set): This data set is available from StemBase (www.scgp.ca:8080/StemBase/) (Perez-Iratxeta et al. 2005). R l , J l ( L i et al. 1992) and V 6 . 5 (Eggan et al. 2001) E S C were transferred onto 0.1% gelatin-coated dishes for 48 hours with L I F prior to inoculation in Petri dishes in the absence o f L I F and R N A extracts obtained from 0 to 96 hours later. R N A samples were generated independently in triplicate and hybridized to M O E 4 3 0 A and B Affymetrix Genechips. (Table 4.1, Gene Expression Omnibus (Edgar et al. 2002) accession numbers: GSE2972 ( R l data), GSE3231 (V6.5 data) and GSE3749 ( J l data)).  125  4.2.6.  Gene Expression Analysis  In this analysis, as is common practice, hybridization data for each probeset was considered independently, although we recognize that transcripts for many genes would be captured by multiple probesets. Furthermore, although the correspondence between probeset and gene is not, as a rule, one-to-one, we refer to the expression from each probeset as i f it reflected the expression o f one gene, unless otherwise stated.  A l l pre-processing, including background correction, normalization, probeset summarization, and log2 transformation, was carried out with the R M A algorithm (Irizarry et al. 2003) in the affy package (Gautier et al. 2004) from Bioconductor (Gentleman et al. 2004) and processed data returned by R M A are referred to as expressions.  In the equations below we follow these conventions: •  Observed intensities were denoted by Y  tc o n d  , where i indexed the biological replicate, i.e.  i€E | l , K , « } , and cond denoted the corresponding condition, i.e. treatment or time. •  A l l models are gene specific although, for the sake o f simplicity, an explicit index for gene was avoided.  •  W i t h i n the observations for one gene, the random errors e were assumed to be independent and identically distributed and to have expectation zero and a finite, gene specific variance o  2  , where exp\s D M S O / R A , R l - L R , or M - L R .  126  To summarize gene expression changes, we fit linear models to the R M A processed data. For the D M S O / R A data we used the following model: ^i,cond  =  where and  P  /J,  +UF  D  M  S  +  Pcond  i,cond  +  0)  £  was the expected intensity i n the +LIF control condition, (PRA)  0  ^+UF  B  +UF  = 0 by definition,  was the effect o f D M S O ( R A ) treatment, relative to +LIF.  For the R l - L R data we used the following model: Y  it  = ft,* + PISH * A.ISA + P™ * ^TZH  where (x was the expected intensity at time 0, j8 0h  (72 hours vs. 18 hours), and I  statement  + u  ()  £  2  ((3 ) was the effect o f 18 hours vs. 0 hours  18A  72h  was 1 i f the statement was true and I  statement  was 0 otherwise.  For the M - L R data we used the following model: Y  it  = Mo + PiJ + P  q  u  J  2  +  (3)  where /J, was the expected intensity at time 0, t was log2 transformed and centered time where 0 0  hours was changed to 3 hours to avoid undefined values, and /3  /in  and f}  quad  gave the linear and  quadratic effects of time, respectively.  4.2.7.  Defining the Embryonic Stem Cell Signature Change in Terms of Gene Expression  For the D M S O / R A data, a gene had to fulfill the following requirements to be included in the E S C signature change:  127  •  Absolute change: fi  •  Change relative to variability:  •  Consistency: P  cond  > C , or fi ahs  for cond = D M S O and cond = R A  rel  or P  DMSO  RA  for cond = D M S O and cond = R A  abs  >C  o  >0&p >0,  DMSO  < -C  cond  <0 & p  M  <0  For the R l - L R data, expression requirements were as follows:  P +p  •  Absolute change:  •  Change relative to variability: l^ *  •  Consistency: /3  l8h  >C  12h  18  {Sh  or, /3  ahs  s 0 & fi  +  + p12h < -C  ahs  & *l > C 2  o  rel  a 0, or fi  12h  18A  nh  =s 0 & fi  12h  £0  For the M - L R data, the definition o f an interesting expression pattern was more complicated. We required a large absolute difference i n the expected expression intensity between the start time, t  , o f the study and the end, f  min  max  . Specifically, we required that  Given equation 3, it can be shown that this is equivalent to the following requirement:  where TD = k  C  TD  TD,  TD,  q  m  d  -C  TD  TD,  TD,  - ?* . The relative expression change requirement that in  E[Y, \  )-E(Y,.  'max /  ) \  *rrun /  >C •rd  re!  o is equivalent to the following condition:  128  (5)  C  PR , >> _i£L(j_ ^ ^ GUA  TD  _ Z _ L ft  ^  P/,„  o  Pft, ^ <<  r  -C r  A  TD  Q--  o  t  ^  1  ft P  m  Consistency was built into the E S C signature change definition by noting the location i n the time course o f the vertex o f the quadratic fit (equation 3). The requirement for strictly increasing or decreasing expression patterns was relaxed by allowing genes whose vertex fell before C after C „ m  to be retained, where C • and C  iTiaX  '  in in  m i n  or  are specified relative to the standardized,  v  rritiA  A  transformed study time. This requirement is captured in the following condition: /  B  ^  PM  min  t  „  TD  \  < C-  m n  2  or  r-quad  I  TD  2  „  >C  In all experiments, the specific values o f the user-specified thresholds are given in Table 4.2. Note that the final results o f this analysis are highly robust to modest differences in these thresholds.  4.2.8.  Confidence Values  One thousand simulated data sets were generated for each o f the original data sets by adding the original fitted averages and a randomly sampled residual (with replacement) from the residuals associated with that gene within the data set. Note that for the M - L R data set, the bootstrap data was derived from time and cell line specific averages and residuals, not from quadratic fits. In order to maintain the covariance between genes, the same random selection o f residuals were used for all genes i n a simulated data set. The simulated data was then assessed relative to the E S C signature change definitions given above. The proportion o f times that a gene fulfilled the definition, i.e. the confidence value ( C V ) , was calculated. A s a practical measure, within each  129  experiment, we used several values for C  abs  and averaged the resulting C V to obtain the C V  used for Pareto Front Analysis ( P F A ) . This was an expedient method for reducing the frequency o f C V equal to 0 or 1, which are highly undesirable for P F A . The use o f multiple thresholds makes the final results o f our analysis robust to modest changes. The most stringent values o f C  abs  were chosen such that the number o f genes retained i n each data set was approximately  equal and a range o f less stringent thresholds was applied to create further distinction among the CVs.  4.2.9.  Comparison of MOE430 and MG_U74v2 Chips  To compare gene samples hybridized to the M O E 4 3 0 and M G U 7 4 chips, 6 different comparisons were used. Two comparisons were generated from the Affymetrix-defined "good" or "best" comparisons (for more information see www.affymetrix.com). Resourcerer (Tsai et al. 2001) was used to generate lists o f comparable probesets based on E G O , Unigene, Locuslink and Refseq comparisons. A list o f 2-way comparable probesets was generated by ordering evidence for comparison as follows: Affymetrix Best > Affymetrix Good > E G O > Unigene = Locuslink = Refseq. In this way 21, 271 one-to-one mappings between the 2 chips were made.  4.2.10.  Gene Ontology  Functional information about differentially expressed genes was obtained by loading Affymetrix identifiers into the Database for Annotation, Visualization, and Integrated Discovery 2.0 (Dennis et al. 2003). Gene ontologies were determined and compiled at several different levels o f the ontology.  130  4.2.11.  Quantitative RT-PCR  Quantitative R T - P C R ( Q - R T - P C R ) was performed as described i n Chapter 2 and primer sequences are shown in Appendix A .  4.3.  4.3.1.  Results  Functional Assay Analysis  To define the time course o f changes i n the biological properties o f E S C subjected to the differentiation protocols used for gene expression analyses, R l E S C were cultured on 0.1% gelatin coated tissue culture dishes with L I F ± R A or without L I F ± D M S O and aliquots then sequentially tested i n 2 colony assays for undifferentiated cell activity - the colony forming cell ( C F C ) assay performed i n liquid cultures containing L I F and the embryoid body (EB)-forming cell assay performed i n a semi-solid medium without L I F (Figure 4.1 A , B ) . The loss o f these activities more closely parallels the loss o f stem cell activity measured by contribution to chimeric mice than the loss o f expression o f S S E A - 1 or Oct4 (Chapter 2). In the starting population 20.9 ± 0.2% o f the R l cells were detectable as C F C and 11.2 ± 0.4% gave rise to E B . Exposure to R A had the fastest effect causing a reduction o f both these values ~20-fold within 24 hours, whereas simply removing L I F (with or without D M S O addition) caused a corresponding reduction o f these values ~6 and 12-fold i n the same time frame. After 96 hours, C F C and E B forming cell frequencies were less than 1% i n a l l treated cultures.  In control cultures the  frequencies o f both C F C and EB-forming cells were sustained at half o f the starting value as noted previously when R l cells are transferred from cultures containing feeders to gelatin-coated dishes (Chapter 2).  131  To verify that each treatment induced cells to differentiate towards different lineages, we used QR T - P C R to monitor changes in transcript levels for 5 differentiation markers in samples obtained after 96 hours o f treatment with the 3 differentiation protocols (Figure 4.1 C ) . A l l fold-changes were statistically significant relative to the +LIF condition unless otherwise stated. A s expected, removal o f L I F , with or without D M S O treatment induced the increased expression o f genes associated with ectoderm, neural and mesoderm differentiation: fgf5 (Haub and Goldfarb 1991), nestin (Dahlstrand et al. 1995), and brachyury  (Wilkinson et al. 1990), respectively. However,  there was little effect on the expression o f genes associated with endodermal differentiation: foxa2 and soxl 7 (Kubo et al. 2004). In contrast, treatment with R A strongly induced the markers o f neural and endodermal differentiation, but decreased the expression o f  brachyury  (mesodermal differentiation) and had no significant effect on fgf5 expression (ectodermal differentiation). Overall, all treatments generated a mixed populations o f cells.  4.3.2.  Gene Expression Analysis  For the data from each experiment we applied a gene specific linear model to separate the observed expression into a level for that gene under a reference condition (e.g. +LIF or time 0) plus effects due to treatment and random fluctuation due to b i o l o g i c a l variability and measurement error. For example, to analyze the D M S O / R A data set, the following model was used: expression = expression in "+LIF" + change due to treatment ( D M S O or R A ) + noise In this case, the three model parameters o f primary interest were the change attributable to the effect o f D M S O , the change attributable to the effect o f R A , and the typical magnitude o f the  132  noise.  These changes can be visualized easily by plotting parameter estimates on a  "transcriptome plot" where each gene is represented by a single point (Figure 4.2). For the data from the D M S O / R A experiments, most o f the genes in such a plot were found to lie close to the origin (Figure 4.2 A ) indicative o f their unaltered expression following either treatment. However, it is interesting to note that for those genes whose observed expression change was greater than 2 i n either treatment (463 genes), both treatments appeared to have similar effects, as indicated by the fact that 98% o f these were either increased (209 genes, top right quadrant) or decreased (243 genes, bottom left quadrant) by both treatments. A similar model was fit to the R l - L R data set to obtain estimates o f expression changes 18 and 72 hours after the removal o f L I F , as w e l l as a measure o f the noise when this differentiation induction protocol was used (Figure 4.2 C ) .  For the M - L R data set, use o f an analysis o f variance-type model, such as those described above, would have resulted i n a large number o f model parameters.  Since interpretability o f the  parameters is so important i n our context, we preferred a smaller, smoother quadratic model based on time. This model was able to capture the temporal trends o f expression changes and principal component analysis strongly suggested that a linear combination o f constant, linear and quadratic terms explained almost all o f the data variability.  4.3.3.  Identification of a Robust Set of Early Gene Expression Changes that Indicate Decreased Frequency of Undifferentiated Embryonic Stem Cells  We defined the E S C signature change as a set o f gene expression changes that were associated with decreased frequencies o f E S C as indicated by functional assay readouts during E S C culture.  133  To identify genes that exhibit patterns consistent with the E S C signature change, we imposed three requirements on each data set. When customized to a specific experimental context, these requirements constitute an expression-based definition o f the E S C signature change, a prerequisite for developing an appropriate statistical procedure.  The 3 requirements used to select expression changes for inclusion were: (1) large change i n absolute magnitude, (2) consistent change for all treatments and cell lines, and (3) large change relative to gene specific variability. The first two requirements can be visualized as retaining expression changes falling i n certain regions o f the transcriptome plots shown i n Figure 4.2 (namely, those regions containing black points). The third requirement cannot be visualized directly in a transcriptome plot, but its effect is revealed by the fact that some expression changes in the highlighted regions are not retained, due to large gene specific variance. Applications o f these requirements are shown i n Figure 4.2 with full explanation detailed i n Section 4.2.7. Specific values o f the thresholds used for each data set are shown in Table 4.2.  4.3.4.  Confidence Values  One way to detect E S C signature change genes is to identify those genes whose observed expression patterns fall i n regions o f interest i n transcriptome plots (Figure 4.2). However, this approach ignores the biological and technical noise contained in the observed data. Furthermore, it fails to distinguish between genes whose observed expression changes barely fulfill our requirements from those that substantially exceed the specified thresholds.  For genes o f the  latter type, we have more confidence that their true, long-run expression patterns are compatible with our definition o f the E S C signature change. W e therefore decided to define and exploit a  134  probabilistic quantity that measured our confidence, given the observed data, that a gene exhibits an expression pattern consistent with the E S C signature change (Bryan et al. 2002; van der Laan and B r y a n 2001).  W i t h i n each experiment, we defined a quantity p for each gene g: the g  probability that, i n a hypothetical repeat o f the experiment, the observed expression change o f this gene would fulfill our requirements. Genes with true expression changes that substantially exceed all relevant thresholds have a p greater than those that barely fulfill the requirements. I f g  two genes shared common expression changes but differed with respect to their background variability, the p o f the gene with less variability w o u l d be greater. g  A l s o , as biological  replication increases, the p o f true E S C signature change genes approach 1 and those o f all other g  genes approach 0. Just as p-values are used to rank genes with respect to differential expression, we used p to rank genes with respect to their consistency with the E S C signature change. Note g  that genes o f primary interest have a p value near 1, not near 0, as is the case with p-values. g  B y definition, knowing p requires knowledge o f the true change i n expression following g  induction o f differentiation, which is not available. Therefore, we estimated p by calculating the g  proportion o f bootstrap data sets i n which gene g exhibited data that fulfilled our requirements (van der Laan and Bryan 2001) and referred to this quantity as the confidence value ( C V ) (Efron and Tibshirani 1998).  4.3.5.  Meta-Analysis via Pareto Optimization  After calculating C V for all genes in the 3 experiments, we conducted a meta-analysis to identify the gene expression changes across all data sets most compatible with the E S C signature change. Genes with expression changes most correlated with decreased frequency o f E S C pluripotency  135  would have a C V near 1 in all 3 experiments. If we were working with only one data set, we could rank the genes by C V in decreasing order. However, with C V arising from 2 or more data sets, the task o f ranking becomes considerably more difficult.  In fact, it is only possible to  partially order the genes and we accomplished this with P F A (Fleury et al. 2002). Briefly, i n P F A , a comparison is made between all pairs o f genes and gene g is said to dominate gene k if, in all experiments, the C V s o f gene g are greater than or equal to those o f gene k, with strict inequality in at least one experiment. The set o f genes not dominated by any others is called the 1 Pareto front (PF) and contains the most promising candidates for the E S C signature change. st  This set is removed from the analysis and the same principle o f non-domination is then used to derive successive PFs. A more detailed explanation o f P F A can be found i n (Fleury et al. 2002). The first 5 PFs identified changes i n expression o f 89 probesets representing 88 genes (10, 7, 17, 27 and 28 probesets on PFs 1-5, respectively). The genes on PFs 1 to 5 are shown in Table 4.3.  4.3.6.  Q - R T - P C R Verification of A r r a y Results  Experiments were undertaken to test, by an independent strategy, the consistency o f the candidates identified from the analysis with the definition o f the E S C signature change. Accordingly, R N A extracts were prepared from R l and J l cells cultured for 0, 24, 72 and 96 hours with L I F ± R A or without L I F ± D M S O . Q - R T - P C R was used to measure the changes in levels o f 22 selected transcripts (relative to the cells cultured in the presence o f L I F ) . Nine o f these were for genes in the 1 P F {103728_at, esrrb, nrObl, tell, hck, gbx2, kl/2, fbxol5  and  st  sppl), 4 for genes i n the 2 (sox2,jam2,  morc,podxl,  n d  P F (tcfcp2ll, 8430410A17Rik,  sodl),  2 in the 4  th  P F (nrld2,  zfp42, klf4), 5 for genes in the 3 P F rd  kit) and 2 in the 5  th  P F (mt/2, nmycl).  These genes were purposefully chosen to have both high and moderate confidence i n their E S C  136  signature change membership (i.e. from the I  s  to the 5  PFs).' This tested the breadth o f the  correlation between the Q - R T - P C R and array results across the complete set o f genes contained in the first 5 PFs.  Q - R T - P C R results were compared to their corresponding array data, except i n the R l - L R data set where the 24-hour Q - R T - P C R results were compared to the 18-hour array data. A l l comparisons o f the data sets for matched treatments are plotted in Figure 4.3. Results from the Q - R T - P C R measurements and the microarray analyses were strongly correlated in both cell lines ( R l cell line: Figure 4.3 A , r=0.76; J l cell line: Figure 4.3 B , r=0.82) although the individual changes i n gene expression measured by Q - R T - P C R were generally larger than those apparent from the microarray data. There was also a strong correlation between the data obtained for the 2 different cell lines tested (r = 0.86, Figure 4.3 C ) . Overall, o f the 22 genes tested, 18 demonstrated the expected kinetics when assessed using Q - R T - P C R .  In particular, 7 genes evaluated (i.e. from P F 1: 103728_at, klf2, nrObl and tell; from P F 2: 8430410A17Rik  and zfp42; and sox2 from P F 3) showed rapid (within 24 hours) and sustained  changes i n expression in both E S C lines in all differentiation induction protocols (Figure 4.4). These 7 genes were tested for their ability to predict the time course o f functional changes i n populations o f E S C treated with another differentiation protocol, i.e. exposure to 50 [xg/mL ascorbic acid ( A A ) i n the absence o f L I F , a treatment reported to promote the generation o f cardiac myocytes from undifferentiated E S C (Takahashi et al. 2003). Accordingly, R l E S C were cultured for 5 days on 0.1% gelatin coated tissue culture dishes i n standard maintenance  137  conditions and with A A ± L I F and changes i n gene expression compared to the loss o f E B forming potential.  A s expected, E B potential decreased to 40% o f its starting value i n the first 24 hours after transferring the cells to the control (+LIF) conditions (without feeders) and then stayed constant over the remaining 5 days o f the experiment (Figure 4.5 A ) . Cells cultured without L I F with added A A showed a rapid decrease in E B potential to almost undetectable levels by day 3. In the presence o f A A and L I F , there was an enhanced yield o f EB-forming cells (with a doubling o f the proportion o f EB-forming cells when compared to the control +LIF conditions, Chapter 3).  Figure 4.5 C shows the time course o f changes i n transcript levels for the 7 genes that had previously been identified as showing rapid changes i n expression i n all tested differentiation conditions. It can be seen that all were reduced in the cells exposed to A A in the absence of L I F , consistent with the concordant rapid loss in E B potential. Moreover, when A A was added i n the presence o f L I F , the level o f expression o f these genes increased relative to the +LIF control cells, consistent with their opposite biological response to the A A treated cells i n the absence o f L I F . Zfp42 showed the most rapid increase in expression in the AA+LIF-treated cells and the increase in expression o f 103728_at and sox2 was the most delayed. Nevertheless, significantly increased expression o f all 7 genes relative to the +LIF controls was seen by 96 hours. Together, these results indicate that changes in expression o f these 7 genes can be used to infer concordant functional changes in populations o f E S C i n culture.  138  4.4.  Discussion  In this work, we have identified a small set o f genes that exhibit the E S C signature change, i.e. whose altered expression is consistently and temporally correlated with an altered frequency o f functionally-defined, undifferentiated cells in E S C cultures. This result is important because we had previously found that significant changes o f more established molecular markers o f undifferentiated mouse E S C (Oct4 and S S E A - 1 ) may not occur until well after the biological hallmarks o f these cells have been lost (Chapter 2). B y undertaking an integrated analysis o f gene expression changes induced by exposure o f E S C to multiple differentiation stimuli and the use o f objective statistical methods, we identified 7 genes whose altered expression correctly predicted concomitant functional changes induced by other treatments.  Importantly, the  expression changes o f these genes reflected both decreased and increased frequency o f E S C . It is interesting to note that while we did not specifically require a high expression level, the genes identified all exhibited expression above up to two fold higher than the average intensity o f expression seen on the microarrays. This was due to our requirement that genes exhibit a large fold change relative to the estimated error. Since genes that are expressed at low levels in cells tend to have a high error estimate, these are effectively eliminated.  Four o f these 7 genes have been shown previously to be involved i n the maintenance o f mouse E S C or during early development. These include nrObl (Swain et al. 1998), sox2 ( A v i l i o n et al. 2003), tell (Narducci et al. 2002) and zfp42 (Rogers et al. 1991). A m o n g the remaining 81 genes on the first 5 P F s , an additional 11 have been reported to be involved i n some aspect o f development (see Table C I , A p p e n d i x C ) . M o s t notably hck (Ernst et al. 1994),  139  fbx015  (Tokuzawa et al. 2003), dppa3/stella  (Bortvin et al. 2003) and klf4 ( L i et al. 2005) have all been  specifically implicated i n maintenance or differentiation o f E S C , while expression o f eed ( M o r i n - K e n s i c k i et a l . 2001) is required for embryonic v i a b i l i t y before implantation. Interestingly, oct4 (Niwa et al. 2000) and nanog (Chambers et al. 2003) were not included in the first 5 PFs. While oct4 was differentially expressed in both the D M S O / R A and M - L R data sets, it was not changed i n the R l - L R data set as shown previously (Chapter 2).  Nanog  was  differentially expressed in both the R l - L R and D M S O / R A data sets but did not show any change in the M - L R data set.  Several previous mieroarray studies have been performed to uncover signalling pathways and regulatory factors required for the maintenance o f human and mouse E S C (Bhattacharya et al. 2004; Brandenberger et al. 2004; Ivanova et al. 2002; Ramalho-Santos et al. 2002; Sato et al. 2003; Sharov et al. 2003; Sperger et al. 2003). These have each uncovered large numbers o f genes whose expression was increased or absent i n undifferentiated cells and, i n some cases, little overlap has been found between the genes thus affected (Evsikov and Solter 2003; Fortunel et al. 2003).  A s a further step towards assessing the validity o f our identified genes, we  compared our data to 2 previously published data sets that sought to identify genes uniquely expressed i n mouse E S C (Ivanova et al. 2002; Ramalho-Santos et al. 2002). O f the 88 genes highlighted here, 75 were also changed i n the other data sets (Table C 2 , Appendix C ) . This high degree o f correspondence supported the validity o f our very different approach to a similar biological question. Interestingly, a comparison o f our results to genes whose expression has been reported to accompany the differentiation o f human E S C (Bhattacharya et al. 2004;  140  Brandenberger et al. 2004; Sato et al. 2003; Sperger et al. 2003), revealed far fewer similarities, as reported by others (Ginis et al. 2004). W e found that only 26 o f the 88 genes, exhibited similar expression changes in at least one o f the 4 human E S C studies. Moreover, in some cases, the gene expression level changed i n the opposite direction. For example, i n this work, podxl was found to be strongly increased as mouse E S C differentiate, whereas the human homolog was found to be decreased i n 3 studies o f human E S C differentiation (Bhattacharya et al. 2004; Brandenberger et al. 2004; Sato et al. 2003). Overall, only 6 o f the 88 genes were not identified as being altered in any other published data sets o f differentiating E S C .  The goal o f this work was to identify a small number o f genes, suitable for the development o f an expression-based assay to estimate the frequency o f E S C i n culture. To achieve this, we have taken care to seek gene expression changes that fulfill several criteria beyond simple differential expression, including large, rapid and consistent changes in more than one cell line following the induction o f differentiation using multiple methods. In terms o f statistical analysis, we required (1) a quantitative index that reflected each gene's compliance with the predefined E S C signature change (for use within each data set); and (2) a meta-analytic procedure for ranking genes based on their compliance with the E S C signature change definition i n multiple data sets.  A s our quantitative index, we chose the probability that a gene would exhibit the E S C signature change in a hypothetical repeat o f the experiment, instead o f the more conventional p-value, the probability that a gene would exhibit data as, or more "extreme' than, the observed change i f its true expression were unchanged i n the study.  This choice was necessitated because i n our  141  application, the E S C signature change is defined by more than a requirement for differential expression. In the majority o f microarray applications, the genes o f interest are characterized by differential expression, where the complementarity o f the null hypothesis (no differential expression) and the biologically interesting state (differential expression) permits the p-value to serve as an index o f biological interest.  Here we employed an index that originates with an  explicit definition o f the biologically interesting target profile. This is an application o f methods previously described for identifying genes with biologically specific expression patterns (Bryan et al. 2002; van der Laan and Bryan 2001). The task o f identifying E S C signature change genes can be viewed also as an instance o f the so-called "problem o f regions" (Efron and Tibshirani 1998), i n which the term "confidence value" is first established. In certain settings, C V s can be shown to be approximations o f Bayesian a posteriori probabilities. A l t h o u g h not formally established here, the C V can be interpreted heuristically as the posterior probability that a gene truly exhibits the E S C signature change.  A s our method o f meta-analysis, we used Pareto Front Analysis ( P F A ) (Fleury et al. 2002) to (partially) rank genes based on three independent measures o f E S C signature change compliance, as opposed to the more prevalent practice o f integrating experiment-specific fold-changes (Stevens and Doerge 2005; Wang et al. 2004), p-values (Levesque et al. 2006; Rhodes et al. 2002), test statistics (Estrada et al. 2006; Ghosh et al. 2003), or effect size estimates (Choi et al. 2003; H u et al. 2005). In these works, the common goal is a unified list o f differentially expressed genes that is accompanied by an estimated error rate, usually the false discovery rate (Benjamini and Hochberg 1995; Storey and Tibshirani 2003). The methodological choices and  142  innovations o f this work are motivated by departures from this common goal and our techniques may prove useful i n other studies where biological interest is not synonymous with differential expression. P F A was first applied to gene expression data by Fleury, et al (Fleury et al. 2002). In that work, P F A was used to optimize multiple indices within one study as opposed to our use, which is the optimization o f a comparable index, the C V , across distinct but related studies. Y a n g et al present another compelling technique for the synthesis o f competing measures o f differential expression within a single experiment (Yang et al. 2005) and it may be possible to extend their methodology for use in meta-analysis.  Meta-analysis o f mieroarray data is an increasingly common technique to capitalize on the combined power o f biologically related but distinct data sets (Choi et al. 2003; Estrada et al. 2006; Ghosh et al. 2003; H u et al. 2005; Levesque et al. 2006; Rhodes et al. 2002; Stevens and Doerge 2005; Wang et al. 2004). In addition to the usual advantage o f increasing the effective sample size, the primary benefit o f meta-analysis in our application is to insulate our biological findings from confounding experimental and biological effects (Estrada et al. 2006; Levesque et al. 2006). For example, in the R l - L R data set, changes induced by differentiation could have been confounded with changes caused by the removal o f feeders from the culture. However, this effect was not present i n the other data sets, therefore any common gene expression changes cannot be attributed to the removal o f feeders. A n example o f profound differences i n gene expression caused by small changes i n culture conditions was reported by Skottman et al., who demonstrated effects on 1417 genes i n human E S C cultured i n serum containing vs. serum-free  143  conditions, despite comparable levels o f expression o f other markers o f their  undifferentiated  state (Skottman et al. 2006).  In summary, meta-analysis o f multiple gene expression data sets from populations o f mouse E S C induced to differentiate has revealed multiple genes whose altered expression provides a robust and timely indication o f changes in pluripotency. These findings suggest the importance o f the products o f these genes i n the molecular regulation o f the undifferentiated state o f E S C and provide a useful basis for developing high throughput approaches for the bio-monitoring o f E S C cultures.  144  Figure 4.1 The effect o f leukemia inhibitory factor removal with or without addition o f D M S O or retinoic acid on the maintenance and differentiation o f embryonic stem cells (A) Colony-forming cell ( C F C ) frequency and (B) embryoid body (EB)-forming ability o f cells from the E S C cultures assessed at varying times after initiation o f the treatment (+LIF controls = • , - L I F = or - L I F + D M S O = • , +LIF+RA = T ) . * denotes the data for the +LIF sample are significantly different from all other treatments. (C) Gene expression o f differentiation markers was monitored by Q - R T - P C R after 96 hours o f treatment. Results shown are relative to the +LIF control cells.  T i m e (hours)  Abbreviations: E S C - embryonic stem cell, L I F - leukemia inhibitory factor, R A - retinoic acid, Q - R T - P C R - quantitative R T - P C R  Figure 4.2 Transcriptome plots o f estimated expression changes, based on fitting models to each data set A l l plots have density shading to demonstrate the number o f points (genes) in different regions. Lines illustrate examples o f some o f the requirements that make up the definition o f the E S C signature change and observed gene expression patterns that fulfilled all requirements are marked as #. Experiment-specific implementations o f requirements are explained below. A ) D M S O / R A data set. The requirement for large absolute changes is illustrated by the solid lines. Consistency across conditions implied that genes must exhibit a change i n the same direction in both treatments (bottom left or top right quadrant). (B) R l - L R data set. Note that the y axis is the change seen at 72 hours relative to that seen at 18 hours. The requirement for large absolute changes is illustrated by the solid lines. The criterion for consistency was applied by requiring that the change 18 hours after L I F removal be i n the same direction as that after 72 hours (i.e., in the lower left or upper right quadrants), regardless o f its magnitude. (C) M - L R data set. The requirement for large absolute changes is illustrated by the dashed lines. To meet the consistency criterion, we required that a temporal gene expression trend either increase or decrease continuously over the duration o f the experiment (solid lines). This requirement was relaxed slightly to retain trends with a direction change occurring either very early or very late.  1:8  1:4  1:2  1:1  2:1  4:1  8:1  -  1:2  1:1  2:1  Figure 4.3 Comparison o f differently measured changes i n gene expression within and between two embryonic stem cell lines grown for 0, 24, 72 and 96 hours in + L I F ± R A or - L I F ± D M S O (A) Comparison o f Q - R T - P C R and mieroarray results for the R l E S C line (r = 0.76). (B) Comparison o f Q - R T - P C R and mieroarray results for the J l E S C line (r = 0.82). (C) Comparison o f results between the R l and J l E S C lines (r = 0.87).  " B  -  c  • •  •  Q-RT-PCR  Q-RT-PCR  .* * •  *  * *  •  •  •  *  • •  Jl  1:4  • *  •  • » • *  •  •  « -T  T 1:64  1:16  I 4  1:1 Amiv  4:1  16:1  1:64  1 1:16  ^  t 1:4  1:1 Array  4:1  1  i 1:256  i  i  i  1:64  1:16  1:4  1:1  Rl  Abbreviations: L I F - leukemia inhibitory factor, R A - retinoic acid, Q - R T - P C R - quantitative R T - P C R , E S C - embryonic stem cell.  Figure 4.4 Quantitative R T - P C R profiles o f transcript levels for seven genes that showed rapid decrease in embryonic stem cells subjected to several differentiating conditions A l l levels o f expression are relative to the +LIF control.  - L I F = ® , or  +LIF control  - L I F + D M S O = JI, + L I F + R A = 6. 1.5-,  103728_at t  11  •  N  M i l 1.0 • 11 0.5  20  40  60  _  80  100  8430410A17Rik  1i 1  i  ¥  A  ¥  1.5T  Klf2  1.04  20  0  40  60  80  100  1.5  NrObl  Tcl1  -¥  20  40  60  ¥  80  t  i-o-  100  m  20  Zfp42 II  0  1  20  •1  ¥  40  60  i  80  n  11  100  Time (h)  Abbreviations: L I F - leukemia inhibitory factor, R A - retinoic acid.  148  1  40  60  80  II  100  Figure 4.5 Comparison o f biological and molecular changes i n embryonic stem cells stimulated to differentiate by exposure to ascorbic acid (A) Changes i n embryoid body ( E B ) frequency. (B) Changes in the levels o f transcripts for seven E S C signature change genes measured by Q - R T - P C R . +LIF ( • ) , + L I F + A A ( • ) and - L I F + A A ( A ) 103728 at  B  A  +  o  0  0  20  ~-' 4 T3 03 3  40  60  80  Nr0b1  100  o 03  60 Time (h)  80  40  60  80  100  o0  20  40  60  Tcl1  Sox2  80  100  A  1  I40  20  k  § 2 c  0  -4  co  o  Klf2  i  a  10n  8430410A17Rik  A  0  20  40  60  80  100  80  1  o6  20  40  60  80  100  0  20  40  60  80  100  Zfp42  X 0)  2|  / 1* 0  20  40  60  °°  T i m e (h)  Abbreviations: E S C - embryonic stem cell, Q - R T - P C R - quantitive R T - P C R , L I F - leukemia inhibitory factor, A A - ascorbic acid  Table 4.1 Summary o f all microarray experiments used i n this study Note that a l l replicates are biological. .'/ DMSO/RA l % D M S O / n o LIF 2 \iM retinoic acid/10 ng/mL LIF 10 ng/mL LIF (positive control) Rl 96  Rl-LR LIF removal  M-LR LIF removal  Rl 0, 18, 72  2(6)  3(9)  R l , J l , V6.5 0, 6, 12, 18,24, 36, 48, 96 3(72)  MOE430  M G U74v2  MOE430  ...v  Condition  Cell line Timepoint (hours) Number of replicates per condition (total number of chips) Affymetrix Chip  Abbreviations: L I F - leukemia inhibitory factor  150  Table 4.2 Thresholds used i n the definition o f the embryonic stem cell signature chang Parameter  DMSO/RA  Rl-LR  Cabs.  2.0,2.3,3.0  1.5, 1.7, 2.0  1.84(DMSO), 2.72 ( R A ) 265  1.22  Cmin •"* Cmax C ] re  Number o f genes, 269 CV>0.5 Abbreviations: R A - retinoic acid, C V - confidence value  151  M-LR 1.4, 1.8, 2.2 0.4 (12 h) 0.9 (68 h) 1.57 240  Table 4.3 Genes identified on the first five Pareto fronts  MG_U74v2  MOE430  Pareto Front 1 1456521 at 103728 at 1436926 at 168508 at  Fold change Fold change following L I F following Fold change at L I F removal removal for 96 hours (MLR) (R1LR) 96 hours Confidence Values Rl V6.5 Gene Name RA 18 h 72 h Jl DMSO/RA R1LR M L R DMSO 1.00 1.00  1.00 0.86  0.07 0.35  0.18 0.09  0.19 0.09  0.65 0.63  0.24 0.20  0.58 0.36  0.64 0.61  93141 at 1417760 at 93296 at 1422458 at 93483 at 1449455 at 94200 at 1420337 at 96109 at 1448890 at 1427238 at 96162 at 1449254 at 97519 at 99561 f at 1448393 at Pareto Front 2 1418091 at 103761 at 1450626 at 108097 at 1434917 at 108712 at  1.00 1.00 1.00 0.92 1.00 0.83 0.02 0.00  0.81 0.98 0.85 0.98 1.00 1.00 0.99 1.00  1.00 0.10 0.67 1.00 0.01 0.53 0.55 1.00  0.06 0.19 0.18 0.23 0.11 0.11 0.08 5.23  0.08 0.19 0.17 0.30 0.11 0.33 0.77 0.67  0.84 0.78 0.89 0.69 0.74 0.63 0.54 1.34  0.10 0.22 0.15 0.22 0.41 0.10 0.19 4.15  0.09 0.48 0.36 0.26 0.49 0.53 0.54 4.67  0.12 0.73 0.44 0.29 0.78 0.51 0.44 7.25  0.29 Transcribed locus 0.24 estrogen related receptor, beta nuclear receptor subfamily 0, group 0.08 B, member 1 0.42 T-cell lymphoma breakpoint 1 0.50 hemopoietic cell kinase 0.25 gastrulation brain homeobox 2 0.31 Kruppel-like factor 2 (lung) 0.18 F-box protein 15 0.10 secreted phosphoprotein 1 3.34 claudin 7  1.00 0.93 0.98  1.00 0.80 0.77  0.07 0.10 1.00  0.19 0.15 0.26  0.26 0.29 0.29  0.64 0.74 0.73  0.13 0.27 0.32  0.60 0.69 0.36  0.66 0.71 0.38  0.22 transcription factor CP2-like 1 0.25 mannosidase, beta A, lysosomal 0.28 cordon-bleu  160684 at 1423786 at 1426810 at 95033 at 1418362 at 98414 at 99622 at 1417394 at Pareto Front 3 100009 r at 1416967 at 101560 at 1415856 at  1.00 0.50 1.00 0.70  0.73 0.97 0.73 1.00  1.00 0.97 0.97 0.00  0.27 0.42 0.09 0.09  0.23 0.30 0.10 0.33  0.97 0.84 0.88 0.26  0.46 0.41 0.14 0.07  0.29 0.31 0.28 0.30  0.24 0.33 0.40 0.14  0.17 0.39 0.11 0.11  1.00 0.00  0.67 0.60  0.00 1.00  0.16 1.22  0.07 0.43  0.89 1.09  0.36 1.85  0.59 3.37  1.11 4.71  0.00 0.38  0.87 0.99  0.30 0.00  0.61 0.45  2.18 0.42  0.74 0.79  0.48 0.38  0.54 1.02  0.66 0.95  0.39 SRY-box containing gene 2 3.22 em big in src family associated 0.52 phosphoprotein 2 0.78 Unc-51 like kinase 1 (C. elegans)  102012 at 102332 at  1418895 at 1448370 at  RIKEN cDNA 843041 OA 17 gene jumonji domain containing 1A zinc finger protein 42 Kruppel-like factor 4 (gut)  Gene Symbol — Esrrb NrObl Tell lick Gbx2 Klf2 FbxolS Sppl Cldnl TcfcplU Manba Cobl 843041 OA 17 Rik Jmjdia Zfp42 Kl/4 Sox2 Emb Scap2 Ulkl  MG_U74v2 108784 at 112828 at 115445 at  Fold change Fold change following following L I F Fold change at L I F removal removal for 96 hours 96 hours Confidence Values (R1LR) (MLR) MOE430 D M S O / R A R 1 L R M L R D M S O Jl Rl V6.5 RA 18 h 72 h 0.46 0.63 0.58 0.92 0.02 0.32 0.25 0.51 0.36 1455604 at 0.91 1.18 1.45 10.45 4.96 4.45 0.87 1.00 3.07 5.89 1448688 at 0.11 0.75 0.56 0.38 0.84 0.88 0.03 0.22 0.32 0.64 0.34 1435374 at  116872 at 1435437 at 1448845 at 117246 at 1436568 at 133365 at 133819 at 1419418 a at 163005 s at 1429366 at  0.82 0.69 0.41 1.00 0.99  0.72 0.27 0.86 0.66 0.76  0.96 1.00 0.87 0.99 0.00  3.08 0.23 0.23 0.08 0.19  3.01 0.31 0.44 0.09 0.22  1.27 0.77 0.74 0.79 0.65  2.07 0.65 0.42 0.24 0.28  2.78 0.29 0.41 0.38 0.49  2.49 0.26 0.31 0.33 0.91  92770 at 93864 s at  1421375 a at 1421624 a at  0.38 0.04  0.98 0.48  0.00 1.00  2.1 1 0.30  5.38 0.65  0.33 1.03  0.16 0.36  0.36 0.38  0.35 0.35  94745 f at 96752 at  1427479 at 1424067 at  0.01 0.00  1.00 0.99  0.00 0.06  0.73 0.63  5.61 0.80  0.49 0.69  0.26 0.41  0.89 0.67  0.35 0.75  0.83  0.88  0.06  0.21  0.32  0.87  0.38  0.57  0.73  0.34 1.00 0.11 0.00  0.91 0.57 0.86 0.95  0.01 0.44 0.61 0.03  0.47 0.21 0.45 0.38  0.17 0.21 0.55 1.16  0.83 0.92 0.72 0.73  0.36 0.54 0.49 0.47  0.25 0.40 0.41 0.39  0.39 0.57 0.33 0.39  97890 at 1416041 at Pareto Front 4 1448562 at 100030 at 1422986 at 100301 at 103342 at 1448653 at 103653 at 1449590 a at  Gene Name expressed sequence AI427138 podocalyxin4ike Transcribed locus  2.84 SET domain-containing protein 7 0.32 ribonuclease P 25 subunit (human) 0.44 junction adhesion molecule 2 0.09 microrchidia 0.34 leucine rich repeat containing 34 SI00 calcium binding protein A6 0.05 (calcyclin) 0.34 enabled homolog (Drosophila) eukaryotic translation initiation 1.00 factor IA 0.61 intercellular adhesion molecule serum/glucocorticoid regulated 0.23 kinase  104139 at  1452094 at  0.00  0.45  1.00  1.50  1.88  0.91  0.57  5.73  3.69  0.85 uridine phosphorylase 1 0.37 estrogen related receptor, beta 0.25 embryonic ectoderm development 0.41 muscle and microspikes RAS procollagen-proline, 2-oxoglutarate 4-dioxygenase (proline 42.91 hydroxylase), alpha 1 polypeptide  104544 at  1423327 at  0.53  0.90  0.00  0.16  0.41  0.75  0.43  0.88  0.36  0.42  107103 at  1416958 at  0.00  0.96  0.00  0.51  0.73  0.60  0.43  0.80  0.91  0.72  RIKEN cDNA 4930517K11 gene nuclear receptor subfamily 1, group D, member 2  Gene Symbol AN27138 Podxl — MGl: 192050 1 Rpp25 Jam2 More Lrrc34 S100a6 Enah Eifla leaml Sgk Uppl Esrrb Eed Mras  P4hal 4930517K11 Rik Nrld2  M G U74v2  MOE430  108010 at 108048 at  1418318 at 1454788 at  113673 at 115804 at 133204 at  1423508 at 1438237 at 1455425 at  160370 at 160828 at 93063 at  1416552 at 1426858 at 1427442 a at  93104 at 94270 at 94354_at  1426083 a at 1448169 at 1421840 at  Fold change Fold change following L I F following Fold change at L I F removal removal for 96 hours (MLR) (R1LR) 96 hours Confidence Values V6.5 Gene Name Jl Rl RA 18 h 72 h DMSO/RA R1LR M L R DMSO 2.54 ring finger protein 128 1.17 1.87 4.61 3.73 0.54 0.99 3.13 16.53 0.87 3.02 3.47 3.13 ADP-ribosylation factor-like 7 2.62 1.00 1.37 0.67 0.08 1.00 5.18 M Y S T histone acetyltransferase 0.57 0.59 0.36 monocytic leukemia 4 0.28 0.42 0.69 0.38 0.56 0.72 0.02 0.22 0.12 RIKEN cDNA C230088H06 gene 0.49 0.16 0.14 0.88 0.50 1.00 0.57 0.68 0.33 0.26 Expressed sequence BB001228 0.14 0.28 0.47 0.99 0.08 0.96 0.43 1.00 developmental pluripotency 1.08 0.44 associated 5 0.93 0.70 0.00 0.17 0.25 0.91 0.44 0.99 0.38 0.38 0.31 inhibin beta-B 0.62 0.25 0.31 0.33 0.64 0.53 0.76 3.84 3.02 amyloid beta (A4) precursor protein 2.70 6.52 0.71 1.00 9.60 0.50 0.85 0.00 B-cell translocation gene 1, anti3.04 1.93 proliferative 2.74 0.92 1.83 0.65 0.48 0.00 0.79 0.62 12.20 18.17 1.76 keratin complex 1, acidic, gene 18 4.12 12.01 1.27 2.57 0.85 0.86 0.00 ATP-binding cassette, sub-family A 1.12 0.67 0.75 (ABC1), member 1 0.83 0.53 0.36 0.00 0.98 0.00 0.37  95518 at 95531 at  1424683 at 1454890 at  0.00 0.35  0.13 0.08  1.00 1.00  1.74 3.90  3.14 2.22  1.22 0.79  1.43 3.31  7.05 20.76  5.20 7.86  96042 at 96841 at  1448610 a at 1451069 at  0.53 0.00  0.72 1.00  0.94 0.00  0.43 0.71  0.32 0.46  0.75 0.73  0.52 0.41  0.30 1.39  0.42 1.26  96900 at  1433720 s at  1.00  0.62  0.00  0.18  0.12  0.92  0.54  0.45  0.85  97317 at 97426 at 97520 s at 99956 at  1415894 at 1416529 at 1423506 a at 1452514 a at  0.00 0.00 0.08 0.00  1.00 0.96 0.97 1.00  0.00 0.00 0.00 0.00  1.05 0.67 1.73 0.67  0.79 6.50 3.84 0.96  2.00 0.28 1.21 0.49  5.20 0.15 2.33 0.25  1.05 0.75 0.66 2.31  1.00 0.63 1.13 1.57  3.40 RIKEN cDNA 1810015C04 gene 5.62 angiomotin superoxide dismutase 2, 0.28 mitochondrial 1.09 proviral integration site 3 0.48 Nur77 downstream gene 2 Ectonucleotide pyrophosphatase/phosphodiesterase 1.21 2 0.57 epithelial membrane protein 1 1.39 neuronatin 1.04 kit oncogene  Gene Symbol Rnfl28 Aril Myst4 — BB001228 Dppa5 Inhbb App BtgJ Krtl-18 Abcal 1810015C04 Rik Amot Sod2 Pim3 MGL214355 8  Enppl Empl Nnat Kit  MG_U74v2  MOE430  Fold change Fold change following L I F following Fold change at L I F removal removal for 96 hours (MLR) (R1LR) Confidence Values 96 hours Rl V6.5 Gene Name Jl RA 18 h 72 h DMSO/RA R1LR M L R DMSO  Gene Symbol  Pareto Front 5 0.35 0.43 0.40  0.50 0.35 0.63  neuroblastoma myc-related 0.35 oncogene 1 0.45 neurofilament, heavy polypeptide 0.65 —  103048 at 103234 at 103737 at  1417155 at 1424847 at 1418753 at  0.98 0.92 0.53  0.39 0.01 0.69  0.64 0.78 0.33  0.30 0.29 0.39  0.25 0.25 0.43  0.96 1.02 0.95  0.58 0.94 0.51  108279 at 110429 at  1455300 at 1455333 at  0.50 0.01  0.52 0.80  0.63 0.08  0.35 0.28  0.42 0.66  0.93 0.72  0.35 0.36  0.37 0.39  0.51 0.32  0.29 RIKEN cDNA E130014J05 gene 0.25 cDNA sequence BC023928  111970 at 115058 at 116214_at  1460711 at 1434362 at  0.76 0.41  0.39 0.68  0.46 0.91  0.36 2.65  0.24 2.23  0.92 1.25  0.57 2.04  0.54 2.47  0.54 2.28  0.40 RIKEN cDNA 4930461P20 gene 2.83 expressed sequence AW550831  1456329 at  0.58  0.37  0.72  2.44  3.11  1.19  1.91  2.29  2.11  2.45  1418076 at  0.00  0.93  0.01  1.90  0.78  1.65  2.22  1.70  2.03  1423754 at 1427912 at 1443892 at 1437015_x_a 162522 f at t  0.06 0.75 1.00  0.74 0.75 0.00  0.31 0.00 0.97  0.34 0.35 0.21  0.56 0.29 0.16  0.82 1.27 1.09  0.51 1.92 0.78  0.53 0.80 0.36  0.66 0.75 0.21  0.17  0.00  0.98  0.51  0.22  0.95  0.78  0.29  0.42  RIKEN cDNA A230098A12 gene suppression of tumorigenicity 14 1.24 (colon carcinoma) interferon induced transmembrane 0.34 protein 3 0.77 carbonyl reductase 3 0.20 . . . phospholipase A2, group IB, 0.39 pancreas  0.98  0.70  0.00  0.23  0.26  0.82  0.36  0.78  0.93  0.40  0.00 1.00 0.56  0.85 0.50 0.00  0.42 0.32 1.00  1.52 0.19 0.42  1.06 0.17 0.37  1.35 0.92 0.98  2.24 0.47 1.02  3.03 0.34 0.30  2.75 0.49 0.23  1.72 0.59 0.29  RIKEN cDNA 2410146L05 gene receptor-interacting serine-threonine kinase 4 ring finger protein 125 purine-nucleoside phosphorylase  1.00 0.41  0.00 0.47  0.97 0.78  0.13 0.34  0.13 0.45  1.16 0.98  0.96 0.13  0.39 0.27  0.27 0.47  0.17 0.24  RIKEN cDNA D230005D02 gene LOC433110  116435 at 160253 at 161042 at 161106 r at  163288 at  1460471 at  163489 at 1418488 s at 1429399 at 163715 at 165699 r at 1453299 a at 166142 r at 167088 r at  1436799 at 1456242 at  Nmycl Nefh — E130014J05 Rik BC023928 4930461P20 Rik AW550831 A230098A12 Rik Stl4 lfitm3 Cbr3 — Pla2glb 2410146L05 Rik Ripk4 Rnfl25 Pup D230005D0 2Rik LOC433110  MG_U74v2 92275 at 92476 at 92550 at  93271 s at  Fold change Fold change following L I F following Fold change at L I F removal removal for 96 hours (R1LR) (MLR) 96 hours Confidence Values MOE430 Rl V6.5 Jl 18 h 72 h DMSO/RA R1LR M L R DMSO RA 1.25 1.48 0.92 0.45 0.77 0.42 0.00 0.95 0.00 j 0.56 1418147 at 0.47 0.29 0.16 0.85 0.62 0.43 0.33 0.75 0.23 1449288 at 1.00 1.22 1.70 1.22 2.04 1.90 5.12 5.63 1417156 at 0.98 0.63 0.00  1450186 s at 1453223_s_at  0.58  0.01  0.88  2.44  3.17  0.82  1.38  2.24  2.90  2.98  95584 at 96203 at  1424713 at  0.03 0.06  0.08 0.27  1.00 0.92  0.25 0.50  0.64 0.59  1.08 1.06  0.63 0.54  0.34 0.23  0.23 0.43  0.11 0.11  97083 at  1441023 at  0.00  0.99  0.00  1.00  0.92  0.51  0.35  1.10  0.93  1.07  97283 at  1424295 at  0.68  0.83  0.00  0.37  0.32  0.84  0.36  1.14  2.48  0.44  97442 at  1416832 at  0.00  0.14  1.00  1.20  1.05  1.08  1.47  7.27  3.55  4.90  Gene Name transcription factor AP-2, gamma growth differentiation factor 3 keratin complex 1, acidic, gene 19 G N A S (guanine nucleotide binding protein, alpha stimulating) complex locus developmental pluripotency associated 2 calmodulin-like 4 eukaryotic translation initiation factor 2, subunit 2 (beta) developmental pluripotencyassociated 3 solute carrier family 39 (metal ion transporter), member 8  Gene Symbol Tcfap2c Gdft Krtl-19  Gnas Dppa2 Calml4 Eif2s2 Dppa3 Slc39a8  4.5.  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A t present, this is hindered by incomplete knowledge o f intra- and extra-cellular  determinants o f stem cell fate.  This thesis reports the development and exploitation o f novel  endpoints for the detection o f loss o f pluripotency within a population, the discovery o f new reagents that w i l l enable enhanced expansion o f undifferentiated E S C , the use o f statistical design o f experiments to optimize the expansion o f E S C and the identification o f candidate genes that may be involved in determining E S C fate.  Integral to determining effectors o f E S C fate decisions are adequate methods to demonstrate changes in the developmental potential o f cells following a particular treatment. Conventional methods so rely on the detection o f expression o f markers such as Oct4 or S S E A - 1 .  While  assessment o f these is relatively easy and lends itself to many high-throughput technologies, such as fluorescent-activated cell sorting, work reported i n Chapter 2 demonstrates that loss o f these markers is a lagging indicator o f loss o f pluripotency as measured by the ability o f cells to contribute to chimeric mice when injected back into recipient blastocysts. This result suggests that while cells not expressing Oct4 or S S E A - 1 have indeed lost pluripotency, there is developmental heterogeneity among those cells that are still expressing these markers. T w o in vitro assays, the embryoid body ( E B ) and colony-forming cell ( C F C ) assays, show a high degree  163  of correlation with the chimeric mouse assay following L I F removal, thus providing more reliable indicators o f the frequency o f pluripotent cells in a population.  B y tracking gene  expression changes in parallel with loss o f readout in these assays, several early indicators o f loss of pluripotency were identified. One o f these, c-kit, is a cell surface marker and thus lends itself to easy detection by antibody staining.  It was shown that loss o f c-kit expression closely  parallels loss o f ability to read out in the E B assay and this provides a more reliable marker o f stem cell state than Oct4 or S S E A - 1 .  Subsequent experiments by our lab and others have  demonstrated that c-kit is involved i n the survival o f E S C as they differentiate (Bashamboo et al. 2006; L u et al. 2006), demonstrating the utility, o f using functional assays to determine novel markers o f pluripotency.  Chapter 3 reports the discovery o f two new factors that enhance E S C expansion - ascorbic acid ( A A ) and chondroitin sulphate (CS) - and the use o f statistical design o f experiments to integrate and optimize these reagents along with PD98059 (PD), a specific inhibitor o f M A P K signalling which has previously been shown to increase E S C expansion (Burdon et al. 1999), into existing protocols for the expansion o f E S C . potential.  For this work, the E B assay was used to measure E S C  Several advantages exist to using statistical design o f experiments.  In particular,  factorial (FD) and central composite designs ( C C D ) enable a small number o f experiments to be run which w i l l optimally cover a large amount o f state space and identify potential negative interactions or synergies between tested factors. Here, a 3-fold greater expansion o f E S C was demonstrated relative to untreated controls i n an optimized combination o f these three factors determined from a series o f F D and C C D experiments.  164  Since a negative interaction was  demonstrated between A A and P D , optimal expansion resulted from a reduction o f the concentration o f P D that was added to the cultures. Furthermore, since the molecular target o f P D is known, the discovery o f the negative interaction between this factor and A A , suggests that ascorbic acid enhances E S C maintenance by suppressing the M A P K pathway, although this remains to be proved directly. Thus, in addition to allowing the optimization o f the culture environment, the design o f experiments approach also allowed the discovery o f unanticipated interactions between media components and suggests future directions for research to understand the specific effect o f A A .  The success o f the optimization efforts demonstrated in Chapter 3 hinged on the sensitivity o f the E B assay that was used to determine E S C potential. Unfortunately this assay does not lend itself to the high-throughput experiments that would be required for full optimization studies due to its labour-intensive nature.  Thus there is a need for the development o f more sensitive, high-  throughput assays. The goal o f the study described in Chapter 4 was to discover genes that are changed concurrent with a loss o f pluripotency with the intention that these could be used as the basis o f a high-throughput assay for detection o f E S C in culture. In addition, it was hypothesized that these genes would be enriched with those that had a biological role i n conferring the undifferentiated state on E S C .  Several studies with similar goals have been performed but all  have resulted i n large lists o f candidate genes that are too numerous for the subsequent lowthroughput studies that must be performed to determine their biological significance. In this study we were able to improve on this by using a more inclusive experimental design and by developing novel statistical tools that allowed a combined analysis o f data sets that we generated  165  in conjunction w i t h several data sets that had been generated by other, independent groups. Gene expression profiling by microarray was used to monitor the expression level o f up to 45,000 transcripts simultaneously. L i k e many large-scale data generating technologies, optimal value was obtained by careful consideration o f the samples analyzed. Here, gene expression changes at short time intervals following the loss o f pluripotency i n mouse E S C following the induction o f differentiation by multiple stimuli were chosen.  This approach relied on the  inclusion o f previously generated data sets resulting in greater robustness to the findings since they were independent o f the lab that the cells were grown in and the individual that had processed the gene expression samples, both factors having been shown to influence gene expression data (Irizarry et al. 2005).  U s i n g novel statistical techniques, we were able to  determine a short list o f 88 genes - considerably smaller than determined in other studies. U p o n further investigation, we were able to propose a gene expression assay consisting o f seven genes that we demonstrated was able to assess both increases and decreases in the frequency o f stem cells within a population. Since assessment o f a small number o f gene expression changes can be done i n a high-throughput way using a variety o f technologies, this provides a rapid assay that can be used in subsequent experiments where high-throughput capacity is required. Since this work was performed, several genes that were reported have been shown to be functionally involved i n maintenance and differentiation o f E S C indicating the value o f this approach in identifying relevant genes. Specifically Esrrb and T e l l have been shown to be required for selfrenewal (Ivanova et al. 2006). Overexpression o f K l f 4 , i n combination with Oct4, Sox2 and cM y c , was able to dedifferentiate terminally differentiated adult fibroblasts i n to E S C - l i k e cells (Takahashi and Yamanaka 2006).  166  5.2.  Future Directions  Work performed i n this thesis is intended to guide future scale-up efforts o f clinically relevant stem cell populations. Here, all work was performed on mouse E S C that have no clinical value. Human E S C are closely related to mouse E S C in terms o f tissue o f origin, and while differences between the two cell types means that direct application o f these findings may not be possible, extension o f many o f the methods described here w i l l be o f great value. Specifically, there is a great need for more assays i n the human E S C system. For ethical reasons, chimera contribution can not be assessed and clonal assays are still impractical due to their inefficiency.  Thus, an  assay based on specific gene expression changes would not only allow more high-throughput discovery but may also allow discovery of new biology.  Direct application to the human system o f the findings that A A , C S and P D are beneficial for the expansion o f E S C would be interesting. A t present, optimal expansion o f h E S C takes place on a layer o f inactivated feeders (Thomson et al. 1998) although this can be replaced with matrigel and conditioned media ( X u et al. 2001). This similarity with the mouse system, and the fact that C S is an integral part o f the extracellular matrix, suggests that it may also have beneficial effects for h E S C . The relevance o f P D and A A to the human system may be more questionable though since it appears that very different signalling pathways are operational i n the two different systems ( W e i et al. 2005). This is based on the assumption that A A acts through suppression o f the M A P K pathway. If the antioxidant power o f A A also has other effects, then A A may have other, unanticipated effects on h E S C .  Nevertheless, the statistical designs o f experiments  described here w i l l have great power to determine optimal protocols for the both the expansion  167  and differentiation o f h E S C . Indeed, F D experiments appear to have already applied to develop optimal differentiation protocols for the generation o f pancreatic cells from h E S C (D'Amour et al. 2006).  Chapter 3 uses F D experiments to determine a negative interaction between the A A and P D . From this data, I hypothesize that A A has its effect by indirectly suppressing the M A P K pathway by reducing the concentration o f intracellular reactive oxygen species.  This remains to be  definitively demonstrated and a greater exploration o f the role o f reactive oxygen species i n E S C maintenance and differentiation would be interesting. Increased intracellular R O S concentration has been shown to have a role in driving m E S C to differentiate towards cardiomyocytes (Sauer et a l . 2000).  W h i l e this seems to contradict previous reports that A A also increases  differentiation towards cardiomyocytes (Takahashi et al. 2003), it is clear that modulation o f R O S does have an influence on cell fate decisions.  The gene list reported i n Chapter 4 provides many new candidates that require testing to understand their role in maintenance and differentiation o f E S C . Genes with unknown function such as 103728_at and 8430410A17Rik would be particularly interesting to focus on. Podxl provides an interesting candidate to investigate because it is expressed on the cell surface and thus may provide a useful marker for m E S C . Subsequent experiments have demonstrated that podxl protein expression tracks gene expression ( C . Glover, unpublished observations). Furthermore, podxl has been shown to be an anti-adhesive molecule (Takeda et al. 2000) thus  168  investigation o f this molecule may provide further clues as to how cell fate decisions can be modulated by cell-cell contact.  Finally, the gene expression assay described i n Chapter 4 is designed to be used in highthroughput settings where screenings o f multiple compounds or optimization type experiments are being performed. Testing o f this assay in such a setting would be very interesting. W i t h the recent advances made in combinatorial chemistry, studies o f this type are becoming increasingly important to identify novel modulators o f cell fate decisions as well as to identify new biological pathways that may be active in cells (Chen et al. 2006).  Furthermore, the development o f  microfluidic systems for both culture ( K i m et al. 2006) and analytical purposes (Warren et al. 2006) mean that experiments o f this type can be performed rapidly and inexpensively.  169  5.3.  References  Bashamboo A , Taylor A H , Samuel K , Panthier JJ, Whetton A D , Forrester L M . 2006. The survival o f differentiating embryonic stem cells is dependent on the S C F - K I T pathway. J C e l l Sci 119(Pt 15):3039-46. Burdon T, Stracey C , Chambers I, Nichols J, Smith A . 1999. Suppression o f S H P - 2 and E R K signalling promotes self-renewal o f mouse embryonic stem cells. Dev B i o l 210(l):30-43. 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Ivanova N , Dobrin R, L u R, Kotenko I, Levorse J, DeCoste C , Schafer X , L u n Y , Lemischka IR. 2006. Dissecting self-renewal i n stem cells with R N A interference. Nature 442(7102):533-8. K i m L , Vahey M D , Lee H Y , Voldman J. 2006. Microfluidic arrays for logarithmically perfused embryonic stem cell culture. Lab Chip 6(3):394-406. L u M , Glover C H , Humphries R K , Piret J M , Helgason C D . 2006. The role o f c-kit i n maintaining murine embryonic stem cell pluripotency. in preparation. Sauer H , Rahimi G , Hescheler J, Wartenberg M . 2000. Role o f reactive oxygen species and phosphatidylinositol 3-kinase in cardiomyocyte differentiation o f embryonic stem cells. F E B S Lett 476(3):218-23. Takahashi K , Yamanaka S. 2006. Induction o f pluripotent stem cells from mouse embryonic and adult fibroblast cultures by defined factors. C e l l 126(4):663-76. Takahashi T, L o r d B , Schulze P C , Fryer R M , Sarang SS, Gullans SR, Lee R T . 2003. Ascorbic acid enhances differentiation o f embryonic stem cells into cardiac myocytes. Circulation 107(14):1912-6.  170  Takeda T, Go W Y , Orlando R A , Farquhar M G . 2000. Expression o f podocalyxin inhibits cellcell adhesion and modifies junctional properties i n Madin-Darby canine kidney cells. M o l B i o l C e l l ll(9):3219-32. Thomson J A , Itskovitz-Eldor J, Shapiro SS, Waknitz M A , Swiergiel JJ, Marshall V S , Jones J M . 1998. Embryonic stem cell lines derived from human blastocysts. Science 282(5391): 1145-7. Warren L , Bryder D , Weissman I L , Quake SR. 2006. Transcription factor profiling i n individual hematopoietic progenitors by digital R T - P C R . Proc Natl A c a d Sci U S A 103(47): 17807-12. W e i C L , M i u r a T, Robson P, L i m S K , X u X Q , Lee M Y , Gupta S, Stanton L , L u o Y , Schmitt J, Thies S, W a n g W , Khrebtukova I, Z h o u D , L i u E T , R u a n Y J , Rao M , L i m B . 2005. 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Nat Biotechnol 19(10):971-4.  171  Appendix A Table A l Primers used for quantitative R T - P C R throughout this study 5'-primers CCCAAACGTGAGGAACAACT CTACAACAAGAGCCCGCAGT GCACCTGCCTTACCAACTCT AAAGCGAGAAACTGCGAGAG TGAGGAGTTTCCATCACGAA CCTGTCGCAGTTGGGTTC CTGCGCTTCAAGGAGCTAAC TATGTGGCCCTGCTACAACA TATCAAGGCCCTGACCACTC ATTGGATGTGTGAGCAGACG TGTACTGCAGAGTGGGCATC CATCCGACTGGAGCAGCTA ACGTCGGGAGTCTGGATCT AACTTTGGCATTGTGGAAGG AGACGGCAAAGCCTTCTTG TCGGAGAGCTCATCTGGAAT CAGGAACTCGTGCTCCACTA GATGTCACTGCTCTGCAAGG GAGTGGAAGAAGGTGGGACA GATCTGCTCTGCGTCCTGTT AGCCTATCTTGCCGTCCTTT CTCTGCTCCCGTCCTTCTC CAGAGGAGAGTGGCTGAAGG GCAGGGAGGATTCATGTTGT GCAGGCCATAAAACCAATGT ATGCCTGCAGTTTTTCATCC CTGCAGGCCACTGAAAAGTT AGCACCTCCGGAGAGGATAC TATCTGAAAGGGACCGTGCT CAGTGCTCCTGGCATGACTA GTTGGAGAAGGTGGAACCAA AGAGCCAAATTGACCCAGAT CAAGAGGAGACCTCCAGCAG CGCTGCTCAAGGACACAGT TTCACGATGAGCCGTACACT TGACATGGAAGCCCACTACA CTTCGCTCTGGAGCAGATTT TGGCTTCCCTGACAGATACC CCTGTCTGTCAGGAGGTGCT CCGAGGAGAAGTACCACGAG  3'-primers ACTTTGGGCAGCATGACAAG CCCATCGCATGGGAATAAT TTTCAGGGCATTTTTCAAGG GGCTCCTTCACAACTGGAAC CACCTCATTCTCTGGGATGC ATTGCCATTGCTGAAGTGC CCAGGCCTGACACATTTACC GGAGGTCTCTGACACCAGGA CTCATCTGGTCCCCAAGTGT AATGCAGCTGCGTACTTCCT ACAATCCCCTGAGACACAGC TGTGTTCATGCCATTCATCC CATAGTCTCCTGCGGCCATA ATGCAGGGATGATGTTCTGG TTGACTCGTCTTTCCCTTGC TCTCTGGCGAGTGAAGATCC CATCTTTCTCCCATGGCTTC AATTGGCTGAGTTTGGCAAC CACAGCGATACTCTCCAGCA CTGATTGTGCTGGATGGATG CCATGGAGAGGATGAAGTCC AGAGAGTTCCTCACGCCAAC CCAGGACTCAATCCCTGTGT CCATATTGCCCGATGAACTT GCAGCACTGCAGCATCTTTA GAGGCAGGTCTTCAGAGGAA TTCCAGGATCTGAGCGATCT AAGTGGTTACCGCCTTGTTG ATCTGCTGGGTTCTCCACTG TGGAATCCTGATGCCACAT CCAAGGTGATCCTCTTCTGC ATGGGACGCGTTCTACTCTG CCAAGTGAGAAACGTGAGCA GTAGCGATGGTAGCGAATCC CAGGGCATGGTACTGGTGTC ACTGTTGCAGTGTGGAGACG GCCGCAGTTCTTTTGAATGT CCTTCGAACGTGCACTGATA TCCACGACATAGGGGACAAT AATATGTCCCCCACCATTGA  172  Product Length 101 104 95 148 107 105 91 105 114 77 121 92 112 130 113 95 103 168 145 109 138 104 116 129 84 109 90 133 92 99 107 • 125 90 84 88 111 121 120 123 95  Gene Name 103728 at 8430410A17Rik Akp2 Ankrdl Bmp4 Bnip3 Brachyury Ebaf, Leftb Esrrb FbxolS Fgf5 Foxa2 Fzd5 Gapdh Gbx2 Gus Hck Inpp5d, s-SHIP Jam2 Kit Kl/2 Klf4 Lox More Mtf2 Nanog nestin Nmycl NrObl Nrld2, Thra Oct4, Pou5fl Pbx3 Pcaf Piml Pim2 Podxl Ptch Rexl Rnfl38 Sod2  5'-primers  3'-primers  Product Length  CAGAACCCAGATCTGCACAA  GCTTCTCTGCCAAGGTCAAC  79  GGGGTGCAAAAAGAGGAGAG  CTAGTCGGCATCACGGTTTT  101  GCTTGGCTTATGGACTGAGG  AGGTCCTCATCTGTGGCATC  82  GGCACCTTGGATTGAGAGTC  ACTCTTGCAGGAATCGGCTA  122  ACAAGCGGGGTACAGAGATG  CCAGTCCAGAGCACCTCACT  81  CCGCCCCTACAGTATGTGTT  AGCCGGATTTCATACGACTG  104  GATCTGGGAGAAGCACGTGTA  CGCAAGATCACCTGGAATTT  93  173  Gene Name Sox 17 Sox2 Sppl Stat3 T-box3 Tcfcp2ll Tell  Appendix B Table B1 Genes decreased between 0 and 18 hours after leukemia inhibitory factor removal Gene Symbol Lox Cd44 Bgn Ankrdl Collal Fos Cd44 Vcaml Cd44 CxclU Socs3 Thbsl Serpinel BB120430 Fbln2 Col3al Tnc Bgn Flrt3 Apbblip Cav Socs3 Fosb Fosl2 AA 408868 Inhba Klf4 Hnrphl Bdnf Csfl 2310046G15Rik Caldl App Bcl3 Tbx3 Timp2 Eif2s2 Mras Aebp2 Ch25h  Probe TD 160095 at 114697 at 167023 f at 102048 at 94305 at 160901 at 103005 s at 92559 at 109403 at 160511 at 92232 at 160469 at 94147 at 105858 at 100928 at 98331 at 101993 at 96049 at 164233 at 102710 at 160280 at 162206 f at 103990 at 109982 at 98988 at 100277 at 99622 at 108850 at 102727_at 101450 at 94238 at 109059_at 93063 at 105489 at 103538 at 93507 at 97083 at 103653 at 105298 at 104509 at  Accessions N M 010728 N M 009851 N M 007542 N M 013468 N M 007742 N M 010234 N M 009851 N M 011693 N M 009851 N M 013655 N M 007707 N M 011580 N M 008871 AI847445 N M 007992 X52046 N M 011607 N M 007542 N M 178382 N M 019456 N M 007616 AV374868 N M 008036 N M 008037 N M 030612 N M 008380 N M 010637 N M 021510 N M 007540 N M 007778 N M 029614 N M 145575 N M 007471 N M 033601 N M 011535 N M 011594 N M 026030 N M 008624 N M 009637 N M 009890  174  Ratio 0 to 18 hours 0.02 0.03 0.03 0.04 0.05 0.06 0.07 0.08 0.08 0.1 0.11 0.11 0.11 0.14 0.14 0.14 0.14 0.15 0.17 0.18 0.2 0.22 0.25 0.26 0.27 0.32 0.32 0.33 0.35 0.36 0.36 0.37 0.37. 0.38 0.41 0.41 0.43 0.43 0.44 0.44  p-value 0.0027 0.0311 0.0406 0.0161 0.0079 0.0399 0.0095 0.0381 0.0093 0.0341 0.006 0.0173 0.0007 0.0136 0.0198 0.0352 0.0256 0.0149 0.0244 0.041 0.0288 0.0006 0.0231 0.0292 0.0103 0.0305 0.0036 0.0418 0.0171 0.0337 0.0441 0.0403 0.0136 0.0027 0.0102 0.0359 0.0074 0.0439 0.0075 0.0065  Gene Symbol AI504685 Jagl AI158842 M32461 Stambp Atf3 Egrl 5730501N20Rik Nek6  Probe ID , 116239 at 116304 at 137194 r at 93022 i at 112159 at 104155 f at 98579 at 163583 at 109012 at  Accession??; AI504685 N M 013822 AI158842 N M 054045 N M 024239 N M 007498 N M 007913 AI882080 N M 021606  175  Ratio 0 to 18 hours 0.44 0.45 0.45 0.45 0.46 0.48 0.49 0.49 0.49  p-value 0.0151 0.0494 0.0184 0.0233 0.0254 0.0193 0.0236 0.0193 0.0447  Table B 2 Genes increased between 0 and 18 hours after leukemia inhibitory factor removal Gene Symbol 2410012C07Rik AI461653 2600010E01Rik Hebpl Fbxw5 9430093H08Rik Nedd8 Six4 Myb Dgka Cldn4 2310010J17Rik Lamrl C730015A04 BC027231 Dnmt3a Cldn6 4933429DllRik Casp9 Slc40al Grhl2 E130303B06Rik Erbb3 Sixl 1810037117Rik Nqol Lefl  Fxfl5  Probe ID 166904 at 115158 at 165640 at 103085 at 96799 at 97391 at 162180 r at 93000 g at 92644 s at 103596 at 101410 at 161018 at 96016 at 105817 at 109595 at 100479 at 163262 at 171343 at 100368 at 109069 at 162724 at 160997 at 96771 at 92722 f at 102234 at 94351 r at 103629_g_at 97721 at  Accessions AW046085 N M 178920 N M 175181 N M 013546 N M 013908 N M 145462 AV367714 N M 011382 N M 010848 N M 016811 N M 009903 AI661590 AW045665 N M 177788 N M 145972 N M 007872 N M 018777 AV259517 N M 015733 N M 016917 N M 026496 N M 198299 AI006228 N M 009189 N M 024461 N M 008706 N M 010703 N M 008003  176  Ratio 0 to18 hours 8.4 7.5 3.2 2.9 2.7 2.6 2.5 2.5 2.5 2.5 2.4 2.3 2.2 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2 2 2  p-value 0.0464 0.0194 0.0051 0.0314 0.019 0.0082 0.0141 0.0008 0.038 0.0079 0.0472 0.0137 0.0001 0.0122 0.0156 0.0282 0.0081 0.0379 0.0331 0.037 0.0036 0.0025 0.0352 0.0354 0.0381 0.0176 0.0099 0.0113  Table B 3 Genes decreased between 18 and 72 hours after leukemia inhibitory factor removal Gene Symbol Klf2 Gbx2 NrObl Esrrb A ass Zfp42; Rexl Tell Pfkp Epasl 0610013D04Rik D930018N21Rik Fbxol5 Klf4 Pfkp Gja7 Pcolce B830022L21Rik A230106A15Rik 0610013D04Rik Gli MovlO AA 183875 AU046074 Ddc • Adaml9 Ryr3 Ptch2 Enah 4930404NllRik C77370 Leftb Bmp4 Myolf Bnip3 GU2 Cdldl Mybl2 Texl9 Ceacaml Ptk9l Pern Acas2l Trap la Aire  Probe ID 96109 at 94200 at 93141 at 100301 at 103389 at 98414 at 93296 at 97834 g at 102698 at 96786 s at 103761 at 96162 at 99622 at 97833 at 104635 r at 93349 at 95156 g at 104030 at 96785 at 94097 at 103025 at 103728 at 110041 s at 160074 at 103554 at 97126 at 96557 at 93864 s at 112858 at 95963 at 102345 at 93456 r at 101708 at 93836 at 100395 at 103422 at 100023 at 102418 at 102805 at 94020 at 101368 at 160921 at 102764 at 97159 at  Accessions N M 008452 N M 010262 N M 007430 N M 011934 N M 013930 N M 009556 N M 009337 N M 019703 N M 010137 N M 030697 N M 023755 AA683807 N M 010637 N M 019703 N M 008122 N M 008788 N M 133206 AI848841 N M 030697 N M 010296 N M 008619 A A 183875 AU046074 N M 016672 N M 009616 D38218 N M 008958 N M 010135 AI854616 C77386 N M 010094 N M 007554 N M 008660 N M 009760 X99104 N M 007639 N M 008652 N M 028602 N M 011926 N M 011876 N M 008818 N M 080575 N M 011635 N M 009646  177  Ratio 18 to 72 hours 0.04 0.07 0.09 0.1 0.11 0.11 0.12 0.13 0.14 0.14 0.15 0.16 0.17 0.18 0.2 0.21 0.22 0.22 0.22 0.22 0.24 0.24 0.24 0.24 0.24 0.25 0.25 0.25 0.26 0.26 0.26 0.26 0.27 0.27 0.27 0.27 0.27 0.28 0.28 0.28 0.28 0.28 0.29 0.3  p-yalue 0.0016 0.0089 0.0067 0.0021 0.0062 0.0005 0.0001 0.0008 0.0048 0.021 0.0067 0.0316 0.0022 0.001 0.0376 0.0328 0.0207 0.0024 0.0045 0.0044 0.0101 0.0094 0.0424 0.0012 0.0161 0.0315 0.0054 0.0074 0.0495 0.0099 0.0124 0.0409 0.009 0.0045 0.0111 0.0291 0.0005 0.0026 0.0139 0.0066 0.0245 0.0049 0.0043 0.0004  Gene Symbol Tm7sf3 6720457D02 Rik 2810008P14Rik TcflS Slc29al Rex2 D930018N21Rik Abcbla Tcea3 G2an Sppl Piml Foxpl AI451904 Aldo3 Rbpsuh Ncoa3 Catnall Smarcad1 B3gntl AI847795 843041OA 17Rik Gabbrl A730024F05Rik Anxa9 C230069C04 5830411El ORik Ptch 2700038C09Rik Ltb Gadd45a Icaml Dgka C530030I18 Pcqf Dnajb6 Ulkl Acp6 Empl Txnip Sec 1411 Utrn Pltp Mtf2 Bmp4 Epb4.1l4a  Probe ID 103402 at 97129 at 104150 at 97717 at 95733 at 95346 at 97193 at 102910 at 102344 s at 135386 r at 97519 at 99384 at 96183 at 104938 at 160546 at 101902 at 102024 at 94174 at 98991 at 160137 at 104266 at 160684 at 98011 at 114567 at 97798 at 100321 f at 95655 at 104031 at 93838 at 102940 at 102292 at 96752 at 103596 at 161096 at 104070 at 104625 at 102332 at 96744 at 97426 at 160547 s at 95664 at 92507 at 100927 at 103657 i at 93455 s at 92231 at  Accession^ AI848522 AA617494 N M 146164 N M 009328 N M 022880 N M 009051 N M 023755 N M 011076 N M 011542 AW228115 N M 009263 N M 008842 N M 053202 AI451904 AW121134 N M 009035 N M 008679 N M 018761 X69942 N M 016888 AI847795 N M 173737 N M 019439 N M 198295 N M 023628 N M 172929 N M 028696 N M 008957 N M 025598 N M 008518 N M 007836 N M 010493 N M 016811 AA209597 N M 020005 AA874130 N M 009469 N M 019800 N M 010128 AI839138 N M 028777 N M 011682 N M 011125 AI836552 N M 007554 N M 013512  178  Ratio 18 to 72 hours 0.31 0.31 0.31 0.31 0.32 0.32 0.32 0.32 0.32 0.33 0.33 0.34 0.34 0.34 0.34 0.35 0.35 0.35 0.35 0.35 0.35 0.35 0.36 0.36 0.36 0.36 0.36 0.37 0.37 0.37 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.39 0.39 0.39 0.39 0.39 0.4 0.4 0.4 0.4  p-value 0.0179 0.0446 0.0052 0.0261 0.0081 0.0096 0.0019 0.0213 0.0337 0.0402 0.0486 0.0106 0.0381 0.0463 0.0004 0.0061 0.0051 0.0138 0.008 0.0263 0.0007 0.0057 0.0005 0.0098 0.0033 0.0181 0.0184 0.0135 0.0024 0.0202 0.0082 0.0236 0.0128 0.0037 0.0104 0.0083 0.0024 0.0025 0.0302 0.0288 0.0399 0.0013 0.0179 0.0002 0.0051 0.0204  Gene Symbol Gfpt2 DlPasl Rasgrp2 Enah Rasa4 1190005106Rik Btebl Bcap29 Gabl BC004728 Jmjdl 6430402H10Rik E2fl Pdelb Cox7al AW490810 Akl 1810037I17Rik Rnfl38 C77009 1110036003Rik Ldh2 Slc2a3 1600025H15Rik D530020C15Rik Akp2 Pim3 Stk38 Scdl Trim25 Ii Sox2 D17H6S56E-5 Dmclh Xlr3b Cd9 9430077C05Rik Ppap2a Soxl5 Xbpl Cdc5l Eifla Dmrtl Trfr Elovl2 Jmj  Probe ID 103737 at 93201 at 103282 at 100472 at 160965 at 93143 at 93527 at 160876 at 93902 at 94423 at 95033 at 97279 at 102963 at 93382 at 102749 at 168859 at 96801 at 102234 at 99529 f at 95028 r at 100471 at 101990_at 93804 at 97320 at 100977 at 92796 at 96841 at 94460 at 94056 at 100475 at 162171 f at 100009 r at 104333 at 96498 at 101883 s at 95661 at 97138 at 98508 s at 96824 at 94821 at 95610 at 94745 f at 100374 at 103957 at 94393 r at 94341 at  Accessions N M 013529 N M 033077 N M 011242 N M 010135 A A 163960 N M 197988 N M 010638 N M 007530 N M 021356 N M 174992 AW049513 N M 145567 N M 007891 N M 008800 N M 009944 AV304692 N M 021515 N M 024461 N M 019706 C77009 N M 176830 N M 008492 N M 011401 N M 028064 N M 172665 N M 007431 N M 145478 N M 134115 N M 009127 D63902 AV363696 N M 011443 N M 033075 N M 010059 N M 011727 N M 007657 N M 178059 N M 008903 N M 009235 N M 013842 AA636547 N M 010120 N M 015826 N M 011638 N M 019423 N M 021878  179  Ratio 18 to 72 hours 04 0.41 0.41 0.41 0.41 0.41 0.41 0.42 0.42 0.42 0.42 0.42 0.43 0.43 0.43 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.47 0.47 0.47  p-value 0.0059 0.0487 0.0243 0.0125 0.0023 0.0444 0.0155 0.0378 0.0035 0.0109 0.0072 0.0124 0.0228 0.0228 0.0096 0.029 0.0304 0.0252 0.0095 0.017 0.0022 0.0436 0.0124 0.0062 0.0255 0.0168 0.0196 0.0113 0.0105 0.0407 0.0293 0.0136 0.0124 0.0157 0.0462 0.0452 0.0285 0.0033 0.0396 0.0154 0.0036 0.0269 0.0052 0.039 0.0189 0.046  Gene Symbol Ung Zfp292 Trim23 Ppm lb Pipox Mtf2 Ccnd3 4930461P20Rik Znrfi Ldbl Scap2 H2qfy AW046396 6330406L22Rik Stx8 4931417E21Rik Trim 13 Mxil 9230112O05Rik Brcal Sgk Acadm AA415014 Ezhl C80913 Jadel  Probe ID 102792 at 99001 at 163698 at 101836 at 101844 at 102069 at 160545 at 93478 at 104396 at 160498 at 102012 at 97452 at 106872 at 100511 at 97977 at 104556 at 103691 at 160138 at 102807 at 102976 at 97890 at 92581 at 161106 r at 100486 at 104589 at 95883 at  Accessions N M 011677 N M 013889 N M 030731 N M 011151 N M 008952 N M 013827 N M 007632 AA612483 N M 199143 N M 010697 N M 018773 N M 012015 N M 177836 N M 027521 N M 018768 AI847766 N M 023233 N M 010847 N M 173347 N M 009764 N M 011361 N M 007382 AA415014 N M 007970 N M 011274 N M 172303  180  Ratio 18 to 72 hours 0.47 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.5 0.5 0.5 0.5 0.5 0.5  p-value 0.0377 0.0148 0.0041 0.0199 0.0486 0.0243 0.0015 0.0297 0.0289 0.046 0.0338 0.0059 0.0246 0.0306 0.0346 0.0033 0.012 0.0313 0.0171 0.0009 0.0161 0.0212 0.0285 0.0096 0.0458 0.0381  Table B 4 Genes increased between 18 and 72 hours after leukemia inhibitory factor removal Gene Symbol AI836367 MGC25977 BC023829 Tpm4 Tpm4 3 U 0003Al 7Rik Acadl App Atf3 Dpi ldb3 Itm2a Modi Proml Proml Pxmp3 Satbl Sox 11 Sparc T Tpd52ll Cask Ccngl Cd24a Col4al DM Gng2 Gp38 Hmga2 Idbl ldb2 Krtl-18 Lmo4 Psmb8 Pitx2 Psme2 Tead2 Copeb Cpe Tap! Ptpla Enpp2 Nes Pbx3  Probe ID 100154 at 103078 at 103756 at 95542 at 95543 at 96135 at 95425 at 93063 at 104155 f at 103886 at 92614 at 93511 at 101082 at 93389 at 93390_g_at 103047 at 99665 at 101631 at 97160 at 93941 at 101446 at 102248 f at 160127 at 100600 at 101093 at 101975 at 100418 at 104469 at 99058 at 100050 at 93013 at 94270 at 98122 at 102791 at 102788 s at 100588 at 96940 at 98083 at 99642 i at 103035 at 166430 i at 97317 at 103549 at 93615 at  Accessions AI836367 AA881018 AA832886 AI835858 AI843046 AA833425 N M 007381 N M 007471 N M 007498 N M 007874 N M 008321 N M 008409 N M 008615 N M 008935 N M 008935 N M 008994 N M 009122 N M 009234 N M 009242 N M 009309 N M 009413 N M 009806 N M 009831 N M 009846 N M 009931 N M 010052 N M 010315 N M 010329 N M 010441 N M 010495 N M 010496 N M 010664 N M 010723 N M 010724 N M 011098 N M 011190 N M 011565 N M 011803 N M 013494 N M 013683 N M 013935 N M 015744 N M 016701 N M 016768  181  Ratio 18 to 72 hours 2 2.1 2.2 2.4 2.6 2.1 2.1 2.3 2.3 2.1 2.7 2.9 3 3.5 4.4 2.1 2.5 2.1 2.2 15.2 2 2.4 2.1 3 2 2.2 2 2.1 2.7 2.8 5.2 2.5 2.3 2.3 7.8 2.2 2.3 2 2.4 2.1 2.4 2.9 2.9 2.5  p-value 0.0255 0.0034 0.0302 0.038 0.0367 0.0003 0.0497 0.0161 0.0103 0.005 0.0036 0.004 0.0013 0.0061 0.0023 0.0113 0.0022 0.0208 0.0209 0.0034 0.0103 0.0052 0.0186 0 0.0212 0.0291 0.0491 0.0323 0.0307 0.0147 0.0132 0.0415 0.021 0.0021 0.0013 0.024 0.0192 0.0257 0.0313 0.0077 0.0369 0.0118 0.0431 0.0226  Gene Symbol Vamp8 Cldnl Ctps2 Tm4sf2 Ptpn9 Tm4sf6 Perp Ifi30 Tpml 2610301B20Rik 1110003B01Rik 3632413B07Rik 2310058J06Rik Arhu Pim2 Hip] Snk Amot 1810057B09Rik 5730453H04Rik B430218L07Rik  Probe ID 100345 f at 99561 f at 160652 at 93326 at 100976 at 92555 at 97825 at 97444 at 160532 at 94893 at 160367 at 97958 at 103100 at 96747 at 101926 at 167859 at 92310 at 95531 at 98958 at 94247 at 93467 at  Accession^ N M 016794 N M 016887 N M 018737 N M 019634 N M 019651 N M 019656 N M 022032 N M 023065 N M 024427 N M 026005 N M 026131 N M 027230 N M 133784 N M 133955 N M 138606 N M 146001 N M 152804 N M 153319 N M 175009 N M 175338 N M 198037  182  Ratio 18 to 72 hours 2.2 4 2.1 2.2 2.9 2 2.3 2.1 2.3 2.3 2.2 2.5 2 2.2 2.2 2.3 3.7 5.4 2.2 2.3 3.8  p-value . 0.0116 0.0003 0.0004 0.0209 0.0141 0.0095 0.0198 0.0458 0.0066 0.0067 0.0246 0.0002 0.0157 0.0309 0.0237 0.0026 0.0066 0.0093 0.0093 0.0405 0.0003  Table B 5 Genes decreased between 0 and 72 hours after leukemia inhibitory factor removal Gene Symbol Ccl2 Bgn Tnc Klfl Socs3 Abcal Klf4 Gbx2 Lox Mras Collal Ndp52ll NrObl Vcaml Empl Fbln2 Tnc D930018N21Rik Nfib Acta2 Esrrb Bgn Fbxol5 Tell Fos Sdpr Zfp42 Thbsl A ass Tbx3 Cish3 S100a6 Eif2s2 Pfkp AA183875 Pcolce BB120430 Osmr Sppl Cd44 Mras Gja7 C730049F20Rik Cav  Probe ID 102736 at 167023 f at 162362 f at 96109 at 92232 at 94354 at 99622 at 94200 at 160095 at 103653 at 94305 at 95562 at 93141 at 92559 at 97426 at 100928 at 101993 at 103761 at 99440 at 93100 at 100301 at 96049 at 96162 at 93296 at 160901 at 167384 s at 98414 at 160469 at 103389 at 166427 f at 162206 f at 92770 at 97083 at 97834 g at 103728 at 93349 at 105858 at 102255 at 97519 at 109403 at 164306 f at 104635 r at 96038 at 160280 at  Accessions N M 011333 N M 007542 AV230686 N M 008452 N M 007707 N M 013454 N M 010637 N M 010262 N M 010728 N M 008624 N M 007742 AA615127 N M 007430 N M 011693 N M 010128 N M 007992 N M 011607 N M 023755 N M 008687 N M 007392 N M 011934 N M 007542 AA683807 N M 009337 N M 010234 N M 138741 N M 009556 N M 011580 N M 013930 N M 011535 AV374868 N M 011313 N M 026030 N M 019703 AA183875 N M 008788 AI847445 N M 011019 N M 009263 AW121933 AV226634 N M 008122 AI840339 N M 007616  183  Ratio 0 to 72 hours 0.02 0.03 0.03 0.03 0.04 0.05 0.05 0.06 0.06 0.07 0.08 0.08 0.08 0.08 0.09 0.09 0.09 0.09 0.09 0.1 0.1 0.1 0.11 0.11 0.11 0.11 0.12 0.13 0.13 0.14 0.15 0.16 0.17 0.17 0.17 0.17 0.18 0.18 0.19 0.19 0.2 0.22 0.22 0.23  p-value 0.0279 0.0257 0.0258 0.0007 0.0122 0.006 0.002 0.0072 0.01 0 0.0278 0.0136 0.0083 0.0344 0.0381 0.0054 0.0134 0.0014 0.0047 0.0472 0.0011 0.0071 0.0212 0.0001 0.0017 0.0278 0.0018 0.0203 0.007 0.0329 0.0055 0.0186 0.017 0.0033 0.0029 0.0231 0.0224 0.0321 0.0126 0.043 0.0292 0.0473 0.0361 0.0254  Gene Symbol Kit D6Ertd32e D930018N21Rik Pfkp Ferll3 Trpsl Lamal Wdt2-pending Pern A230106A15Rik Myolf Akap2 RM2 Eifla My lip Gli Inhba Tbx3 Trim2 Dnajb9 All 15454 Icaml G2an 6720457D02Rik AA 175286 AI427138 Ly75 Ulkl Nrld2 Tcfap2c Mllt2h Trap la Dpys Fosb Mybl2 Pim3 AI504685 MovlO Abcbla Fgfl7 GH2 C77370 Scap2 Piml Nidi 8430410A17Rik  Probe ID 99956 at 95618 at 97193 at 97833 at 112941 f at 110414 at 103729 at 107952 i at 101368 at 104030 at 101708 at 163655 s at 95617 at 94745 f at 106479 at 94097 at 100277 at 103538 at 163115 at 96680 at 115333 at 96752 at 135386 r at 97129 at 113283 at 108784 at 103258 at 102332 at 99076 at 92275 at 115212 at 102764 at 113671 at 103990 at 100023 at 96841 at 116239 at 103025 at 102910 at 98730 at 100395 at 95963 at 102012 at 99384 at 100120 at 160684 at  Accessions N M 021099 AI843884 N M 023755 N M 019703 AA960514 N M 032000 N M 008480 AA606601 N M 008818 AI848841 N M 008660 N M 009649 N M 011250 U28419 N M 153789 N M 010296 N M 008380 N M 011535 N M 030706 N M 013760 N M 175345 N M 010493 AW228115 AA617494 N M 010156 AI427138 N M 013825 N M 009469 N M 011584 N M 009335 N M 133919 N M 011635 N M 022722 N M 008036 N M 008652 N M 145478 AI504685 N M 008619 N M 011076 N M 008004 X99104 C77386 N M 018773 N M 008842 N M 010917 N M 173737  184  Ratio 0 to 72 hours 0.23 0.24 0.24 0.24 0.24 0.25 0.26 0.26 0.27 0.27 0.27 0.28 0.28 0.28 0.28 0.29 0.29 0.3 0.3 0.31 . 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.31 0.32 0.32 0.33 0.33 0.33 0.33 0.34 0.34 0.34 0.35 0.36 0.36 0.36 0.36 0.36 0.36 0.37  p-value 0.0016 0.0496 0.0113 0.0002 0.029 0.0369 0.0346 0.0439 0.0293 0.0012 0.0083 0.0159 0.039 0.0091 0.0452 0.0496 0.0297 0.0214 0.0307 0.0216 0.0344 0.0188 0.0253 0.0462 0.0402 0.0228 0.035 0.0012 0.0194 0.0482 0.0277 0.0109 0.0005 0.0094 0.0013 0.0107 0.0004 0.0104 0.02 0.0044 0.0122 0.0303 0.0001 0.0154 0.0471 0.007  Gene Symbol Gfpt2 Leftb Nrld2 AI85U55 4930422J18Rik C85523 Mapt D130027M04 Bnip3 493043 lPHRik Tuba3 AW490810 AI837485 1110035O14Rik Jmjdl Kif3a Bmp4 Camk4 Yes Il6st Stat3 Sox2 Ddc Catnall AUO14924 Epb4.1l4a 0610013D04Rik Cnot4 Zfhxla Tm7sf3 Scmhl Rbpsuh 493243 lF02Rik Msc Texl9 Aebp2 9930116P15Rik Ptk9l 2810008P14Rik Uppl Slc29al Aldo3 Epb4.1l4a Mtf2 Zfp36ll Slc2a3  Probe ID 103737 at 102345 at 107103 at 117246 at 161817 f at 99849 at 102742_g_at 112257 at 93836 at 108866 at 160420 r at 168859 at 133552 at 94192 at 95033 at 161275 at 93455 s at 104455 at 100015 at 135888 at 99100 at 100009 r at 160074 at 94174 at 109299 f at 92231 at 96785 at 163709 i at 99052 at 103402 at 103399 at 101902 at 95964 at 93234 at 102418 at 105298 at 111970 at 94020 at 104150 at 100030 at 95733 at 160546 at 161603 r at 103657 i at 93324 at 93804 at  Accessions N M 013529 N M 010094 N M 011584 N M 133982 AV376312 C85523 N M 010838 AA958476 N M 009760 N M 172723 N M 009446 AV304692 AI837485 N M 021524 AW049513 AV324729 N M 007554 N M 009793 N M 009535 N M 010560 N M 011486 N M 011443 N M 016672 N M 018761 AU014924 N M 013512 N M 030697 N M 016877 N M 011546 AI848522 N M 013883 N M 009035 N M 028475 N M 010827 N M 028602 N M 009637 AI616223 N M 011876 N M 146164 N M 009477 N M 022880 AW121134 AV308245 AI836552 N M 007564 N M 011401  185  Ratio.0 to 72 hours 0.37 0.37 0.37 0.38 0.38 0.38 0.38 0.38 0.39 0.39 0.39 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.41 0.41 0.41 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.43 0.43 0.43 0.43 0.43 0.43 0.44 0.44 0.44 0.44 0.44 0.44 0.44 0.44  p-value 0.0018 0.0186 0.0474 0.0126 0.0119 0.0449 0.0203 0.0427 0.0111 0.0444 0.0215 0.0134 0.0075 0.0349 0.0209 0.0223 0.0052 0.0329 0.0079 0.0058 0.0128 0.02 0.0046 0.017 0.0398 0.0173 0.019 0.013 0.0096 0.0287 0.0115 0.0004 0.0304 0.025 0.0075 0.0002 0.0454 0.0141 0.0067 0.0096 0.015 0.0257 0.0005 0.0031 0.0123 0.0027  Gene Symbol Egrl Anxa9 B3gntl Klf5 Adaml9 2310005N03 Rik AI851470 Echdcl 5730501N20Rik Gadd45a Cd9 5830411ElORik H2afy C230069C04 Jadel Sgk C530030I18 Ac as 21 D7Ertdl43e Gjb3 Timp2 Xbpl Ddahl Nfatc2ip Stx8 Cdc37l 2410043F08Rik Il6st Jagl Aard Arg2 2310009N05Rik Rgsl6 8430420F16Rik Mtf2 Rgsl6 3230402J05Rik Btgl Ppap2a 9430079M16Rik 4930553M18Rik Smarcadl 3110031O14Rik Ak4 2700017106Rik Sod2  Probe ID 98579 at 97798 at 160137 at 97937 at 103554 at 160316 at 112406 at 113214 at 163583 at 102292 at 95661 at 95655 at 97452 at 100321 f at 95883 at 97890 at 161096 at 160921 at 95954 at 104232 at 93507 at 94821 at 115112 at 96561 at 97977 at 115976 at 115028 at 94345 at 116304 at 160968 at 98473 at 160801 at 94378 at 111789 at 102069 at 161609 at 106144 at 93104 at 98508 s at 104091 at 104639 i at 98991 at 116738 at 99959 at 162909 at 96042 at  Accessions N M 007913 N M 023628 N M 016888 N M 009769 N M 009616 N M 025511 AI851470 N M 025855 AI882080 N M 007836 N M 007657 N M 028696 N M 012015 N M 172929 N M 172303 N M 011361 AA209597 N M 080575 AA437728 N M 008126 N M 011594 N M 013842 N M 026993 N M 010900 N M 018768 N M 025950 N M 133754 N M 010560 N M 013822 N M 175503 N M 009705 N M 025861 N M 011267 AI642433 N M 013827 AV349152 AI846635 N M 007569 N M 008903 N M 175414 N M 029248 X69942 N M 026081 N M 009647 AI846731 N M 013671  186  Ratio 0 to 72 hours 0.44 0.44 0.44 0.44 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.46 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.47 0.48 0.48 0.48 0.48 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.49 0.5  p-value 0.0258 0.0025 0.0041 0.0236 0.002 0.023 0.0186 0.0469 0.0068 0.0011 0.0345 0.0384 0.0115 0.0231 0.0267 0.0009 0.0208 0.0283 0.0038 0.0423 0.0298 0.0045 0.0289 0.0087 0.0046 0.032 0.0161 0.0119 0.0411 0.0367 0.0037 0.0086 0.0082 0.0444 0.0096 0.0026 0.0425 0.0418 0.0041 0.031 0.0253 0.024 0.0208 0.01 0.0234 0.0018  Gene Symbol 1810013L24Rik Fut9 AI595304 U10008H02Rik E130012A19Rik  Probe ID 131177 r at 105675 at 113495 at 96156 at 160712 r at  Accessions AA636427 N M 010243 AI595304 AA874329 N M 175332  187  Ratio 0 to 72 hours 0.5 0.5 0.5 0.5 0.51  p-value 0.0038 0.0125 0.0489 0.0463 0.0217  Table B 6 Genes increased between 0 and 72 hours after leukemia inhibitory factor removal Gene Symbol Prphl Thbs4 Evxl Btla A1461653 Aloxl5 Enpp2 T Pitx2 Calcr Cldn7 Nef3 Proml Galnt3 Eomes Proml Amot Foxhi B430218L07Rik Krtl-18 Nudtll Cldn6 AU067722 AV022166 Cldn6 Pbx3 Nefl Ssbp2 Cxcll2 Wnt3 AA407151 Krt2-8 Msxl Cnnl Plcg2 Gatm 1110003B01Rik Hes6 Cbr3 Crlfl Cbr3 Bhlhb5 Ifi30 Dapkl  Probe ID 104751 at 113830 at 98815 at 116608 at 115158 at 98758 at 97317 at 93941 at 102788 s at 101135 at 99561 f at 117269 at 93390 g at 99011 at 103532 at 93389 at 95531 at 97789 at 93467 at 94270 at 166849 at 93703 at 135354 s at 168363 i at 163262 at 93615 at 103575 at 103504 at 100112 at 99325 at 94663 at 101009 at 101526 at 99942 s at 103503 at 96336 at 160367 at 97334 at 168153 i at 161046 at 161042 at 129880 s at 97444 at 114748 at  Accessions N M 013639 N M 011582 N M 007966 N M 177584 N M 178920 N M 009660 N M 015744 N M 009309 N M 011098 N M 007588 N M 016887 N M 008691 N M 008935 N M 015736 AW120579 N M 008935 N M 153319 N M 007989 N M 198037 N M 010664 . N M 021431 N M 018777 AU067722 AV022166 N M 018777 N M 016768 N M 010910 AI837107 N M 013655 N M 009521 AA407151 N M 031170 N M 010835 N M 009922 N M 172285 N M 025961 N M 026131 N M 019479 AV217999 N M 018827 N M 173047 N M 021560 N M 023065 N M 029653  ' Ratio 0 to 72 hours 17.6 16.2 14.7 14.3 13.9 11.1 9.1 7.3 6.1 6 6 5.6 5.54.7 4.7 4.5 4.1 3.9 3.9 3.9 3.9 3.7 3.6 3.5 3.4 3.4 3.43.4 3.3 3.3 3.3 3.2 3.2 3.1 3.1 3.1 3.1 3 3 3 2.9 2.9 2.9 2.8  p-value 0.0122 0.0003 0.0337 0.0033 0.0058 0.0353 0.0165 0.0126 0.0002 0.0338 0 0.0415 0.001 0.0391 0.0162 0.0312 0.0073 0.0376 0.001 0.0124 0.0101 0.013 0.005 0.036 0.027 0.0073 0.0494 0.0102 0.0231 0.0027 0.004 0.0451 0.0275 0.0228 0.0147 0.0072 0.0069 0.0109 0.0253 0.0045 0.0048 0.0227 0.0162 0.0197  Gene Symbol Itga8 2600010E01Rik Idb2 Tead2 Modi Nes Idbl Mdk Ankrd3 Tdpl Cd24a 5330410G16Rik Gatm Erbb3 Arhgef5 St6gall Arhu Nnat MGC27770 5730467H21 Rik Podxl Ctps2 Tc/7 Rnfl28 Tor2a Vamp8 Spintl Ptpla Prphl 2810432L12Rik 2610319K07Rik Lamrl Gclm Tpm4 Ptpn9 Stl4 Facl5 Nme3 Tm4sf6 Cldn7 Nudtll Kiflc Edg4 Gng3 Ddrl Perp  Probe ID 166680 at 165640 at 93013 at 96940 at 101082 at 103549 at 100050 at 160561 at 163489 at 103035 at 100600 at 162573 at 109824 f at 96771 at 160977 at 94432 at 96747 at 97520 s at 97116 at 168550 at 112828 at 160652 at 97994 at 108010 at 97557 at 100345 f at 97206 at 166430 i at 161482 f at 95440 at 104744 at 96016 at 160335 at 95543 at 100976 at 116435 at 97456 at 94982 f at 92555 at 129928 f at 163611 f at 160679 at 114162 at 94986 at 100155 at 97825 at  Accessions AI447669 N M 175181 N M 010496 N M 011565 N M 008615 N M 016701 N M 010495 N M 010784 N M 023663 N M 013683 N M 009846 N M 182991 AI835703 AI006228 . AA726063 N M 009175 N M 133955 N M 010923 N M 145986 N M 175270 N M 013723 N M 018737 N M 009331 N M 023270 AI841457 N M 016794 N M 016907 N M 013935 AV068234 N M 025944 N M 028264 AW045665 N M 008129 AI843046 N M 019651 N M 011176 AI838021 N M 019730 N M 019656 AW214436 N M 021431 N M 153103 N M 020028 NM010316 N M 007584 N M 022032  189  _  Ratio 0 to 72 hours 2.8 2.8 2.8 2.8 2.8 2.8 2.8 2.7 2.7 2.7 2.7 2.7 2.7 • 2.7 2.6 2.6 2.6 2.6 2.6 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.5 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4 2.4  . p-value 0.0298 0.0054 0.022 0.0058 0.0016 0.0384 0.0314 0.0139 0.04 0.0074 0.0001 0.048 0.0023 0.0146 0.0094 0.0498 0.0009 0.0092 0.0038 0.0013 0.0019 0.0082 0.0322 0.0453 0.0016 0.0024 0.0138 0.0019 0.0028 0.0038 0.0002 0.0284 0.0315 0.0049 0 0.0319 0.006 0.0085 0.0113 0.0147 0.0207 0.0069 0.0301 0.0321 0.0264 0.0268  Gene Symbol Sox 11 Mapkl2 Ssbp3 Snk MGC25977 6030440G05Rik 3632413B07Rik Slc39a8 Zdhhc2 Ptpnl3 D10Ertd755e Cyfip2 Sfrp2 1810029F08Rik Lrp8 Unc5b Kras2 AU024549 Trip6 Oxct SST3 Igsf9 2300002F06Rik Tapbp Phfl5 Cugbp2 Cyb5 Pcbd Hipl AW228646 Satbl AI838057 Mx2 Clu Psme2 Binl Them2 Dpi 2810428I15Rik Rem2 Hmga2 Frda AW214234 Lmo4 AW046694 Laptm4b  Probe ID 101631 at 92323 at 117147 at 92310 at 103078 at 108014 at 97958 at 97442 at 162858 at 98424 at 130824 i at 162711 at 93503 at 104302 f at 138065 at 112955 at 97991 at 131137 at 94948 at 92845 at 109554 at 129147 r at 96672 at 100154 at 105005 at 97255 at 98533 at 99056 at 167859 at 135355 at 99665 at 117151 at 102699 at 95286 at 100588 at 92220 s at 93780 at 96115 at 160278 at 163542 at 99058 at 101407 at 129180 f at 98122 at 104445 at 100571 at  Accessions , N M 009234 N M 013871 N M 023672 N M 152804 AA881018 N M 145148 N M 027230 N M 026228 N M 178395 N M 011204 AUO19706 N M 133769 N M 009144 N M 029635 N M 053073 N M 029770 N M 021284 AU024549 N M 011639 N M 024188 N M 172463 N M 033608 N M 175606 AI836367 N M 199299 N M 010160 N M 025797 N M 025273 N M 146001 AW228646 N M 009122 AI838057 N M 013606 N M 013492 N M 011190 , N M 009668 N M 025790 N M 007874 N M 025577 N M 080726 N M 010441 N M 008044 AW214234 N M 01Q723 AW046694 N M 033521  190  Ratio 0 to 72 hours 2.4 2.4 2.4 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.3 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.2 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1  p-value 0.0434 0.0047 0.0416 0.0295 0.0114 0.0166 0.0055 0.0208 0.0158 0.0191 0.0428 0.0268 0.015 0.0145 0.0255 0.0367 0.0467 0.004 0.0044 0.0073 0.0155 0.005 0.0081 0.0202 0.0024 0.018 0.0248 0.044 0.0073 0.0184 0.0285 0.0311 0.0299 0.0018 0.0149 0.0234 0.0318 0.0455 . 0.027 0.0225 0.0439 0.0052 0.0099 0.0243 0.0241 0.0369  ;  Gene Symbol 1600022A19Rik 2810453I06Rik 2610301B20Rik Vamp8 1200003O06Rik Tpd52U A hey Siat8c 130001 lC24Rik AI586067 Purg All 73484 Ppplrl4c Nme3 Ube2d2 PsmblO 1110049F12Rik BC023829 Mab21l2 AW547186 Rabl5 Ttc8 0610013E23Rik D10Ertd755e AW550831 BC026370 Vapb AW555464  Probe ID 113913 at 104393 at 94893 at 93305 f at 94549 at 101446 at 96026 at 99504 at 93327 at 105461 at 110145 at 131796 at 115575 at 94981 i at 93069 at 101486 at 160329 at 103756 at 95379 at 116943 at 113199 at 163404 at 160507 at 130825 f at 115058 at 162607 i at 112389 at 131216 f at  Accessions N M 146062 N M 026050 N M 026005 N M 016794 N M 025813 N M 009413 N M 016661 N M 009182 N M 029564 AI586067 N M 152821 A l l 73484 N M 133485 N M 019730 N M 019912 N M 013640 N M 025411 AA832886 N M 011839 N M 177592 N M 134050 N M 029553 N M 029788 AUO19706 AA756546 N M 198167 N M 019806 AI463306 '  191  Ratio 0 to 72 Hours 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2 2 2 . 2 2 2 2 2 2 2 2 2 2 2 2 2 ,  p-value 0.0374 0.0496 0.0214 0.0173 0.0031 0.0038 0.0068 0.0399 0.0054 0.0388 0.0468 0.0487 0.0418 0.0003 0.0034 0.0058 0.0197 0.0415 0.0108 0.026 0.019 0.0435 0.0037 0.0043 0.0084 0.011 0.0419 0.0421  Table B 7 Comparison of changed genes to previously published data sets  to  Gene Name  Affy ID  Tnc Lox Fbln2 Bdnf Tbx3 2310()46G15Rik Bgn Cav Serpinel Ankrdl Atf3 Bgn Collal Col3al Cxcll2 Egrl Klf4 Socs3 Socs3 Thbsl  101993 at 160095 at 100928 at 102727 at 103538 at 94238 at 96049 at 160280 at 94147 at 102048 at 104155 f at 167023 f at 94305 at 98331 at 160511 at 98579 at 99622 at 162206 f at 92232 at 160469 at  Leftb Jadel Smarcadl Ung Sox2  Overlap Kelly Overlap Bhattac harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap mouse et al. with all human etal. Rizzino Santos et et al. et al. rger et al. al. etal. data sets 2004) data sets al. 2002) 2003) 2000) Accession 2004) 2004) 2003) 2003) data sets 2002) Decreased between 0 and 18 hours 1 4 1 3 0 0 0 1 1 1 0 N M 011607 0 1 3 0 3 1 1 0 0 0 0 0 0 N M 010728 1 2 2 0 0 0 0 0 1 0 N M 007992 0 0 2 1 0 2 1 0 0 0 N M 007540 0 0 0 0 1 2 2 0 0 0 1 0 N M 011535 0 0 0 0 1 2 2 0 0 1 0 N M 029614 0 0 0 0 0 1 2 2 0 0 1 0 N M 007542 0 0 0 0 0 2 1 2 0 0 0 1 0 0 0 0 0 N M 007616 1 2 0 2 0 1 0 0 0 0 0 N M 008871 0 1 1 0 0 1 0 0 0 0 0 0 N M 013468 0 1 0 0 0 0 0 0 1 0 1 N M 007498 0 0 1 1 0 1 0 0 0 0 0 N M 007542 0 0 0 1 0 1 1 0 0 0 0 0 N M 007742 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 X52046 1 1 0 1 0 0 0 0 N M 013655 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 N M 007913 0 1 1 0 0 0 0 1 0 0 0 N M 010637 0 0 1 0 0 0 0 0 0 1 1 0 0 N M 007707 0 1 0 0 0 0 1 1 0 0 N M 007707 0 0 0 1 0 0 0 0 0 0 1 0 0 1 N M 011580 0 Decreased between 18 and 72 hours 2 0 6 1 0 4 1 0 D83921 1 1 1 1 6 0 2 0 1 1 1 4 1 0 1 1 N M 172303 6 1 1 3 0 1 0 1 1 1 3 X69942 0 0 5 1 0 3 1 1 1 1 2 N M 011677 0 0  102345 at 95883 at 98991 at 102792 at 100009_r_a N M 011443 t  1  1  0  0  2  I  0  0  0  1  1  4  m  o  —  —  o  o  as  e  5  Overlap with all data sets  -t-  o o o  - oi  o  o  -  --  -  o o  o  o  --  -  o  o —  CN  —  o  -  =1  -r -t  s  © O  o -H  O  o O  o  O o o o o  CN CN ol  CN CN CN  o - i  o —  CN  -  <N  1  -  ol  -  O  o  o o o  CN  o  -  o  <*i  q o  CN  -t -r -1m  O o  o  ----  - --—  o  -  o  o o o  o o  —  - O  -  o o o  o —  o  CN  -  o  -  o  —  —  o  -  -  o o  o  o CN  © —  -  o  o o  o o o o o  o c c  6 o  CS  1  4 i  "*  1  193 010207 013512 007431 009337 007554 021356 010262 022880 015826 008818 173347 009616 183875  o  -t  NM NM NM NM NM NM NM NM NM NM NM NM AA  o  -r  NM 013827 NM 013529 NM 011401 NM 021878 U28419 WI 009556 NM 019706 NM 010135 NM 172665 NM 013827 NM 011635  •  Accession NM 008652 NM 008952 NM 009035 NM 008492  «  Affy ID 100023 at 101844 at Pipox 101902 at Rbpsnh 101990 at 103657_i_a t Gfpt2 103737 at 93804 at 94341 at Jmi Eifla 94745 f at 98414 at 99529 f at Enah 100472 at D530020C15Rik 100977 at 102069 at Trap la 102764 at 162171_f_a t 92231 at Akp2 92796 at Tell 93296 at 93456 r at 93902 at Gabl 94200 at Slc29al 95733 at Dmrtl 100374 at Pern 101368 at 9230112005Rik 102807 at Adaml9 103554 at AA183875 103728 at  .23 • S~ 55  Gene Name  Overlap Kelly Overlap Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov| with all human et al. Rizzino Santos et etal. ij mouse rger et al. et al. ai. 2000) al. 2002) 2003) data sets data sets 2002) 2004) 2003) 2003) .E  -i-  m  o O  o O  — CN o CN  o o O  o  o  m  -  -  o  1  o  -- - o  — CN o Ol  — O o  —  ©  o o o o  o o  © © o  © © o  © © ©  o o  © o  o  O  1  S—  —i  o  -  o  -  0  O  o o o  fN  — CN PI  o o  o  o  - o  o  o  o  o o  o o  — —  -  o  •»  B.  03  5  -  -  CN  o  -  o  o o  © © ©  © — —  © ©  o rl o o  © o  o o  © o o  -  r i CN Ol  CN  r-1  O  © ©  o S  CN CN  o  ©  --  o  --  r1  - -  © O  o  -  o  o  Afly ID 103761 at 104589 at 160546 at 160684 at 161106_r_a AA415014 t Pcolce 93349 at Bmp4 93455 s at 93527 at Btebl 93836 at Bnip3 93864 s at Enah 94174 at Catrial 1 C77009 95028 r at 96162 at Fbxol5 96557 at Ptchl 0610013D04Rik 96785 at 6720457D02Rik 97129 at Empl 97426 at Sppl 97519 at 97890 at Sgk Ppap2a 98508 s at 99384 at Piml GH2 100395 at 1110036O03Rik 100471 at Pltp 100927 at Ppm lb 101836 at 101883_s_ Xlr3b at 102024 at Ncoa3 102292 at Gadd45a  Gene Name D930018N2IRik C80913 Aldo3 843041OA 17Rik  5  Accession N M 023755 N M 011274 AW121134 N M 173737  Overlap Kelly Overlap Bhattac harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap mouse et al. with all human et al. Rizzino Santos et et al. et al. rger et al. al. et al. 2000) al. 2002) 2003) data sets 2004) data sets 2004) 2004) 2003) 2003) data sets 2002) 1 2 2 0 0 1 0 0 0 0 0 0 2 1 2 0 0 1 0 0 0 0 0 0 2 1 0 0 1 0 1 0 1 0 0 1 2 2 0 0 1 0 0 0 0 0 0  AA415014 N M 008788 N M 007554 N M 010638 N M 009760 N M 010135 N M 018761 C77009 AA683807 N M 008958 N M 030697 AA617494 N M 010128 N M 009263 N M 011361 N M 008903 N M 008842 X99104 N M 176830 N M 011125 N M 011151  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1  0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0  0 0 2 0 1 1 1 0 0 0 0 0 0 0 0 1 2 1 1 1 1  1 1 0 1 0 0 0 1 1 0 1 1 1 1 1 0 0 0 0 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  N M 011727 N M 008679 N M 007836  0 0 0  0 0 0  0 0 0  0 0 0  0 0 0  1 0 0  0 0 0  1 1 1 1 1 1 1 1 1 1 0 0 0 0 0  0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0  2 2 0 2 1 1 1 2 2 2 2 2 2 2 2 1 0 0 0 0 0  0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 1  0 1 0  1 1 1  0 0 0  1 1 1 1  2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1  , 1 1  ©  o o  o ©  o o © ©  o o ©  © ©  o ©  ©  ©  o © © ©  o  -  -  ©  © ©  ©  © ©  o o  o ©  o o  o ©  o o o © ©  o ©  o o o o © ©  — ©  — ©  ©  © © © © ©  ©  —  o o  ©  o  —  ©  o  © ©  & B>  &;  -  ©  ©  -  © © © ©  o © ©  o © © © © ©  © © © © © © ©  © © © ©  ©  o © © © © ©  © © ©  ©  -  ©  ©  -  o ©  o ©  ©  -  -  so  §  g  195  © © ©  o  — © ©  ©  o © ©  o © ©  © © © © ©  ©  ©  o ©  — © ©  CN  e a ©  I  03  -  © ©  © ©  © ©  — ©  © © ©  © ©  -  ©  © ©  -  -  © ©  © ©  o  © ©  o © ©  © ©  ©  © © © ©  ©  © © © ©  © ©  a,  CN  &rtt•o  U  -  -  05  o CO  <"3"n  o  Utrn Acadm  —  ©  ©  007707 007707 011682 007382  o o  ©  NM NM NM NM  -  ©  AI839138 NM 007530 AA209597  o  ©  Txnip  ©  ©  Ldbl  ©  ©  008060 016672 016888 010697  o  ©  NM NM NM NM  ©  o  160074 at 160137 at 160498 at 160547_s_ at 160876 at 161096 at 162206_f_a t 92232 at 92507 at 92581 at  o  ©  Ddc  ©  -  ©  -  NM 008122 NM 177836  O  ©  AW046396  o  o o o  ©  ©  Gjal  NM 011542 NM 028602 NM 019456 NM 011076 NM 009764 NM 011242 NM 013930 NM 016811 NM 023233 NM 011638 NM 008957 AA874130  --  ©  Aass Dgka Trim 13 Trfr Ptch  Any ID 102344_s_ at 102418 at 102710 at 102910 at 102976 at 103282 at 103389 at 103596 at 103691 at 103957 at 104030 at 104625 at 104635_r_a t 106872 at 135386_r_a  ©  Apbblip Abcbla Brcal  Tcea3  Gene Name  Overlap Kelly Overlap Bhattac harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap etal. with all human mouse etal. etal. Rizzino Santos et et al. rger et al. al. et al. 2000) j\ al "M)02^ 2003) data sets 2004) data sets 2004) data sets 2002) Accession 2004) 2003) o © ©  -  -  © © ©  © © —  © © © ©  © © © ©  ©  ©  © ©  -  sO ON  Gene Name Affy ID App 93063 at NrObl 93141 at U90005106Rik 93143 at C330022M23 93478 at Scdl 94056 at 94097 at Gli Jmjdl 95033 at Rex2 95346 at Cdc5l 95610 at 5830411E10Rik 95655 at C77370 95963 at Kip 96109 at 96498 at Dmclh 96744_at Acp6 96752 at lcam I 9430077C05Rik 97138 at D930018N21Rik 97193 at 1600025H15Rik 97320 at H2afy 97452 at 97717 at TcflS Pfkp 97833 at 97834 g at Pfkp Klf4 99622 at Leftb Jadel Smarcad I Sox2 Ak4 Mybl2  102345 at 95883 at 98991 at 100009 r at 99959 at 100023 at  Accession N M 007471 N M 007430 N M 197988 AA612483 N M 009127 N M 010296 AW049513 N M 009051 AA636547 N M 028696 C77386 N M 008452 N M 010059 N M 019800 N M 010493 N M 178059 N M 023755 N M 028064 N M 012015 N M 009328 N M 019703 N M 019703 N M 010637 D83921 N M 172303 X69942 N M 011443 N M 009647 N M 008652  Overlap Kelly Bhattac Overlap harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap et al. with all mouse et al. human etal. rger et al. al. etal. Rizzino Santos et et al. 2000) al. 2002) 2003) data sets 2004) data sets 2004) 2004) 2003) 2003) data sets 2002) 1 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 Decreased between 0 and 72 hours 1 1 2 0 6 1 0 0 1 1 4 1 1 1 2 0 6 1 0 1 4 1 0 1 1 1 1 3 0 6 1 1 3 1 0 0 1 1 1 0 1 5 0 0 1 0 3 1 2 1 1 0 5 1 0 0 1 3 1 0 4 1 3 0 1 0 1 1 1 0 0 0  Z  s  CU  a ©  -  o  ©  o  ©  ©  o  — — —  1 0 ©  CN CN  ©  -©  ©  —  cu o  o  o  ©  -  o  o  ©  ©  ©  i*i Q <N "o &a  -  O  CN CN  ©  ©  -  ©  ©  CN  O  ©  CN  ©  ©  -  ©  o  1  o  ©  CN  o  o  o  CN  --—  1  -  ©  --  o  ©  o  O  O  o  o  CN cn CN CN CN rn cn  ©  ©  o  ©  ©  --  o  --  o  o  o  >  -  5-  >  rn  rn  cn cn  Ol  CN  Ol  Ol  Ol  Ol  CN r i CN CN  ©  o  ©  ©  ©  ©  ©  ©  ©  ©  CN  Ol  CN  Ol  CN  CN —  ©  o  ©  ©  ©  © © © ©  — ©  © ©  © ©  © ©  ©  -  197  -  ©  ©  -  <N  'O  ©  -  ©  ©  ©  oi  ©  ©  ©  ©  ©  ©  -  ©  ©  ©  ©  o  ©  ©  ©  o  -  ©  ©  o  ©  ©  ©  ©  ©  — CN  -  ©  © ©  ©  O  ©  o  O — —  -  ©  ©  n  r-  I  FzdS Dpys  CN on CN CN rn CN  -  103761 at NM 023755 107952 i at 108784 at NM 022721 1 13671 at NM 022722  o  o  ©  Adaml9 AAl83875 D93()0l8N2lRi k  -  o  -  ©  m rn rn m rn rn  Sic 2 9a 1 Yes Fblnl Pern  o  O  rn  ylcta2 Tell  CN  — O  ©  Ccl2 Mapt Trapla Lox Epb4.1l4a  o  ©  Eifla Zfp42  o  ©  Tnc  o  -t -T -r -r  109909VV  O  Rbpsuh Tnc Mtf2  Overlap Kelly Bhattac Overlap harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap mouse et al. with all etal. rger et al. human et al. Rizzino Santos et et al. et al. al. 2000) al 2002^ 2003) data sets 2004) data sets 2004) 2004) Accession 2003) data sets 2002) Affy ID 101902 at NM 009035 101993 at NM 011607 103657 i at NM 013827 103737 at NM 013529 162362 f at NM 011607 93804 at NM 011401 94745 f at U28419 98414 at NM 009556 102069 at NM 013827 102736 at NM 011333 102742 g at NM 010838 102764 at NM 011635 160095 at NM 010728 161603 r at NM 013512 92231 at NM 013512 93100 at NM 007392 93296 at NM 009337 94200 at NM 010262 95733 at NM 022880 100015 at NM 009535 100928 at NM 007992 101368 at NM 008818 103538 at NM 011535 103554 at NM 009616 103728 at AAl 83875 -t  ©  -  ©  o  O  o  o  o  ©  ©  ©  ©  ©  ©  ©  ©  ©  ©  ©  o ©  ©  O  o  o  o  o  o  o  ©  ©  o  ©  o  o  ©  ©  ©  ©  © © ©  ©  ©  ©  ©  -  -  O  Gene Name Cav Tuba3 Aldo3 8430410AllRik E130012A19Rik Trim! Akap2 Cnol4 Tcfap2c Msc Pcolce Bmp4 Bnip3 Catnal 1 D7Ertdl43e Bgn Fbxol5 0610013D04Rik 6720457D02Rik Empl Sppl Sgk Klf5 Ppap2a Nrld2 Piml Uppl Nidi GU2 Osmr  Affy ID 160280 at 160420 r at 160546 at 160684 at 160712 r at 163115 at 163655 s at 163709 i at 92275 at 93234 at 93349 at 93455 s at 93836 at 94174 at 95954 at 96049 at 96162 at 96785 at 97129 at 97426 at 97519 at 97890 at 97937 at 98508 s at 99076 at 99384 at 100030 at 100120 at 100395 at 102255 at  Accession N M 007616 N M 009446 AW121134 N M 173737 N M 175332 N M 030706 N M 009649 N M 016877 N M 009335 N M 010827 N M 008788 N M 007554 N M 009760 N M 018761 AA437728 N M 007542 AA683807 N M 030697 AA617494 N M 010128 N M 009263 N M 011361 N M 009769 N M 008903 N M 011584 N M 008842 N M 009477 N M 010917 X99104 N M 011019  Overlap Kelly Overlap Bhattac harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap etal. rger et al. al. et al. human et al. Rizzino Santos et et al. mouse et al. with all 2000) al. 2002) 2003) data sets 2004) data sets 2004) 2004) 2003) 2003) data sets 2002) 0 2 0 2 1 1 0 0 0 0 0 0 0 2 0 1 0 0 1 1 0 0 1 0 1 0 2 0 0 1 1 0 1 0 0 0 0 2 1 0 2 1 0 0 0 0 0 0 2 2 0 1 0 1 0 0 0 0 0 0 2 0 0 0 0 2 0 1 1 0 0 0 0 2 0 1 1 1 0 0 1 0 0 0 0 2 0 0 0 1 1 2 0 0 0 0 1 2 1 0 0 1 1 0 0 0 0 0 1 2 2 0 0 0 1 0 0 0 0 0 1 2 2 0 0 0 1 0 0 0 0 0 2 0 0 0 2 0 1 1 0 0 0 1 2 1 0 0 1 0 1 0 0 0 0 1 2 0 1 0 1 0 0 1 0 0 0 1 1 2 0 2 0 0 0 0 0 0 0 1 1 2 0 2 0 0 0 0 0 0 0 1 1 0 2 2 0 0 0 0 0 0 0 1 1 2 2 0 0 0 0 0 0 0 0 1 1 2 0 2 0 0 0 0 0 0 0 1 1 2 0 2 0 0 0 0 0 0 0 1 1 2 0 2 0 0 0 0 0 0 0 1 1 2 2 0 0 0 0 0 0 0 0 1 1 2 0 2 0 0 0 0 0 0 0 1 2 1 0 0 1 0 0 0 0 1 0 1 2 0 0 1 1 0 1 0 0 0 2 0 0 0 2 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 1 o  Gene Name Gadd45a TexJ9 Abcbla Aass Lama I Ptch G/b3  sC sC  4930553MI8Rik Fut9 My lip Nrld2 Trpsl C330022M23 Dl30027M04 Ferll3 A1595304 2410043F08Rik Ddahl 3110031O14Rik G2an Ddc B3gntl Thbsl Aard C530030118 4930422J18Rik Mras Tbx3 Bgn  Affy ID 102292 at 102418 at 102910 at 103389 at 103729 at 104030 at 104232 at 104635 r at 104639 i at 105675 at 106479 at 107103 at 110414 at 111970 at 112257 at 112941 f at 113495 at 115028 at 115112 at 116738 at 135386 r at 160074 at 160137 at 160469 at 160968 at 161096 at 161817 f at 164306 f at 166427 f at 167023 f at  Accession N M 007836 N M 028602 N M 011076 N M 013930 N M 008480 N M 008957 N M 008126 N M 008122 N M 029248 N M 010243 N M 153789 N M 011584 N M 032000 A1616223 AA958476 AA960514 AI595304 N M 133754 N M 026993 N M 026081 N M 008060 N M 016672 N M 016888 N M 011580 N M 175503 AA209597 AV376312 N M 008624 N M 011535 N M 007542  Overlap Kelly Overlap Bhattac harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap et al. with all mouse human et al. Rizzino Santos et etal. etai. etal. rger et al. al. 2000) al. 2002) 2003) data sets 2004) data sets 2004) 2003) data sets 2002) 2003) 2004) 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 1 0 0 1 0 0 0 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0  Affy ID Gene Name 93141 at NrObl 93324 at Zfp36ll 94097 at Gli 94305 at Collal H6st 94345 at 94354 at Abcal 95033 at Jmjdl 583041 IE WRik 95655 at C77370 95963 at 96042 at Sod2 Klf2 96109 at 1110008H02 Rik 96156 at Nfatc2ip 96561 at 96680 at Dnajb9 96752 at learn 1 D930018N21Ri 97193 at k 97452 at H2afy 97833 at Pfkp 97834 g at Pfkp Egrl 98579 at 99440 at Nfib 99622 at Klf4 99956 at Kit  Accession N M 007430 N M 007564 N M 010296 N M 007742 N M 010560 N M 013454 AW049513 N M 028696 C77386 N M 013671 N M 008452 AA874329 N M 010900 N M 013760 N M 010493 NM NM NM NM NM NM NM NM  023755 012015 019703 019703 007913 008687 010637 021099  Overlap Kelly Overlap Bhattac harya Brandenbe Sato et Sperger with all Ivanova and Ramalho- (Sharov with all (Ginis Overlap mouse et al. with all human et al. Rizzino Santos et et al. et al. rger et al. al. etal. 2000) al. 2002) 2003) data sets 2004) data sets 2004) 2003) 2003) data sets 2002) 2004) 1 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0  0 1 0 0 0 0 0 0  0 0 0 0 1 0 0 1  0 1 0 0 1 0 0 1  1 0 1 1 0 1 0 0  0 0 0 0 0 0 1 0  0 0 0 0 0 0 0 0  0 0 0 0 0 0 0 0  1 1 1 1 1 0  0 0 0 0 0 0 0 0  1 1 1 1 1 1 1 1  Appendix C Table C I Function of the 88 genes on the first five Pareto fronts  Gene Ontology Term  Count  Percentage  Physiological Process Metabolism Cellular Physiological Process Cell Growth And/Or Maintenance Regulation O f Biological Process Macromolecule Metabolism Protein Metabolism Nucleobase, Nucleoside, Nucleotide And Nucleic Acid Metabolism Regulation O f Metabolism Regulation O f Physiological Process Development Regulation O f Transcription Regulation Of Nucleobase, Nucleoside, Nucleotide And Nucleic Acid Metabolism Transcription Regulation Of Transcription, Dna-Dependent Transcription, Dna-Dependent Protein Modification Morphogenesis Phosphate Metabolism Phosphorus Metabolism Protein Amino Acid Phosphorylation Phosphorylation Organogenesis Axonogenesis Cell Migration Regulation Of Cell Proliferation Cell Motility Response To Chemical Substance Chemotaxis Taxis Negative Regulation Of Transcription Negative Regulation Of Nucleobase, Nucleoside, Nucleotide And Nucleic Acid Metabolism Growth Negative Regulation O f Cytokine Biosynthesis Negative Regulation Of Cytokine Production Negative Regulation Of Protein Biosynthesis Negative Regulation Of Biosynthesis  56 40 28 24 23 22 20  62.2 44.4 31.1 26.7 25.6 24.4 22.2  18 16 16 15 14  20 17.8 17.8 16.7 15.6  14 14 13 13 11 10 9 9 8 8 8 4 4 4 4 4 3 3 3  15.6 15.6 14.4 14.4 12.2 11.1 10 10 8.9 8.9 8.9 4.4 4.4 4.4 4.4 4.4 3.3 3.3 3.3  3 3 2 2 2 2  3.3 3.3 2.2 2.2 2.2 2.2  201  Table C 2 Comparison o f genes on the first five Pareto Fronts with previously published data sets  0  0  0  0  0  1  1  2  2  Emb  0  0  0  0  0  0  0  0  0  Scap2  0  0  0  0  0  1  0  1  1  Ulkl  0  0  0  0  0  0  0  0  0  Nmycl  0  0  0  0  0  1  1  2  2  Nefh  0  0  1  1  2  0  1  1  3  Eed  0  0  0  0  0  1  0  1  1  Mras  0  0  0  0  0  1  0  1  1  0  0  0  0  0  1  1  2  2  1  0  1  2  Sox2  1  100030 at  1448562 at  uridine phosphorylase 1  Uppl  101560 at  1415856 at  102012 at  1418895 at  embigin src family associated phosphoprotein 2  102332 at  1448370 at  103048 at  1417155 at  103234 at  1424847 at neurofilament, heavy polypeptide embryonic ectoderm development 1448653 at  103737 at  1418753 at  103761 at  1418091 at  104139 at  1452094 at  104544 at  1423327 at  107103 at  1416958 at  108010 at  1418318 at  Transcribed locus —  —  transcription factor CP2-like 1 Tcfcp2ll procollagen-proline, 2oxoglutarate 4-dioxygenase (proline 4-hydroxylase), alpha 1 polypeptide P4hal R I K E N c D N A 4930517K11 4930517 KllRik gene nuclear receptor subfamily 1, group D, member 2 Nrld2 ring finger protein 128  108048 at  1454788 at  ADP-ribosylation factor-like 7  108097 at  1450626 at  mannosidase, beta A , lysosomal  108279 at  1455300 at  4  3  SRY-box containing gene 2  muscle and microspikes RAS  1  1  Gene Name  1416967 at  103653 at 1449590 a at 103728^at 1456521 at  0  Sperger  0  MOE430  100009 r at  103342 at  Overlap with all  1  M G U74v2  Unc-51 like kinase 1 (C. elegans) neuroblastoma myc-related oncogene 1  Overlap with mouse  Overlap with human  Gene Bhatta Branden Symbol charya burger Sato  Rnfl28 Arl7  Manba E130014 RIKEN cDNA E130014J05 gene JOSRik  Ramalho Ivanova -Santos 1  0  0  0  1  1  0  0  0  0  0  1  1  2  2  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  2  2  0  0  0  0  0  1  0  1  1  0  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  1  1  0  0  0  0  0  1  0  1  1  0  0  0  0  0  1  0  1  1  M G U74v2  MOE430  Gene Name  108712 at  1434917 at  cordon-bleu  108784 at  1455604 at  expressed sequence AI427138  110429 at  1455333 at  tensin 3  111970 at  1460711 at  1 12828 at  1448688 at  113673 at  1423508 at  Gene Bhatta Branden Symbol charya burger Sato  116872 at 117246 at  2  1  2  1  1  1  0  0  0  0  0  ,  0  Tns3 4930461 R I K E N cDNA 4930461P20 gene PlORik  0  0  0  1  1  1  0  0  0  0  0  0  0  1  podocalyxin-like M Y S T histone acetyltransferase monocytic leukemia 4  1  1  1  0  3  0  3  0  0  0  1  1  0  2  0  0  0  0  0  junction adhesion molecule 2  160253 at  1423754 at  microrchidia interferon induced transmembrane protein 3  160370 at  1416552 at  developmental pluripotency associated 5  133819 at 1419418 a at  1  0  Podxl  1436568 at  116435 at  Overlap with all  0  133365 at  115804 at 116214_at  0  Overlap with mouse  0  133204 at  115445 at  Ramalho Ivanova -Santos  Gobi AI42713 8  Myst4 AW5508 31 1434362 at expressed sequence AW550831 — 1435374 at Transcribed locus R I K E N cDNA C230088H06 gene 1438237 at R I K E N cDNA A230098A12 A230098 gene A12Rik 1456329 at suppression of tumorigenicity 14 Stl4 1418076 at (colon carcinoma) MG1.192 0501 1435437 at SET domain-containing protein 7 ribonuclease P 25 subunit (human) Rpp25 1448845 at BB00122 8 1455425 at Expressed sequence BB001228  1 15058 at  Sperger  Overlap with human  Jam2  1  1  0 1  0  1  1  0  0  0  0  0  0  0  0  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0  0  1  0  0  0  0  0  1  2  0  1  0  0  1  1  0  1  2  0  1  1  More  0  0  0  0  0  1  lfitm3  0  0  0  0  0  0  0  0  0  Dppa5  0  0  0  0  0  1  1  2  2  M G U74v2  MOE430  160684 at  1423786 at  160828 at  1426858 at  161042 at  1427912 at  161106 r at  1443892 at  162522 f at 1437015 x at  Gene Name R I K E N cDNA 8430410A17 gene  Gene Bhatta Branden Symbol charya burger Sato 8430410 0 0 A17Rik 0  163715 at  1429399 at  0  0  0  0  0  carbonyl reductase 3  Cbr3  0  0  0  0  0  0  0  0  0  0  0  1  0  1  2  phospholipase A2, group IB, pancreas  Pla2glb  leucine rich repeat containing 34  ring finger protein 125  Rnfl25  estrogen related receptor, beta  Esrrb  168508 at  1436926 at  92275 at  1418147 at transcription factor AP-2, gamma Tcfap2c  93063 at  growth differentiation factor 3 Gdf3 keratin complex 1, acidic, gene Krtl-19 19 1417156 at SI00 calcium binding protein A6 (calcyclin) SW0a6 1421375 a at amyloid beta (A4) precursor protein App 1427442 a at  93104 at  1426083 a at  92550 at 92770 at  0  Inhbb  Pnp 165699 r at 1453299 a at purine-nucleoside phosphorylase RIKEN cDNA D230005D02 D230005 166142_r_at D02Rik gene 1436799 at LOC433 110 LOC433110 167088 r at 1456242 at  92476 at  0  inhibin beta-B  Lrrc34 2410146 163288 at 1460471 at R I K E N cDNA 2410146L05 gene L05Rik receptor-interacting serineRipk4 threonine kinase 4 163489 at 1418488 s at  163005 s at 1429366 at  Sperger  Overlap with human  1449288 at  B-cell translocation gene 1, antiproliferative  BM  Overlap with mouse  Overlap with all  1  2  2  1  2  2  1  1  1  1  2  2  0  1  3  0  1  1  0  1  1  Ramalho Ivanova -Santos  1 1  1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1  1  0  0  0  0  0  1  0  1  1  0  0  0  0  0  1  0  1  1  0  0  0  0  0  0  1  1  0  0  0  0  0  0  1  1  0  0  0  0  0  1  0  1  1  0  0  0  1  1  1  1  2  3  1  0  1  0  2  1  1  2  4  0  1  1  1  i->  0  0  3  0  0  0  0  0  0  1  1  0  0  1  1  2  0  1  3  0  0  0  0  0  0  1  1  1  M G U74v2  MOE430  93141 at  1417760 at  93271 s at 1450186 s at 93296 at  1422458 at  93483 at  I449455  at  93864 s at 1421624 a at 94200 at 94270 at 94354 at 94745_fat  Gene Name nuclear receptor subfamily 0, group B, member 1 G N A S (guanine nucleotide binding protein, alpha stimulating) complex locus  Gene Bhatta Branden Symbol charya burger Sato  Sperger  Overlap with human  Ramalho Ivanova -Santos  Overlap with mouse  Overlap with all  NrObl  0  0  0  0  0  1  1  2  2  Gnas  0  1  0  1  2  0  0  0  2  T-cell lymphoma breakpoint 1  Tell  0  0  0  0  0  1  1  2  2  hemopoietic cell kinase  Hck  0  0  0  0  0  1  0  1  1  enabled homolog (Drosophila)  Enah  0  0  0  1  1  0  0  0  1  1  1  2  2  0  0  0  0  0  1  1  1  0  3  0  0  0  3  0  0  0  0  0  1  0  1  1  0  0  0  1  1  1  1  2  3  0  0  1  1  2  1  1  2  4  0  0  0  0  0  1  0  1  1  Amot  0  0  0  1  1  1  1  2  3  Dppa2  0  0  0  0  0  1  1  2  2  Sod2  0  0  1  0  1  1  1  2  3  gastrulation brain homeobox 2 Gbx2 keratin complex 1, acidic, gene 18 Krtl-18 1448169 at ATP-binding cassette, sub-family Abcal A (ABC1), member 1 1421840 at similar to Eukaryotic translation initiation factor 1A (elF-lA) MGC107 (eIF-4C) 533 1427479 at 1420337 at  95518 at  Jmjdia 1810015 1424683 at RIKEN cDNA 1810015C04 gene C04Rik  95531 at  1454890 at  95584 at  1453223 s at  96042 at  1448610 a at  angiomotin developmental pluripotency associated 2 superoxide dismutase 2, mitochondrial  96109 at  1448890 at  Kruppel-like factor 2 (lung)  Kip  0  0  0  0  0  0  1  1  1  96162 at  1427238 at  F-box protein 15  Fbxol5  0  0  0  0  0  1  1  2  2  96203 at  1424713 at  calmodulin-like 4  Calml4  0  0  0  0  0  1  0  1  1  0  1  1  0  1  1  95033 at  1426810 at  jumonji domain containing 1A  96752 at  1424067 at  intercellular adhesion molecule  learn 1  0  0  0  0  0  1  96841 at  1451069 at  proviral integration site 3  Pim3  0  0  0  0  0  1  M G U74v2  MOE430  96900 at  1433720 s at  Gene Name  97317 at  Nur77 downstream gene 2 eukaryotic translation initiation factor 2, subunit 2 (beta) 1441023 at developmental pluripotencyassociated 3 1424295 at ectonucleotide pyrophosphatase/phosphodiestera se 2 1415894 at  97426 at  1416529 at  97442 at  1416832 at  97083 at 97283 at  97519 at  1449254 at  97520 s at 1423506 a at 97890 at 1416041 at 98414 at  1418362 at  epithelial membrane protein 1 solute carrier family 39 (metal ion transporter), member 8 secreted phosphoprotein 1 neuronatin serum/glucocorticoid regulated kinase zinc finger protein 42  Gene Bhatta Branden Symbol charya burger Sato MG1.214 0 0 0 3558  0  0  1  0  1  1  0  0  0  1  1  1  0  1  2  Dppa3  0  0  0  0  0  0  1  1  1  Enpp2  0  0  0  0  0  0  0  0  0  Empl  0  0  0  0  0  1  1  2  2  Slc39a8  0  0  0  0  0  1  0  1  1  2  2  Sppl Nnat  0  0  0  0  0  1  1  0  1  0  1  2  0  0  0  2  Sxk  0  0  0  0  0  1  1  2  2  Zfp42  1  0  0  0  1  1  1  2  3  1  1  0  1  2  0  1  0  1  1  1  1  0  1  2  1448393 at  claudin 7  Cldn7  0  0  0  99622 at  1417394 at  Kruppel-like factor 4 (gut)  Klf4  0  0  0  0  kit oncogene  Ramalho Ivanova -Santos  Eif2s2  99561 f at  1452514 a at  Overlap with all  Sperger  1  99956 at  Overlap with mouse  Overlap with human  Kit  0  0  0  1  

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