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Systems biology of cellular signaling : quantitative experimentation and systems genetics approaches Taylor, Robert James 2009

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Systems Biology of Cellular SignalingQuantitative Experimentation and Systems Genetics ApproachesbyRobert James TaylorB.A.Sc., The University of British Columbia, 2002A THESIS SUBMITTED IN PARTIAL FULFILLMENTOF THE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinTHE FACULTY OF GRADUATE STUDIES(Genetics)The University Of British Columbia(Vancouver)April 2009c Robert James TaylorAbstractCellular regulation is governed by dense biomolecular networks consisting of pro-teins, nucleotides, lipids, and metabolites that dynamically coordinate cellular de-cision making in the face of complex and time-varying environmental stimuli. Ob-taining predictive models of these complex networks is a central goal of systemsbiology and requires sophisticated technologies for the acquisition and integrationof many disparate data types. Recent genomic, proteomic and cellular imagingdevelopments have greatly enabled systems-level studies, but further technologi-cal advances are needed. For instance, current high-throughput biochemical andcellular measurement techniques are generally limited to the analysis of cell pop-ulations, and the development of single-cell technologies are needed to advancepredictive models of cellular networks. Large-scale genetic analyses are highlyinformative of the complex architecture of cellular networks but further compu-tational methods are required to manage data complexity. In this thesis I presentthe development of two technologies, a microfluidic single-cell experimental plat-form and a genetic-network computational analysis platform, to address these is-sues and apply them to the study of prototypical eukaryotic signaling systems inSaccharomyces cerevisiae.First I describe microfluidic technology for the high-throughput analysis ofsingle-cells subject to complex environmental conditions. Using this platform, Istudied cellular response of the mating pathway in Saccharomyces cerevisiae undera series of genetic and time-varying environmental perturbations. This analysisrevealed dynamic phenotypes that are not observable under static conditions andallowed for the stratification of system components into distinct functional roles.In addition, I describe advances to this technology that allow for the tracking ofiiindividual cells over long experimental time frames. These developments enabledthe investigation of sources of cell-to-cell variability not detectable otherwise.Second I describe a computational platform for analyzing complex genetic in-teraction networks. These networks describe functional relationships between genesystems and can be used to delineate information flows through complex cellularcircuits. Genetic interactions networks are dense and information rich, and re-quire sophisticated computational methods for their analysis. In this work, I de-veloped network algorithms to identify biologically informative patterns withina multi-mode genetic interaction network to reveal functional sub-networks andinformation-hubs of the filamentation pathway in Saccharomyces cerevisiae.iiiTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ivList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ixGlossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiCo-Authorship Statement . . . . . . . . . . . . . . . . . . . . . . . . . . xiv1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1 General Introduction . . . . . . . . . . . . . . . . . . . . . . . . 21.2 Thesis Summary . . . . . . . . . . . . . . . . . . . . . . . . . . 41.3 Yeast Signaling . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3.1 Model Organisms . . . . . . . . . . . . . . . . . . . . . . 51.3.2 Signaling Pathways in Yeast . . . . . . . . . . . . . . . . 51.3.3 Mating and Filamentation/Invasion Pathways . . . . . . . 61.3.4 Complex Systems Models: Yeast . . . . . . . . . . . . . 81.4 Microfluidic Quantitative Single-cell Analysis . . . . . . . . . . . 91.4.1 Quantitative Single-Cell Studies . . . . . . . . . . . . . . 91.4.2 Single Cell Microfluidics . . . . . . . . . . . . . . . . . . 10iv1.5 Systems Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . 131.5.1 Genetic Analysis of Signaling Pathways . . . . . . . . . . 131.5.2 Genetic Interaction Networks . . . . . . . . . . . . . . . 151.5.3 Network Analysis . . . . . . . . . . . . . . . . . . . . . . 17Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 Dynamic Analysis of MAPK Signaling Using a Microfluidic Live-Cell Imaging Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . 412.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 412.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 452.2.1 High-Throughput Microfluidic Live Cell Imaging Platform 452.2.2 Imaging Studies of Pheromone Response Pathway . . . . 492.2.3 Response Under Chemostatic Conditions . . . . . . . . . 502.2.4 Pathway Response Under Dynamic Stimulation . . . . . . 552.2.5 Response to Periodic Stimulation . . . . . . . . . . . . . 572.2.6 Mutants Implicated in Dynamic Phenotypes . . . . . . . . 602.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 632.4.1 Cell loading. . . . . . . . . . . . . . . . . . . . . . . . . 632.4.2 Microfluidic Control . . . . . . . . . . . . . . . . . . . . 642.4.3 Microfluidic Fabrication . . . . . . . . . . . . . . . . . . 652.4.4 Image Acquisition . . . . . . . . . . . . . . . . . . . . . 65Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 673 Network Motif Analysis of a Multi-Mode Genetic-Interaction Net-work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . 763.2.1 Multi-Mode Genetic-Interaction Network . . . . . . . . . 763.2.2 Genetic-Interaction Patterns Reflect the Underlying Molec-ular System . . . . . . . . . . . . . . . . . . . . . . . . . 773.2.3 Statistical Model of a Null Hypothesis . . . . . . . . . . . 79v3.2.4 Genetic-Interaction Network Motifs . . . . . . . . . . . . 803.2.5 Molecular Information and Genetic-Interaction NetworkMotifs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 863.2.6 Comparing Network Patterns in a Similar Genetic-InteractionNetwork . . . . . . . . . . . . . . . . . . . . . . . . . . . 913.2.7 Open Source Software . . . . . . . . . . . . . . . . . . . 923.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 933.4 Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . 943.4.1 Network Randomization . . . . . . . . . . . . . . . . . . 943.4.2 Motif Enumeration Techniques . . . . . . . . . . . . . . . 963.4.3 GoSlim Molecular Function Annotations . . . . . . . . . 97Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 984 Conclusions and Recommendations for Further Work . . . . . . . . 1054.1 Summary of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . 1054.2 Dynamic Single Cell Analysis . . . . . . . . . . . . . . . . . . . 1074.2.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 1074.2.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . 1094.3 Systems Genetics . . . . . . . . . . . . . . . . . . . . . . . . . . 1124.3.1 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . 1124.3.2 Future Directions . . . . . . . . . . . . . . . . . . . . . . 114Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116A Appendix to: Dynamic Analysis of MAPK Signaling Using a Mi-crofluidic Live-Cell Imaging Matrix . . . . . . . . . . . . . . . . . . 125A.1 SI Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126A.1.1 Fabrication Protocol . . . . . . . . . . . . . . . . . . . . 126A.1.2 Microfluidic Control . . . . . . . . . . . . . . . . . . . . 126A.1.3 Chemicals and Media . . . . . . . . . . . . . . . . . . . . 127A.1.4 Cell Preparation . . . . . . . . . . . . . . . . . . . . . . 127A.1.5 Chemical Mixing and Perfusion . . . . . . . . . . . . . . 127A.1.6 Constant Stimulation Protocol . . . . . . . . . . . . . . . 128viA.1.7 Single Transient Pulse Protocol . . . . . . . . . . . . . . 131A.1.8 Short Repeated Pulses Protocol . . . . . . . . . . . . . . 131A.1.9 Biological Constructs . . . . . . . . . . . . . . . . . . . . 133A.1.10 Image Analysis Pipeline Algorithms . . . . . . . . . . . . 133A.2 Supporting Text . . . . . . . . . . . . . . . . . . . . . . . . . . . 140A.2.1 Experimental Variability of Microfluidic Platform . . . . . 140A.2.2 Morphology Classifications Under Constant Stimulation . 141A.2.3 Single Pulse Analysis . . . . . . . . . . . . . . . . . . . . 144A.2.4 Pulse-Width Dependent Growth Rate . . . . . . . . . . . 152A.2.5 Calculation of d[GFP]/dt . . . . . . . . . . . . . . . . . . 155Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156B A Microfluidic Platform for High-Throughput Single-Cell Trackingand Dynamic Environmental Control . . . . . . . . . . . . . . . . . 158B.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158B.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162B.2.1 Microfluidic Chip Overview and Operation . . . . . . . . 162B.2.2 High-Throughput Multimode Live-Cell Imaging . . . . . 166B.2.3 Single-Cell Tracking Reveals Heterogeneous Decision Mak-ing in a Narrow Pheromone Concentration Range . . . . . 171B.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174B.4 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176B.4.1 Chip Fabrication . . . . . . . . . . . . . . . . . . . . . . 176B.4.2 Segmentation and Tracking Algorithm . . . . . . . . . . . 177Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179viiList of TablesA.1 Pumping Protocol for Creating 32 Different a-factor Concentrations.129A.2 Strains Used in this Study . . . . . . . . . . . . . . . . . . . . . . 136viiiList of Figures2.1 Schematic of the Microfluidic Device . . . . . . . . . . . . . . . 472.2 Mating Response to Persistent a-factor Stimulation . . . . . . . . 522.3 Morphological Response and Transient Stimulation Responses. . 552.4 Mating Response to Short Repeated Pulses of a-factor . . . . . . 603.1 Multi-Mode Genetic-Interaction Motifs and the Underlying Molec-ular System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793.2 Motifs in the Yeast-Invasiveness Genetic-Interaction Network . . . 833.3 Motif Subnetworks . . . . . . . . . . . . . . . . . . . . . . . . . 853.4 Examples of Motifs Integrating Gene Annotations. . . . . . . . . 883.5 Annotation-Motif Subnetworks . . . . . . . . . . . . . . . . . . . 91A.1 Sieve Valves . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130A.2 Stimulation protocols . . . . . . . . . . . . . . . . . . . . . . . . 132A.3 Image Segmentation Algorithms . . . . . . . . . . . . . . . . . . 138A.4 Morphological Response of the Yeast Strains to a-factor Concen-tration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144A.5 Single Pulse Analysis . . . . . . . . . . . . . . . . . . . . . . . . 148A.6 Response Variability within a Single Microfluidic Device. . . . . . 149A.7 Reproducibility of Results. . . . . . . . . . . . . . . . . . . . . . 152A.8 Image Focus over 12 hours . . . . . . . . . . . . . . . . . . . . . 155B.1 Microfluidic Chip Design and Operation . . . . . . . . . . . . . . 164B.2 On-Chip Reagents Mixing and Temporal Control of Chemical En-vironment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167B.3 High-Throughput Gene Expression Measurements . . . . . . . . . 170ixB.4 Lineage Tracking with Single-Cell Resolution; Identification of aSwitch-Like Response . . . . . . . . . . . . . . . . . . . . . . . 174xGlossaryBSA Bovine serum albuminCV Coefficient of variabilitydSLAM Diploid-based synthetic lethality analysis on microarraysE-MAP Epistasis miniarray profileFACS Fluorescent-activated cell sortingFRET Fluorescence resonance energy transferGFP Green fluorescent proteinGPCR G-protein coupled receptorMAPK Mitogen activated protein kinaseMSL Multilayer soft-lithographyPAK P21-activated kinasePCR Polymerase chain reactionPDMS Poly(dimethylsiloxane)SCD Synthetic complete dextroseSGA Synthetic genetic analysisSGD Saccharomyces genome database (www.yeastgenome.org)xiAcknowledgmentsI would like to thank everyone who has helped in my research and my transitioninto the biological sciences. I particularly indebted to my supervisors Dr. TimGaliski who taught me much about biology and research, and Dr. Phil Hieter whoenabled me to conduct research from afar, at the Institute for Systems Biology. Iwould like to thank my supervisory committee, Dr. Anne Condon, Dr. ElizabethConibear, and Dr. Steven Jones, for guidance and direction. I am also grateful forthose who supported my research, directly or indirectly at the University of BritishColumbia: Carl Hansen, Didier Falconnet, Shay Ben-Aroya, Kirk McManus, JanStoepel, Irene Barrett, and Dave Thomson, and at the Institute for Systems Biology:Greg Carter, Susanne Prinz, Iliana Avila-Campillo, Song Li, Ramsey Saleem, JohnBoyle, Jennifer Smith, James Spotts, and Alan Diercks. Finally, I am grateful tothe Michael Smith Foundation for Health Research for my graduate scholarships.xiiDedicationTo my parents and sister who have supported me throughout my academicendeavors.xiiiCo-Authorship StatementChapters 2, 3, Appendix A, and Appendix B were co-authored work. In Chapter2 and Appendix A, I designed the study, designed and fabricated the microfluidicplatform used, constructed the biological strains used, performed many of the mi-crofluidic experiments, designed and coded the image analysis pipeline, conductedall analysis, and wrote the manuscript. D. Falconnet performed many of the mi-crofluidic experiments and assisted in preparation of the manuscript. A Niemistodesigned and coded the image segmentation algorithms. S.A. Ramsey assisted withexperimental design and analysis. S. Prinz assisted with experimental design. I.Shmulevich assisted with experimental design. T. Galitski assisted with study andexperiment design and manuscript preparation. C.L. Hansen assisted with studyand experiment design, microfluidic platform design, and manuscript preparation.In Chapter 3, I designed the study, designed and coded the analysis software,and prepared the manuscript. A.F. Siegel assisted in the statistical analysis of mo-tifs with respect to multiple hypothesis testing. T. Galitski assisted in study design,analysis design, and manuscript preparation.In Appendix B, I assisted in study design, microfluidic design, and designedand coded the image pipeline used. D. Falconnet lead the microfluidic design andxivconducted all experiments and analysis. A. Niemisto designed and coded the im-age segmentation and tracking algorithms. T. Galiski assisted with study design.I. Shmulevich assisted with study design. C. Hansen assisted with study design,microfluidic design, and manuscript preparation.xvChapter 1Introduction”The completion of the Human Genome Project fueled the expectationfor the rapid translation of ”genes to drugs.” Unfortunately these hopesand dreams shattered with the realization that disease biology is muchmore complex than we had first realized. Despite all the sophisticatedtechnology for drug discovery, the expected acceleration in innovativemedical therapies reaching patients has not occurred. This is becausedeciphering the mechanisms of disease requires a deep knowledge ofhow signaling transduction pathways operate. This is why biologyis undergoing a fundamental shift from a descriptive to a quantita-tive, predictive science. This transition is being driven by advancesin genome sequencing, massive amounts of data, rapidly expandingcomputational resources, and the introduction of powerful new ana-lytical technologies.1Advancements in biology in the 21st century will be dominated by re-search at the convergence of biological, physical, and information sci-ences. Disease will be understood at a systems level, which promisespredictive, preventive, and personalized medicine. Although systemsbiology is still in its infancy, we are at a turning point in our under-standing of what the future holds for biology and human medicine.”Biotech 2008. Life Sciences: A 20/20 Vision to 2020. Burrill & Com-pany. [14]1.1 General IntroductionBiological systems are irreducible: function and phenotype are emergent proper-ties of complex biomolecular networks that govern cellular regulation and physi-ology. Obtaining a predictive understanding of biological processes thus requiresa global view of biomolecular systems and cannot be achieved by the independentstudy of individual genes or gene products. At the multicellular organism level,tissues are interdependent, communicating through dedicated endocrine, nervous,and circulatory systems. Individual organs cannot exist unaided outside of theorganism, and perturbations occurring in one tissue cause measurable effects inother non-neighboring tissues [20]. At the single-cell level, complex systems ofproteins, lipids, nucleotides, and metabolites interact together in dense biomolecu-lar networks to regulate phenotype. Analysis of complex single-cell and multicellsystems thus requires sophisticated experimental and analysis tools. Systems biol-ogists are developing these tools and have begun analyzing biological response atthe network and system level [63]. Systems biology is an emerging field, and con-2tinued efforts in the development of both experimental and analysis technologiesare needed.Obtaining predictive models of biological systems is a primary goal of systemsbiology. This requires a quantitative understanding of the regulatory mechanismsof the cell such that response to genetic and chemical perturbations may be pre-dicted under a wide variety of environmental conditions [62]. Traditional biologi-cal methods rely on qualitative descriptive models that simplify biological processto allow for conceptual accessibility. Although successful in defining key biologi-cal themes, these methods are limited in their predictive power. For example, cellsignaling systems were originally described as linear signaling cascades leading tothe idea of a signaling ’pathway’. Such descriptions can predict qualitative phe-notypes, such as the loss of signaling upon removal of key system components,however are unable to predict quantitative phenotypes, such as the percentage ofgene expression increase upon increased amount of stimuli. The pathway descrip-tion of signaling is now being replaced by a network view [27], and sophisticatednetwork models of cellular regulation are leading to impressive predictive powerof cellular response [10].Although much progress has been made, constructing high-quality quantitativemodels of biological processes remains a challenge. Recent works have describedmethodologies for this task, with an emphasis on data driven reverse engineeringtechniques [10, 57]. The methods presented in these papers can be broadly classedinto three major steps: 1) identify all parts involved in the process of interest; 2)delineate network topology by identifying and analyzing functional and physical3connections between parts; 3) iteratively describe the system quantitatively, exper-imentally test, and refine. Although stated simply, each task is challenging due tothe complexity of cellular systems and of the environments for which cell live, andsophisticated experimental and analysis tools are required. In this thesis I presentthe development of such tools and their application to the study of cellular signal-ing. These works are summarized as follows.1.2 Thesis SummaryIn Chapter 2 I describe an experimental platform that gives exquisite control ofenvironmental conditions, allowing for the study of cellular response under time-varying environmental perturbations. I used this platform to screen for componentsof the mating pathway in Saccharomyces cerevisiae that are involved in regulatingdynamic cellular phenotypes. In Chapter 3 I describe a computational methodfor extracting meaningful biological data from dense multi-mode genetic interac-tion networks. I used this method to identify paths of information flow within thefilamentation response of Saccharomyces cerevisiae. Preceding these works is in-troductory background material describing the signaling pathways studied (Chap-ter 1.3), and gives an overview of relevant technical fields including single-cellanalysis (Chapter 1.4) and the use of genetic interaction networks to delineatebiological systems (Chapter 1.5). In Chapter 4 I present conclusions and futuredirections.41.3 Yeast Signaling1.3.1 Model OrganismsThe study of human biology is challenged by the complexity of the organismand the ethical implications of human experimentation. The majority of biolog-ical knowledge to date has been obtained through the study of model systemsand the inference of understanding to humans. Models commonly used includecomplex multi-cellular organisms like rodents and non-human primates, simplemulti-cellular model organisms like flies and worms, and single-cellular modelslike human cell lines, yeast, and bacteria. These models have provided a wealthof information translatable to human biology with homologous process occurringboth at the single gene level and at the systems level. In this thesis, Saccharomycescerevisiae is studied to obtain novel understandings relating to the basic principlesof eukaryotic cellular signaling.1.3.2 Signaling Pathways in YeastDue to the obvious phenotype, facility of genetic manipulations, and the availabil-ity of genome-wide deletion and fusion libraries, Saccharomyces cerevisiae (yeast)has become a prototypical model of single-cell eukaryote biology. Yeast cell sig-naling has been particularly well studied, with many individual components char-acterized, including G-protein coupled receptors (GPCRs) [12, 50, 80], mitogenactivated protein kinases (MAP kinases) [41], second messengers [76], and tran-scriptional regulators [29, 42]. In addition, common signaling network motifs havebeen well described, allowing for the construction of quantitative and predictivesignaling models [22, 65, 100].5One such highly studied motif is the MAP kinase signaling cascade [93, 121],a highly conserved signal transduction module used to relay external stimuli in-formation to the nucleus. MAP kinase cascades consist of two or three kinasesthat function through sequential phosphorylative activation, with the MAP kinasebeing the final active component. Five MAP kinases exist in S. cerevisiae, andare components in pathways involved in sensing environmental changes in: matingresponse, filamentation/invasion, high osmolarity growth, cell integrity, and sporewall assembly [45]. Years of genetic and biochemical analysis, and more recentlyhigh-throughput proteomic [36, 38] and microarray studies [119], have resulted ina detailed understanding of the core signaling circuitry of yeast response to thesestimuli. Studies in this thesis focused on MAP kinase signaling in the mating andfilamentation/invasion response.1.3.3 Mating and Filamentation/Invasion PathwaysThe yeast mating system, which regulates the desire to mate between haploid yeastcells, is arguably the most well understood eukaryotic signaling pathway. Decadesof research have identified all components involved in mating signal transduction,and many regulatory aspects of the network have been uncovered. Briefly, hap-loid yeast exist as one of two mating types: MATa and MATa. Haploid cells ofopposite type can mate through a cellular fusion event to create a single diploidcell. Mating between haploid cells is initiated through the binding of pheromones(a-factor and a-factor) to mating type specific GPCRs (Ste2 for MATa or Ste3 forMATa) [12, 80] causing the activation of the signaling pathway leading to a largetranscriptional response [119], mating morphology, and cell cycle arrest [116].6Ste2/Ste3 activation causes the liberation of b g subunits (Ste4 and Ste18) [50].The liberated Ste4 and Ste18 complex recruits to the membrane the MAPK mod-ule, consisting of the kinases Ste11 [91], Ste7 [106], and Fus3 [41], and the scaffoldprotein Ste5 [87, 105]. Ste20 (a homolog of the mammalian P21-activated kinase,PAK) [31] phosphorylates Ste11 (a MEKK homolog) to initiate a kinase cascadein which Ste7 (a MEK homolog) and Fus3/Kss1 are sequentially phosphorylated.This activated MAP Kinase translocates to the nucleus where it phosphorylates theCKI/scaffold protein Far1 [110] and the master transcription factor Ste12 [29]. Ac-tivation of Far1 enforces G1 growth arrest and initiates a morphological transitionof shmoo growth towards the mating partner [15, 116]. Activation of Ste12 ini-tiates a transcriptional program of pheromone response involving a suite of 200genes [119].The filamentous growth pathway (invasive growth in haploids and pseudohy-phal growth in diploids) is also a very well studied signaling system and is usedto understand principles of cellular differentiation and signaling specificity. Thefilamentous response is induced when the cells are placed under certain starva-tion conditions: glucose depletion for haploids [28, 93] and nitrogen limitationfor diploids [43]. The filamentation system shares many of the same componentsas the mating pathway including Ste20, Ste11, Ste7, Dig1, Dig2, and Ste12. Fil-amentation specific components include environmental receptors (which are notyet well defined), the lack of Ste5 scaffolding [3], cyclic AMP signaling [94, 95],and the use of the transcription factor Tec1 [42, 119]. In addition, upon pathwayactivation cells enter a filamentation specific cell cycle. In contrast to the the ax-ial budding (budding near bud scar and birth end) or bipolar budding (alternating7budding near and opposite bud scar and birth end) patterns of haploid and diploidvegetative yeast respectively, filamentous yeast elongate and bud in a unipolar pat-tern (daughter cells budding opposite of birth end). In addition, cells demonstrateincreased adherence and invasion into the substratum, and increased cell-cell ad-hesion. These phenotypes give a colony of non-motile yeast a means to prospectfor new territory when faced with nutrient limiting conditions.1.3.4 Complex Systems Models: YeastThe richness of data and detail surrounding these pathways allow for new stud-ies of systems function to be placed upon a backdrop of molecular network data,and S. cerevisiae has become a key model for systems biology [57]. Researchhas progressed beyond studying the mechanisms of how information is propagatedfrom the cellular membrane to the nucleus (signal transduction), to studying themechanisms of information processing that allow cells to thrive in complex envi-ronments. For example, a long-standing question in cell biology is how signalingsystems are able to translate multiple environmental signals into specific responseswhile using a shared set of core components. Work over the past decade has ex-plored this question using yeast as a model, and has found regulatory mechanismsat the scaffold [3, 37, 71], MAP kinase, and transcription factor level [5, 21, 35].Recently, research has begun to focus on even more questions relating to the com-plexity of cellular regulation, including how signaling pathways communicate withother cellular modules, [92, 103], process cellular information in the face of bio-chemical noise [25], and regulate response in dynamically changing environments[8, 49, 78]. The collection of these studies reflect the complexity of cellular signal-ing and demonstrate that although well studied, much about these model systems8is still unknown. The work presented in this thesis focuses on the development ofnew technologies that help to manage this complexity, with specific application tostudying the information processing mechanisms of the mating and filamentationsignaling circuits in Saccharomyces cerevisiae.1.4 Microfluidic Quantitative Single-cell Analysis1.4.1 Quantitative Single-Cell StudiesThe construction and refinement of predictive biomolecular models requires anabundance of high quality quantitative data. Given the complexity of cellular regu-lation, these data need to be globally acquired under vast numbers of environmentalconditions. Until recently, biomolecular analyses consisted of single measurementsconducted serially, limiting early biomolecular models to be qualitative and oversimplified. Recent technological advances have greatly improved the experimen-talist’s toolbox and high-throughput methods have enabled data driven methodsfor model building [10]. For example, advances in microarray and proteomic tech-nologies enabled the quantitative and global analysis of transcripts [69, 97] andproteins [38]. Yet, although these methods have greatly improved our ability tomeasure global response, they are not high-throughput in regards to the numberof environmental conditions tested and are limited in their ability to study the re-sponse of individual cells. Further, such measurement tools have not improved ourability to test cellular response under complex environments such as multiple orsequential stimuli conditions. Advances are required for the high-throughput studyof single cells under large numbers of well controlled conditions. This will require9the advancement of technologies that can manipulate and measure individual cells,such as microfluidics, for high-throughput analysis.1.4.2 Single Cell MicrofluidicsMicrofluidics allows for fluidic manipulation at the scales of individual cells andis a promising technology class for single cell studies. Microfluidic devices ex-ist in glass, silicon, and polymers, and devices from all material types have mademuch progress in single cell analysis in recent years [34]. Recent studies havedemonstrated on-chip live cell assays such as cell capture and culturing [4, 17,26, 44, 56, 86, 104, 118], cell sorting [39, 40, 52, 55, 113], exquisite control ofthe microenvironment both in terms of chemical composition [1, 56, 59, 83] andchemical sequence [8, 49, 78], along with many biochemical assays such as micro-assay PCR [68, 82], DNA/RNA analysis [9, 13, 51, 74, 114], and protein analysis[16, 54, 61, 96, 117].Recent studies have combined single cell microfluidic technologies with sys-tems biology studies of yeast signaling. These works demonstrate the power ofapplying advanced microfabrication techniques to analyze single cells. For exam-ple, in Paliwal et al. [83], microfluidic devices were fabricated to create preciseconcentration gradients without flow, allowing for sensitive studies of the concen-tration dependent pheromone response at the single cell level. Three recent studiesdeveloped microfluidic devices to rapidly exchange the media surrounding cells,allowing for the study of the frequency dependent response of cells under oscillat-ing stimuli. In these studies Hersen et al. [49] and Mettetal et al [78] measuredresponse in the osmolarity system while Bennett et al. [8] measure response in the10galactose metabolic pathway. Lastly, Charven et al. [19] used a simple microflu-idic device to track single cell lineages while analyzing properties of the inducibleMET3 promoter. Although these studies have identified novel biological under-standings, the platforms used lack scalability both in terms of the numbers andcomplexity of assays completed, ultimately limiting the breath of biological analy-ses possible. This limitation is technological and due to the microfluidic techniquesinvolved.A recent technological advance called multilayer soft-lithography (MSL) promisesto solve microfluidic issues of scalability and integration. MSL’s predecessor, soft-lithography (SL), is a microfluidic fabrication technique [32, 115] that casts micro-channels into a soft elastomer, usually poly(dimethylsiloxane) (PDMS), using ahard pre-fabricated master mold [32]. PDMS is an inert silicone elastomer withexcellent biocompatibility and optical properties [102] making it an ideal materialfor biological studies. Soft lithography allows for the inexpensive and rapid fab-rication of devices, but is limited in its ability to manipulate fluids due to the lackof micro-mechanical control elements such as valves and pumps. MSL remediesthis issue by stacking planar fluidic layers separated by a thin flexible PDMS mem-brane. In this way, orthogonal crossing channels can be used to create monolithicsoft elastomer valves [112]. In contrast to mechanical valves fabricated in glassand silicon devices, MSL valves are extremely robust and fabricated with highyield, allowing for valve densities of thousands per cm2 to be routinely achieved.As a fundamental element, multiple MSL valves can be used to create higher or-der fluidic control elements. Demonstrated components include on chip: i) pumpsto allow for high-precision fluidic control and metering without the need to tightly11regulate fluid pressures [112], ii) multiplexers that enable fluidic addressing using areduced number of pin-out control connections [53, 107], iii) cell sorting junctionsto allow for rapid sorting of fluorescently labeled cells, and iv) cell culturing mi-crochamber traps to capture non-adherent cell types [8, 19, 26]. These componentscan be easily integrated into the same device allowing for previously unattainablelevels of on chip fluid handling, component integration, and assay parallization[47, 68, 74, 77, 107].High-throughput MSL microfluidic technologies have recently been developedfor various biochemical assays including protein crystallization [46, 47], PCR[75, 82, 114], mRNA analysis [74, 75, 114], and protein-DNA interaction analysis[33, 70]. Cellular analyses are following suit, and a small number of cell culturetechnologies have been presented [4, 44]. Further development is required how-ever, before cell based microfluidic assays fulfill the promise of high-throughputanalysis of live single cells under complex environments. One area of neededdevelopment is in the ability to conduct single cell studies in time-varying stim-uli in a high-throughput manner. In chapter 2 I present such technology that, forthe first time, combines the microfluidic ability to rapidly exchange environmen-tal conditions with MSL parallelization. We applied this technology to the high-throughput dynamic study of the mating response pathway in yeast under complextime-varying environmental conditions. In addition, in Appendix B I describe anextension of this technology that our group has developed for the tracking of indi-vidual cells over time.121.5 Systems Genetics1.5.1 Genetic Analysis of Signaling PathwaysWith the advent of genome sequencing came the ability to obtain a global ’partslists’ of an organism [66]. Coupling this to high-throughput phenotypic screens,collections of genes involved in a system of interest can be efficiently obtained [81].Understanding how these genes function as networks to process cellular informa-tion is now the more challenging aspect of cell biology and methods to generatephysical and functional networks are needed. High-throughput strategies for test-ing physical connections between biomolecules have recently emerged and includetechnologies like yeast two hybrid [58, 111], mass spectrometry [36], ChIP-on-chip [90], and protein arrays [88]. These techniques have generated dense physicalinteraction maps and provide possible paths of information flow through a bio-logical system of interest. Although topologically informative, these maps do nothowever detail how cellular networks actually process information. Further tech-niques are needed to delineate the functional relationships between components ofthese physical interaction maps.One such technique is the use of genetic interactions. Genetic interactionsdescribe the phenotypic outcome of dual gene perturbations, and can be used tofunctionally relate gene pairs. Combined with an understanding of the underlyingmolecular network, these functional relationships can dissect flows of informationthrough complex biological systems [18]. A genetic interaction comprises phe-notype measurements from four genotypes: the reference genotype (WT), a singlegene perturbation (A), a second single gene perturbation (B), and the dual gene per-13turbation (AB). The relative ordering of these four measurements determines thetype of genetic interaction. For example, a commonly studied interaction called’synthetic’, describes two genes that when perturbed simultaneously results in astrong phenotype not seen if either gene is perturbed in isolation. It has been pro-posed that this interaction can indicate that the genes are involved in the sameprocess but in parallel redundant pathways, indicating gene buffering [11]. A sec-ond important example is the epistatic genetic interaction, which can be defined intwo different ways. In the first definition, the term epistasis refers to a statisticaldeviation of the double mutant from what would be expected given that the twogenes act in independent processes. In this way an epistatic interaction describesone of three functional digenic relationships in that the double mutant phenotypeis measured to be: i) more severe than expected (aggregating interaction), ii) lesssevere than expected (alleviating interaction), and iii) as expected (no interaction).It has been shown that these different epistatic types can inform information flowbetween cellular processes [99]. In the second definition, the term epistasis refersto the masking of one genetic perturbation by a second (the double perturbation hasthe same outcome as the masking single perturbation). In this classic genetic de-scription, this relationship indicates that the gene products act in a common processwith one ’upstream’ of the second. In this way an epistatic interaction is directionaland indicates information flow. Cell biologists have used this second definition fordecades in focused low-througput analyses [48].141.5.2 Genetic Interaction NetworksIn recent work, high-throughput techniques have been used to generate networksof genetic interactions. The nodes of these networks indicate genes of interest andedges indicate functional relationships between gene pairs. Comprehensive anal-ysis of genetic interaction relationships within a biomolecular system promisesto deliniate the functional organization of biomolecules and pathways. Whereassingle genetic interactions describe functional relationships between gene pairs,networks of genetic interactions describe functional relationships between genesystems.Genetic network analysis requires the high-throughput acquisition of gene in-teraction data, and many approaches have been developed. These methods varyin the type and precision of the interaction, resulting in the construction of var-ious genetic network types. For example, in Tong et al. [108] a method calledthe synthetic genetic array (SGA) approach was described for systematically test-ing gene pairs for synthetic interactions. SGA uses a series of mating and meioticrecombination steps to take an input set of single gene perturbations to generatethe full set of double perturbations. Synthetic interactions were recorded whendouble mutant fitness was much reduced as compared to the two single mutantcounterparts. This method can be fully automated using fluid handling robotics,and in a follow up study [109] a large  1000 node  4000 edge synthetic lethalinteraction network was constructed. Analysis of this network allowed for the al-gorithmic grouping of genes into functionally related modules and gave insightsto the global structure of genetic interaction networks. Diploid-based synthetic15lethality analysis on microarrays (dSLAM) is a related method [85] where insteadof growing yeast in spots on plates the entire set of mutants are grown in bulkin liquid. To demultiplex individual mutants from the bulk culture, genome spe-cific barcodes are used and growth rates are quantified with genomic microarrays.Pan et al. used this method to construct a large synthetic lethal DNA integritynetwork in Saccharomyces cerevisiae, allowing for the stratification of genes intodistinct functional modules [84]. In a more quantitative approach, Schuldiner etal. constructed epistatic miniarray profiles, (E-MAPS), using growth rates as aquantitative phenotype [98]. This method allowed for the identification of func-tional sub-modules of the early secretory [24, 98] and chromosome manipulation[23] systems in yeast. And Segre et al used similar methods to identify functionalmodules in yeast metabolism [99]. Lastly Drees et al., demonstrated a more com-plex description of genetic interactions, defining nine discrete types, four of whichwere directional [30]. This methodology was applied to the filamentation signal-ing pathway in Saccharomyces cerevisiae, and by identifying mutually informativepatterns of genetic interaction between perturbations, genes could be placed in ahigh-resolution mapping of filamentation signaling.Taken together, these studies demonstrate the ability to rapidly construct densenetworks of functional gene relationships, and initial analyses demonstrate therichness of information that they contain. Due to the complexity of these net-works, experimentation techniques need to be coupled with computational algo-rithms for the extraction of meaningful biological information. Preliminary analy-ses used simple techniques to study network topology and cluster functional mod-ules [23, 24, 30, 98, 99, 109]. Although informative, much opportunity exists to16develop sophisticated computation methods to further extract meaningful biolog-ical data from these information rich genetic interaction networks. One area ofconsiderable potential for methods comes from the broad field of network analysis.1.5.3 Network AnalysisThe creation of large networks of biological data has encouraged parallel advance-ments in complex network analysis. For example, pivotal studies analyzing net-works of physical interaction data have uncovered organizational structures ofbiomolecular systems [6, 7, 89, 92]. In these studies, Barabasi and colleagues iden-tified that biological networks exhibit a scale free architecture [60], similar to thatseen in the internet and social networks. Although these studies do not give directinsight into molecular functions of specific molecules or processes, they provide afoundation for further complex systems analysis and uncover general properties ofbiological networks including modularity [89, 92] and robustness [2, 64]. Furtherwork by Alon and colleagues provided tools to identify network sub-structures thatrepresent the basic building blocks of complex networks, called network motifs.Network motifs are small network structures that occur more often than expectedby random, implying relevant function [79, 101]. The functions of these networksubstructures are directly testable [73] and network motifs have been found in bac-teria [101], yeast [67], and worms [79]. Quantitative modeling and experimentationhas revealed specific functions of these motifs and highlighted the importance oftheir use in biological systems. For example, in the context of protein-DNA net-works, the coherent feed-forward motif uses multiple time scales to filter incominghigh-frequency transient signals[72, 73]. Further, recent studies have found certain17network motifs dysregulated in human disease [27].The tools developed to analyze networks of physical interaction data can be ap-plied to genetic interaction networks. For example, in Zhang et al [120], the idea ofnetwork motifs was used to integrate genetic interaction data with other interactiontypes including protein-protein, protein-DNA, sequence homology, and expressioncorrelation interactions. This method allowed a single edge-type (synthetic) ge-netic interaction network to be placed in context of the the underlying molecularsystem, and higher-order network structures were identified. Further network stud-ies of more complex genetic interaction network types holds the potential to de-lineate high resolution maps of information flows through local regulatory systems[18]. In Chapter 3, I present work using network motifs to identify biologically in-formative patterns of genetic interaction in the dense multi-mode genetic networkof Drees et al. [30]. 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Mol Cell Biol, 13(4):2069–80,Apr 1993.40Chapter 2Dynamic Analysis of MAPKSignaling Using a MicrofluidicLive-Cell Imaging Matrix 12.1 IntroductionCellular processes are governed by complex protein signaling networks that func-tion as robust and dynamic control systems, ensuring appropriate responses to sus-tained and transient stimuli. These networks feature emergent properties includingbistability, adaptation, and memory that make their behavior inherently dependenton previous stimulation and current cell states. As examples: system bistabilityprovides a selective advantage by allowing populations of cells to test the responses1A version of this chapter has been accepted for publication. R. J. Taylor, D. Falconnet, A.Niemist, S. A. Ramsey, S. Prinz, I. Shmulevich, T. Galitski and C. L. Hansen. Dynamic Analysisof MAPK Signaling Using a Microfluidic Live-Cell Imaging Matrix. Proceedings of the NationalAcademy of Sciences.41of alternative states to a given condition [22, 28]; network adaptation to a sustainedchange in stimulant concentration limits the metabolic cost of a sustained response[9, 41]; and network memory allows more rapid accommodation of recurrent stim-ulations [1, 34]. Due to experimental tractability, these emergent properties werefirst studied in model systems and have recently been uncovered in key mammalianregulatory networks, include those dysregulated in disease [37].Owing to the facility of genetic manipulations and availability of reporters,yeast has emerged as the prototypical model of cell signaling. In particular, thepheromone response pathway in Saccharomyces cerevisiae is arguably the bestcharacterized mitogen activated protein kinase (MAPK) signaling network, and hasbeen a particularly fruitful model of eukaryotic signaling. MAPK signaling is ofcentral importance to a wide range of cellular decision making processes, respond-ing to a staggering range of stimuli including growth factors, cytokines, hormones,cellular adhesion, stress, and nutrient conditions [5]. Regulated signaling governscellular growth and differentiation while deviations from normal MAPK regulationare implicated in the onset of disease including cancer [10].The yeast pheromone response is initiated by the binding of a mating-peptide,either a-factor or a-factor, to a membrane-localized G-protein-coupled receptor,either Ste2 or Ste3 on MATa or MATa cells respectively. Pheromone signalingis communicated through a MAPK signaling cascade that ultimately results in thephosphorylation of key substrates including the cyclin-dependent-kinase inhibitorFar1, which initiates growth arrest, and the transcription factor Ste12, which ac-tivates a program of gene expression involving over 200 genes [5, 7, 24, 32, 40].Despite the wealth of detailed information gleaned from 30 years of biochemi-cal and genetic studies, the vast majority of these data are qualitative and derived42from measurements of cellular response under conditions of constant a-factor lev-els. More recently genome-wide analysis of transcription, protein expression, andprotein interactions have been applied to systems-level studies of the pheromoneresponse, delineating the tapestry of protein-protein interactions that mediate sig-naling [13, 31]. However, these studies are limited by poor temporal and spatialresolution, making it difficult to probe the dynamics of network function. Per-haps most importantly, these methods require the study of large populations ofcells and are completely blind to cell differences that arise from a combination ofde-synchronization, bistability, and stochastic variation in gene expression.The combination of fluorescent reporters and quantitative microscopy has re-cently been used to address cell-cell variability in the yeast mating response underconstant environmental conditions [8, 28]. Such techniques are scalable to high-throughput formats in multi-well plates but provide only crude control over themicroenvironment and are poorly suited to the study of response dynamics or his-tory effects. Indeed, little is known regarding cellular regulation in dynamicallychanging environments. This dearth of understanding is largely due to the techno-logical challenges involved in precisely controlling time-varying conditions, andlimitations in throughput. Achieving a quantitative understanding of protein net-work function requires new tools for high-throughput studies under a large numberof genetic perturbations and changing chemical environments, and with single-cellresolution [39].In particular, microfluidics offers the combined advantages of precision fluidcontrol necessary for exchange of media conditions around cells, and scalabilityfor parallel analysis of multiple conditions on a single device. In yeast the pre-cise microfluidic control of conditions has been applied to investigations of modest43number of genotypes or chemical conditions under both constant [16, 28] or chang-ing [2, 20, 26] media conditions. More scalable devices have been applied to theculture and analysis of mammalian cells on-chip [21, 23] although these have todate be focused primarily on adherent cell types [6, 15, 23]. In particular, mi-crofluidic large-scale integration [35, 36] of devices having hundreds to thousandsof valves has proven a powerful technique for simultaneously realizing the advan-tages of temporal control over media conditions and scalability of culture. Herewe further extend the throughput and functionality of this approach in the devel-opment of a microfluidic high-throughput single cell analysis platform optimizedfor live-cell imaging studies of yeast. This system features a throughput of 256simultaneous perfusion experiments with non-adherent yeast, integrated on-chipmixing and control software for programmable control of media conditions, andimage processing algorithms and computational infrastructure for large-scale dataanalysis.We use our platform to investigate the role of signaling genes in network mem-ory and the filtering of transient stimulation. Recent studies have demonstratedthe capacity for memory in cellular circuits, including the pheromone pathway[25] but were limited to long time scales [1] or in very few conditions [25, 34].Using our microfluidic platform we examined cellular memory by performing over3000 experiments investigating the combined effect of gene deletions and changingstimulant conditions on the mating response. These studies show that the matingsystem depends strongly on the frequency of stimulation and identifies genes thatplay a dominant role in regulating memory.442.2 Results2.2.1 High-Throughput Microfluidic Live Cell Imaging PlatformTo test the combined effect of genetic perturbations and chemical sequences onmating response we developed a microfluidic live cell imaging matrix in which 8yeast strains are tested against a total of 32 stimulant-concentration sequences fora total of 256 simultaneous experiments (Fig. 2.1A). Unique genetic and chemicalconditions are created along the matrix columns and rows, respectively. Duringcell loading all columns are isolated by actuation of row-valves (top to bottom)(Fig. 2.1B top), confining each yeast strain to a unique column without cross-contamination. At each vertex of the matrix the loaded cells were trapped in aperfusion chamber formed by a series of cell traps designed to immobilize yeastwhile allowing for the rapid and complete exchange of surrounding media (Fig.2.1B bottom, Fig. 2.1C). Each cell trap consists of a partially closed valve that,when actuated, creates a cell filter to allow media exchange while retaining thecells (Fig. A.1). Cells are loaded through each column unimpeded and then trappedstochastically upon hydraulic actuation of the trap valves at approximately 120 kPapressure. At each of the 256 perfusion chambers 5 sieve valves are actuated over adoubled flow line to create 8 cell traps (2048 per device). The size of these cham-bers is easily adjusted and in the current experiments was designed to accommodateapproximately 600 cells before reaching confluence.4546Figure 2.1: Schematic of the Microfluidic Device (A) Layout of microflu-idic live-cell imaging matrix. Device features two layers of channelsincluding a flow structure (Blue) in which cells and reagents are intro-duced, and a control structure (Red) for pneumatic valves. Regions ofthe device are indicated including 1) Cell loading ports, 2) Experimentmatrix, 3) Chemical inputs and control, 4) Peristaltic pump, 5) Fluidicmultiplexer, And 6) Waste outlet. Each column of imaging matrix cor-responds to a single yeast genotype. Each row corresponds to a singleexperimental condition. (B) Control architecture for cell loading andperfusion. Dark red and orange or transparent red and orange lines in-dicate actuated and non-actuated control lines, respectively. Valve actu-ation isolated columns to direct flow through matrix during cell loading(top). During perfusion (middle) rows are isolated and flow is directedhorizontally across the matrix perfusion chambers (bottom) formed byarrays of pneumatically actuated cell-traps (dark orange). (C) DIC im-age of cell yeast cells in perfusion chambers. (D) Stimulation condi-tions: 1) Step function: the cells are stimulated with constant a-factorsolutions, with different pulse heights (Dh) (a-factor concentrations). 2)Pulse function: the cells are stimulated with a transient a-factor solu-tion, with different pulse heights (Dh) and pulse widths (Dw) (durationof stimulus) analyzed. 3) Short Repeated Pulses: the cells are stimu-lated with short repeated pulses of a-factor with different pulse heights(Dh) and different delays between pulses (Dd). (E) Pheromone pathway.Grey nodes indicate genes deleted in this study.47Perfusion of immobilized yeast allows for studies under well-defined and time-varying chemical conditions. Our device features fluidic elements for the periodicprogrammable mixing and delivery of chemical formulations to each row of thematrix to generate arbitrary chemical sequences of nutrient and stimulant concen-trations in time. On-chip generation of programmable chemical conditions is ac-complished by a peristaltic pump that precisely meters varying proportions of 8stock reagents to enable accurate and continuous control of stimulant concentra-tion. Sequences of varying numbers of 120 pL aliquots of the input reagents aremixed in line by Taylor dispersion as they are transported from the mixing elementto the array (Fig. 2.2E). A single mixing element controls all rows of the matrixusing a time-division multiplexing strategy in which each row is sequentially ad-dressed using a fluidic multiplexer [35]. Between sequential perfusions the entirefluidic path connecting the mixer and matrix is purged through wash channels lo-cated between every row, thereby eliminating cross-contamination. Experimentsusing a fluorescent tracer show that contamination between rows is less than 1part in 10,000 which was the detection limit of our detector. Automated perfu-sion of each row is performed periodically during experiments at approximately100 s intervals to maintain nutrient levels, remove metabolites, and change condi-tions, thereby enabling long-term study of response under constant (chemostatic)or changing (chemodynamic) conditions. The measured generation time for yeastgrown in SCD media at room temperature was 220 min and was found to be inde-pendent of seeding density below 200 cells per chamber, resulting in approximatelya 10x increase in cell number over a 12 h experiment. This growth rate is consistentwith off-chip measurements in bulk culture (240 min).Throughout each experiment the cells are confined in the vertical direction by483.5 mm height of the perfusion chambers, restricting them to a monolayer of cellsin a single focal plane and allowing for long-term imaging over multiple genera-tions. In each experiment high-resolution brightfield and fluorescence images of all256 chambers were taken with 15 min time resolution over the entire length of eachexperiment (12.5 h). Two fields of view are required for complete imaging of eachchamber so that a single experimental run generates over 50,000 images capturingmillions of single-cell measurements. To handle the volume of raw image data wedeveloped an image analysis pipeline to record single-cell data including cell num-ber, cell size, cell morphology, and concentration of a fluorescent gene-expressionreporter molecule (GFP).2.2.2 Imaging Studies of Pheromone Response PathwayMicrofluidic parallelization allows for the simultaneous collection of unified datasets in a single experiment, thereby allowing for sensitive comparisons of wild-type with multiple mutant responses under a wide array of changing chemicalconditions. We investigated the signaling response of wild type cells and a panelof 11 mutants having deletions of mating signaling genes (DIG2, RGA1, RGA2,SLT2, MSG5, PTP2, FUS3, KSS1, STE50, FAR1, BEM3) that are reported tohave subtle or complex mutant phenotypes under constant a-factor stimulation[3, 27, 30, 32, 38, 40]. Mutant response was screened against a wide range ofstatic and time-varying (Fig. 2.1D) conditions including: 1) constant stimulationunder finely-varied concentrations to measure dose response of pathway activationand morphological variability; 2) transient pulses of varying concentration and du-ration to measure pathway deactivation and adaptation; 3) repeated short pulses ofvarying concentration and frequency to measure cellular memory of transient stim-49ulation. Mating-specific gene expression was reported using an enhanced greenfluorescent protein (GFP) gene under the control of a minimal promoter includingthe tandem pheromone-response elements of the PRM1 promoter [19]. The BAR1gene, encoding a secreted a-factor protease, was deleted from all strains to focuson the roles of intracellular elements. Details of strain construction are included inAppendix A.2.2.3 Response Under Chemostatic ConditionsFrequent media exchange allows for precise control of chemical conditions overlong times to perform highly resolved studies of the dose response of signalingoutput. Using this control we validated our platform in the high-throughput anal-ysis of all 12 genotypes under static conditions of finely varied a-factor concen-trations. Using 5 identical devices we tested 8 strains per device with at least 3replicates for each of the 12 strains. This analysis rapidly and faithfully repro-duced a broad range of observations collated from previous studies [8, 28] andfurther extended these results in terms of the number of chemical conditions, rangeof genetic perturbations, and temporal resolution. Signaling response of all strainswas measured across 32 exponentially distributed a-factor concentrations, rangingfrom 1 to 100 nM. Gene expression was detectable over the full range of concen-trations and showed a 15-fold increase at saturating concentrations of 30-100 nMa-factor [8]. A representative data set for one of the 256 experiments, showingthe distribution of single cell GFP expression and growth rate for wild type cellsunder 20 nM a-factor stimulation, is shown in Fig. 2.2A-B. Signaling responsewas mapped for each mutant as high-resolution GFP expression surfaces, show-ing the interplay between stimulation strength, time, and GFP concentration (Fig.502.2C for wild type). The simultaneous testing of identical stimulation conditions inmultiple strains allows for precise comparative analysis by normalization of expres-sion to wild type response (Fig. 2.2D). Under constant stimulation we identifiedhyper-responders (kss1D, msg5D, ptp2D), wild-type-like responders (fus3D, slt2D,dig2D, rga1D, rga2D, bem3) and hypo-responders (far1D, ste50D) (Fig. 2.2D).Generally, the degree of differential expression was found to be concentration-dependent with hyper- and hypo-responding phenotypes exhibited most strongly atlow non-saturating a-factor concentrations, highlighting the context-specific effectof non-essential genetic perturbations to network output [8, 28].51Figure 2.2: Mating response to persistent a-factor stimulation. (A,B) WTtime course data showing mean and variation of response to constantstimulation with 20 nM a-factor. (A) Measured GFP concentration, re-porting mating specific gene expression, of each cell with mean of pop-ulation indicated in red. (B) Number of cells in the chamber as a func-tion of time showing arrest under alpha-factor stimulation. (C) Timecourse and dose response of mean GFP concentration in WT cells forall a-factor concentrations. (D) Strain comparison of signaling underconstant stimulation. Initial dGFP/dt for all strains at the given concen-trations relative to WT (see SI text). Initial dGFP/dt is calculated asthe slope of a line fitted to the population averaged GFP concentrationsbetween 30-180min. (E) Performance of on-chip chemical formulation.Fluorescent measurements of 32 concentrations generated on-chip asdetailed in SI methods and Table S1.52High-throughput imaging allows for the direct comparison of morphologicaltransitions across varying genetic backgrounds as a function of alpha factor dose.Gene expression, cell-cycle arrest, and cell morphology changes are tightly cou-pled in the mating response. However, in contrast to mating-pathway-dependentgene expression, which increased continuously with increasing a-factor concen-tration, cell-cycle arrest and morphological transitions were found to exhibit de-fined thresholds. Analysis of single-cell morphology after 6 hours under varyinga-factor concentrations reveals three distinct cell types: proliferating ovoid cells atvery low concentrations (<4 nM), highly elongated cells at intermediate concen-trations (4-20 nM), and cells with shmoos (mating projections) at high concentra-tions (>20nM). At intermediate a-factor concentrations (4-20 nM) we find the co-existence of all three morphological types [11, 12, 18, 28] with characteristic levelsof transcriptional output, a phenomenon that has been attributed previously to net-work bistability[28]. Fig. 2.3A depicts average wild-type (WT) gene expressionin each morphological cluster after 6 h. Interestingly, some mutant strains werefound to undergo morphological transitions at different thresholds of a-factor con-centration and to support the coexistence of phenotypes over differing concentra-tion ranges (Fig. A.4). For example, the morphological switch in msg5D mutantsis more sensitive, exhibiting elongated morphologies at lower a-factor concentra-tions than WT (Fig. 2.3B). In contrast, ste50D presents no elongated morphologyat any concentrations tested at 6 hours (Fig. 2.3C). Interestingly, fus3D displayed adelayed morphological response, with no observable elongation or shmooing until10 h (data not shown).5354Figure 2.3: Morphological response and transient stimulation responses.Morphological response of (A) WT, (B) msg5D, (C) ste50D across alla-factor concentrations under constant stimulation. Color code for mor-phology shows representative images of yeast-form cells (YF) at 3.2nM(red); hyperelongated cells (HE) at 17 nM (blue), and shmoo cells (S)at 90 nM (green). Figure indicates mean GFP concentration, reportingmating specific gene expression, as a function of a-factor concentra-tion for each classification with dot opacity indicating the fraction ofcells with that morphology. Error bars represent standard deviation ofmeasured GFP expression for cells of each morphology. Measurementswere taken 360 min after exposure to pheromone. (D,E) WT time courseresponse to a 180 min duration 50 nM a-factor pulse. Cells are stim-ulated with a-factor at t=0; shading indicates the presence of a-factor.(D) GFP concentration, reporting mating specific gene expression, percell with mean of population indicated in red with nearest neighbor timepoint averaging used to smooth the mean curve. (E) Total number ofcells vs. time showing transient growth arrest during a-factor pulse.Each blue diamond is the total number of cells in the microchamber ar-ray at a given time. (F) Representative data of population mean GFPconcentration in response to transient a-factor pulse of varying dura-tion. Data is shown for 20 nM a-factor condition.2.2.4 Pathway Response Under Dynamic StimulationSingle Pulse Experiments: Microfluidics offers unique opportunities for measuringcellular response to precisely controlled time-varying stimulation and with high55temporal resolution [2, 4, 20]. We used this temporal control to investigate differ-ences in network memory between mutants. In particular, the propagation of signalthrough the MAPK network results in changes in the dynamic state of the system,including alterations in protein expression, phosphorylation, localization, and com-plex assembly. These changes modulate the network’s ability to transmit signals(network capacity), giving rise to memory effects in which the cellular response ishistory-dependent [1].We first measured cellular response to transient a-factor, and tested whethercellular recovery depends on duration of stimulation. We stimulated yeast withsingle transient pulses of a-factor across a broad range of both stimulation strengthand pulse duration: all combinations of four a-factor concentration (5, 10, 20, and50 nM), and eight pulse widths (20, 40, 60, 90, 120, 150, 180, 210 min) were tested.Consistent with experiments under static conditions, we observed no threshold ofresponse and measured expression in all conditions (Fig. 2.3F).Release from stimulation resulted in a characteristic decay time of 3.6 hours,beginning approximately 30 minutes after release, which was independent of pulseduration and the maximum level of GFP. This is consistent with reported GFP mat-uration times and dilution of GFP during cell growth, suggesting that the rapiddeactivation of signaling output is independent of input dose (Fig. 2.3F). In con-trast to the case of periodic stimulation (described below), single-pulse stimulationrevealed no new differences between mutants, suggesting that any changes in net-work dynamics arise through transients with fast characteristic time scales or adap-tation occurring at very long time scales. Similarly, analysis of cell cycle response(Fig. A.5C) indicates that cell growth quickly resumes upon a-factor removal (Fig.2.3E). No morphological changes were observed in any cells for pulses shorter than5690 min even at saturating a-factor concentrations indicating that the emergence ofa full mating response requires sustained stimulation. Directly probing signalingat faster time scales using single-pulse experiments is limited by low expressionand the long maturation time of GFP, and will require future studies with fasterreporters such as those using fluorescence resonance energy transfer (FRET), pho-toactivatable GFP [29], or mRNA tagging[14].2.2.5 Response to Periodic StimulationUnder constant stimulation different deletion mutants may exhibit phenotypes thatare indistinguishable, thus making it difficult to assign unique functions to thesegenes. These genes may nonetheless have distinct roles in controlling short time-scale network dynamics and thus may be separated by analysis under varying stim-ulation. Although these differences are difficult to detect with fluorescent proteinreporters, the monitoring of response under periodic stimulation allows for integra-tion of the GFP output to amplify subtle differences across conditions and mutantgenotypes. We used this strategy to investigate mutant variations in pathway mem-ory by measuring transcriptional output to repeated 10 min pulses of pheromoneof varying frequency. All strains were tested under repeated pulse conditions ofvarying concentrations (5, 10, 20 and 50 nM) and delay times (15, 40, 65, and140 min) between pulses (Fig. 2.4A). Although the wild-type response was foundqualitatively to increase with total time-averaged alpha-factor dose, there were no-table deviations from this trend that suggest a more complicated dependence on thefrequency response of signaling. Cells were found to respond comparatively morestrongly to repeated intermittent pulses of low pheromone than would be expectedunder a model of response to simple time-averaged concentration. For instance,57conditions of repeated 10 minute pulses of 5 nM pheromone every 25 minutes,corresponding to a time averaged dose of 2 nM, resulted in a gene expression re-sponse similar to conditions of 50 nM and 20 nM stimulation with pulse delays of40 minutes (corresponding to time-averaged dose of 10 nM and 4 nM respectively).Similarly, 10 minute pulses of both 5 and 50 nM pheromone every 75 minutes, cor-responding to 0.67 nM and 6.7 nM time-averaged doses respectively, show verysimilar response. Taken together these observations under periodic stimulation in-dicate that pathway output depends strongly on cell history and the frequency ofsignal input.5859Figure 2.4: Mating response to short repeated pulses of a-factor. (A) Pop-ulation mean GFP concentration, reporting mating specific gene expres-sion, of wild-type cells for the 50, 20, 10, and 5 nM a-factor concen-trations across four pulse delay lengths (15, 40, 60, 140 min). In eachcondition, 10 minute pulses of a-factor are used. The green rectanglesdepict the 4 pulse patterns of a-factor stimulation over time; values in-dicated delay time in min. (B) Sensitivity of kss1D, fus3D, msg5D, andptp2D mutants under periodic stimulation under conditions of varyinga-factor concentration (columns) and pulse delays (row). Mean popu-lation GFP concentration over three experimental replicates are shownnormalized to WT. Data are taken at t=600 min.2.2.6 Mutants Implicated in Dynamic PhenotypesAnalysis under periodic stimulation allows for the classification of mutants on thebasis of differing dynamic response. To test this idea we compared the dynamicresponses of all mutants (Fig. A.7B) and found distinct patterns of hypersensitivityfor mutants lacking two highly related kinases, Fus3 and Kss1[24], and two phos-phatases, Msg5 and Ptp2 [40]. All four of these mutants exhibit WT-like matingphenotypes, are indistinguishable under saturating pheromone concentrations (50nM) [8], and were found to be increasingly hypersensitive at low a-factor concen-trations (Fig. 2.4B). This divergence from WT behaviour was greatly amplifiedunder low frequency periodic stimulation across all concentrations tested. More-over, the frequency-response of mating-pathway-dependent gene expression undervarying pheromone concentrations was unique to each mutant.The ability to unambiguously stratify mutants on the basis of response to time-60varying stimulation provides a stringent test for the development and testing ofquantitative network models and suggests new regulatory roles of for signaling pro-teins. Across all conditions, kss1D mutants exhibited the greatest divergence fromWT. In addition to previously reported hypersensitivity at low pheromone con-centrations, kss1D mutants display hypersensitivity under intermittent pheromonestimulation. This effect was evident for all transient conditions, even when pulsesare delayed by as little as 15min, and was most pronounced for low frequencystimulation with high pheromone concentrations. By comparison, mutant fus3Dcells, which show similar pathway output to WT under all constant stimulationconditions, exhibit hypersensitivity to transient stimulation only for pulse delaysof 40 min or more. Also, whereas the degree of hypersensitivity for fus3D cellswas found to depend primarily on the frequency of stimulation, the sensitivity ofkss1D mutants exhibits both concentration and frequency dependence, being mostpronounced for transient pulses of high concentration. Taken together these re-sults implicate Kss1 in a regulatory mechanism which acts to filter both weak andintermittent a-factor stimulation while Fus3 appears primarily to filter transientsignals (Fig. 2.4B). Analysis of the phosphatase mutants reveals a similar trend inwhich the hypersensitivity of msg5D mutants is largely determined by frequency,whereas ptp2D mutants exhibit hypersensitivity depending on both pulse frequencyand pheromone concentration. Similarities in behavior were also noted betweenkinase and phosphatase deletion mutants. The sensitivity trend for fus3D mutantsunder dynamic stimulation, having a frequency threshold with little concentrationdependence, is similar to that of msg5D mutants at pulse delays longer than 15minutes. At short pulse delays (15 minutes) kss1D mutants exhibit hypersensitivitysimilar to that of msg5D mutants.612.3 DiscussionHere we have presented a new system for high-throughput measurements of single-cell response over a broad array of precisely controlled and time-varying condi-tions. Further improvements will allow for both increased genotypic throughputand more refined analysis of single cells in time. Straightforward device modifica-tions will allow for the parallel analysis of 40 strains, allowing for comprehensivenetwork-scale analysis on a single device run. Additionally, an important compo-nent of single-cell analysis is the ability to track single-cell response and lineagethrough time, something that is difficult to automate in the current format due to themotion of cells within the traps during perfusion sequences. We are currently refin-ing cell immobilization techniques, image processing algorithms, and data analysismethods to enable automated high-throughput tracking of tens of thousands of sin-gle cells, spanning multiple genotypes and stimulation conditions, through time(Appendix B).Microfluidics provides a powerful method for high-throughput imaging anal-ysis with programmable control over the chemical environment, offering a newtemporal dimension to live-cell imaging studies. Our present study demonstratesthat the analysis of cellular networks under static conditions or with coarse chem-ical resolution is insufficient to reveal the function of genes in regulating networkresponse. Indeed, dynamic analysis of mutants compromised for genes knownto be key players in the pheromone response, including Kss1, Fus3, Msg5, andPtp2, reveals unique properties of network response that are invisible under con-stant stimulation and that suggest possible mechanisms of network regulation. Forinstance the similarity in frequency threshold for the hypersensitivity of kss1D and62msg5D mutants may be due to increased Fus3 activity in both of these mutants. TheKss1 kinase competes with Fus3[33] and Msg5 phosphatase acts on Fus3 [40].Usually, the response to transient stimulus is discussed in the context of signalfiltering to prevent unproductive response. Conversely, we speculate that frequency-dependent signal memory enables productive responses in natural environments.For instance, one might expect that the paracrine induction and auto-repressionof the mating response, including the secretion of pheromone and pheromone-degrading enzymes, coupled with hydrodynamics, varying cell density, and cellmotion, creates spatiotemporal variations in pheromone concentration and inter-mittent opportunities for successful mating. Pathway mechanisms selected to filteror remember stimulations over appropriate time-scales could act to prime cells formore rapid response, thus increasing mating success. Testing of such hypotheseswill ultimately require combined approaches based on quantitative modeling andexperiment. We contend that high-throughput single-cell measurements of net-work dynamics will provide a stringent test for in silico models and are essentialfor ultimately developing a quantitative and predictive understanding of cellulardecision-making.2.4 Methods2.4.1 Cell loading.For each experiment yeast cells were grown with aeration overnight in YPD (30C),diluted and grown to log-phase in SCD on and loaded into the microfluidic deviceat 3-15 p.s.i. (20.7 103.4 kPa). Prior to loading the cells were sonicated for 5s,concentrated to an OD600 of 3 and manually aspirated into tygon tubes (internal63diameter 0.020 in) with a 1ml syringe. The chip was primed with SCD (syntheticcomplete media with 2% dextrose) medium containing 20mg/ml of bovine serumalbumin (BSA) for approximately 3 h prior to cell loading. Adsorption of BSA onPDMS channel walls allowed performing reproducible experiments by preventingcell adhesion and significant non-specific binding of alpha factor to the channelwalls.Chemical Mixing and Perfusion.Between every exchange or refreshing of medium, the following refresh proto-col was used: 1) Prime and wash multiplexer: Perfuse adjacent waste row withchemical solution for 20s. 2) Refresh microchambers: Perfuse experiment rowwith chemical solution for 70s. 3) Wait: A wait of 3s is used to dissipate pressurebuild up that occurs across the high-impedance microchamber traps. This protocolallowed us to fully refresh an experimental row approximately every 100s.2.4.2 Microfluidic ControlMicrofluidic operation was fully computer controlled, excluding cell loading andtrapping. Custom software was designed and executed in LabVIEW (National In-struments, Austin, TX). Microfluidic valves were actuated using high-speed com-puter controlled solenoid micropump manifolds (Fluidigm Inc., San Francisco,CA) via a PCI-6533 digital input/output (DIO) card (National Instruments). Asingle LabVIEW program operated all experiment types, with user designed ex-periments inputted as parsed text files. Scheduling algorithms were included tomaximize the frequency of experiment refresh of all 32 chemical sequences.642.4.3 Microfluidic FabricationFabrication of the microfluidic device was accomplished using multi-layer softlithography [17, 36]. Our chips used a 3-layer design: The top layer was a ’flowlayer’, containing the cells and the chemical channels. The middle layer was a’control layer’, containing channels used for pneumatic valves. The bottom layerwas a ’blank layer’, used to tightly seal the control channels to the glass slide. Alldevices were made from polydimethylsiloxan (RTV615 General Electric, Fairfield,CT).Briefly, photolithography masks were designed using AutoCAD software (Au-todesk, Inc., San Rafael, CA) and used to generate high-resolution (20,000 dpi)transparency masks (CAD/Art Services). Molds were fabricated by photolithogra-phy on 4 inch silicon wafers (Silicon Quest International, Santa Clara, CA). Theflow layer consisted of two different channel profiles: 3.5 mm high rectangulartrapping channels allowing for sieve valving, and 12 mm high rounded channelsused for standard flow. The 3.5 mm layer was made with SU8-5 negative pho-toresist (Microchem Corp., Newton, MA) and the 12 mm rounded layer was madewith SPR220-7 positive photoresist (Microchem Corp.). The control master wasa single layer mold consisting of 25 mm high squared features made with SU8-2025 negative photoresist (Microchem Corp.). Resist processing was performedaccording to the manufacturer’s specifications.2.4.4 Image AcquisitionMicrofluidic devices were mounted onto a Leica DMIRE2 fluorescent microscopemodified with a custom LED brightfield source to increase acquisition speed. Cellswere imaged with a 40x air objective (HCX, long working distance, FLUOTAR PL65with correction collar, NA=0.6) and 1344x1024 pixel cooled CCD camera (ORCA-ER, Hamamatsu Photonics, Hamamatsu Japan). Z focus was determined indepen-dently for each acquisition position. Differential interference contrast (DIC) andfluorescent images were captured with a 100 ms and 250 ms exposure respectively.Each set of microfluidic cell traps was acquired in two fields of view, giving a totalof 256 experiment traps x 2 = 512 differential interference contrast / fluorescentimage pairs acquired per time point. We acquired a full set of images every 15min,which was slightly longer than the time it took to iterate over and acquire the 512image pairs. 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Negative feedback that improves information transmission inyeast signalling. Nature, 456(7223):755–61, Dec 2008.[39] Eli Zamir and Philippe I H Bastiaens. Reverse engineering intracellular bio-chemical networks. Nat Chem Biol, 4(11):643–7, Nov 2008.[40] X L Zhan, R J Deschenes, and K L Guan. Differential regulation of fus3map kinase by tyrosine-specific phosphatases ptp2/ptp3 and dual-specificityphosphatase msg5 in saccharomyces cerevisiae. Genes Dev, 11(13):1690–702, Jul 1997.72[41] J Zhou, M Arora, and D E Stone. The yeast pheromone-responsive g alphaprotein stimulates recovery from chronic pheromone treatment by two mech-anisms that are activated at distinct levels of stimulus. Cell Biochem Biophys,30(2):193–212, Jan 1999.73Chapter 3Network Motif Analysis of aMulti-Mode Genetic-InteractionNetwork 13.1 BackgroundThe cell is an elaborate network of biomolecular and environmental interactionsthat together bring about complex phenotypes. Understanding the functional con-sequences of molecular interactions is fundamental to understanding phenotypes.A highly successful approach is the use of genetic interactions. Genetic interac-tions describe the phenotypic consequences of combinations of genetic perturba-tions. Genetic interactions combined with molecular interaction data can delin-eate information flows through complex biochemical systems. The concept of the1A version of this chapter has been published. Network Motif Analysis of a Multi-Mode Genetic-Interaction Network. R. J. Taylor, A. F. Siegel, and T. Galitski. Genome Biology. 8R:160. 200774molecular signaling pathway owes much to this approach.A genetic interaction comprises phenotype measurements of four genotypes:the reference genotype (wild type (WT)); a single gene perturbation A; a pertur-bation B of a different gene; and the double perturbation AB. By themselves, thesingle perturbations link individual genes to specific phenotypes and biologicalprocesses. Studying a double perturbation defines functional relationships betweenthe perturbed genes. The relative ordering of the four phenotype measurements de-fines different genetic-interaction modes [7]. Genetic-interaction modes indicateone or more possible molecular relationships, for example, upstream/downstream.Networks of genetic interaction, and the molecular wiring, constrain these possi-bilities. In this way, genetic-interaction modes are a reflection of the underlyingbiochemical system.Geneticists have formalized collections of genetic interactions into genetic-interaction networks of perturbed-gene nodes and genetic-interaction edges. Tonget al. [31] created a network consisting of edges representing a single type of ge-netic interaction, synthetic lethal. Zhang et al. [35] integrated this network withdisparate data types, including protein-protein and protein-DNA interactions, se-quence homologies, and expression correlations. In this study, network patternswere used to reduce the overall system into a thematic map of biological rela-tionships. The E-MAP method [4, 24] creates high-density genetic-interactionnetworks consisting of aggravating or alleviating edge types. This method hasbeen fruitful for identifying both system-level and protein-complex-level func-tional modularity.Further work has generated networks of multiple genetic-interaction modes(edge types). In Drees et al. [7], all possible genetic interactions were classified75into nine modes, of which four are asymmetric (directed edges). A multi-modegenetic-interaction network was derived from a large set of quantitative phenotypedata. This work revealed local and global genetic-interaction patterns suggestingthe prevalence of information contained in the structure and distribution of geneticinteractions within the network. Further network information can be extracted fromsuch complex networks by identifying significantly repeated genetic-interactionpatterns, network motifs [14, 16, 28]. In this study, we report a network-motifanalysis of the dense multi-mode genetic-interaction network of Drees et al. [7].3.2 Results and Discussion3.2.1 Multi-Mode Genetic-Interaction NetworkIn the network of Drees et al. [7], there are 1,760 genetic interactions among 128perturbed genes controlling the agar-invasion phenotype of diploid budding yeast.The perturbations included gene deletions as well as overexpressers and domi-nant alleles. This yeast-invasiveness network contains all nine possible genetic-interaction modes, including noninteracting, epistatic, synthetic, suppressive, ad-ditive, conditional, asynthetic, nonmonotonic, and double-nonmonotonic interac-tion. Four of these modes (epistatic, suppressive, conditional, and nonmonotonic)are directional, giving thirteen possible edges between any pair of nodes. Note thatthe genetic-interaction modes discussed in this paper refer to those defined in Dreeset al. [7], and that there are semantic differences between the Drees definitions andother genetic-interaction classifications. Example interactions for each mode areshown in Additional data file 22 [20].763.2.2 Genetic-Interaction Patterns Reflect the Underlying MolecularSystemPrior to rigorous statistical motif analysis, we inspected the yeast-invasiveness net-work to discern possible patterns of genetic interactions reflecting the underlyingmolecular system. Fig. 3.1 shows genetic interactions among components of threemain signaling pathways controlling yeast invasiveness [2, 3, 5, 8, 15, 17–19, 21–23, 29, 30, 34]. Subsequently, we investigated our preliminary observations (de-scribed below) quantitatively and globally in the network.We initially observed that there are local patterns incorporating both edge typeand network topology. For example, consider the interactions between the overex-pressers of CDC42 and GLN3 and the deletions of DIG2 and TPK2. Both CDC42and GLN3 interact asynthetically with DIG2 and nonmonotonically with TPK2,creating a two-mode bi-fan interaction pattern.Also, we observed that patterns of genetic interaction can reflect the direc-tion of information flow through the molecular network. For instance, epistaticinteractions involving the STE12 overexpresser originate from upstream signalingcomponents. Also, many genetic interaction modes occur repeatedly between par-allel information paths. For instance, the HOG1 deletion interacts syntheticallywith deleted components of the cAMP pathway and additively with overexpressedcomponents of the filamentation/invasion MAP-kinase (fMAPK) pathway.7778Figure 3.1: Multi-mode genetic-interaction motifs and the underlyingmolecular system. Genetic-interaction edges are superimposed ontoa diagram of the cAMP, fMAPK, and HogMAPK signaling pathways.Gene perturbations are marked: hc, high copy overexpresser; D , dele-tion.3.2.3 Statistical Model of a Null HypothesisBiologically relevant genetic-interaction patterns can be identified by finding thoseoccurring more frequently in the genetic network than expected at random. Thiscan be done by comparing the number of times a given pattern occurs in the geneticnetwork to the number of times it occurs in a set of properly randomized networks.The randomized networks represent a statistical null hypothesis and effectivelymodel the level of pattern noise in the network [16] [24]. In this way, significancecan be assigned to each identified pattern. In this study we highlight those patternswith a significance level of p < 0.05/n, using the Bonferroni multiple-hypothesis-testing correction, where n is the number of patterns tested in each analysis. Al-gorithms were developed to create the set of randomized networks modeling a nullhypothesis. The yeast-invasiveness network contains nine edge types of which fourare directed. Randomized networks were generated by a Monte Carlo method it-eratively selecting a pair of edges at random and swapping their edge types. SeeMaterials and methods for details.Randomizations were subject to specific constraints to preclude the introduc-tion of biases to the results. Each edge represents the results of a given experiment(repeated measurement of the phenotypes of WT, A, B, and AB). Every geneticexperiment creates a resulting genetic edge, with noninteracting edge types used79in the cases of genetically noninteracting loci. This causes the topology of the net-work (the simple presence or absence of an edge of any type linking each pair ofnodes) to be determined by experimental design (the set of experiments performedor not performed), not by genetics. Thus, for proper randomization the networktopology is held constant. The results could also be biased by the selection of mu-tant alleles included in the experiments. As described in Additional data file 22[20], the data for a genetic interaction consist of the ordering of four phenotypes:WT, A, B, and AB. The single-mutant phenotypes could be biased by the selectionof mutant alleles. To preclude this allele-selection bias, in our Monte Carlo switch-ing we restricted edge-type swaps to those in which the two edges have the samerelative ordering of A, B, and WT. Lastly, in some of the analyses below, moleculardata are mapped onto the genetic network. In these cases the genetic-interactionedge types are randomized under the above constraints, while the molecular dataare held constant. Note that our randomization methods are strictly conservativeand restrict the number of significant motifs. Such methods are necessary to en-sure that the calculated significance is due to biological significance rather thanexperimental design.3.2.4 Genetic-Interaction Network MotifsTo identify genetic-interaction network patterns that reflect biological relationshipssuch as those illustrated in Fig. 3.1, we identified network motifs. Network motifsare small repeatedly occurring multi-element components of a network, where therepetition suggests functional significance. Such methods have been successful inextracting information from various other network types [14, 16, 28, 32, 33], aswell as identifying general themes in the evolved organization of molecular sys-80tems [35].The simplest network patterns containing information about the genetic-interactionmodes and their system-level organization are 3-node motifs (3n-motifs). Using thenull hypothesis method described above, we enumerated all 3n patterns in the yeastinvasiveness network and tested each one for biological significance. We found 27significant motifs among the 489 different patterns observed in the network (5.5%).Many of these motifs occur hundreds or thousands of times in the yeast-invasionnetwork. Examples are shown in Fig. 3.2a. The full set is found in Additional datafile 1 [20]. Homogeneous-edge-type motifs were found frequently, with 9 of the13 possible homogeneous 2-edge patterns being significant (3n-motifs 1, 4, 5, 6, 9,10, 11, 23, 27). Examples of such motifs occur in Fig. 3.1. Their global frequencymay reflect the tendency of gene perturbations to show ’monochromatic’ interac-tion [7, 26]. Many heterogeneous motifs also were found (3n-motifs 2, 3, 7, 8, 12,and so on), as were various fully connected motifs (for example, 3n-motifs 22, 24,25, 26, and so on).8182Figure 3.2: Motifs in the yeast-invasiveness genetic-interaction network.(a) Examples of significant 3-node motifs. The number of instancesof each motif is indicated as is the p value. A statistical cutoff of p= 0.05/489 = 1.02  10 4 was used to define significant patterns. (b)Examples of significant 4-node motifs. The number of occurrences isshown as the percentage of the full number of patterns sampled. P val-ues are shown and a statistical cutoff of p = 0.05/1,505 = 3.32  10 5was used to define significant patterns. Edge colours indicate geneticinteraction mode as indicated in Fig. 3.1. The full collection of motifsis in Additional data files 1 and 4 [20].We also identified significant 4-node patterns (4n-motifs). Because the num-ber of pattern instances contained in a network scales combinatorially with localnetwork density and pattern order (number of nodes in the pattern), the full enu-meration of 4n pattern instances was computationally infeasible. Thus, a samplingalgorithm (Materials and methods) [28] was employed. Of the 1,505 4n patternssampled from the original network, 190 (12.6%) were repeated significantly. Thefull list of 4n-motifs can be found in Additional data file 4 [20]. Fig. 3.2b showsexamples. We found 4n-motifs exhibiting the edge-type homogeneity detectedamong 3n-motifs, as well as mixed-edge-type motifs.We noted that specific nodes (gene perturbations) often appear repeatedly amongthe numerous instances of a specific motif. This suggested that the instances of mo-tifs are connected structural units of larger single-motif subnetworks. Such subnet-works can highlight the main perturbations contributing to a motif, and show thelarge scale organization of instances of the motif. Fig. 3.3 shows an example ofsingle-motif subnetworks, and additional examples are in Additional data file 2383[20]. In Fig. 3.3 is the incoming epistatic motif network of 3n-motif 9. In anepistatic interaction, the phenotype of the double mutant is the same as one of thetwo gene perturbations, and depending on the allele type (hypermorphic or hypo-morphic), orders the epistatic gene upstream or downstream (see mode definitionsin Drees et al. [7]). In this way, epistatic interactions have been commonly used tohelp identify and delineate directed information flows in biochemical systems. Asshown in Fig. 3.3, the epistatic motif network is organized around six main geneperturbation hubs: the overexpressions of STE20, STE12, CDC42 and GLN3, andthe deletions of IPK1 and HSL1. Extending the concept of single epistatic inter-actions, these repeated interactions suggest critical hubs of information flow, andgenes whose influences are likely to flow through them.84Figure 3.3: Motif subnetworks. An example of a motif subnetwork. A mo-tif subnetwork is the union of all instances of a specific motif. Shownhere is the subnetwork of 3n-motif 9. The gene perturbations compris-ing the genetic interactions are marked with the suffixes: hc, high copyoverexpresser; D, deletion.853.2.5 Molecular Information and Genetic-Interaction NetworkMotifsFig. 3.1 illustrates genetic-interaction patterns describing specific functional rela-tionships within and between the signaling pathways. To identify significant rela-tionships between genetic interactions and molecular-function data, we integratedthese data types [7, 9, 11, 12, 24, 25]. Patterns from such integrated networkscan be tested for statistical significance allowing for the identification of signifi-cant network motifs. In our case, these motifs are genetic-interaction patterns thatexhibit significance in the context of the molecular system [31].Filamentation/invasion signaling is a directed system that can be characterizedloosely by the molecular functions of the system components. Plasma-membranereceptors transfer information to cytoplasmic signaling components that then regu-late nuclear transcription factors. These molecular functions capture a first approx-imation of the directionality of the system. By mapping the GoSlim [10] ’molec-ular function’ annotations onto the nodes of the yeast-invasiveness network, weidentified genetic-interaction network motifs involving these loosely directed rela-tionships.Fig. 3.4a,b shows examples of the significant 2-node and 3-node motifs forthe molecular-function annotations, respectively. The full sets are found in Addi-tional data files 7 and 10 [20], respectively. Of the 575 observed 2-node GoSlimmolecular function patterns in the original network, 6 (1.0%) were found signifi-cant (2nGO-motifs). Of the 23,286 observed 3-node molecular-function patterns,116 (<0.5%) were found significant (3nGO-motifs). These significant patternsillustrate a correspondence between the genetic-interaction modes and the under-lying biochemical system. For example, 2nGO-motif 1 (Fig. 3.4a) shows additive86interactions between perturbations of protein-binding proteins and transcriptionalregulators. Among the instances of this motif are additive interactions of a deletionof DIG2 with overexpression of FLO8 and deletion of SFL1. The Dig2 proteinbinds and inhibits the Ste12 protein, a transcriptional activator of the filamenta-tion/invasion MAP-kinase (fMAPK) pathway. DIG2 deletion interacts additivelywith perturbations of FLO8 and SFL1, encoding transcription factors of a differentfilamentation/invasion-promoting pathway, the cyclic-AMP pathway. The addi-tive interaction reflects the separate contributions of these pathways. As anotherexample, 3nGO-motif 166 (Fig. 3.4b) shows perturbations of protein kinase/trans-ferase activity proteins interacting supressively to transcriptional regulator proteinsand to hydrolase activity proteins. In the context of filamentation signaling, envi-ronmental signals are transmitted through hydrolase (for example, GTPase) andkinase activity proteins to transcriptional regulators. In a suppressive genetic in-teraction, a suppressor gene perturbation ameliorates the effects of the suppressedperturbation, indicating the suppressor perturbation reverses or short-circuits thesuppressed perturbation. A specific instance of this is that a deletion of the cAMP-dependent protein kinase subunit Tpk3 abrogates the effects of overexpression ofboth the membrane localized hydrolase Cdc42 and the transcriptional regulatorSte12. Cdc42 is an upstream activator of the fMAPK signaling pathway, and Ste12is a downstream transcription factor of the same pathway [1–3, 6]. This motif in-stance suggests that loss of TPK3 activity in the parallel cAMP pathway offsets theeffects of overexpression of CDC42 or STE12 activity in the fMAPK pathway.87Figure3.4:Examplesofmotifsintegratinggeneannotations.Examplesofsignificant(a)2-nodeand(b)3-nodemotifsinvolvegenetic-interactionedgesandGOSlimmolecular-functiongene-annotationnodes.Thenumberofinstancesandcalculatedpvalueofeachmotifisindicated.Forthe2nGO-motifsastatisticalcutoffofp=0.05/575=8.7 10 5wasused.Forthe3nGO-motifsastatisticalcutoffofp=0.05/23,286=2.14 10 6wasused.ThefullcollectionofmotifsisinAdditionaldatafiles7and10[20].88To investigate the distribution of these motif examples within the full network,motif subnetworks were generated. Fig. 3.5a,b shows the motif subnetworks for2nGO-motif 1 and 3nGo-motif 166, respectively. The 2nGo-motif 1 network isorganized around the transcription factor tri-hub MSN1, PHD1, and FLO8, andthe two separate single transcription factor hubs, SFL1 and GLN3. This networkexhibits a high degree of mutually informative genetic interactions. Each of theeight protein binding proteins that interact with the tri-hub (AGA1, BMH1, LIN1,SSA4, MSN5, URE2, DIG2, and ENT1) interacts with each tri-hub member. Thissuggests overlapping pathway functionality within the set of protein binding pro-teins and within the set of transcription factors. This motif-instance organizationcontrasts with that of 3nGo-motif 166. The 3nGo-motif 166 subnetwork centers onthe single protein kinase/transferase hubs TPK3, PBS2, HOG1, and HSL1. Thesekinases are information flow constriction points in their respective signaling path-ways: TPK3 in the cAMP pathway, PBS2 and HOG1 in the osmolarity sensingpathway, and HSL1 in the morphogenic checkpoint pathway. In contrast to the2nGo-motif network, these single hubs primarily act independently of each other,with two hubs having at most only two nodes in common. This likely reflectsthe differing roles these pathways play in the invasion phenotype. Interestingly,the osmolarity sensing kinases Pbs2 and Hog1 show differing interaction patterns,although they are implicated in the same pathway. This possibly reflects subtlydiffering roles of the two kinases. These examples illustrate how the aggregationof motif information in motif subnetworks highlights biological information notpresent in individual motif instances.8990Figure 3.5: Annotation-motif subnetworks. (a) The union of all instancesof 2nGO-motif 1, which comprises perturbations of protein binding pro-teins and transcriptional regulators acting additively. (b) The union ofall instances of 3nGO-motif 166, which comprises perturbations of pro-tein kinase/transferase activity proteins interacting supressively to tran-scriptional regulator proteins and to hydrolase activity proteins. Geneperturbations are marked: hc, high copy overexpresser; D, deletion.3.2.6 Comparing Network Patterns in a Similar Genetic-InteractionNetworkThe diversity of networks that can be formed from 13 edge types and large numbersof nodes is enormous. Thus, the yeast-invasiveness genetic-interaction networkprobably contains a sample of biologically relevant genetic-interaction motifs. Togauge the scope of our analysis we made a comparison of motifs in the yeast inva-siveness network (derived from yeast diploid strains) to a similar network, a yeastdiploid agar-adhesion network. The adhesion network was created in parallel tothe invasion network reported in Drees et al. [7] (data not shown), and althoughthe two phenotypes are related, many genetic interactions differed between the two(652 of 1,751 (37.2%)). To compare the networks, we enumerated their 3-nodemotifs. For consistency, we pruned the networks such that they had exactly thesame topological set of nodes (128) and edges (1,751). We found 27 motifs in boththe invasion network and the adhesion network out of 419 and 414 candidate pat-terns (6.4% and 6.5%, respectively). Of these 27 motifs, 20 (74%) were commonto both. This indicates that although common genetic-interaction motifs exist inthe two networks, each genetic network also contains a unique subset. The fact91that these are related phenotypes underscores this observation.To further understand the different motif sample spaces of the two networks,we compared the null hypotheses generated by the invasion and adhesion networks.Using the 378 3n patterns common to both networks, we compared the mean num-ber of times each pattern occurred in the adhesion randomized network set to thatof the invasion randomized network set. By making this comparison across all pat-terns, an understanding of how similar the global null hypotheses are is obtained[24]. The comparison was accomplished by calculating the correlation coefficientbetween the mean number of occurrences of the 378 network patterns in the adhe-sion and invasion randomized network, obtaining a value of 0.974. A completelycorrelated null hypothesis would have given a correlation coefficient close to 1,while a completely uncorrelated null hypothesis will give a value close to 0 (dueto randomization). This shows that though the networks contain different motifsets, they display similar null hypotheses. These observations demonstrate the sig-nificance of the network comparison and suggest that there is no universal set ofgenetic-interaction motifs that will apply uniformly to all genetic-interaction net-works. Rather, analyses of each network will be necessary.3.2.7 Open Source SoftwareTo facilitate the application of the analyses used in this study to other networks,we developed an open source software package entitled Network Motif Finder.Network Motif Finder was designed to identify motifs in any network type, andto include any number of edge and node types. Network Motif Finder acts as aplugin to the network analysis platform Cytoscape [27], and identifies significantmulti-mode genetic interaction patterns. In addition, Network Motif Finder has the92functionality of extracting motif sub-networks as shown in Fig. 3.3 and 3.5. Theplugin is available as open source, with a user manual, at [37].3.3 ConclusionIn this study we develop methods to address the challenges of analyzing complexgenetic-interaction networks. Specifically, we use statistical techniques to identifybiologically significant multi-mode genetic interaction network patterns, networkmotifs. Utilizing randomized null hypotheses of the genetic network, those pat-terns that occur more frequently than randomly expected can be identified. Thesemotifs highlight biologically informative network patterns of the genetic network.Further, the union of all instances of a motif forms a motif subnetwork. Thesesubnetworks illustrate the distribution of the motif instances within the full geneticnetwork. This allows for the identification of all genes involved in such a motifand can highlight those genes that dominate the motif’s occurrence. In this way,motif subnetworks extract the biological information that was identified by motifanalysis.We also identified network motifs that reflect the underlying biochemical net-work. This was done by integrating our genetic network with gene-annotation data.In this way, we describe an unbiased approach to understand how genetic interac-tions reflect the biological properties of the underlying system. Lastly, this analy-sis has been developed into an open source plugin to the network analysis softwareCytoscape, allowing users to analyze their own multi-mode genetic-interaction net-work datasets.933.4 Materials and Methods3.4.1 Network RandomizationStatistical significance of each network pattern was calculated by comparing thenumber of times the pattern occurred in the observed genetic-interaction network,to a set of randomized networks. The randomized networks represent the null hy-pothesis. To ensure that pattern significance was due solely to the genetics of thesystem and not experimental design, we constrained our randomizations in the fol-lowing way. First, as described in the text, the topology of the genetic interactionnetwork defines which genetic interaction experiments were conducted, while theinteraction types describe the genetic results. Thus, in all our randomizations, thetopology of the network is held constant and the genetic interaction types (edgecolors) are switched. Second, as described in Drees et al. [7] and Additional datafile 22 [20], each genetic interaction consists of the four phenotypes: fWT, fA,fB, fAB. These quantitative phenotypes are ordered into 1 of 75 possible geneticinteraction inequalities, and the inequalities are grouped into 9 possible geneticinteraction types. As the phenotypes of the single genetic perturbations (fA, fB)are dependent on experimental allele selection, it is necessary to avoid randomizingthese single-gene phenotypes to prevent allele-selection bias in the results. Thus, inour Monte Carlo switching we strictly maintain the ordering of each edge’s single-perturbation and wild-type phenotypes (fWT, fA, fB). In all randomizations weuniformly chose a random pair of ordered edges and exchanged their genetic in-teraction types only if the inequality relationship of fWT, fA, and fB (regardlessof fAB) was identical for both edges. In the case of nonidentical inequality rela-tionships, we retested after swapping the positions of fA and fB in the inequality94of the second edge of the pair and exchanged only if the resulting edge inequalityrelationship of fWT, fA, and fB was identical. These methods conserve the to-tal number of each genetic interaction edge type in all randomizations and ensurethat statistical significance does not depend on initial experimental design or alleleselection.We employed a Monte Carlo method of genetic-interaction edge-type switch-ing for the randomization algorithm. Each edge was switched in the Monte Carloalgorithm at least ten times per randomization. This level of switching has beenshown to provide good mixing [16]. A sample size of 1,000 randomized networksto represent the null hypothesis was used for each analysis unless specified below.Modifications to this scheme were employed for the motifs involving annotationdata and are described below. All algorithms are implemented in our open-sourcesoftware package, Network Motif Finder.In the motif analyses including GOSlim annotations, the positions of the GOSlimnode annotations were held constant, and only the genetic interaction types wererandomized as described above. This ensures that the underlying molecular struc-ture of the system remains constant, while only the resulting genetic relationshipsare randomized. As well, we identified both 2-node and 3-node motifs. In theenumeration of 3-node network pattern instances the total number of 2-node net-work pattern instances was held constant. This ensures that the significance of a3-node pattern is due to its 3-node architecture and not because it contained a sig-nificant 2-node pattern. Edge directions are conserved in this restriction. Also,the relationships between node annotations and the single gene perturbation datawere maintained. Due to the extra calculations that are made during these random-izations this algorithm was much slower, particularly for the 3-node analysis. To95compensate, we reduced the sample size representing the null hypothesis in the3-node analysis from 1,000 to 500. This null hypothesis reduction was conductedfor the dual invasion/adhesion network comparison as well.Lastly, to avoid significance due to multiple testing, we corrected our signifi-cance threshold by applying the conservative Bonferroni correction. Specifically, astatistical threshold of p < 0.05/n was used, where n is the total number of patternstested for significance in each analysis. For the 3n-motifs, 4n-motifs, 2nGO-motifs,and 3nGo-motifs, n was 489, 1,505, 575, and 23,286, respectively. To obtain a pvalue resolution greater than what is possible empirically (p < 1  10 3 for a1,000 randomized network set), we parametrically fit the null hypothesis networkpattern distributions to Gaussian (or Poisson when the pattern’s mean count was<3). Please see Additional data files 3, 6, 9, 20 and 21 [20] for the network patterndistributions and parametric fits.3.4.2 Motif Enumeration TechniquesIn all analyses except those containing 4-node patterns, a full enumeration of thenetwork pattern instances was conducted. However, this was not computationallyfeasible for the 4-node patterns, and a sampling algorithm was employed [28].There are >3  106 individual 4-node network pattern instances in our analyzednetwork; we sampled 100,000 without replacement. This sample rate is compara-ble to those used in other sampling studies [13].In enumerating network patterns involving GoSlim annotations, we needed toaccount for genes having multiple annotations. For instance, a particular GoSlimmolecular function gene may be annotated as both a transferase and a protein ki-nase. In enumerating a specific network pattern, we allowed genes sharing a sin-96gle common annotation to be considered equal. For instance, consider the set of1-node patterns annotated transferase, transferase/protein kinase, and protein ki-nase, respectively. In our scheme, we would have three patterns (transferase, trans-ferase/protein kinase, and protein kinase), containing two, three, and two instances,respectively.In the general motif analysis we identified motifs containing purely noninter-acting edge types. It is possible that these motifs occur due to gene perturbationsirrelevant to the filamentation phenotype. In our analyses using GoSlim annota-tions, we included such motifs when stating the percentage of significant patterns,but removed them from the Additional data files to avoid highlighting relativelyuninformative patterns.3.4.3 GoSlim Molecular Function AnnotationsThe GoSlim molecular function annotations were downloaded on 5 June 2006 fromthe Saccharomyces Genome Database [39].97Bibliography[1] L Bardwell, J G Cook, D Voora, D M Baggott, A R Martinez, and J Thorner.Repression of yeast ste12 transcription factor by direct binding of unphos-phorylated kss1 mapk and its regulation by the ste7 mek. Genes Dev,12(18):2887–98, Sep 1998.[2] L Bardwell, J G Cook, J X Zhu-Shimoni, D Voora, and J Thorner. Differen-tial regulation of transcription: repression by unactivated mitogen-activatedprotein kinase kss1 requires the dig1 and dig2 proteins. Proc Natl Acad SciUSA, 95(26):15400–5, Dec 1998.[3] Song Chou, Shelley Lane, and Haoping Liu. 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These methods extend the toolset available tocell biologists, and allow for the analyses of questions not previously answerable.Each technology was applied to the study of cellular signaling in Saccharomycescerevisiae and novel understandings were achieved.In Chapter 2 I present a novel microfluidic platform for high-throughput sin-gle cell analysis of signaling pathways under complex environmental conditions.105Our platform leveraged recent advances in microfluidic technologies to allow forthe design of highly integrated fluidic handling systems. Using a parallel assayarchitecture we conducted over 3,000 live cell experiments across three major ex-periment types. These included the analysis of signaling activation using constantstimulation conditions, analysis of signaling deactivation using transient stimula-tion conditions, and analysis of signaling memory using periodic stimulation con-ditions. For these experiment types we mapped cellular response across a broadrange of stimuli parameters including stimuli strength, duration, and frequency.We used our technology to study the mating response in Saccharomyces cerevisiaeunder dynamic stimuli conditions, comparing a set 11 gene deletions to WT to im-plicate genes involved in the dynamic regulation of mating response. Our resultsuncovered cellular regulation not observable under static conditions, emphasizingthe need to study cellular systems in dynamic environments.In Chapter 3 I present computational work to extract biologically relevant in-formation from a complex multi-mode genetic interaction network. Here I identifynetwork patterns that occur more often than expected by random, implying biolog-ical relevance. The identified patterns revealed common regulatory themes withinthe system including that gene perturbations often interact similarly with a broadclass second perturbations. By compiling all motif instances of a specific type, Iwas able to identify motif sub-networks that delineated information flow throughthe filamentation system. For example, by extracting all instances of a significantlyoccurring 3-node epistatic network, genes representing key information hubs wereidentified. Finally, through analysis of a second similar genetic interaction net-work, we determined that a universal set of significant genetic interaction patterns106is unlikely.4.2 Dynamic Single Cell Analysis4.2.1 DiscussionCurrent methods for the analysis of many single cells rely on either fluorescent-activated cell sorting (FACS) [13] or high-throughput multi-well plate imaging[4].Both methods excel in certain experiment types but do not provide complete sin-gle cell solutions. For instance, FACS has the ability to rapidly acquire fluores-cent reporter expression data from tens of thousands of individual cells creatingrobust distributions of population response. Yet due to its fluorescent-only andflow-through methods this technique is unable to reliably obtain morphology dataor measure the same cell multiple times. Multi-well high-throughput imaging hasthe ability to measure morphology repeatedly on the same set of cells, however itis extremely difficult to control the microenvironment and studies in time-varyingconditions are not realistic. As demonstrated in Chapter 2 the ability to measurethe combination of morphology data, the same set of cells repeatedly, and cell re-sponse to time-varying environments is critical in the analysis of dynamic cellularnetworks.The issues of studying single cells in complex dynamic environments has beenapproached in other recent studies. For example, Bennett et al. [11] ,Hersen et al.[29] and Mettetal et al. [41] have demonstrated the utility of studying signalingsystems under dynamic conditions. These works however, were limited by theiruse of serial low-throughput microfluidic technologies and as a result each study107focused only a small number of environmental conditions and genotypes. Otherstudies have developed devices for parallel cell culture, but lack either precise on-chip fluidic control for high-throughput media exchange [28, 30, 33] or the abilityto trap non-adherent cell types [27, 30, 33]. These limitations inhibit the ability totest the vast number of environmental conditions required to reverse-engineer largeprotein networks[51], as well as the ability to test important cell types includingmany single cell organisms like yeast and bacteria. The technology we present inChapter 2 combine microfluidic large scale integration, novel cell-trapping meth-ods, and high-throughput image analysis to enable hundreds of simultaneous livecell-imaging experiments under programmable time-varying conditions.We applied our technology to the study of dynamic signaling in the matingresponse in Saccharomyces cerevisiae. This was, to the best of our knowledge,the most comprehensive quantitative analysis of dynamic cellular signaling to dateand enabled us to uncover many novel regulatory processes of the mating pathway.For instance, we uncovered that the mating system has the ability to rememberprior stimulation and that this memory dissipates over time. Previous studies haveinvestigated memory in other cellular networks, including in the yeast galactoseinduction system [1], . These studies analyzed cellular circuits over long periodsof stimulation [1] and in very limited number of conditions [40, 44]. Utilizing ourmicrofluidic platform we were able to examine cellular memory to brief stimula-tion under many different conditions and genotypes. We first stimulated wild typecells periodically under many different stimulation strengths and frequencies. Wecompared our measured results to an in silico model simulating a null hypothesisof a memoryless response, and found that as stimuli frequency increased, cellu-lar response became greater than our model predicted. This indicated that as time108between stimulation decreased, cells were influenced by their previous response.We speculate that this frequency dependent memory is due to the relaxation timeneeded for the system to return to a pre-stimulated state upon stimuli removal. Inthis scenario, if a deactivating system is stimulated again before it has a chance tofully recover, the system may respond greater than if it was stimulated from a fullydeactivated state. By stimulating the system with different frequencies we wereable to obtain a characteristic time for pathway recovery. We found that for 10min stimulation pulses, the responses of wild type yeast differed greatly betweenshort (15 min, 40 min) versus long (65 min, 140 min) deactivation times indicat-ing the cells likely need between 40 - 65 min to fully return to a pre-stimulatedstate. Further analysis of mutants comprised of deletions of genes involved in themating system implicated network components involved in this memory response.Interestingly, mutant analysis in these dynamic environments uncovered hypersen-sitivity phenotypes that were not-observable under static conditions, supporting thenotion that cellular systems contain important circuitry for regulating time-varyingstimuli. This has broad implications for cell signaling research, particularly that itdemonstrates that much cell circuitry will not be discoverable by studying systemsin static conditions, the primary technique used today.4.2.2 Future DirectionsOur platform can be applied broadly enabling many areas of future study, includ-ing further analysis of yeast signaling networks and application of the technologyto study mammalian cells. In the continuing study of yeast signaling, our abil-ity to generate defined complex environments should be used to ask important yetdifficult questions in cell biology, for example how cells respond to dynamically109changing stimuli [11, 29, 41] and how signaling systems are able to integrate mul-tiple types of information using a shared set of components [3, 6, 17, 22, 24, 39].In Chapter 2 we lay the foundation for the former, and further work should beconducted to obtain a circuit-level understanding of how the mating system regu-lates dynamically changing environments. Our initial analysis implicated the keyMAP kinases, Kss1 and Fus3, and the key phosphatases, Msg5 and Ptp2, in thiscircuitry: these mutants each displayed a hypersensitive response under periodstimulation in a frequency dependent manner that is not observable under con-stant stimulation conditions. Quantitative analysis revealed that the hypersensi-tivity profiles between homologous kinases or between homologous phosphataseswere different, indicating that these components have unique dynamic regulatoryroles. Similar to our initial genetic screen, the reverse engineering of this circuitrywill require a combination of microfluidics, genetic perturbations, and molecularreporters. However, whereas we initially used full gene knockouts to implicategenes in regulatory processes, more sophisticated genetic perturbations will be re-quired to investigate circuit dynamics. For instance, Fus3 and Kss1 are highly ho-mologous MAP kinases [9] and under certain conditions, the loss of one kinase iscan be masked by the other ([38], Chapter 2). Previous studies have stratified func-tions specific to Fus3 or Kss1 by using a collection of mutant alleles that allow forthe disruption of protein function without full deletion. These include alleles thatare catalytically inactive (removing kinase function)[37, 48], un-phosphorylatable[8] (removing kinase function and affecting inhibitory protein binding activity), ordisrupt protein-binding interactions [16] . It is currently unclear if dynamic reg-ulation relies primarily on the kinase or protein-protein binding activities of Fus3and Kss1 (or both), and testing response to dynamic stimuli using a collection of110these alleles can begin to tease these roles apart. Further, techniques using highlyspecific small-molecule kinase inhibitors for Fus3 and Kss in combination withsite directed mutagensis enable real-time perturbation of kinase activity [12, 50].Such techniques allow for dynamic network perturbations by modulating the lo-cal chemical environment (adding or subtracting the inhibitor to growth media),which is ideally suited to our microfluidic platform. Using these tools, furtherinsights into the memory response can be obtained. For example, testing if thesystem’s frequency-dependent hypersensitivity is reversible or not could be easilytested by inhibiting kinase activity for a duration and then release by washing outthe inhibitor. Lastly, Fus3 has recently been implicated in a negative feedback loopresponsible for down regulating signal response following stimulation [50]. Dueto their dynamic nature, feedback loops are particularly relevant time-varying cir-cuit connections and disruption of such interactions may uncover specific dynamicregulatory mechanisms. Lastly, computational models of the pathway should beemployed to compare experimental results to quantitatively described hypothe-ses. Many computational model classes exist including those that successfullymodel circuitry dynamics (differential equation models) and cell-to-cell variability(stochastic models).A second important area of future development will be in the transfer of ourtechnology to mammalian cell culture. Simple modifications can be made to ourdesign to allow for mammalian cells to grow and be analyzed in chip [27]. Suchwork has implications in both mammalian cellular research as well as in drug dis-covery. In particular, our work analyzing yeast cellular response to complex envi-ronmental conditions is directly translatable to mammalian systems. Mammaliansignaling networks are presumed larger and integrate more environmental cues,111and thus the need for sophisticated experimental tools is even greater. In addition,our platform acts as a live-cell high-content screening platform for measuring com-bined chemical and genetic interactions. Systems biology theory predicts that drugtherapy of disease networks likely requires chemical perturbations of networks atmultiple critical foci [2, 7, 34]. This indicates that drug combinations will be re-quired to target disease to achieve desired efficacy and to avoid drug resistance,with drug combinations being applied in parallel or in sequence[35]. The use ofhigh-throughput microfluidic technoloqies has already found success in the field ofdrug discovery [21], and the immediate application of our platform into the areasof oncology and infectious disease are realistic.4.3 Systems Genetics4.3.1 DiscussionThe work in Chapter 3 revealed that complex genetic networks contain emergentinformation that is not observable from the set of interactions alone: biologicallymeaningful patterns can be identified once interactions are abstracted into a net-work format. Extraction of such information first requires the construction of densegenetic networks followed by network analyses with sophisticated computationalalgorithms. In this thesis I focused on the computational aspects of genetic analy-sis, leveraging recent advancements in the field of complex systems.Complex system analysis has rapidly matured, largely due to the excitementgenerated by the recent flux of new large biomolecular network data sets [23, 26,32, 36, 46, 47]. One such advance is in the identification of network motifs, sig-112nificant network patterns that are biologically informative. Network motif analysishas been primarily applied to physical interactions networks, for example networksof protein-to-DNA or protein-to-protein interactions. Our work(Chapter 3, [45] ),and that of Zang et al [52], were the first examples to apply network motif tech-niques to study genetic interaction network data. In Zang et al [52] network motifswere identified in a single edge-type (’synthetic lethal’) genetic interaction net-work. They used network motif analysis to globally integrate their genetic networkwith other network types like physical interaction networks and gene co-expressionnetworks. This method was successful in identifying global thematic maps of cel-lular regulation, however their use of single genetic interaction edge-type funda-mentally limits the resolution of the functional maps. In our study (Chapter 3)we analyzed a dense multi-color interaction network that comprised of 9 geneticinteraction types. Our analysis identified significant patterns of genetic interac-tion that often consisted of multiple genetic types, demonstrating the additionalinformation obtained by studying multi-color genetic interaction networks. Wecompiled all instances of a significant pattern into a motif sub-networks to identifypaths of information flow through the underlying biological system. For example,the aggregation all instances of a significant epistatic interaction pattern uncoverednetwork nodes that are key points of information processing, and by aggregatingall instances of certain multi-color pattern integrated with functional ontology datauncovered overlapping pathway functionality between a set of protein binding pro-teins and a set of transcription factors. The richness of the multi-colored geneticinteraction network allowed us interrogate our specific signaling system at high-resolution, a technique that has gained favor in recent years [18, 19, 43].1134.3.2 Future DirectionsOur methods can be applied broadly and future analyses should be extended to adiverse set of genetic interaction data sets. An obvious direction will be to ap-ply our methods to other pathways and organisms. For example, with the adventof RNA interference gene knock-downs[42], a rush of genetic studies are beingcompleted in higher organisms, for example in worms[10, 49] and flies[5]. Thiswill allow for similar analyses to ours to be applied to these data sets, helping toidentify information flows through multicellular regulatory systems. In addition,in Chapter 3 we demonstrated that the overlapping set of signification genetic net-work patterns using two similar phenotypes, the filamentation system in diploidyeast and the invasion system in hapliod yeast, did not product a universal set ofsignificant genetic patterns. It would be interesting to see if in the analysis of manygenetic networks a universal set would emerge.A second future direction will be to apply our methods to the analysis of net-works relating gene variation to disease susceptibility. Advances in genotyping hasenabled researchers to begin exploring how allelic variation leads to complex phe-notypes in humans. Genome-wide association studies, which rely on population-based samples to associate disease with common genetic traits, has become verypopular of late [20, 25]. 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Galitski and C. L. Hansen. Dynamic Analysisof MAPK Signaling Using a Microfluidic Live-Cell Imaging Matrix. Proceedings of the NationalAcademy of Sciences.125A.1 SI MethodsA.1.1 Fabrication ProtocolDevices were made using Multilayer Soft Lithography in which consecutive replicamolding and bonding steps are used to realized monolithic multilayer devices. Pho-tolithography masks were designed using AutoCAD software (Autodesk, Inc., SanRafael, CA) and used to generate high-resolution (20,000 dpi) transparency masks(CAD/Art Services). Molds were fabricated by photolithography on 4 inch siliconwafers (Silicon Quest International, Santa Clara, CA). The flow layer consisted oftwo different channel profiles: 3.5 mm high rectangular trapping channels allowingfor sieve valving, and 12 mm high rounded channels used for standard flow. The 3.5mm layer was made with SU8-5 negative photoresist (Microchem Corp., Newton,MA) and the 12 mm rounded layer was made with SPR220-7 positive photoresist(Microchem Corp.). The control master was a single layer mold consisting of 25mm high squared features made with SU8-2025 negative photoresist (MicrochemCorp.). Resist processing was performed according to the manufacturer’s specifi-cations.A.1.2 Microfluidic ControlMicrofluidic operation was fully computer controlled, excluding cell loading andtrapping. Custom software was designed and executed in LabVIEW (National In-struments, Austin, TX). Microfluidic valves were actuated using high-speed com-puter controlled solenoid micropump manifolds (Fluidigm Inc., San Francisco,CA) via a PCI-6533 digital input/output (DIO) card (National Instruments). Asingle LabVIEW program operated all experiment types, with user designed ex-126periments inputted as parsed text files. Scheduling algorithms were included tomaximize the frequency of experiment refresh of all 32 chemical sequences.A.1.3 Chemicals and MediaYeast cells were grown with aeration overnight in YPD (30C), diluted and grownto log-phase in SCD on the day of the experiment. BSA (20 mg/mL) was addedto all SCD solutions to act as an anti-fouling agent. We found that this helpedavoiding adherence of yeast cells to PDMS walls as well as reducing non-specificbinding of the a-factor to the PDMS or tubing walls[2]. a-factor was purchasedfrom ZymoResearch (Orange, CA).A.1.4 Cell PreparationCells were diluted from overnight culture into fresh SCD and grown to log-phase.To improve uniform cell loading, the cell solution was sonicated at low powercausing the dissociation of cell clumps. To achieve ideal seeding density, cellswere concentrated to an OD600 of 3 immediately before loading. This allowed fora seeding density of 25 cells per microchamber, and with 8 microchambers perexperiment, 20 - 30 initial cells per experiment. Once loaded onto the microfluidicdevice, the cells were perfused with fresh SCD for at least 2 h prior to initial a-factor stimulation. Image acquisition was started at least one hour before a-factorstimulation in order to record basal fluorescence level as well as initial cell number.Cells were grown and stimulated at room temperature.A.1.5 Chemical Mixing and PerfusionBetween every exchange or refreshing of medium, the following refresh proto-col was used: 1) Prime and wash multiplexer: Perfuse adjacent waste row with127chemical solution for 20s. 2) Refresh microchambers: Perfuse experiment rowwith chemical solution for 70s. 3) Wait: A wait of 3s is used to dissipate pressurebuild up that occurs across the high-impedance microchamber traps. This protocolallowed us to fully refresh an experimental row approximately every 100s.A.1.6 Constant Stimulation ProtocolYeast strains were continuously stimulated with 32 exponentially distributed a-factor concentrations ranging from 0 - 100 nM beginning at t = 0 s. a-factor con-centrations were calculated as: a-factor concentration = 1.16i nM; where i = rownumber. The calculated concentrations were then rounded to account for discreteratio mixing. The first row (row index 0) was used as a negative control (a-factor= 0 nM). All 32 a-factor concentrations were created on chip using ratio mixingenabled by the peristaltic pump. Mixing protocols are found in Table A.1. Eachchemical mixture protocol was based on a 10 pump cycle period. To continuallyadminister a particular mixture, the 10 pump cycle was repeated.128i 1:16i Used (nM) 0 nM 1 nM 10 nM 100 nM0 1 0 10 0 0 01 1.16 1 0 10 0 02 1.35 1.3 6 3 1 03 1.56 1.5 4 5 1 04 1.81 1.8 1 8 1 05 2.10 2 8 0 2 06 2.43 2.4 4 4 2 07 2.82 2.8 0 8 2 08 3.27 3.2 5 2 3 09 3.80 4 6 0 4 010 4.41 4.4 2 4 4 011 5.12 5 5 0 5 012 5.94 6 4 0 6 013 6.89 7 3 0 7 014 7.99 8 2 0 8 015 9.27 9 1 0 9 016 10.75 10 0 0 10 017 12.46 12.5 2 5 2 118 14.46 14.5 0 5 4 119 16.78 17 2 0 7 120 19.46 20 8 0 0 221 22.57 22.5 1 5 2 222 26.19 26 2 0 6 223 30.38 30 7 0 0 324 35.24 35 2 0 5 325 40.87 41 5 0 1 426 47.41 46 0 0 6 427 55.00 50 5 0 0 528 63.80 64 0 0 4 629 74.01 73 0 0 3 730 85.85 90 1 0 0 931 99.60 100 0 0 0 10Table A.1: Pumping protocol for creating 32 different a-factor concentra-tions. The first column is the row index. The second column is theexponentially calculated a-factor concentration. The third column is theactual a-factor concentration used. The final four columns are in unitsof number of pumps for a 10 pump cycle.129Mixing of pumped solutions occurred through mechanisms of Taylor dispersion[8].We tested the mixing in our chip by creating a concentration gradient of fluorescentdye (fluorescein). Effective concentration was measured by taking a fluorescent in-tensity measurement of the dye as it passed the entrance of an experiment row. Aplot of measured fluorescence intensity vs. relative concentrations is shown in Fig.2.2E. The results show good agreement, indicating our solutions are well mixed,and our concentration profiles are precise.Figure A.1: Sieve Valves. (A) Standard MSL valves deflect the elastomermembrane into a rounded flow channel, causing complete closure. (B)Sieve valves deflect the elastomer membrane into a rectangular flowchannel, causing incomplete closure.130A.1.7 Single Transient Pulse ProtocolScheduling of the single pulse experiment is shown in Fig. A.2A Protocols of thesame concentration were grouped together to allow for the simultaneous refreshingof multiple experiment rows. Concentration groups were staggered by 2.5min.Approximately 16 p.s.i. (110.3 kPa) was applied to the different chemical lines.A.1.8 Short Repeated Pulses ProtocolScheduling of the repeated pulse experiment is shown in Fig. A.2B. Concentrationgroups were staggered by 2.5min. Approximately 16 p.s.i. (110.3 kPa) was appliedto the different chemical lines.131FigureA.2:Stimulationprotocols.(A)singlepulseexperiments.Colorindicatestheadministereda-factorconcen-tration.Therowsindicatedtheexperimentalrowsinthemicrofluidicmatrix.Horizontalaxisiselapsedtime.(B)Repeatedpulseexperiments.Colorindicatestheadministereda-factorconcentration.Therowsindicatedtheexperimentalrowsinthemicrofluidicmatrix.Horizontalaxisiselapsedtime.132A.1.9 Biological ConstructsThe full list of strains is given in Table A.2. Deletion mutants were obtained fromthe Open Biosystems Yeast Knockout (YKO) collection, as was the wild type (WT)BY4714 strain. All strains are derived from the parental S288C strain and have thefollowing base genotype: MATa his3D1 leu2D0 met15D0 ura3D0. Deletion mu-tants in the YKO collection were derived using a polymerase chain reaction (PCR)based strategy to replace the open reading frame with a KanMX deletion cassette.We confirmed each deletion using PCR methods. To report on mating-pathway-dependent gene expression we transformed the strains with the gene coding forenhanced green fluorescent protein (GFP) under control of a mating-specific pro-moter. The promoter consisted of 3 consecutive pheromone response elements(PREs) from the PRM1 promoter, plus 3 flanking PRM1 promoter bases on eachside, plus a CYC1 core promoter placed just prior to the ATG start site. The PRE-promoter-GFP construct (PRE-GFP) was placed adjacent to the HIS3 selection ele-ment using the Longtine et al. cassettes[5] and integrated at the his3D1 locus usingflanking sequences introduced by long-oligo PCR. The integration was verified byPCR. In each strain the pheromone protease gene, BAR1, was deleted to precludecomplications resulting from genotype-dependent Bar1 activity. The BAR1 cod-ing sequence was deleted using PCR methods with the pFA6a-hphNTI hygromycincassette as described in Janke et al [4].A.1.10 Image Analysis Pipeline AlgorithmsEach set of microfluidic cell traps was acquired in two fields of view, giving a totalof 256 experiment traps x 2 = 512 differential interference contrast / fluorescentimage pairs acquired per time point. We acquired a full set of images every 15min,133which was slightly longer than the time it took to iterate over and acquire the 512image pairs. Over a 12.5 h experiment this resulted in over 50,000 images ( 120Gb of image data). To process data a customized image analysis pipeline wasdeveloped using MATLAB software (Mathworks, Natick, MA).Central to our image analysis pipeline were algorithms for automated cell seg-mentation and enhanced green fluorescence protein (GFP) concentration calcula-tion. These algorithms consisted of the three major steps described below.Identification of Microfluidic Region of InterestPrior to cell segmentation the images were cropped to exclude the non-yeast con-taining regions outside of the cell flow channels. This reduced computational timeas well as false positives due to out-of-channel segmentation. To detect the channelboundaries, the detection algorithms took advantage of 1) the horizontal orienta-tion of the yeast flow channels and 2) the dark illumination of channel edges. Thehorizontal projection of the image, calculated by summing each image column, re-sulted in a one-dimensional signal in which four sharp local minima representedthe four channel edges. The projected values were then normalized to the range [0,1] and the local minima were detected by thresholding.The edge detection algorithm was an iterative procedure that used the a prioriknowledge that there should be two channels whose approximate width and sepa-ration from each other (in pixels) was known. The threshold was at first set to auser-defined initial value. If thresholding found two local minima whose distancefrom each other was less than 50 pixels, only the one whose value was smaller waskept. If this procedure detected four local minima, these local minima were kept asthe channel edges. If fewer than four local minima were found, the procedure was134repeated with the threshold incremented by 0.01 (a.u.); and if more than four localminima were found, the procedure was repeated with the threshold decremented by0.01. If the procedure failed to converge to four local minima, channel detectionfailed, and cell segmentation was performed on the whole image. Fig. A.3B showsthe detected channel edges overlaid in red on the DIC image.135IdentifierGenotypeWTMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3bem3DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3bem3D::KanMX4dig2DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3dig2D::KanMX4far1DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3far1D::KanMX4fus3DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3fus3D::KanMX4kss1DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3kss1D::KanMX4msg5DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3msg5D::KanMX4ptp2DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3ptp2D::KanMX4rga1DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3rga1D::KanMX4rga2DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3rga2D::KanMX4slt2DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3slt2D::KanMX4ste50DMATaleu2D0met15D0ura3D0bar1D::HphNT2his3::PRE-GFP-HIS3ste50D::KanMX4TableA.2:Strainsusedinthisstudy.136137Figure A.3: Image Segmentation Algorithms. (A) Original cell image. (B)Microfluidic channel detection. (C) Enhanced bright-field image. (D)Yeast cell wall segmentation. (E) Cell wall segmentation after removalof channel edges. (F) Cell area mask. (G) Cell segmentation beforepost-processing steps. (H) Final cell segmentation.Cell SegmentationFollowing channel detection, the DIC image was enhanced using background sub-traction. The background was estimated by spatially averaging the image using a21  21 mean filter. The resultant image is shown in Fig. A.3C.The first step in cell segmentation identified the cell walls. Yeast cell walls wereclearly visible as continuous borders that were darker than the background, givingtwo useful properties: the local mean was low and the local variance was high. Cellwall pixels were marked as those pixels whose local mean was below a thresholdTm and whose variance was above a threshold Tv[6]. A 5  5 neighborhood wasused to calculate the local mean and variance. The threshold Tm was set to mm- (1/2) sm, where the mm and sm were the global mean and standard deviationof the local mean image respectively. Similarly, the threshold Tv was set to mv +(1/3) sv, where the mv and sv were the global mean and standard deviation of thelocal variance image respectively. The cell wall segmentation result is shown inFig. A.3D.This segmentation result has both false negatives and false positives (apparentdiscontinuities in cell walls and non-cell wall pixels detected as cell wall pixelsrespectively), and further processing was required. First, small holes inside the de-tected cell walls were removed by a morphological closing with a 5  5 structuring138element. Second, detected channel edges were removed by assigning to zero eachrow of pixels five pixels above and below the detected channel edges followed byhysteresis thresholding[1]. The result is shown in Fig. A.3E. Typically some partsof the channel edges were still falsely detected as cell wall pixels, but these pixelswere removed in the subsequent processing steps.The image with the detected cell walls was used to obtain a mask of cell ar-eas. First, the image was dilated by a circular structuring element inside a 11  11 square. Following, holes inside the objects were filled and the image was sub-sequently eroded by the same circular structuring element. The result is shown inFig. A.3F. The detected cell walls were then removed from the mask (Fig. A.3F).Due to noise in cell wall detection, some cells were incorrectly grouped togetherand recognized as a single cell. These were separated from each other with thewatershed of the Euclidean distance function of the complement image[7]. Theh-maxima transformation was used in order to prevent over-segmentation. Finally,objects that were smaller than 80 pixels in size were excluded. The final cell seg-mentation result is shown in Fig. A.3H.Measurement of GFP ConcentrationGFP concentration values were calculated from the fluorescent channel images.Average background florescence, as calculated by the mean fluorescence of valuesoutside of the segmented cells, was first subtracted from each pixel value. Totalfluorescence of each cell was then summed from the image pixel values boundedby the segmented regions identified from the segmentation algorithms. For eachcell, the GFP concentration was obtained by dividing the total cellular fluorescenceintensity by a volume estimate for the cell. The volume estimate was based on the139cross-sectional cell area that was obtained from the cell segmentation result andcalculated using the conical method as described previously[3].A.2 Supporting TextA.2.1 Experimental Variability of Microfluidic PlatformExperimental variability is due to condition differences between experiments withina single device and between experiments taken on different devices on differentdays. Sources of in-chip variability include precision limits of chemical mixing,consistency of media conditions across experimental positions, and variations inz-position. To maximize chemical mixture precision we used on-chip pumpingand mixing of a-factor stock solutions to create precise final stimulant concen-trations (demonstrated in Fig. 2.3E and discussed in paper text). To ensure re-producibility across experimental positions (different columns of the microfluidicdevice) we used FITC and food dye tracers to determine the perfusion time neededto reliably replace the well-mixed media solutions across a row of microchambers(30 s). During experimentation we used a much longer refresh time (70s) to ro-bustly exchange the media. We empirically characterized in-chip reproducibilityby stimulating eight identical yeast genotypes (WT) with a series of constant a-factor concentrations (Fig. A.6). We find variability is minimal (<10 - 20%) andnot dependent of column position. To reduce variability due to image focus, wemanually defined focus positions for all images prior to image acquisition and pro-grammed our microscope to return to these positions at each time-point. We foundthis method was robust and long time courses could be obtained with minimal fo-cus drift (Fig. A.8). In addition, we used a 40x long distance objective (NA=0.6)140with a depth of field (estimated 1.5 mM) similar to the diameter of yeast cells. Thisincreased the robustness of the fluorescent measurement, which was advantageousover a higher magnification and numerical aperture objectives.Variability across experiments taken with different chips on different days isprimarily due to precision limitations of stock a-factor solutions and day-to-dayfluctuations in fluorescent excitation intensity. Quantitative comparisons betweengenotypes were possible by internally controlling all measurement by normalizingresponse to a WT control. In each experiment we reserved one column per-chip(chosen randomly) for WT and mutant responses were analyzed after normaliza-tion to WT.Lastly, we found that variability in population averaged statistics (e.g. meanresponse) increased when initial seeding density was low, due to sampling error ofthe biological response. An initial density of 20-30 cells (2.5 - 3.75 cells per microchamber) greatly reduced this source of error and we conducted our experimentsin this regime. Increases in chamber size will further address issue, and technicalmodifications to the trapping scheme are in development (Appendix B).A.2.2 Morphology Classifications Under Constant StimulationThroughout our experiments we found a-factor concentration-dependent morpho-logical responses. This was most apparent when the cells were subjected to con-tinuous stimulation for long times under chemostatic conditions. In our study, weclassified the morphologies after 6 hours of constant a-factor stimulation usingthree general morphology types:1. Budding Yeast. Yeast cells maintained their vegetative rounded shape seenfor exponentially growing cells. At the lowest concentrations these cells did141not cell cycle arrest (<5nM).2. Elongated. Elongated cells demonstrated rod shaped phenotypes. The de-gree of elongation varied and at long time scales (>14 h) we observed cellswith major axes > 5 minor axis. Elongated morphologies were observedat intermediate a-factor concentrations (4nM - 20nM).3. Shmooing. Shmooing cells were rounded with small sharp protrusions andcell cycle arrest. These cells were mostly observed at higher a-factor con-centrations (>20nM).Fig. A.4 demonstrates morphology analysis of each strain across all a-factorconcentrations taken at 6 h after initial stimulation. The concentration range foreach morphology varied drastically among some mutants. Some mutants like far1Donly showed the budding yeast morphology and lacked any other type. Others likemsg5D and ptp2D displayed an extended shmoo concentration range (extending tobelow 4nM) as compared to WT.142143Figure A.4: Morphological response of the yeast strains to a-factor con-centration. Color dots represent morphologically stratified populationmean GFP response, with dot opacity indicating the percentage of cellswith that morphology for the specific experiment. Red = yeast form;blue = hyperelongated; green = shmoo. Error bars represent standarddeviation of response. Measurements were taken from the t = 360 mintime point for all strains except fus3D which was taken at t = 600 min.Mating morphologies in fus3D we not observable until this time.A.2.3 Single Pulse AnalysisTo investigate the pulse-width-dependence of the rate of pathway deactivation, wequantitatively examined the kinetics of pathway shut down following a-factor re-lease in multiple ways. First, we measured the delay between a-factor release andtime to reach maximal GFP concentration across all pulse-widths (Fig. A.5A). Wefind that this time was independent of pulse-width, occurring 30min after releasefrom a-factor stimulation for all conditions. Second, we measured GFP decayrates upon a-factor release by fitting the post-maximum GFP time-course data to amodel of exponential decay. Across replicates (n=3) and across all conditions giv-ing significant GFP expression (all combinations of 50 and 20 nM x 60, 90, 120,150, 180, and 210 min), we found a condition-independent decay rate of 0.0046  0.0014 min 1. Third, we compared the integrated GFP response to total integratedinput dose. As we know that the activation rate for a given a-factor concentrationis constant, as is the time to initiate deactivation upon release, deactivation ratesthat are independent of pulse-width should map linearly to input dosage. We infact observed this relationship (Fig. A.5B). Taken together, these results indicate144that, for the pulse-widths tested, we do not measure network adaptation effectsresulting in increased or decreased rate of pathway shutdown.145146147Figure A.5: Single Pulse Analysis. (A) Time to reach maximal GFP con-centration following a-factor release. The green bar indicates a-factorstimulation. The orange line indicates approximate time of GFP con-centration maximum. A higher image acquisition rate of every 7.5minwas used to increase the accuracy of peak finding. (B) Total integratedGFP out vs. total integrated a-factor input for wild-type yeast. Dotcolor represents different input a-factor concentration: black = 50 nM;blue = 20 nM. (C) Growth curves of WT cells stimulated with a sin-gle 50nM a-factor pulse. Pulse duration: 20 min; 40 min; 60 min; 90min; 120 min; 150 min: 180 min; 210 min. a-factor stimulation wasinitiated at t = 0. Yeast cell proliferation arrests upon stimulation witha-factor and resumes when the a-factor is washed out.148Figure A.6: Response variability within a single microfluidic device. Eachdata point is the steady-state population averaged GFP concentrationnormalized by the basal fluorescence (y-axis) for a given a-factor con-centration (x-axis). Each microfluidic column contains the same WTgenotype, with each dot color representing a different column. Reddots and lines represent the mean and standard deviation response ofall columns. Cases where an experimental position contained zero cellswere removed from this analysis.149150151Figure A.7: Reproducibility of results. (A) dGFP/dt measurements. Eachbar plot gives the mutant initial dGFP/dt over WT initial dGFP/dt acrossall a-factor concentrations between 5nM and 100nM. Initial dGFP/dtis calculated as the slope of a line fitted to the population averaged GFPconcentrations between 30-180min. Error bars give standard deviationacross experiments (n = 2 to 5). (B) Periodic a-factor stimulation forall mutants. Heat plots and bar plots of mutant GFP concentrations vs.WT are given for all tested strains. Heat plot scales are in log2(mutant/ WT) and the bar plots are in (mutant / WT). In both cases, responseis taken at t=600min and averaged across n experiments (n is indicatedfor each strain). Error bars on the bar plot represent the standard de-viation of the measurement across experimental replicates. Number ofreplicates is given for each strain. If replicates from the same chip wereconsidered, the total number of replicates from different chips is indi-cated. Each colored rectangle of the heat plots corresponds to a specifica-factor concentration (column) and delay between successive pulses(row). Experimental conditions of the bars of the bar plot are indicatedby the colored base bar (dark blue = 5nM; orange = 10nM; yellow =20nM; light blue = 50nM) and colored dot (dark blue = constant stim-ulation; yellow = 15 min delay; green = 40 min delay; light blue = 65min delay; grey = 140 min delay).A.2.4 Pulse-Width Dependent Growth RateManual counts of numbers of cells across time were used to obtain exact cellgrowth curves. Fig. A.5C gives the growth curves for wild-type (WT) cells across152all single pulse widths of the 50 nM concentration. Cell-cycle arrest is observedapproximately 75 min after stimulation and approximately persists for a durationequivalent to the pulse width. We were unable to detect any arrest for the <20 nMconditions.153154Figure A.8: Image focus over 12 hours. Representative DIC images yeastcells taken over a 12 hr time-course. Using an automated XYZ posi-tioning, images were acquired at each position of the microfluidic de-vice (256 microchambers x 2 fields of view per microchamber = 512positions) every 15 min for 12 hrs (52 images). Shown here are the 2hr time-points for a single position.A.2.5 Calculation of d[GFP]/dtInitial rate of GFP molecule production (d[GFP]/dt) was calculated as the slopeof the line fitted to the population averaged GFP response for t = 30 min to t =180 min. This time interval was chosen to allow for GFP maturation and to endbefore response saturation. We found that yeast cells demonstrated an a-factorconcentration-dependent rate of GFP expression.155Bibliography[1] J Canny. A computational approach to edge detection. IEEE Transactions onPattern Analysis and Machine . . . , 8(6):679–698, Nov 1986.[2] Alejandro Colman-Lerner, Andrew Gordon, Eduard Serra, Tina Chin, OrnaResnekov, Drew Endy, C Gustavo Pesce, and Roger Brent. Regulated cell-to-cell variation in a cell-fate decision system. Nature, 437(7059):699–706, Sep2005.[3] A Gordon, A Colman-Lerner, T Chin, and K Benjamin. Single-cell quantifi-cation of molecules and rates using open-source microscope-based cytometry.Nat Methods, 4:175–181, Jan 2007.[4] Carsten Janke, Maria M Magiera, Nicole Rathfelder, Christof Taxis, SimoneReber, Hiromi Maekawa, Alexandra Moreno-Borchart, Georg Doenges, Eti-enne Schwob, Elmar Schiebel, and Michael Knop. A versatile toolbox forpcr-based tagging of yeast genes: new fluorescent proteins, more markers andpromoter substitution cassettes. Yeast, 21(11):947–62, Aug 2004.[5] M S Longtine, A McKenzie, D J Demarini, N G Shah, A Wach, A Brachat,P Philippsen, and J R Pringle. Additional modules for versatile and economical156pcr-based gene deletion and modification in saccharomyces cerevisiae. Yeast,14(10):953–61, Jul 1998.[6] Antti Niemist¨o, Matti Nykter, Tommi Aho, Henna Jalovaara, Kalle Marjanen,Miika Ahdesm¨aki, Pekka Ruusuvuori, Mikko Tiainen, Marja-Leena Linne,and Olli Yli-Harja. Computational methods for estimation of cell cycle phasedistributions of yeast cells. EURASIP journal on bioinformatics & systemsbiology, page 46150, Jan 2007.[7] P Soille. Morphological image analysis: Principles and applications. page391, Jan 2003.[8] TM Squires and SR Quake. Microfluidics: Fluid physics at the nanoliter scale.Rev Mod Phys, 77:977–1026, Jan 2005.157Appendix BA Microfluidic Platform forHigh-Throughput Single-CellTracking and DynamicEnvironmental Control 1B.1 IntroductionSystems biology studies based on genome wide analysis of transcript and proteinexpression have greatly advanced our understanding of the molecular interactionsthat govern complex cellular behaviours[28]. However, such studies are typically1Figures B.1, B.2, B.3, and B.4 of this chapter are taken from Didier Falconnet, Antti Niemisto,James Taylor, Tim Galitski, Ilya Shmulevich, and Carl Hansen. A microfluidic platform for high-throughput single-cell tracking and dynamic environmental control. In Preparation. The lead authorconducted the bulk of the work in the said manuscript. My role in this study was in supporting thetechnology development of the microfluidics and image analysis pipeline.158limited by poor temporal and spatial resolution. In addition, these methods onlyprovide an average population response and are hence blind to cell differencesthat arise from a combination of de-synchronization[9], bistability[17, 26], andstochastic variations in expression[10, 20].Technical advances in microscopy, fluorescent reporters and live-cell imagingsetups have allowed the visualization and quantification of gene expression as wellas intra-cellular trafficking in real time within single cells [13, 29]. Studies on cel-lular behavior with single-cell resolution have open new experimental alleys andpioneered work about heterogeneous response and its benefits for a population toexplore simultaneously different phenotypes[2, 26], signaling network adaptationto limit the metabolic cost of sustained response[33], and network memory to allowmore rapid accommodation of recurrent stimuli[1, 5]. Such studies are traditionallyperformed by imaging cells in multi-well plates (eg, high content cell screenings)or by confining them between a microscope slide and an agar pad[13]. However,both approaches lack the ability to finely control and modulate the chemical envi-ronment and suffer accumulation of metabolites in the culture medium. The ten-dency of the cells to grow out of the focal plane further limits the experiment du-ration. Flow cytometry is a high-throughput alternative that allows measuring theresponse of thousands of single cells and provides a snapshot of the cell states[2]however; it doesnt allow repeatedly interrogating individual cells and thus can-not provide time course data with a single-cell resolution nor provide sub-cellularinformation.Systems level studies aiming at unraveling complex signaling networks re-quire the quantitative time resolved analysis of single cells under a range of well-defined chemical conditions and genetic perturbations[32]. Microfluidic devices159combined with state-of-the art microscopy and image analysis have emerged in thepast decade as powerful tools to study cells in a controlled environment and in amultiplexed, high-throughput and high-content manner [3, 12, 14, 18, 21, 26, 30](Chapter 2). This technology allows integrating at a large scale micro- valves,pumps and reaction chambers with astonishing reliability and low cost. The de-vices are easily automated with commercial interfacing software and are thus nowbroadly adopted to conduct novel studies on bacteria, yeast or mammalian cellsunder chemostatic [3, 14, 18], or chemodynamic conditions [5, 12] (Chapter 2).Despite of the relative maturity of this technology existing microfluidic sys-tems for live-cell imaging still suffer from major limitations: the vast majority ofmicrofluidic systems have been developed for studying adherent cells[8, 12, 18]and their fluidic wiring makes them unsuitable for non-adherent cells such as yeastor hematopoeitic lineages as they would be washed away by the fluid flow. Con-sidering the tremendous importance of yeast as a model organism and the prolificresearch on hematopoeitic stem cells there is an obvious need for a new generationof microfluidic devices enabling the study of non-adherent cells with a systemsbiology approach.A few devices including a commercial one have proposed ways to immobilizenon-adherent cells under flow conditions. The proposed immobilization strategiesconsist of seeding cells on a surface and clamping them with a soft permeablemembranes embedded in a single fluidic chamber [6]. The second approach[19]exploits the elasticity of the of the device material to inflate the chambers dur-ing cell loading, upon pressure release the cells are physically trapped between theceiling and the floor of the chamber (http://www.cellasic.com). These devices haveproven extremely useful for studies on a limited number of cell types and chemical160inputs they are however lacking the throughput needed to perform systems levelstudies on living cells across multiple mutants and chemical conditions. Moreover,the later approach doesnt exclude the loss of newly budded cells as these are po-tentially smaller than the trap heights. This often overlooked aspect also applies toadherent mammalian cells since they round up during division and are thus morelikely to be washed away with the flow. To address the lack of current throughputand non-optimal trapping strategies a new type of device is required.Here we present a microfluidic live-cell imaging device capable of runningsimultaneously and in an unattended manner 128 different experiments on tensof thousands of immobilized non-adherent single yeast cells exposed to variouspre-programmed chemical perfusion schemes. The cells are trapped in an agarosenetwork within the chambers by a straight forward in situ gelling procedure whichallows us to track each individual cell in time via a custom built algorithm and thusgenerate multidimensional time plots of gene expression, morphological propertiesand cellular growth throughout 8 different cell types and 16 chemical conditions.Expanding the experimental dimensions (number of strains, chemical conditionsand time points) also results in generating large data sets often in the order ofhundreds of gigabytes. To accompany such developed we built powerful customalgorithms to analyze the images and present the data in useful ways.We demonstrate our microfluidic technology by studying the pheromone in-duced mitogen-activated protein kinase (MAPK) signaling pathway in Saccha-romyces cerevisiae[4]. MAPK signaling is of central importance to a wide rangeof cellular decision making processes, responding to a staggering range of stim-uli including growth factors, cytokines, hormones, cellular adhesion, stress, andnutrient conditions[27]. Yeast is an ideal model organism to study the highly con-161served MAPK signaling cascade as it undergoes rapid division (a few hours) andgenetic manipulations are straight forward. Yeast cells of mating type a can readilybe induced by soluble a-factor pheromones via the membrane-localized G-protein-coupled receptor Ste2 (Ste3 for MATa cells). The pheromone peptide when boundto its receptor triggers a MAPK cascade activating expression of about 200 genesand culminating in cellular growth arrest and formation of a pointed extension (ashmoo) towards the pheromone gradient[4]. To report on mating-pathway activitywe transformed the strains with the gene coding for enhanced green fluorescentprotein (EGFP) under control of a mating-specific promoter (Supplementary meth-ods).B.2 ResultsB.2.1 Microfluidic Chip Overview and OperationOur microfluidic device is composed by an array of 128 chambers (Fig. B.1A,region 1) located at the intersections of 8 columns and 16 rows (Fig. B.1B). Eachcolumn (Fig. B.1A, region 2) is loaded with a different mutant (here single genedeletions) while the rows permit perfusion of 16 different chemical formulations(here different a-factor concentrations) that are mixed on chip automatically from3 stock solutions. The innovative fluidic and valving circuitry was designed so thatliquid gel suspensions of yeast cells could be flown into the columns and cham-bers of the chip at room temperature and subsequently flushed away in areas thatare not part of the 128 experimental cell-chambers. The cells were then gentlyimmobilized in the chambers by cooling down the chip and consequently forminga gel (agarose). The low density gel prevents any fluid flow from the perfusion162channels into the chambers and thus do not displace the cells. Diffusion conduitsconnect the chambers to the perfusion channels to deliver nutrients (pheromonesor any molecule of interest) to the cells while simultaneously remove metabolites(Fig. B.1B).The fluidic device is equipped with 8 chemical inlet ports (Fig. B.1A, region3, Fig. B.2A) for stock solutions prepared off-chip. They can either be perfusedto the cells as prepared or they can be further mixed on-chip. Each row can beindividually perfused or a combination of rows can be opened simultaneously toallow multiple row perfusions with an identical solution (Fig. B.2B). On-chip mix-ing is achieved by sequentially opening the appropriate chemical input valves fora predefined number of pump cycles. For example a 23 nM solution is preparedby running the two first pump cycle with 100 nM stock solution, then three cycleswith the 10 nM valve open and finally completing with 5 cycles of 0 nM (puremedia) to reach a fluidic slug of 23 nM with a total of 10 pump cycles. The pro-cess is repeated for the time required to replace the whole volume of a perfusionchannel (approx. 35 s). Mixing between the small volumes of stock solutions isachieved by Taylor dispersion and solutions were fully mixed when they entered aperfusion row. To characterize the efficiency and accuracy of our mixing protocolwe generated onchip 16 different fluorescein concentrations from 3 stock solutions(1x, 10x, 100x) and measured the fluorescence intensity in each chamber. FigureB.2C is a plot of the fluorescence intensity in function of the relative concentra-tions. Y-error bars are standard deviations across all eight chambers of each row.An almost perfect linear fit (R2 = 0.9996) demonstrates the accuracy of the on-chipformulator. Besides being fast and reproducible on chip mixing alleviates humanerror. Nutrients as well as any molecules used to induce a response in the cells163will diffuse from the perfusion channels to the cells. Figure B.2D demonstratesthe kinetics of fluorescein diffusion under continuous flow. The chambers reachequilibrium with the feeding channel within 9 min (for non-continuous flow seesupplementary notes). Microfluidics offer unique capabilities for precise handlingof fluids including switching between different chemicals as illustrated by the arbi-trary sequence in Figure B.2D alternating between fluorescein solution and media.These switching speeds were adequate for the biological experiments conductedin this work. If faster solution exchange is needed, faster switching can easilybe achieved by simply reducing the chamber width or widening or shortening thediffusion channels connecting the chambers to the perfusion channels.164Figure B.1: Microfluidic chip design and operation.. (a) The central part ofthe chip (1) is composed of an array of 128 chambers (8 columns  16rows). Each column (2) is loaded with a different strain at the desireddensity. The rows are fed by 8 chemical inlets (3) which controlled byindependent valves. Single or multiple rows can be perfused simulta-neously by combined actuation of the multiplexer valves (5). Replacedfluids are collected into a 1 ml bottle connected to the chip by outletports (4). Fluids are moved into the channels by applying positive pres-sure to the chemical inlets or by using the on-chip integrated peristalticpump (6). (b) Detailed chip architecture; each chamber (light blue) isconnected to the perfusion channels (dark blue) by diffusion channels(dark blue). All diffusion channels can be closed by actuating the dif-fusion valves (yellow). Each of the 16 rows has its own waste channel(dark blue above each chamber) in order to flush out the binary treebetween placed between the cell array (1) and the chemical inlets (3).The chambers are partitioned during the experiment by the side-valves(pink). The loading valves (red) are actuated when the cells are loadedinto the chip in order to avoid cross-contamination between strains. (c)A chamber with cells trapped in the agarose gel and growing in mediaat a density 3.5 109 cells/ml. Note that each chamber is imaged with2 fields of views. The chambers are 684 260 4.4 mm3 filling a vol-ume of 0.7 nl. The 6 squares are posts providing mechanical stability.(d) Frequent media refreshing allows the culturing cells in chemostaticconditions and thus achieving high densities as shown by the growthcurve.165B.2.2 High-Throughput Multimode Live-Cell ImagingEach chamber can be repeatedly imaged with a temporal resolution as low as 10min while the perfusion protocols run fully unattended for multiple days. The tem-poral resolution is limited by two factors; the speed of the automated stage movingto each chamber and the number of chambers to image. The chamber dimensionsare 684 260 4.4 mm3 filling a volume of 0.7 nl only. Each chamber can hostover 6,000 cells corresponding to 8.5 109 cells/ml. For comparison a yeast culturereaches stationary phase at about 2-3 108 cells/ml. Exceptionally high cell densi-ties can be achieved in the chip by frequently refreshing the entire volume of theperfusion channels and thus providing nutrients and removing metabolites. Notethat often such high cell numbers are not necessary and may result in unnecessar-ily image analysis time. The height of the chambers (4.4 mm) are similar to thecells diameter and thus confine them to a single focal plane allowing for accuratequantitative measurements on each cell repeatedly.166Figure B.2: On-chip reagents mixing and temporal control of chemicalenvironment. (a) 8 chemical inlet ports shown with food dyes. The3 valves on the right constitute the on-chip peristaltic pump. (b) Vi-sual illustration using different food dyes perfusing separate rows. (c)On-chip formulation of 16 different concentrations of fluorescein from3 stock solution of fluorescein (10 , 1 , 0 ). The intensity wasrecorded in each row and plotted in function of relative concentrations.The error bars are standard deviations across the 8 chambers on eachrow. (d) Less than 9 min are sufficient to reach concentration equilib-rium within the experimental chambers. Temporal modulation of thechemical environment is demonstrated by arbitrary pulses alternatingbetween media and fluorescein.167We monitored the response of over 60,000 individual yeast cells from 8 mutantsexposed to 16 different a-factor concentrations (Supplementary Table 2) using a 20min sampling period. During a typical 24h experiment over 40,000 images (brightfield and fluorescence) are recorded holding 4 million cell measurements. We builta custom image analysis pipeline in Matlab (Mathworks, Inc) to rapidly processthe images, the algorithms performed the following tasks: (i) segment and labelthe cells in each image, (ii) calculate total fluorescence in each cell, (iii) calculatestatistics across all cells and experimental conditions and, (iv) present time-courseand steady-state data in human-readable formats like figures, movies, and tables.Figure 3a is a typical time course plot of the GFP concentration (GFP normalizedto the cell volume) within each wild-type cell. The cells were initially perfusedwith medium and at time 0 the media was switched to a 10 nM a-factor solu-tion. After approximately 300 min of pheromone induction the population reachedsteady-state and displayed a 12 fold increase in GFP expression compared to thebasal fluorescent level. We observed a spread of response across the populationwhich highlights the heterogeneity of cellular behavior also commonly referred toas noise[9, 22]. Coefficients of variability (CV = standard deviation/mean) of 0.3were typically measured for such populations. Movies were automatically gener-ated for each chamber to allow rapid visualization of the evolution of cellular re-sponse under each tested condition. This allows to efficiently screening the wholechip for interesting or unexpected behaviors and can thus help focusing the studyon more targeted experiments.Figure B.3 (wild-type) represents the mean wild-type response for the 16 pheromoneconcentrations over approximately 600 min (this corresponds to a complete columnof the chip). Cells show a subtle response at a-factor concentrations as low as 1nM168with response saturating in a range of 22-30 nM. Below saturation, the responseis monotonically graded with pheromone concentration. This device allows study-ing simultaneously the response of 7 mutants in addition to wild-type. Figure B.3also displays the response of each of these mutants; in brief we observe that ptp2D,msg5D and kss1D are hyper-sensitive (in increasing order) while ste50D, far1D,slt2D and fus3D are hypo-sensitive (in decreasing order) with respect to wild-type.These observations are supported by our previous high-throughput study (Chap-ter 2) and by others [7, 9, 26] and thus validate our platform. In addition, differentresponse saturation thresholds are found amongst mutants. For example, slt2D sat-urates at a-factor concentrations as low as 10 nM while ste50D does not reachsteady state within the time of the experiment. We are currently tackling the re-sponse of the different mutant with a high-degree of detail and will thus not bediscussed further as it is outside of the scope of this method article.169FigureB.3:High-throughputgeneexpressionmeasurements.(a)3Dplotsshowingthechamber-averagedGFPconcentrationsinfunctionoftimeanda-factorconcentration.(b)GFPconcentrationofwild-typecellsexposedto10nMa-factormeasuredevery20minover9h.Eachdatapointisacellwithinthisspecificchamber.170B.2.3 Single-Cell Tracking Reveals Heterogeneous Decision Makingin a Narrow Pheromone Concentration RangeYeast cells are known to adopt various morphologies depending on their surround-ing pheromone concentration [11, 16] (Chapter 2). We observed that between 0-3nM wild-type cells displayed a budding yeast morphology while between 4 and8 nM an increasing fraction of the population showed an elongated morphology.At 8 nM the totality of the cells transitioned from budding yeast to an elongatedphenotype accompanied by growth arrest. When the cells were exposed with 30nM or higher they adopted the more common shmooing phenotype characterizedby one or more short and pointed projections. Fig. B.4 is a representative exampleof wild-type cells exposed to 5 nM a-factor. Fig. B.4B is close up with a timesequence of 2 small, a priori, isogenic populations exposed to the same chemicalenvironment (5 nM a-factor). Interestingly, the population circled in red undergoestypical growth arrest and cellular volume increase while the green-circled popula-tion keeps dividing and is apparently insensitive to the presence of pheromones.Furthermore, the GFP intensity reporting on the pheromone pathway activity pro-vides complementary information to the bright field images; we visually observea direct correlation between growth arrest accompanied by elongation with higherGFP expression. To accurately quantify the GFP expression in each cell and overtime we built a cell tracking algorithm which labels each cell (Fig. B.4C) and al-lows plotting their respective time-lapse gene expression profile as shown in Fig.B.4D. Cells undergoing growth arrest (red) display a strong GFP signal while di-viding cells (green) only show a moderate GFP response. The strongest responderdisplays a 12 fold increase over basal fluorescence level while the strongest re-sponding cells in the green population only have a 5 fold increase. Due to the171relatively dim signal in the weakly responding cells and their lack of growth arrestsimple visual observation could lead to erroneously concluding that these cells ig-nore the pheromone stimulus. By quantifying accurately their GFP concentrationwe show that their pheromone pathway becomes activated but to a milder level.This suggests that the activity of the signaling transduction cascade is graded butthat there might be a threshold above which the cells commit to growth arrest andmorphological transition.Our tracking algorithm keeps track of each cell and attributes new labels tonewborn cells however, it is unable to track the lineages (relate the daughter cell toits mother). By analyzing time courses we manually reconstructed the lineages ofthe 2 populations as shown in Fig. B.4E. Specific markers such as budneck stainsfor automating lineage tracking has recently been demonstrate successfully[6].What causes one population significantly less sensitive to pheromones than itsneighbor remains an open question which will drive further studies. Particularlyinteresting would be to decipher the potential lineage-dependent behavior; in otherwords if the decision to growth arrest and elongate is coordinated within the mem-ber of the populations; in the present example, 3 out of 3 cells in the red circleundergo growth arrest and elongation, while the cells in the green population keepbudding. The cells within these small populations are close relatives (separated by1 to 3 generations at most) and maybe the cause of the concerted response. Thispoints to the question of potential epigenetic (non-genetic) inheritance of compo-nents playing a role in the pheromone response.172173Figure B.4: Lineage tracking with single-cell resolution; identification ofa switch-like response. (A) A snapshot of an isogenic colony of bar1Dcells exposed to 5nM a-factor pheromones after 40min. (B) A time-lapse sequence to illustrate the variability of pheromone response ofgenetically identical cells. The red population undergoes growth ar-rest and displays an elongated phenotype while the green keeps bud-ding over multiple generations, seemingly ignoring the pheromone in-put. (C) The tracking algorithm attributes a unique label to each cell,allowing for correlation of lineage, morphology, budding pattern andgene expression. (D) EGFP concentration in each cells reporting thepheromone pathway activity. Note that plotted gene expression is nor-malized for calculated cell volume and therefore represents EGFP con-centration.B.3 DiscussionWe designed and optimized a microfluidic chip that combines multiple impor-tant features: non-adherent single cells can be measured repeatedly under a pre-programmed perfusion protocol sequences with on-chip chemical input switchingand mixing. The device was designed as a tool for systems biology studies wheremultiple cell types (strains) were exposed to a range of stimuli over multiple gen-erations and in an unattended manner. We developed custom software to run thechips in an automated manner as well as sophisticated image analysis algorithms toefficiently and accurately extract relevant information from million single cell data.In this study we report multiple observations that raise questions such as the source174for the heterogeneous response in an isogenic population or the potential epigeneticinheritance of components implicated in the pheromone response. We argue thatour device combined with our software is an ideal tool to tackle such fundamen-tal and current questions with an efficient and precise approach. The accurate andrapid fluid handling capability combined with the minute volumes required in mi-crofluidics have imposed this technology as a standard and cost-effective methodin the areas of analytical biology, drug discovery and diagnostics [21, 23]. It islikely that in future the same will be seen for cellular in vitro assays such as theone presented in this work. The spreading of this technology to biology labs hasbeen refrained by the need for special equipment and know-how however, accessto custom microfluidic chips has recently become widely available: recognizingthe impact of microfluidics, several institutions have created fabrication facilitiesthat can provide any researcher with the desired chips. The devices are fully cus-tomizable and easily modified as the channels are initially drawn on CAD software.This provides virtually unlimited flexibility for adapting the chips to specific ap-plications and for studying other cells types such as bacteria or mammalian cells.Particularly appealing is the rapid turn over time between designing a new chip andperforming the first experiment. This typically required less than 4 days while thechip fabrication step (with molds in hand) was routinely performed in 1 day andproduced 8 functional chips allowing to perform 1024 independent experiments.The footprint of our chip is approximately 4 3 cm2 and can thus be used on anyregular microscope equipped with a motorized stage. Sources of compressed airrequired to run a chip are widely available either from in house lines or throughmobile gas cylinders. The hardware for controlling the valves is commerciallyavailable (see Methods). CAD files and software can be obtained upon request.175This work presents the-state-of-the-art microfluidic sophistication for studyingnon-adherent yeast cells under well control chemical conditions and at a through-put adequate for systems level studies with single cell resolution and track capa-bility. Further, our design is scalable and the number of genotypes measured canbe greatly increased to enable the possibility of genome-wide studies using a mod-est number of devices. Finally, we believe that such large scale data sets shouldprovide stringent tests for in silico models of signaling networks and lead to moreaccurate and ultimately predictive understanding of complex cellular decision mak-ing.B.4 MethodsB.4.1 Chip FabricationFabrication of the microfluidic device was accomplished using standard multi-layersoft lithography techniques[15, 21, 31]. We used a 2-layer design: The top layer isa ’control layer’, containing channels used for pneumatic valving and the bottomlayer was a ’flow layer’, containing the cells and the chemical channels. The chipwas covalently bonded to the 0.7 mm low autofluorescent glass slides (borofloat,S.I. Howard Glass Co., Inc. MA) by a 10 s O2-plasma surface treatment. Theassembled and punched poly-dimethylsiloxane chips (PDMS, RTV615 manufac-tured by General Electric, CT) were baked for at least 15h at 80C. Chip designwas completed using AutoCAD software (Autodesk, Inc., San Rafael, CA). Mas-ter negative molds were fabricated by standard photolithography techniques on 4inch (101.6 mm) silicon wafers (Silicon Quest International, Santa Clara, CA).High resolution transparency masks (20,000 dpi) were printed by CAD/Art Ser-176vices. The flow layer consisted of two feature types: 4.4 mm high rectangularcell microchambers, and 9 mm high rounded flow channels. The rounded channelscross-section was obtained with by placing the wafer on a 130C for 30 min. Eachcell microchamber had a volume of 0.71 nL with dimensions 684x260x4.4 mm3.The 4.4 mm layer was made with SU8-5 negative photoresist (Microchem Corp.,Newton, MA) and the 9 mm rounded layer was made with SPR220-7 positive pho-toresist (Microchem Corp.). The control master was a single layer mold consistingof 25 mm high squared features made with SU8-2025 negative photoresist (Mi-crochem Corp.). Resist processing was performed according to the manufacturer’sspecifications.B.4.2 Segmentation and Tracking AlgorithmCell segmentation was exclusively performed on bright-field images in order to beindependent of any fluorescent reporter for this task. Yeast cell walls were clearlyvisible as continuous borders that were darker than the background. The localmean and variance were calculated for each pixel of the image using a small localneighborhood, and those pixels for which the local mean was below a thresholdand the local variance was above a threshold were marked as cell wall pixels[24].Then, a mask of cell areas was obtained by thresholding the local variance imagewith a threshold found with the help of Otsu’s method[25], followed by a series ofoperations based on mathematical morphology. 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