Applied Science, Faculty of
Mechanical Engineering, Department of
DSpace
UBCV
Lee, JeongHyun
Doctor of Philosophy - PhD
University of British Columbia
Cells are encapsulated decision-making units that measure external stimuli, process incoming information, and develop appropriate responses. A key window into cellular decision-making are the transcriptional programs enacted after specific events. Recent advances in transcriptome analysis by single cell RNA sequencing have revealed the remarkable transcriptional heterogeneity between cells. However, current methods are unable to investigate the changes of the transcriptional profile adopted by specific cells after specific events. This dissertation presents See-N-Seq, a new method to selectively extract RNA from single cells identified by imaging in order to perform transcriptome profiling after specific events. Our process involves embedding the cell sample in a permeable hydrogel in a microwell, and then photo-patterning a second impermeable hydrogel inside the permeable hydrogel. Non-target cells are encapsulated in the impermeable hydrogel, while target cells are encapsulated in the permeable hydrogel. Target cells can then be lysed to release RNA, which is then captured, barcoded, reverse transcribed, amplified, and sequenced at high-depth. We initially tested this process by extracting single cell RNA from mixtures of transgenic cancer cells, which is then analyzed using qPCR. We demonstrated the utility of this technology by sequencing RNA from an in vitro model of immune synapses formed between Jurkat cells and Raji cells with Staphylococcal enterotoxin E (SEE) super-antigen. The RNA from synapse formed single cell pairs were selected and sequenced as well as non-synapse formed single cell pairs at 0, 4, 24 hour time points. As a control, RNA from the bulk and single cell samples of Jurkat cells and Raji cells were also sequenced. The transcriptome sequencing data showed an average of 0.39 +/- 0.11 million unique transcripts for each cell or cell pair, which represent most of the mRNA in single cells. Furthermore, we confirmed the potential for Jurkat cells to adopt divergent transcriptional programs at 24 hours after synapsing. Together, these results demonstrate the potential of See-N-Seq to uncover decision-making processes in critical single cells that ultimately lead to health and disease outcomes.
PLATFORM TECHNOLOGY FOR SELECTIVE SINGLE CELL RNA SEQUENCING OF TRANSIENT EVENTS by JeongHyun Lee B.A.Sc., Myongji University, 2011 M. Sc., Myongji University, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Mechanical Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2020 © JeongHyun Lee, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Platform technology for selective single cell RNA sequencing of transient events submitted by Jeonghyun Lee in partial fulfillment of the requirements forthe degree of Doctor of Philosophyin Mechanical Engineering Examining Committee: Hongshen Ma, Mechanical EngineeringSupervisor Dana Grecov, Mechanical EngineeringSupervisory Committee Member Supervisory Committee Member Mu Chiao, Mechanical EngineeringUniversity Examiner Christian Kastrup, Biochemistry and Molecular BiologyUniversity Examiner Additional Supervisory Committee Members: Boris Stoeber, Mechanical EngineeringSupervisory Committee Member Karen Cheung, Electrical and Computer EngineeringSupervisory Committee Member iii Abstract Cells are encapsulated decision-making units that measure external stimuli, process incoming information, and develop appropriate responses. A key window into cellular decision-making are the transcriptional programs enacted after specific events. Recent advances in transcriptome analysis by single cell RNA sequencing have revealed the remarkable transcriptional heterogeneity between cells. However, current methods are unable to investigate the changes of the transcriptional profile adopted by specific cells after specific events. This dissertation presents See-N-Seq, a new method to selectively extract RNA from single cells identified by imaging in order to perform transcriptome profiling after specific events. Our process involves embedding the cell sample in a permeable hydrogel in a microwell, and then photo-patterning a second impermeable hydrogel inside the permeable hydrogel. Non-target cells are encapsulated in the impermeable hydrogel, while target cells are encapsulated in the permeable hydrogel. Target cells can then be lysed to release RNA, which is then captured, barcoded, reverse transcribed, amplified, and sequenced at high-depth. We initially tested this process by extracting single cell RNA from mixtures of transgenic cancer cells, which is then analyzed using qPCR. We demonstrated the utility of this technology by sequencing RNA from an in vitro model of immune synapses formed between Jurkat cells and Raji cells with Staphylococcal enterotoxin E (SEE) super-antigen. The RNA from synapse formed single cell pairs were selected and sequenced as well as non-synapse formed single cell pairs at 0, 4, 24 hour time points. As a control, RNA from the bulk and single cell samples of Jurkat cells and Raji cells were also sequenced. The transcriptome sequencing data showed an average of 0.39 +/- 0.11 million unique transcripts for each cell or cell pair, which represent most of the mRNA iv in single cells. Furthermore, we confirmed the potential for Jurkat cells to adopt divergent transcriptional programs at 24 hours after synapsing. Together, these results demonstrate the potential of See-N-Seq to uncover decision-making processes in critical single cells that ultimately lead to health and disease outcomes. v Lay Summary A major challenge in biotechnology is the ability to extract and sequence RNA, an important class of molecules, from target single cells in order to determine the biological status of the cell. This dissertation describes a new technology, called See-N-Seq, for extracting and sequencing RNA from specific single cells during transient events. This capability is enabled by embedding cell samples in polymers where the properties of the polymers can be modified using an incident laser beam. By steering the laser beam to certain regions of the polymer, it is possible to allow the extraction of RNA from a specific target cell. See-N-Seq was used to sequence RNA from cells that form an immunological synapse, which are junctions formed between two immune cells that can result in the initiation of different immune responses. RNA sequence data collected at 0, 4, and 24 hours following synapse formation show immune cells developing distinct responses at the 24-hour time point. With this capability, it is possible to obtain molecular information from cells engaged in specific events in order to uncover decision-making processes that ultimately lead to health or disease. vi Preface Research presented in this thesis is the original work of the author. The work was supported by grants from Canadian Institute of Health Research, Natural Science and Engineering Research Council of Canada, Prostate Cancer Canada, as well as a Four Year Fellowship from the University of British Columbia for the author. Chapter 4 describes See-N-Seq technology. Dr. Hongshen Ma., JeongHyun Lee, and Simon P. Duffy conceived the idea. JeongHyun Lee, Emily S. Park, Jane R. Choi, Kerryn Matthews, and Xiaoyan Deng performed the experimental work. JeongHyun Lee and Alice V. Lam developed the laser micropatterning system. JeongHyun Lee and Simon P. Duffy analyzed the sequence data. Chapter 4.2 describes a cell capture imaging reagent. Dr. Hongshen Ma and JeongHyun Lee are listed as inventors on a patent application for this reagent, which has been licensed to MilliporeSigma and is being sold commercially as catalogue number LMR001. A version of chapter 4.2 has been published in JeongHyun Lee, Aline T. S., Emily S. Park., Kerryn M., Simon P. D., and Hongshen Ma (2020) “Lossless immunocytochemistry using photo-polymerized hydrogel thin-films”, Analyst. I conducted all the experiments and wrote the manuscript together with Dr. S. Duffy, Dr. K. Matthews and Dr. Hongshen Ma. Chapter 5 describes application of See-N-Seq to analyze transient cellular events. Dr. Hongshen Ma., JeongHyun Lee, and Simon P. Duffy conceived the idea. JeongHyun Lee, Emily S. Park, Jane R. Choi, and Kerryn Matthews performed the experimental work. JeongHyun Lee and Simon P. Duffy analyzed the sequence data. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Abbreviations ................................................................................................................. xxi Acknowledgements .................................................................................................................. xxvi Dedication ................................................................................................................................ xxvii Chapter 1: Introduction ................................................................................................................1 1.1 Significance and Motivation ........................................................................................... 1 1.2 Research Objective ......................................................................................................... 3 1.3 Organization of the Thesis .............................................................................................. 3 Chapter 2: Background .................................................................................................................5 2.1 Messenger RNA separation by Poly-A tail capture ........................................................ 5 2.2 3’ end sequencing ........................................................................................................... 6 2.3 Single cell whole transcriptome amplification ............................................................... 7 2.4 Template switching ......................................................................................................... 8 2.5 Barcode for cell and mRNA ........................................................................................... 9 Chapter 3: Literature Review .....................................................................................................11 3.1 Non-selective Single Cell Sequencing Methods ........................................................... 11 viii 3.1.1 Encapsulation in droplet ........................................................................................... 12 3.1.2 Encapsulation in nanowell ........................................................................................ 13 3.1.3 Microfluidic-trap based approach ............................................................................. 15 3.1.4 Combinatorial labeling.............................................................................................. 16 3.2 Selective Single Cell Sequencing Methods .................................................................. 17 3.2.1 Micropipette aspiration ............................................................................................. 17 3.2.2 FACS......................................................................................................................... 18 3.2.3 Laser Capture Microdissection ................................................................................. 20 3.2.4 CLaP – Live single-cell laser tag .............................................................................. 22 3.2.5 Transcriptome in vivo analysis (TIVA) .................................................................... 23 3.2.6 RNA in situ hybridization ......................................................................................... 23 3.2.7 Fluorescent in situ sequencing (FISSeq) ................................................................... 25 Chapter 4: Development of See-N-Seq .......................................................................................26 4.1 Overview ....................................................................................................................... 26 4.1.1 Principle of See-N-Seq ............................................................................................. 26 4.2 Development of cell encapsulation using permeable hydrogel .................................... 29 4.2.1 Overview ................................................................................................................... 29 4.2.2 Results ....................................................................................................................... 30 4.2.2.1 Density Testing ................................................................................................. 31 4.2.2.2 Mechanical strength testing .............................................................................. 31 4.2.2.3 Porosity testing.................................................................................................. 31 4.2.2.4 Thickness optimization ..................................................................................... 32 4.2.2.5 ICC of cell sample ............................................................................................ 33 ix 4.2.3 Experimental ............................................................................................................. 37 4.2.3.1 Chemicals and Hydrogel Preparation ............................................................... 37 4.2.3.2 Cell Culture ....................................................................................................... 38 4.2.3.3 Cell Encapsulation ............................................................................................ 38 4.2.3.4 Immunocytochemistry ...................................................................................... 38 4.2.3.5 Cell Counting and Statistical Analysis ............................................................. 39 4.2.4 Summary ................................................................................................................... 39 4.3 Micropatterning hydrogel permeability ........................................................................ 40 4.3.1 Overview ................................................................................................................... 40 4.3.2 Results ....................................................................................................................... 41 4.3.2.1 Laser system setup ............................................................................................ 41 4.3.2.2 Spatial resolution of Micropatterning ............................................................... 42 4.3.2.3 Improving spatial resolution by multistep patterning ....................................... 43 4.3.2.4 Validation of hydrogel permeability ................................................................. 45 4.3.3 Experimental ............................................................................................................. 46 4.3.3.1 Hydrogel preparation ........................................................................................ 46 4.3.3.2 Laser system setup ............................................................................................ 46 4.3.3.3 Cell culture and preparation .............................................................................. 46 4.3.3.4 Prepare for micropatterning .............................................................................. 47 4.3.3.5 Patterning resolution test................................................................................... 47 4.3.3.6 Diffusion test ..................................................................................................... 48 4.3.4 Summary ................................................................................................................... 48 4.4 Selective single cell RNA extraction ............................................................................ 49 x 4.4.1 Overview ................................................................................................................... 49 4.4.2 Results ....................................................................................................................... 49 4.4.2.1 Validation of selective single cell lysis ............................................................. 49 4.4.2.2 Validation of single cell RNA extraction by target gene amplification ............ 50 4.4.2.3 Validation of single cell RNA extraction by WTA .......................................... 51 4.4.3 Experimental ............................................................................................................. 52 4.4.3.1 Cell culture and preparation .............................................................................. 52 4.4.3.2 Cell encapsulation and target cell isolation by laser micropatterning .............. 52 4.4.3.3 RNA extraction, reverse transcription (RT) and qPCR .................................... 53 4.4.4 Summary ................................................................................................................... 55 4.5 Molecular workflow for sequencing library preparation .............................................. 56 4.5.1 Overview ................................................................................................................... 56 4.5.2 Molecular workflow .................................................................................................. 56 4.5.2.1 Process: from mRNA to sequencing library ..................................................... 56 4.5.2.2 Design of oligos ................................................................................................ 58 4.5.3 Experimental ............................................................................................................. 59 4.5.3.1 Primer sequences .............................................................................................. 59 4.5.4 Summary ................................................................................................................... 60 4.6 Single cell sequencing of Raji and Jurkat cells ............................................................. 61 4.6.1 Overview ................................................................................................................... 61 4.6.2 Results ....................................................................................................................... 61 4.6.3 Experimental ............................................................................................................. 61 4.6.3.1 Hydrogel preparation ........................................................................................ 61 xi 4.6.3.2 Cell preparation ................................................................................................. 62 4.6.3.3 Cell encapsulation and target cell isolation ...................................................... 63 4.6.3.4 RNA extraction and WTA ................................................................................ 63 4.6.3.5 Amplicon purification ....................................................................................... 63 4.6.3.6 Library preparation ........................................................................................... 63 4.6.3.7 Sequencing data process pipeline ..................................................................... 64 4.7 Conclusion .................................................................................................................... 64 Chapter 5: The Immunological Synapse ...................................................................................66 5.1 Introduction ................................................................................................................... 66 5.2 Sequencing of immunological synapse ......................................................................... 68 5.2.1 Selection of immunological synapse ........................................................................ 68 5.2.2 Read depth of sequencing ......................................................................................... 73 5.2.3 Distribution of UMI .................................................................................................. 74 5.2.4 Correlation analysis – Scatter plot ............................................................................ 75 5.2.5 Correlation between bulk and single cells – Scatter plot .......................................... 77 5.3 Differential Gene Expression analysis .......................................................................... 79 5.3.1 Jurkat to Raji ............................................................................................................. 79 5.3.2 Jurkat and Raji to non-synapse pair .......................................................................... 80 5.3.3 0 hour post-synapse................................................................................................... 81 5.3.4 4 hour post-synapse................................................................................................... 82 5.3.5 24 hour post-synapse................................................................................................. 83 5.3.6 Transcriptome divergence ......................................................................................... 85 5.4 Experimental ................................................................................................................. 86 xii 5.5 Conclusion .................................................................................................................... 89 Chapter 6: Conclusions ...............................................................................................................90 6.1 Summary of results ....................................................................................................... 90 6.2 Future work ................................................................................................................... 92 Bibliography .................................................................................................................................94 Appendices ..................................................................................................................................105 Appendix A List of genes ....................................................................................................... 105 xiii List of Tables Table 4.1 Sequence of i5 indices. ................................................................................................. 60 Table 5.1 List of samples and its abbreviations. ........................................................................... 73 xiv List of Figures Figure 2.1 Work flow of 3’ end sequencing. .................................................................................. 6 Figure 2.2 Design of DNA barcoded beads and DNA barcode for tagging mRNA. ...................... 9 Figure 3.1 Encapsulate single cell in droplet. ............................................................................... 12 Figure 3.2 Encapsulating single cell using nanowells. ................................................................. 13 Figure 3.3 Capturing single cell by microfluidic-trap. Red arrow indicates single cell captured in chamber. ........................................................................................................................................ 15 Figure 3.4 Fluorescent activated cell sorting mechanism. ............................................................ 18 Figure 3.5 Laser capture microdissection systems. ...................................................................... 20 Figure 3.6 Live single cell laser tagging process. ......................................................................... 22 Figure 4.1 Selective single cell RNA sequencing via micropatterning of hydrogel permeability. (A) The cell sample and the pre-polymer for the permeable hydrogel are added wells in a glass-bottom imaging well-plate. The well-plate is centrifuged to align cells to the glass surface. (B) The cells are encapsulated in the permeable hydrogel thin-film formed by exposure to 375 nm UV. (C) Encapsulated cells are imaged by microscopy to identify a target cell in each well. (D) The pre-polymer for the impermeable hydrogel is added and allowed to penetrate the permeable hydrogel. The impermeable hydrogel is micropatterned inside the permeable hydrogel using a scanning 405 nm laser. (E) (Top view) All cells except for the target cell are encapsulated in the impermeable hydrogel. The exposed cell is washed and then lysed. (F) RNA from the target cell is captured by oligo-dT beads. (G) The oligo-dT beads from each well is transferred to a test tube. The beads are washed, elute mRNA, UMI-barcoded, and reverse transcribed with a template switching oligo. (H) The cDNA library is amplified by long distance PCR (LD-PCR). xv (I) Library preparation process after eluted mRNA is annealed to UMI-primer by poly T capture: 1. PCR handle sequence is added at 5’ end of mRNA by template switching during the reverse transcription. 2. cDNA is amplified using PCR handle at both 5’ and 3’end by LD-PCR. 3. LD-PCR amplicons are tagmented. 4. Illumina sequencing adapter with index is tagged by PCR. ... 27 Figure 4.2 Schematic of (A) cells encapsulated in photo-polymerized PEG-DA hydrogel. (B) Without porogen, PEG-DA hydrogel is impermeable to immunoglobulins. (C) With porogen, PEG-DA hydrogel is permeable to immunoglobulins .................................................................. 30 Figure 4.3 The general approach for lossless immunocytochemistry using hydrogel-encapsulated cells. (A) The PEG-DA pre-polymer solution and cell suspension is added to a standard (unmodified) glass bottom imaging well-plate. The plate is centrifuged to position the cells at the bottom of the plate and the plate is exposed to UV light for 5 s. (B) The supernatant, along with uncured prepolymer solution, is removed from the well by pipetting. (C) Immunostaining steps for fixation, permeabilization, immunostaining, as well as multiple washing steps are performed without additional centrifugation steps. (D) Image acquisition can be performed directly on the imaging plate. ................................................................................................................................ 33 Figure 4.4 Comparison of bright-field microscopy image of cells encapsulated in PEG-DA hydrogels (A) before and (B) after photo-polymerization. The polymerized hydrogel shows a slight opacity after photo-polymerization, but there is no significant change in microscopy image quality. .......................................................................................................................................... 34 Figure 4.5 Micrographs of hydrogel encapsulated cells stained with fluorescent antibodies. (A) 40X image from a well in a 384 well microtiter plate where ~1,000 cells are encapsulated in hydrogel and subsequently stained with DAPI (blue), EpCam-Alexafluor-488 (green), Pan-Keratin-Alexafluor-647 (red). (B-E) Close-ups of merged and separated channels. ................... 35 xvi Figure 4.6 Cell retention following immunostaining of cells using standard ICC, ICC following Cytospin, and hydrogel-encapsulation. Each experiment was performed in triplicate and cells were independently enumerated by two reviewers. Error bars rep resent the standard deviation (n=6). ............................................................................................................................................. 36 Figure 4.7 Design of microscopic laser scanning system. ............................................................ 41 Figure 4.8 Resolution in photopatterning circular voids using 10X and 20X microscope objectives. (A-B) Micrographs of circular voids formed by photopatterning impermeable hydrogel using a 10x objective. Effective diameter of isolated area is 100 µm and 50 µm. (C-D) Micrographs of circular voids formed by photopatterning impermeable hydrogel photopolymerized using a 20x objective. Effective isolated diameter corresponded to 50 µm and 25 µm. Note the voids are patterned by raster scanning. Scale bar: 25 µm. ................................. 42 Figure 4.9 Improvement of spatial resolution and cell isolation. (A) Micrographs of circular voids formed by multistep photopatterning impermeable hydrogel photopolymerized using a 10x objective. Effective isolated diameter corresponded to 25 µm. (B) Multistep photopatterning using a 20x objective. Effective isolated diameter corresponded to 12 µm. (C-E) Multistep micropatterning of impermeable hydrogel to progressively encapsulate all cells except one target cell in impermeable hydrogel. Scale bar: 50 µm. ......................................................................... 44 Figure 4.10 Micropatterning hydrogel permeability (A) Bright-field and fluorescence micrographs of a 50 µm circle of permeable hydrogel in a region of impermeable hydrogel. (B) Diffusion of FITC-Dextran (Mw 10kDa) into permeable hydrogel. Scale bar: 50 µm. ............... 45 Figure 4.11 Selective cell lysis. (A-C) Selective single cell lysis using UM-UC13-mCherry (mCh) and UM-UC13-EGFP (EGFP) cells shown using overlaid bright-field and fluorescence micrographs. (A) mCh and EGFP cells encapsulated in permeable hydrogel. (B) All non-target xvii cells are encapsulated in micropatterned impermeable hydrogel, while exposing a single target EGFP cell. (C) After addition of LiDS lysis buffer, only the target EGFP cell was lysed. Fluorescence of non-target cells is reduced due to dehydration. .................................................. 49 Figure 4.12 mRNA extraction from single cell. qPCR for RNA selectively extracted mixed mCh and EGFP cell samples. Samples include no-template control (NT), bulk mCh, bulk EGFP, single cell mCh, singe cell EGFP, as well as mCh and EGFP cell pair. Vertical black arrows indicate target cDNA undetected. Ct values are averaged from N=5. .......................................... 50 Figure 4.13 mRNA extraction from single cell. qPCR for RNA selectively extracted mixed Jurkat and Raji cell samples. Samples include no-template control (NT), bulk Jurkat (Jb), bulk Raji (Rb), single cell Jurkat (Jsc), singe cell Raji (Rsc). Vertical black arrows indicate target cDNA undetected. Ct values are averaged from N=4. Error bar indicates range. ........................ 51 Figure 4.14 Detailed molecular workflow for sequencing library preparation. (A) The extracted mRNAs are captured using oligo-dT beads. (B) The mRNAs are then eluted from the beads and annealed to oligonucleotides consisting of PCR handle, unique molecular index (UMI), and oligo-dT. (C) Reverse transcription is performed with a template switching oligo, which provides a PCR handle. (D-E) The reverse transcribed cDNA is then amplified by long distance PCR (LD-PCR). (F-G) The amplified cDNA is tagmented using the Illumina Nextera DNA flex kit. (H) After tagmentation with read sequence insertion, the sequencing adapters P7 and P5 are attached by PCR. (I) To specifically sequence the UMI on READ1, a custom primer is used, which contains P5, Illumina’s i5 index and PCR handle. The prepared library sequenced using Illumina Next-seq 550. ................................................................................................................................ 57 xviii Figure 4.15 Comparison between Jurkat and Raji cell. Heatmap illustrates Top 100 genes of highly expressed in Jurkat bulk (Jb) and Raji bulk (Rb) samples with Jurkat single cell samples (Jsc) and Raji single cell samples (Rsc). ....................................................................................... 61 Figure 5.1 Schematic of immunological synapse. The schematic shows interaction between Jurkat cell and Raji cell. Jurkat cells are a model for helper T-cells that express CD4 and the T-cell receptor (TCR) on the cell surface. Raji cells are a model for B-cells that present antigens via major histocompatibility complex II (MHC-II). The staphylococcus aureus enterotoxin E (SEE) superantigen forms an immune synapse between Jurkat and Raji cells without matching the Jurkat TCR to antigens presented by the Raji MHC-II. ................................................................ 67 Figure 5.2 Fluorescent micrograph of immune synapse. (A) 3-channel merged fluorescent micrograph of the immunological synapse formed between Jurkat cell and SEE treated-Raji cells. (B) Jurkat cell, CY3 channel (orange). (C) The interface of synapse has linear localization of TCR, which stained by anti-CD3. (D) Raji cells, in EGFP channel (green). Scale bar: 10 µm........................................................................................................................................................ 68 Figure 5.3 Selection of immune synapse. (A) The image of cell pair that formed immune synapse. (B) Selection of this cell pair via micropatterning hydrogel permeability. (C) The images of non-synapse pair. This cell pair is physically separated. (D) Selection of this cell pair from different well. Scale bar: 50 µm. .......................................................................................... 69 Figure 5.4 The number of mapped UMI counts per each sample. The bulk samples show higher counts than single cell samples. .................................................................................................... 73 Figure 5.5 Nucleotide distribution in sequenced UMIs. ............................................................... 74 Figure 5.6 Scatter and correlation between transcriptomes of bulk cell samples and between single cells. The Log2-CPM of each sample was plotted in matrix of scatter plot. The scatter plot xix in lower-left matrix position shows relationship between compared samples. Each dot represents one gene and each axis indicates Log2-CPM values, while number in the reflected upper-right matrix position indicates Pearson correlation coefficient for compared samples. Samples are grouped as follows: (A) Jurkat bulk (Jb), (B) Raji bulk (Rb), (C) Single Jurkat cells (Jsc), (D) Single Raji cells (Rb), (E) Bulk mixtures of Raji and Jurkat cells containing immune synapse at 0 hour (S0b), (F) Bulk mixtures of Raji and Jurkat cells containing immune synapse at 0 hour (S4b), (G) Bulk mixtures of Raji and Jurkat cells containing immune synapse at 0 hour (S24b), (H) Single Raji-Jurkat synapses at 0 hour (S0sc), (I) Single Raji-Jurkat synapses at 4 hours (S4sc), and (J) Single Raji-Jurkat synapses at 24 hour (S24sc). ................................................... 76 Figure 5.7 Scatter and correlation between transcriptomes of non-synapsing bulk cells and non-synapsing cell pairs. Non-synapsing bulk cells are abbreviated as NSb, while non-synapsing cell pairs are abbreviated as NS0sc. The scatter plots in lower-left matrix position shows relationship between compared samples. Each dot represents one gene and each axis indicates Log2-CPM values, while number in the reflected upper-right matrix position indicates Pearson correlation coefficient for compared samples. ................................................................................................ 77 Figure 5.8 DGE analysis between Jurkat and Raji Cells. The volcano plot shows DGE analysis between sample group of (A) Jurkat bulk and Raji bulk, (B) Single Jurkat and single Raji. X-axis indicates log2-fold change, Y-axis indicates -log10 P-value. Top 5 genes that has lowest P-value highlighted as a gene name. (C) Mean-Difference (MD) plots of DGE analysis between single Jurkat and single Raji cell. The X-axis is average log-CPM (counts per million) and the Y-axis is log-fold-change. The red highlights indicate upregulation while the blue highlights indicate downregulation in comparison. The highlights are applied to adj P < 0.05. ................................ 79 xx Figure 5.9 MD plots of DGE analysis between conjugation of single Jurkat, single Raji cell and non-synapse at 0 hour (NS0sc). .................................................................................................... 80 Figure 5.10 MD plots of DGE analysis between synapse at 0 hour (S0sc) and non-synapse at 0 hour (NS0sc). ................................................................................................................................ 81 Figure 5.11 DGE analysis at 4 hour post-synapse. Volcano plots shows (A) bulk sample comparison at 4 hour post-synapse (S4b) to 0 hour post-synapse (S0b). (B) single cell sample comparison at 4 hour post-synapse (S4sc) to 0 hour post-synapse (S0sc). (C) MD plot shows DGE analysis between S4sc and S0sc. (D) MD plot of DGE analysis between subgroup of 4 hour post-synapse (S4sc1,3,6,7 and S4sc2,4,5). .......................................................................................... 82 Figure 5.12 DGE analysis at 24 hour post-synapse. Volcano plots shows (A) bulk sample comparison at 24 hour post-synapse (S24b) to 0 hour post-synapse (S0b). (B) single cell sample comparison at 24 hour post-synapse (S24sc) to 0 hour post-synapse (S0sc). (C) MD plot shows DGE analysis between subgroup of S241,2,5 (S24sc1, S24sc2, S24sc5) and S0sc. (D) MD plot of DGE analysis between subgroup of S43,4 (S24sc3, S24sc4) and S0sc. ......................................... 84 Figure 5.13 The heatmap illustrates log-CPM of marker genes for T-cell activation, T helper type 1 (Th1) cells, and T helper type 2 (Th2) cells. Red indicates genes more expressed while blue indicates genes less expressed. The highlight box indicates high expression samples for marker genes. ............................................................................................................................................ 85 xxi List of Abbreviations BSA Bovine Serum Albumin cDNA Complementary Deoxyribonucleic Acid CK Cytokeratin CLaP Cell Labelling via Photobleaching EGFP Enhanced Green Fluorescent Protein EpCAM Epithelial Cellular Adhesion Molecule FACS Fluorescene-activated Cell Sorting FISH Fluorescence in-situ Hybridization FISSEQ Fluorescent in situ Sequencing ICC Immunocytochemistry IFC Integrated Fludic Circuits IR Infrared IVT in vitro Transcription Jb1 Jurkat cell Bulk replicate #1 Jb2 Jurkat cell Bulk replicate #2 Jb3 Jurkat cell Bulk replicate #3 Jsc1 Jurkat Single cell replicate #1 Jsc2 Jurkat Single cell replicate #2 Jsc3 Jurkat Single cell replicate #3 LCM Laser Capture Microdissection xxii LD-PCR Long Distance Polymerase Chain Reaction LED Light Emitting Diode MDA Multiple Displacement Amplification MHC Major Histocompatibility Complex MMLV Moloney Murine Leukemia Virus mRNA Messenger Ribonucleic Acid NGS Next Generation Sequencing NS0b1 Cell pair without synapse forming, bulk cells, replicate #1 NS0b2 Cell pair without synapse forming, bulk cells, replicate #2 NS0sc1 Single cell pair without synapse forming, replicate #1 NS0sc2 Single cell pair without synapse forming, replicate #2 NS0sc3 Single cell pair without synapse forming, replicate #3 NS0sc4 Single cell pair without synapse forming, replicate #4 NS0sc5 Single cell pair without synapse forming, replicate #5 NS24sc1 Single cell pair without synapse forming, after 24 hours of incubation, replicate #1 NS24sc2 Single cell pair without synapse forming, after 24 hours of incubation, replicate #2 NS24sc3 Single cell pair without synapse forming, after 24 hours of incubation, replicate #3 NS24sc4 Single cell pair without synapse forming, after 24 hours of incubation, replicate #4 NS24sc5 Single cell pair without synapse forming, after 24 hours of incubation, replicate #5 NS24sc6 Single cell pair without synapse forming, after 24 hours of incubation, replicate #6 NS24sc7 Single cell pair without synapse forming, after 24 hours of incubation, replicate #7 NS4sc1 Single cell pair without synapse forming, after 4 hours of incubation, replicate #1 xxiii NS4sc2 Single cell pair without synapse forming, after 4 hours of incubation, replicate #2 NS4sc3 Single cell pair without synapse forming, after 4 hours of incubation, replicate #3 NS4sc4 Single cell pair without synapse forming, after 4 hours of incubation, replicate #4 PBS Phosphate Buffered Saline PEG-DA Polyethylene Glycol Diacrylate qPCR Quantitative Polymerase Chain Reaction Rb1 Raji cell Bulk replicate #1 Rb2 Raji cell Bulk replicate #2 Rb3 Raji cell Bulk replicate #3 rRNA Ribosomal Ribonucleic Acid Rsc1 Raji Single cell replicate #1 Rsc2 Raji Single cell replicate #2 Rsc3 Raji Single cell replicate #3 RT Reverse Trasncription S0b1 Immeadate after Synapse formed, bulk cells, replicate #1 S0b2 Immeadate after Synapse formed, bulk cells, replicate #2 S0b3 Immeadate after Synapse formed, bulk cells, replicate #3 S0sc1 Immeadate after Synapse formed, singel cell, replicate #1 S0sc2 Immeadate after Synapse formed, singel cell, replicate #2 S0sc3 Immeadate after Synapse formed, singel cell, replicate #3 S0sc4 Immeadate after Synapse formed, singel cell, replicate #4 S0sc5 Immeadate after Synapse formed, singel cell, replicate #5 xxiv S0sc6 Immeadate after Synapse formed, singel cell, replicate #6 S24b1 After 24 hours of Synapse formed, bulk cells, replicate #1 S24b2 After 24 hours of Synapse formed, bulk cells, replicate #2 S24sc1 After 24 hours of Synapse formed, single cell, replicate #1 S24sc2 After 24 hours of Synapse formed, single cell, replicate #2 S24sc3 After 24 hours of Synapse formed, single cell, replicate #3 S24sc4 After 24 hours of Synapse formed, single cell, replicate #4 S24sc5 After 24 hours of Synapse formed, single cell, replicate #5 S4b1 After 4 hours of Synapse formed, bulk cells, replicate #1 S4b2 After 4 hours of Synapse formed, bulk cells, replicate #2 S4b3 After 4 hours of Synapse formed, bulk cells, replicate #3 S4sc1 After 4 hours of Synapse formed, single cell, replicate #1 S4sc2 After 4 hours of Synapse formed, single cell, replicate #2 S4sc3 After 4 hours of Synapse formed, single cell, replicate #3 S4sc4 After 4 hours of Synapse formed, single cell, replicate #4 S4sc5 After 4 hours of Synapse formed, single cell, replicate #5 S4sc6 After 4 hours of Synapse formed, single cell, replicate #6 S4sc7 After 4 hours of Synapse formed, single cell, replicate #7 SEE Staphylococcus Aureus Enterotoxin E TCR T-cell Receptor TIVA Transcriptome in vivo Analysis TSO Template Switching Oligo xxv UMI Unique Molecular Index UV Ultraviolet WGA Whole Genome Amplification WTA Whole Transcriptome Amplification xxvi Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Hongshen Ma for his patient guidance, encouragement and advice he has provided throughout my PhD study. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my PhD study. I also would like to thank all of the members in the Multi-Scale Design Laboratory for their support. Many thanks to Dr. Kerryn Mattews, Dr. Emily Park, Dr. Quan Guo, Aline T Santoso, Jinpyo Jeon, Xiaoyan Deng, Sameual Berryman, Alec Xu, Joyce Teodoro, Eric Lamoureux, Jane Choi and Alice Lam for the stimulating discussions and for all the fun we have had together. Special thanks go to Dr. Simon Duffy for his constructive suggestions he made to this thesis and many other manuscripts we worked together. Special thanks are owed to my family: my wife, my parents and my sister for their love and support during my Ph.D. study abroad. xxvii Dedication This dissertation work is dedicated to my wife, Hae Jung Kang, who has been a constant source of support and encouragement during the challenges of graduate school and life. I am truly thankful for having you in my life. This work also dedicated to my parents, Jungha Lee and Hyunsug kim, and my parents-in-law, who have always loved me and supported me unconditionally. 1 Chapter 1: Introduction 1.1 Significance and Motivation Cells are encapsulated decision-making units that measure external stimuli, process incoming information, and develop appropriate responses. Decisions made in single cells are then propagated through the organizational hierarchy to tissues and organs, which ultimately confer health or disease to the organism. A key window into cellular decision-making are the transcriptional programs enacted after specific events, such as receptor-ligand interactions1,2, cell to cell contact3,4, and micro-environmental modifications5,6. Recent advances in single cell RNA sequencing (scRNA-seq) have revealed remarkable transcriptional heterogeneity between single cells that derive from differences in cell state7–9, function10, fate11,12, and spatial positioning13. However, current methods have been unable to determine the transcriptional profiles enacted by specific cells after specific events. Single-cell RNA sequencing has been valuable for assessing cellular heterogeneity in tissues and for identifying previously unknown cell types14–16. Single-cell technologies have also been used to define subpopulations within a known cell type by searching for differential gene expression patterns within the cell population of interest14,17, as well as effectively isolate signal from subpopulations, which would be hidden in the output from bulk cell population RNA sequencing18–21. Moreover, the technology can be used to infer potentially useful markers, such as cell surface proteins, for cell types with no known markers. Because single-cell sequencing analysis is driven by clustering of cells based on their differentially expressed genes, the genes that drive the clustering can be examined as possible unique markers for the cell population of 2 interest14,22. Lastly, single-cell sequencing can be employed in studies of cell lineage and the regulation of differentiation23. A key challenge in single cell RNA sequencing is the ability to target specific cells based on specific phenotypic information or observed transient cellular events. The majority of current single cell RNA sequencing approaches rely on isolating single cells into separated compartments in order to perform indiscriminate RNA extraction from thousands of single cells in parallel. These strategies include encapsulation into water-in-oil droplets, deposition into microtiter plates by flow cytometry, or deposition into nanowells. This indiscriminate approach cannot easily identify rare cells or cells from specific events. Furthermore, despite efforts to couple post hoc analysis and spatial inference with high throughput sequencing, the nature of the originating cell is difficult to determine by these methods. These methods are reviewed in detail in chapter 3.1. Existing selective single cell RNA sequencing approaches include micropipette aspiration, fluorescent activated cell sorting, and laser capture microdissection. These approaches, however, are very laborious, time-consuming, and have limited throughput and scalability. Therefore, it is difficult to use these methods to isolate single cells from transient cellular events. Recently, few emerging methods have been developed to specifically isolate and sequence single cells including cell labeling via photobleaching (CLaP), transcriptome in vivo analysis (TIVA), RNA in situ hybridization (RNA-ish), and fluorescent in situ sequencing (FISSeq). CLaP and TIVA rely on photoactivation for cell selection and validation, therefore it is limited by imaging capacity and is difficult to collect sufficient data for cellular events. The RNA-ish is limited to 3 number of target transcripts and FISSeq provides very few mRNA transcripts reads which requires multiple single cell data for profiling transcriptome. These methods are reviewed in detail in chapter 3.2. 1.2 Research Objective This dissertation describes the development of See-N-Seq, a new method to selectively extract RNA from single cells identified by imaging in order to perform transcriptome profiling after specific events. By embedding cell samples in a permeable hydrogel in a microwell, and photo-patterning a second impermeable hydrogel inside the permeable hydrogel matrix, enables selective single cell RNA extraction. The development of See-N-Seq workflow includes development and characterization of cell encapsulation in permeable hydrogel thin-film, development and characterization of micropatterning of impermeable hydrogel, as well as single cell RNA extraction and sequencing validation. To demonstrate the utility of See-N-Seq, we used this technology to perform transcriptome sequencing from an in vitro model of an immunological synapse between a T-cell and an antigen presenting cell. By sequencing cell pair at 0, 4, 24 hour time points post-synapse formed, we found that the adoption of divergent transcriptomic profiles of T-cell. By elucidating the transcriptional programs that coordinate key cellular events, See-N-Seq can provide an important avenue to understand these processes in health and disease. 1.3 Organization of the Thesis Chapter 2 reviews current knowledge on RNA sequencing, including mRNA sequencing, 3’ end sequencing, techniques for unbiased whole transcriptome amplification, template switching, as 4 well as cell barcoding. Chapter 3 reviews recent work in single cell isolation and RNA sequencing. Chapter 4 describes the development and validation of See-N-Seq: the novel method for single cell RNA extraction and sequencing via micropatterning of hydrogel permeability, including single cell identification, isolation, RNA extraction that confirmed by qPCR as well as sequencing of single cell. Chapter 5 describes the application of See-N-Seq to transcriptome sequencing of rare cellular events. Specifically, an in vitro model of immunological synapse between a T-cell and an antigen presenting cell. Finally, Chapter 6 presents the conclusions and potential future directions arising from this dissertation. 5 Chapter 2: Background This chapter reviews the current state of knowledge on RNA sequencing. Section 2.1 reviews mRNA transcript sequencing by poly-A capture. Section 2.2 reviews the definition and necessity of 3’ end sequencing. Section 2.3 reviews techniques for whole transcriptome amplification of single cells. Section 2.4 reviews template switching mechanism for unbiased whole transcriptome amplification. Finally, section 2.5 reviews tagging of cells and mRNA molecules. 2.1 Messenger RNA separation by Poly-A tail capture RNA sequencing can measure the expression genes to provide insight into transcription, alternative splicing24, unannotated exons, and novel transcripts25. Expressing a gene means manufacturing its corresponding protein. The first step of this manufacturing process is that information from DNA is transferred to messenger RNA (mRNA) by a process called transcription. Consequently, to efficiently detect transcripts, mRNA must be sequenced among the total RNA. However, ribosomal RNA (rRNA) is the most abundant component, comprising the majority (>80% to 90%) of the molecules in a total RNA sample26. Therefore, mRNA must be enriched while removing the rRNA from the total RNA sample. The common way to enrich mRNA is by using its poly-A tail. Immediately after mRNA transcription, a cap is added to the 5’ end and a poly-A tail (100-150 nucleotides) is added to the 3’ end to improve resistance to degradation. By employing poly-T oligomers, which is complementary of A, the mRNA can be depleted from total RNA. This process is typically performed using poly-T conjugated magnetic beads. Poly-A selection significantly improves the transcriptomic data compared to total RNA sequencing by avoiding non-coding RNA. 6 2.2 3’ end sequencing Figure 2.1 Work flow of 3’ end sequencing. [Reproduced from Ma et. al.27, under CC-BY 4.0] Relative gene expression analysis is based on counting sequencing read alignments to annotated features of a reference genome. When this sequencing read is from the whole transcriptome, the longer transcripts are represented by more reads than the shorter transcripts, because cDNA is randomly fragmented. As the longer transcripts always have more sheared fragments, the more reads are mapped and assigned to each gene27,28. Consequently, in differential expression analysis, the differentially expressed genes are more likely to be enriched for longer than shorter transcripts, as the statistical power is higher for longer transcripts due to the larger counts. To 7 eliminate the biased estimation of gene expression owing to the length of transcripts, generate the sequencing reads only from the 3’ end of the transcripts had been introduced29. This approach is commonly known as 3’ end sequencing. Specifically, one side of pair-ended reads is limited to the 3’ region of the mRNA transcript (Figure 2.1). The count for differential gene expression analysis is then based on the same region of each gene. The main benefit of 3’ end sequencing is the depth of sequencing required to achieve the same level of detection sensitivity as full-length transcript libraries is much reduced, as well as increased statistical estimation30. 2.3 Single cell whole transcriptome amplification To perform RNA sequencing, a certain level of input RNA is required, which is significantly higher than single cell level. This limitation can be resolved by amplifying RNA from single cell before sequencing. There are four common types of single cell whole transcriptome amplification methods: random PCR, multiple displacement amplification (MDA), in vitro transcription (IVT), and template switching. The random PCR based approach is not limited in input amount, as additional PCR cycles may be used. However, owing to random priming of primer, the amplification in non-linear to the input RNA sample and gene coverage is limited to about 10%31. Hence, it is impossible to properly estimate the expression of genes32. The MDA technique also shares this same issue, as phi-29 enzyme randomly binds to synthesized cDNA fragments and synthesizes random lengths of amplicon33. Furthermore, the amplification requires a long time of incubation. In contrast, IVT can provide linear amplification with up to 400 pg of input material. However, the IVT process is very labor intensive, as it requires 3 amplification steps, which require ~5 days of hands-on wet-lab work34,35. Critically, the IVT technique is not adapted for multiplexed sequencing. To overcome non-linear or labor-intensive amplification, 8 PCR based linear amplification methods have been introduced using the template switching mechanism. 2.4 Template switching Template switching refers to the effect of switching the template for synthesizing cDNA from the original RNA to a complimentary oligos during the reverse transcription36. The use of template switching for RNA amplification was first described in 2001, and was used as the switching mechanism at the 5' end of the RNA transcript (SMART) technology (Takara Bio USA, Inc). This technique has shown the ability to generate full-length cDNA libraries from single-cell RNA samples37–39. This technique relies on the terminal nucleotidyl transferase (TdT) activity of some reverse transcriptase enzymes. Different reverse transcriptases possess varying degrees of intrinsic TdT activity40. With wild-type MMLV (Moloney Murine Leukemia Virus) reverse transcriptase, 25–90% of synthesized DNA strands contain extra nucleotides at the 3′ end. Engineered MMLV reverse transcriptases, on the other hand, tend to display reduced intrinsic TdT activity. By combining MMLV reverse transcriptase and a unique template switching oligonucleotide (TS oligo, or TSO), the MMLV reverse transcriptase adds a few additional nucleotides to the 3' end of the newly synthesized cDNA strand upon reaching the 5’ end of RNA. In SMART-seq protocol, these additional nucleotides are 3 bases of cytosine. These additional bases function as a TS oligo-anchoring site. Upon base pairing between the TS oligo and the appended C nucleotides, the reverse transcriptase switches template strands, from RNA to the TS oligo, and continues replication to the 5' end of the TS oligo. By doing so, the resulting cDNA contains the complete 5' end of the transcript. Along with tagging of the cDNA 3' end by oligo dT primers, the cDNA can be amplified by PCR. This approach makes it possible to 9 efficiently amplify the entire full-length transcript pool in a completely sequence-independent manner41. 2.5 Barcode for cell and mRNA Figure 2.2 Design of DNA barcoded beads and DNA barcode for tagging mRNA. [Reprinted from Macosko et. al.42, Copyright (2015), with permission from Elsevier] RNA-seq measures gene expression by counting aligned sequencing reads43,44. To improve counting accuracy by eliminating PCR duplicates and to distinguish pooled sequencing reads into single cells, tagging of sequencing reads with short DNA barcodes have been introduced. Most of single cell sequencing relies on this technique, where reads originating from individual cells are tagged with cell-specific barcodes. Hence, the first step in single cell sequencing analysis is separating reads by barcode to recover single-cell profiles45–48. Then, using molecular barcodes to trace single originating nucleic acid molecule through a sequencing assay. For example, in the Drop-Seq protocol, DNA barcodes along with a PCR handle for amplification are synthesized on a surface of a bead42. The first section of DNA barcode works as a cell 10 barcode, with 12 bases of randomly synthesized sequence (Figure 2.2). After the cell barcode sequence, the unique molecular index (UMI) sequence follows. One bead has one combination of unique cell barcode sequences in 12 bases length, and each bead has a different cell barcode sequence. In contrast, the UMI sequence is distinct on each oligo on the same bead, with 8 bases in length. The cell barcodes are used for separating a pooled sequencing reads into single cells, and the UMI are used to detect and quantify unique mRNA transcripts. This entire barcoded string work as an oligo-dT RT primer, which consist of the PCR handle sequence, cell barcode sequence, UMI, and oligo-dT for mRNA capturing. By employing the cell barcode and UMI, it is possible to distinguish the mRNA from different cells and unique mRNA transcripts in single cell, as well as detect rare transcripts. Furthermore, each transcript can be distinguished and quantified in high resolution with minimized statistical estimation. 11 Chapter 3: Literature Review This chapter reviews recent research in single cell RNA sequencing. Current methods for single cell preparation for transcriptome analysis are classified into two major categories: (1) non-selective and (2) selective approaches. Section 3.1 describe methods to prepare RNA sequencing library from single cells indiscriminately. Section 3.2 describes methods to prepare RNA sequencing libraries from specific single cells identified by microscopy. Several examples of each approach will be presented along with a discussion of their advantages and disadvantages. 3.1 Non-selective Single Cell Sequencing Methods Single cell sequencing requires physically partitioning cells to separated compartments. This can be achieved by encapsulating single cells in isolated containers where RNA can be captured and barcoded. The unique barcodes that associate to each single cell enables separation and discrimination of the sequencing results. Current approaches aim to associate a single cell to a single barcode with minimized cross contamination. Non-selective methods can be classified into four general strategies including (1) encapsulation in droplet, (2) encapsulation in nanowells, (3) encapsulation in microfluidic chambers, as well as (4) combinatorial labeling. The non-selective approach for single cell transcriptome analysis provides high throughput sequencing, but not the ability to select target cells. 12 3.1.1 Encapsulation in droplet Figure 3.1 Encapsulate single cell in droplet. [Reprinted from Macosko et. al.42, Copyright (2015), with permission from Elsevier] Droplet-based encapsulation techniques are currently commonly used as high-throughput, non-selective single cell preparation49. Since Mazutis et al. showed random capture and manipulation of individual cells in nano-liter water droplet in oil emulsions, there are multiple methods following this strategy to isolate single cells in droplets and perform library preparation50,42,51–53. Here, the cell containing flow and barcoded bead or poly-T RT primer containing flow are separated into droplet by oil intervals, then merged to rapidly capture mRNA and RT primer is then annealed. This can parallelize the single cell isolation and mRNA capturing simultaneously, capturing up to 100,000 cells per running. The Drop-seq protocol, by Macosko et al., reported that the parallelized analysis of 44,808 single retina cells by using barcoded beads, identified 39 transcriptionally distinct retina cell populations. In another study, Klevin et al. applied this method to embryonic stem cells, and reported the population structure and heterogeneity of differentiation after leukemia inhibitory factor withdrawal. This report utilized the same fundamental strategies as dropseq, but used a hydrogel drop to carry the RT primer instead of 13 using beads. These droplet-based single cell RNA sequencing are also commercially available from 10x Genomics. Furthermore, Perterson et al. and Stoeckius et al. reported multimodal single cell sequencing, in which they pre-stained the cells with barcoded-DNA conjugated antibodies to examine the relationship between protein expression and mRNA expression. Although cell encapsulation in droplet based systems can massively parallelize the mRNA capturing from single cells, by nature, it requires a large number of cells. Hence, it is not suitable for rare cells, and the transcriptome coverage from each single cell is significantly low due to the large number of samples per one sequence run. Furthermore, this requires a custom-made platform such as microfluidic channels for library preparation. Finally, since there is no identification step during the library preparation, the cell types need to be assigned post sequencing, during the analysis. Since minor variation in transcriptome does not show heterogeneously, this droplet approach has the potential to discover minor variation. 3.1.2 Encapsulation in nanowell Figure 3.2 Encapsulating single cell using nanowells. 14 [Reprinted by permission from Springer Nature: Gierahn et. al.54, COPYRIGHT (2017)] The nanowell based techniques use nanowells to encapsulate cells instead of droplets (Figure 3.2). Similar to the droplet-based system, a barcode string is used and are either pre-deposited inside the well or the surface of beads. This approach starts from generating nanowells in either silicon or PDMS surfaces. Then the cells are deposited into picoliter-sized wells that either contain barcodes or barcoded beads54–56. The same beads as for droplet protocols can be used to deliver RT primers into individual wells, but in most cases, no pumps or other microfluidic equipment are needed. Instead, cells are isolated into the picoliter wells by gravity. Each plate of wells will have an upper limit of cells determined by the number of wells on the plate, and Poisson statistics for the limiting dilution to avoid doublets, so that only a small fraction of the wells will end up containing a single cell. Since the cells are trapped inside the nanowells, it's possible to retrieve minimum information from the loaded cells such as number of trapped cells, type of cells, and presence of doublets. Comparison between number of cells from loading to sequence results shows that only 35% of cells from loading were able to be sequenced54. As it can only provide minimum information regarding cells to be sequenced, it is still hard to apply to rare cells as cell retrieval is not guaranteed. Furthermore, it has the same limitation as droplet-based methods, which is the low transcriptome coverage from each single cell, owing to similar reasons as droplet-based approaches. Recently, BD (Beckton Dickinson and company) launched a single cell sequencing system called Rhapsody, based on nanowells, with extracting mRNA by magnetic beads from the top of the nanowell structures, by loading cells to nanowell by gravity. It is possible to do multimodal 15 analysis with this system, which performs a simultaneous protein expression analysis similar to Cite-seq or REAP-seq, however it still shares the same issues as other well-based approaches. 3.1.3 Microfluidic-trap based approach Figure 3.3 Capturing single cell by microfluidic-trap. Red arrow indicates single cell captured in chamber. [Reproduced from Azizi et. al.57 under CC BY 4.0] The first form of passive cell capture for scRNA-seq was the microfluidic C1 system released by Fluidigm Inc. The company had expertise in producing systems for parallel RT-qPCR in single cells and adapted the technology for sequencing. The key feature of the Fluidigm technology is the design of microfluidics devices which allows the sequential delivery of very small and precise volumes into tiny reaction chambers. Cells are loaded onto this chip and are passively captured in up to 96 isolated chambers in about half an hour. Several steps of the SMART-seq protocol could be performed within the chambers of the chip, before cDNA was extracted from 16 the chip and deposited in microwell plates for the generation of a sequencing library. It is possible to sequence the whole transcriptome with this system, while with the bead based system, only the 3’ end of RNA can be sequenced. The novel splicing event or splicing junction can be investigated by whole transcriptome sequencing. However, it is impossible to separate RNA molecules due to lack of UMI with this technique. Complex single use integrated fluidic circuits (IFC) chips are fundamentally limited to 96 chambers to capture cells in, and in many cases only a fraction of the chambers successfully captures cells, depending on the cell type58,59. Some large-scale studies made use of a large number of IFC’s to create big data sets17,59. 3.1.4 Combinatorial labeling The split-pool ligation-based transcriptome sequencing (SPLiT-seq) has been recently developed by Rosenberg et. al. as a single cell RNA sequencing method that labels the cellular origin of RNA through combinatorial barcoding60. This technique is based on in situ labeling of cells without physical separation, by a multistep of pooling and dividing. This study demonstrates analysis of 156,049 single-nucleus transcriptomes from postnatal day 2 and 11 mouse brains and spinal cords by SPLiT-seq. Over 100 cell types were identified, with gene expression patterns corresponding to cellular function, regional specificity, and stage of differentiation. This technique provides a path toward comprehensive single-cell transcriptomic analysis of other similarly complex multicellular systems. However, the library preparation requires many days of work due to multiple pooling-separating step. Furthermore, this method has high rate, 50 – 70 %, of cell loss, owing to multiple cell centrifugation steps, requiring removal of carryover buffers. Finally, this method also focuses on high throughput sequencing, which share similar issues with droplet base approaches or nanowell based approaches, such as no identification step during the 17 library preparation, such that the cell types need to be assigned post sequencing as well as ignorance of minor variation in transcriptome. 3.2 Selective Single Cell Sequencing Methods Traditional methods for isolating single cells for RNA sequencing include: Error! Reference source not found. micropipette aspiration, 3.2.2 fluorescent activated cell sorting (FACS), and 3.2.3 laser capture microdissection (LCM). Emerging methods for specifically sequencing single cells include 3.2.4 Cell labelling via photobleaching (CLaP), 3.2.5 transcriptome in vivo analysis (TIVA), 3.2.6 RNA in situ hybridization (RNA-ish), and 3.2.7 FISSEQ. 3.2.1 Micropipette aspiration Micropipette aspiration is a classical cell isolation method that aspirates single cells using negative pressure while under observation using an inverted microscope61. Cell samples in a dish or well-plate are visually inspected by brightfield or fluorescent microscopy to identify the target cells. The aspirated liquid, including the cell of interest, can be transferred to a collection tube or a well-plate for further analysis. This manual process is difficult, time-consuming, and require an experienced technician. Tang et al. reported the first single cell whole transcriptome analysis by manually separating single cells62. In this study, a new technique to amplify the whole transcriptome was used. Unlike traditional microarray, the authors used PCR-based amplification, where a poly-A tail was added on the 5’ end of the first-stand cDNA. This study reported that 75% more mouse blastomere genes were detected than the conventional technique, however, the number of samples was significantly low as only 6 cells were studied. Another study performed by Picelli et al. report effective whole transcriptome amplification with 18 minimized amplification bias by using template switching effect to add PCR handles to the 5’end of cDNA63. Although it was possible to record the cell morphology and cellular information, the number of single cells analyzed for both studies were very limited and each isolation process is very time and labor consuming. 3.2.2 FACS Figure 3.4 Fluorescent activated cell sorting mechanism. [Reproduced from Hodne et. al.64, under CC BY 4.0] 19 Fluorescence activated cell sorting (FACS) identifies and separates cells labeled with fluorophore-conjugated antibodies that recognize specific surface markers on target cells 61. As the suspended labelled cells are passed through the cytometer, the fluorescence detector identifies cells based on the cell specific signals. Based on their characteristics, the droplet containing a cell of interest is sorted into appropriate collection tubes for later analysis. FACS has been widely used for the isolation of highly purified cell populations as this method is a very robust cell sorting technology with high-throughput (50,000 cells/sec). Subsequently, many studies have used FACS for single cell RNA sequencing17,20,39,41,65–67. However, it requires the sample population to consist of a minimum ~1x105 cells and contain more than 0.1% positive cells. Also, many phenotypes are impossible to identify by FACS, due to the limitation of imaging sensors. Since the sorting relies on cells in suspension, information about cell morphology and the microenvironment is lost, and cell variability in phenotype is masked by the average effect that only retrieve by post hoc analysis68. Moreover, while FACS can be used for isolating single cells, it has significant limits on detecting and separation cell pairs, which is often interesting for detecting rare cellular events. 20 3.2.3 Laser Capture Microdissection Figure 3.5 Laser capture microdissection systems. [Reproduced from Vandewoestyne et al69. under CC BY-NC-ND 3.0] Laser capture microdissection (LCM) has been widely used to isolate single cells from mostly solid tissue samples, which are typically provided fixed in formalin, embedded in paraffin, or cryo-fixed61. Similar to the micromanipulation method, a tissue section is observed through an inverted microscope to identify target cells. The target cells are marked by drawing a line around it, followed by laser cutting and extraction. While the cutting procedure using the laser is mostly the same, there are two main types to extract target cells using either infrared (IR) or ultraviolet 21 (UV) laser. The Arcturus LCM system performs ‘contact-based’ extraction of single cells using IR laser. In this system, the samples are typically stained for the identification of the target61,69,70. Once target cells are identified, the IR laser activates a transfer film on a cap, located directly above the target region. The cells of interest are then adhered to the film on a cap, and then transferred to a microcentrifuge tube for further application (Figure 3.5 left). On the other hand, the PALM Zeiss system is a ‘contact-free’ method, isolating single cells using UV laser beam61,69,70. The samples are prepared on a glass slide, layered with a thin and transparent membrane. When target cells are identified, the UV laser beam cuts around the region of interest. The desired cells are then catapulted against gravity into the cap of the collection tube using a short-defocused UV laser pulse (Figure 3.5 right). The thin membrane acts as a stabilizing backbone during lifting. The collected samples can be visually confirmed on a collection tube by moving the sample stage to focus the samples collected on the cap. Park et al. demonstrate the single cell isolation and genotyping by combining hydrogel matrix with LCM71. However, the cell loss was occurred during the isolation step, and analysis was limited to DNA, amplified by whole genome amplification using phi-29 enzyme. In other study, Nichterwitz et al. demonstrated LCM can be sensitive down to a single neuron72. By combining LCM with Smart-seq263, this study reported RNA sequencing from human and mouse tissues. While the LCM system has been extensively used in tissue samples, providing a straight-forward method for the isolation of cells from tissue, the LCM system requires specialized coated glass-slides, and is hard to apply to different cell types that not derived from tissue. 22 3.2.4 CLaP – Live single-cell laser tag Figure 3.6 Live single cell laser tagging process. [Reproduced from Binan et. al.73, under CC BY 4.0] Cell labeling via photobleaching (CLaP) is a noninvasive, laser-based labeling technique for single cells73. The method uses lasers to crosslink fluorescent biotin-tags to the plasma membrane of specific cells before isolating individual cells for sequencing. After tagging, streptavidin-conjugated fluorescent labels are bound to biotinylated cells (Figure 3.6). These steps can be repeated to tag multiple cell types with a variety of fluorescent tags. The tagged cells are subsequently isolated and processed to generate cDNA libraries for sequencing. This technique provides noninvasive, cell targeted approaches, as well as image-based cell selection. Furthermore, multicolored stains maybe used to phenotype the cells. However, diffusion of reagents through the extracellular matrix and continuous laser illumination limits the process to applying 3-dimensional environments. Furthermore, the fluorescent tagged cells are transferred for sequencing library preparation, which is then performed by the microfluidic based Fluidgm C1 system. Finally, cellular specificity may be decreased slightly in primary cell cultures as dissection debris may yield non-specific staining. 23 3.2.5 Transcriptome in vivo analysis (TIVA) TIVA (Transcriptome in vivo analysis) uses photo-activatable tags to directly tag the mRNA inside the target cell and retrieve tagged mRNA by affinity purification. The TIVA technique utilizes a functionalized tag which consists of biotin, Cy3, poly-A tail binding 2′-F RNA poly-U oligo, photocleavable linker, 2′-OMe RNA poly-A oligo, Cy5, disulfide bond and cell penetrating peptide (CPP)74. Cells are exposed to the TIVA tag, and the cytosolic environment cleaves the CPP from the tag. By laser based photoactivation of the selected cell, the tags inside the cell are excited and the photo-cleave site of the tag is cleaved. Then the rest of the tag is annealed to mRNA inside the cells. The mRNA is captured by streptavidin beads after cell lysis. This technique can effectively select cells in vivo, however, due to lack of cell barcode or unique molecular index (UMI), it’s impossible to separate more than one single cell from tissue. Also, this method depends on efficient uptake of the capture oligo and may underrepresent RNA transcripts within protected cell structures. Furthermore, owing to the nature that TIVA uses photo-activation during the tagging, and validate the tag using FRET technique at the CY3 and CY5 channel, there is significant limitation on cell identification using fluorescent microscopy. The wavelength for photo-tagging is overlapping with the excitation wavelength for fluorescent microscopy, and it is therefore impossible to visualize the cell under the CY3 and CY5 channel because of the FRET signal from the tag. 3.2.6 RNA in situ hybridization Transcriptomic profiling the single cell level could also be achieved using RNA in situ hybridization (RNA-ISH). This process involves using sequence-specific DNA or RNA probes that conjugated with fluorochromes, typically 10 – 25 nucleotides in length, followed by target 24 visualization using a chromogenic reaction. The in situ hybridization of RNA molecules has been instrumental for revealing embryo patterning and studying developmental processes75. A quantitative development of this approach came from the introduction of single-molecule RNA fluorescence in-situ hybridization (smRNA FISH) techniques76–78. In smRNA FISH, DNA oligonucleotides that are complementary to the target RNA are labelled with fluorescent dyes, which enable visualization of individual transcripts as diffraction-limited spots. Using this approach, a panel of 15 cell type-specific marker transcripts were charted in situ in the mouse intestinal crypt, which unravelled the design principles of stem cell progenitor hierarchy formation in the crypt during embryonic development77. In other studies, Waks et al. discovered splice variants by smFISH method while Markey et al. and Semrau et al. detected the fusion transcript, particularly in complex tissues and tumor sections, to study spatial heterogeneity79–81. With the emergence of single-cell transcriptomic assays, smFISH also represents a powerful means to validate individual RNA expression values from single-cell transcriptome-wide measurements, as recently shown for a variety of transcripts in mouse embryonic stem cells82. Recently, Torre et al. reported rare cell detection by transcriptome analysis through multi-round of multiplexed smRNA FISH, quantifying 26 genes that are rarely expressed in the cell-cycle state83. However, although this approach enables robust transcript quantification in morphologically intact tissues, the number of simultaneously visualized transcripts is small. These limits are due to the availability of fluorophores with non-overlapping spectra, preventing highly multiplex measurements. Furthermore, due to probe-based detection, probe-specific noise can occur. 25 3.2.7 Fluorescent in situ sequencing (FISSeq) FISSeq is an improvement of RNA-ish by adding in situ sequencing step to sequence RNA in intact cells. In this method, RNA is sequenced using SOLiD (Sequencing by oligoneucleotide ligation and detection) technique, which overcome the limitation of probe-specific semi-quantitative nature of RNA-ish. The FISSeq has a very distinctive advantage that not only retrieves the sequence of RNA, but also the location of sequenced RNA which can be found inside the cells. However, since the sequencing technique relies on SOLiD, based on ligation, the read lengths are extremely short and number of reads are very limited. The study that used FISSeq to sequence fibroblasts, reported a read length of 27 nucleotide with 156,762 reads, while typical NGS reads are 100~150 bp in pair-ended with 10~100 millions of reads84. Also, since there is no purification or isolation step for the mRNA, the major output is rRNA sequence, which is less relevant to transcriptome analysis. The report shows low as only 7% was mRNA sequence overall reads, which makes it impossible to retrieve whole transcriptome information for single cells84. 26 Chapter 4: Development of See-N-Seq 4.1 Overview This chapter describes the development and characterization of the selective single cell RNA isolation technology See-N-Seq. Section 4.1 describes the principle of the See-N-Seq to extract RNA from target single cell using a hydrogel with spatially varying permeability to biomolecules. Section 4.2 describes the development and characterization of cell capturing strategy by encapsulation in a permeable hydrogel thin-film. Section 4.3 describes the development and characterization of a process for micropatterning of hydrogel permeability. Section 4.4 describes the development and characterization of a process to extract RNA from single cells encapsulated in permeable hydrogel. Section 4.5 describes molecular workflow for library preparation for RNA sequencing. Section 4.6 presents characterization of the selective single cell RNA sequencing. Section 4.7 summarizes material presented in this chapter. 4.1.1 Principle of See-N-Seq See-N-Seq is a method to sequence single cell RNA selectively using spatially varying hydrogel permeability via micropatterning. This process involves embedding the cell sample in a permeable hydrogel in a microwell, and then photo-patterning a second impermeable hydrogel inside the permeable hydrogel. Non-target cells are encapsulated in the impermeable hydrogel, while target cells are encapsulated in the permeable hydrogel. Target cells can then be lysed to release RNA, which is then captured, barcoded, reverse transcribed, amplified, and sequenced at high-depth. 27 G G G405 nm laserPre-polymer for 2nd hydrogelEmbedded cellsImpermeable 2nd hydrogelLysed celldT beadsMicroscope ObjectiveDPre-polymer for 1st hydrogel Permeable hydrogel thin-filmCentrifugeA CImageBF375 nm UVEncapsulateSelectively EncapsulateRNA Extraction Library PreparationG HIC C CTagmentationrGrGrGC C CLD-PCRTemplate SwitchingAdapter TaggingPCR Handle UMI dT(30)poly-A mRNA TS OligoPCR Handle UMI dT(30) cDNA TS Oligo*PCR HandlePCR Handle* UMI*PCR HandleP5 i5P7i7R2R2*TransposomeSelectively LysisEC C CcDNA Figure 4.1 Selective single cell RNA sequencing via micropatterning of hydrogel permeability. (A) The cell sample and the pre-polymer for the permeable hydrogel are added wells in a glass-bottom imaging well-plate. The well-plate is centrifuged to align cells to the glass surface. (B) The cells are encapsulated in the permeable hydrogel thin-film formed by exposure to 375 nm UV. (C) Encapsulated cells are imaged by microscopy to identify a target cell in each well. (D) The pre-polymer for the impermeable hydrogel is added and allowed to penetrate the permeable hydrogel. The impermeable hydrogel is micropatterned inside the permeable hydrogel using a scanning 405 nm laser. (E) (Top view) All cells except for the target cell are encapsulated in the impermeable hydrogel. The exposed cell is washed and then lysed. (F) RNA from the target cell is captured by oligo-dT beads. (G) The oligo-dT beads from each well is transferred to a test tube. The beads are washed, elute mRNA, UMI-barcoded, and reverse transcribed with a template switching oligo. (H) The cDNA library is amplified by long distance PCR (LD-PCR). (I) Library preparation process after eluted mRNA is annealed to UMI-primer by poly T capture: 1. PCR handle sequence is added at 5’ end of mRNA by template switching during the reverse transcription. 2. cDNA is amplified using PCR handle at both 5’ and 3’end by LD-PCR. 3. LD-PCR amplicons are tagmented. 4. Illumina sequencing adapter with index is tagged by PCR. The process to selectively isolate RNA from target single cells begins by suspending the cell sample in the pre-polymer of the permeable hydrogel in a glass-bottom imaging well-plate. The prepolymer has lower specific gravity than cells, allowing the cells to be sedimented to the bottom surface by centrifugation (Figure 4.1A). The mixture is then polymerized by exposure to 28 a 5 s pulse of 375 nm UV, which forms a hydrogel thin-film 100-200 µm in thickness (Figure 4.1B). This hydrogel is permeable to molecules, such as polymers, nucleic acids, and proteins. Consequently, the cell sample can be washed and immunostained after encapsulation, if desired. This hydrogel thin-film also exhibit high transparency and low autofluorescence, enabling high-quality imaging to identify target cells (Figure 4.1C). After identifying the target cell, the prepolymer for a second impermeable hydrogel is added to the well where it penetrates the first permeable hydrogel. The impermeable hydrogel is formed by laser micropatterning using a 405 nm laser (Figure 4.1D). Specifically, in each well, the laser is raster scanned to polymerize all regions except a void around the target cell. After patterning, the remaining prepolymer is removed by washing, leaving a single exposed target cell encapsulated in the permeable hydrogel, while all other cells are embedded inside the impermeable hydrogel (Figure 4.1E). The exposed single cells in each well is lysed using LiDS lysis buffer. The released mRNA is captured using oligo-dT beads (Figure 4.1F). The beads are then transferred to PCR tubes, where they are washed. The mRNA molecules are then eluted from beads and hybridized oligo-dT primers containing a PCR handle and a 16-bp unique molecular index (UMI) (Figure 4.1G). The mRNA is then reverse transcribed using a template switching oligo (TSO) to attach PCR handle at 5’end of cDNA. The resulting cDNA library is then amplified via long-distance PCR (LD-PCR) (Figure 4.1H). The amplicons then cleaned up using Ampure XP beads followed by tagmentation using Nextera DNA flex kit. The sequencing adapter was tagged by PCR with illumina’s standard P7-i7-R2 primer and custom primer that illumina’s P5-i5 sequences are encoded with our PCR handle. The prepared library could then be pooled to produce a transcriptome library from multiple single cells, where the transcripts of each cell were labeled 29 with a single combination of indices that could be directly traced to the image of the originating cell (Figure 4.1I). 4.2 Development of cell encapsulation using permeable hydrogel 4.2.1 Overview In this chapter, a cell encapsulation using permeable hydrogel thin-film has been developed for immobilizing cells and phenotyping. To identify cell by its phenotype, immunocytochemistry (ICC) is essential. However, when the specimen contains few cells, many cells may be lost during ICC protocol. The developed cell encapsulation material has lower density than typical cells in order to enable the alignment of cells by centrifugation to the bottom surface of a standard and unmodified imaging microtiter plate. The hydrogel thin-film permeability and cell trap performance been tested by ICC. Also, the density, mechanical strength, porosity, as well as thickness of hydrogel thin-film been optimized. This material is permeable to immunoglobulins, optically transparent with minimal coloration and auto-fluorescence, and mechanically robust to withstand repeated washing. 30 4.2.2 Results Figure 4.2 Schematic of (A) cells encapsulated in photo-polymerized PEG-DA hydrogel. (B) Without porogen, PEG-DA hydrogel is impermeable to immunoglobulins. (C) With porogen, PEG-DA hydrogel is permeable to immunoglobulins Our general approach is to mix cells in a pre-polymer solution that can be crosslinked into a hydrogel upon ultraviolet (UV) light exposure. To enable lossless ICC, the prepolymer solution must be less dense than cells to allow them to sink to the imaging surface via centrifugation. The hydrogel thin film must have sufficient mechanical strength to withstand normal pipetting. Finally, the hydrogel must be sufficiently thin and porous to allow the diffusion of immunoglobulins in a reasonable timeframe (~1 hour)(Figure 4.2). 31 4.2.2.1 Density Testing In order to align cells by centrifugation to the bottom glass surface of the imaging microtiter plate, the cell capture solution density must be less than that of typical cells. Given that the lowest density cells are likely to be monocytes, which have a density between 1.067 and 1.077 g/ml28, the cell capture solution should be less than 1.067 g/ml in density. Also, the density must be higher than 1 g/ml to sink and encapsulate the cells in bottom plane. We aimed the density of cell capture solution as 1.058 g/ml. 4.2.2.2 Mechanical strength testing The mechanical strength of the hydrogel thin-film is important for retaining its structural integrity during pipetting. This property was tested by repeatedly pipetting 40 µl of PBS onto the surface of the photopolymerized hydrogel until signs of structure disintegration, such as cracks, tears, and delamination, began to be observable. The polymerized hydrogel thin-film had sufficient mechanical strength to survive >100 rounds of repeated pipetting. 4.2.2.3 Porosity testing Immunoglobulins have an estimated size of ~ 14 nm85,86. The porosity of hydrogel thin-film must be sufficiently large to permit diffusion of immunoglobulins to the cell sample in a reasonable amount of time. Conventional method for producing macroporous hydrogels include freeze-drying, solvent casting, and gas forming87–92. While these methods have been used in tissue engineering applications to produce hydrogels with >100 µm pores93–97, these hydrogels have poor mechanical strength and image quality98, which makes them incompatible with immunostaining of embedded cells. We evaluated whether ICC could be performed on cells 32 embedded in lossless hydrogel by embedding 22RV1 cancer cells and then using fluorophore-conjugated antibodies to stain the extracellular EpCAM protein, and the intracellular cytokeratin proteins. The 90% of cells were stained after 1 hour incubation. 4.2.2.4 Thickness optimization In addition to porosity, the thickness of the hydrogel thin-film is important for determining the time required for immunoglobulin diffusion. Immunoglobulin stains are introduced on the top surface of the hydrogel and must diffuse to cells located at the bottom surface of the hydrogel, which interfaces with the glass substrate. The thickness of the hydrogel thin-film can be controlled by the UV light intensity and exposure time. Based on the Beer-Lambert law, UV light intensity diminishes exponentially as it penetrates absorbing material. Therefore, UV light applied at the bottom of the imaging plate polymerizes a hydrogel thin-film with thickness directly controlled by exposure time. We tested hydrogel thin-film formation by exposure using a long-wavelength UV LED (λ=375 nm) for 1, 3, 5, 7, or 10 seconds. We also tested hydrogel thin-film formation by exposure using standard UV gel imaging system for 5, 10, 15, 20, and 30 seconds. The hydrogel thickness was then estimated by first focusing on a cell along the imaging plate surface and then the top hydrogel surface. The z-position of each focal point was obtained from Nikon NIS-BR software and used to estimate the distance between the two points. Three measurements were performed for each experimental condition. Using an UV LED, exposure for 1 and 3 s failed to form a hydrogel, while 5, 7, and 10 s exposures produced hydrogels with thickness of 100 +/- 20 µm, 500 +/- 20 µm, and 1000 +/- 20 µm, respectively. Therefore, we selected the 5 s as the optimal exposure time for UV LED source because it produced a stable 33 hydrogel while minimizing UV exposure and minimizing hydrogel thickness. When using the UV gel imaging system, exposure <20 seconds failed to form a hydrogel, while 20 seconds exposure produced 100 +/- 20 µm thickness hydrogel and 30 seconds exposure produced 300 +/- 20 µm thickness hydrogel. Therefore, for gel imaging system, we used an exposure time of 20 seconds to minimize hydrogel thickness. 4.2.2.5 ICC of cell sample Figure 4.3 The general approach for lossless immunocytochemistry using hydrogel-encapsulated cells. (A) The PEG-DA pre-polymer solution and cell suspension is added to a standard (unmodified) glass bottom imaging well-plate. The plate is centrifuged to position the cells at the bottom of the plate and the plate is exposed to UV light for 5 s. (B) The supernatant, along with uncured prepolymer solution, is removed from 34 the well by pipetting. (C) Immunostaining steps for fixation, permeabilization, immunostaining, as well as multiple washing steps are performed without additional centrifugation steps. (D) Image acquisition can be performed directly on the imaging plate. To evaluate this cell fixation method for a thin immunoglobulin-permeable hydrogel thin-film, we evaluate its use in immunocytochemistry. Our process involves first mixing the cell sample with the pre-polymer and then using a single centrifugation step to collect the target cells at the base of the imaging plate. Next, the hydrogel thin-film is formed using a 5 s UV exposure, at which point, the specimen is immunostained using standard ICC reagents (Figure 4.3). Figure 4.4 Comparison of bright-field microscopy image of cells encapsulated in PEG-DA hydrogels (A) before and (B) after photo-polymerization. The polymerized hydrogel shows a slight opacity after photo-polymerization, but there is no significant change in microscopy image quality. Since previous efforts to visualize cells in macroporous hydrogels have been limited by image quality98, we first evaluated the image quality of PEG-DA encapsulated cells by bright field microscopy. While the polymerized hydrogel shows a slight opacity compared to standard 35 buffer, the encapsulated cells were clearly visible under microscopy with no visible degradation in image quality (Figure 4.4). Figure 4.5 Micrographs of hydrogel encapsulated cells stained with fluorescent antibodies. (A) 40X image from a well in a 384 well microtiter plate where ~1,000 cells are encapsulated in hydrogel and subsequently stained with DAPI (blue), EpCam-Alexafluor-488 (green), Pan-Keratin-Alexafluor-647 (red). (B-E) Close-ups of merged and separated channels. We then assessed whether the hydrogel impaired immunofluorescence microscopy by staining 22RV1 tumor cells using fluorescence conjugated antibodies specific for EpCAM and pan-cytokeratin (Figure 4.5). The cells were clearly visible and the absence of background fluorescence indicates that the unbound antibodies were not adsorbed by the hydrogel, but were efficiently washed away. An important consideration was that all staining parameters were optimized using the standard ICC protocol and the conditions did not require re-optimization for 36 PEGDA-encapsulated cells. Thus, cell staining could be achieved under standard conditions in <2 hours. Figure 4.6 Cell retention following immunostaining of cells using standard ICC, ICC following Cytospin, and hydrogel-encapsulation. Each experiment was performed in triplicate and cells were independently enumerated by two reviewers. Error bars rep resent the standard deviation (n=6). The key advantage to this process is that, unlike ICC conventional protocols or ICC following Cytospin, the cells are encapsulated in hydrogel during staining and washing, which eliminates any mechanisms for cell loss. To quantify cell loss from standard ICC processes, we generated a 10-fold dilution series of samples containing 10 to 10,000 22RV1 cells, and then immunostained the samples using standard ICC, Cytospin, and hydrogel encapsulation. We performed triplicate experiments wherein cells were enumerated by two independent reviewers before and after each 37 process (Figure 4.6). Regardless of the number of cells in the initial sample, both cells processed by standard ICC and Cytospin resulted in losses of greater than 50% during subsequent immunostaining. In contrast, 97-99% of input cells were enumerated following immunostaining of PEG-DA encapsulated cells. This small deviation likely resulted from incomplete staining rather than cell loss since there are invariably a small fraction of a sample that will not stain. The most significant difference in cell loss was observed when fewer than 100 input cells were stained. Under these situations, almost all (>84%) cells were lost using standard ICC and Cytospin. In fact, Cytospin failed to retain any cells when there were only 10 cells in the initial sample. Together, these results show that hydrogel encapsulation permits virtually lossless immunostaining that is robust regardless of the starting number of cells in the sample. 4.2.3 Experimental 4.2.3.1 Chemicals and Hydrogel Preparation The hydrogel PEG-DA as a scaffold material and the PEG as a porogen material, as well as photo-initiator 2-Hydroxy-4′-(2-hydroxyethoxy)-2-methylpropiophenone (Irgacure 2959), paraformaldehyde (PFA), and Tween-20 were all purchased from Sigma-Aldrich, Canada. The Irgacure 2959 photo-initiator was selected since photopolymerization could be performed with a short pulse (≤5s) of long-wave UV light. Furthermore, Irgacure 2959 is non-toxic at the concentration of 0.1% (w/v) and is optically transparent. The PEG-DA was diluted in phosphate buffered saline (PBS) and then tested for curing time, mechanical stability, and staining time. The hydrogel macromer solution selected for the lossless ICC experiments was prepared. Each solution was freshly prepared prior to experiments. 38 4.2.3.2 Cell Culture The cell line 22RV1 (human prostate carcinoma, ATCC CRL2505) was used for immunostaining experiments. This cell line was cultured in RPMI-1640 culture media containing 10% Fetal Bovine Serum (Gibco) and 1% penicillin-streptomycin (Gibco) at 5% CO2 at 37°C. Cells were re-suspended using 0.25% Trypsin-EDTA (Gibco), to generate a 10-fold dilution series from 104-10 cells per 40 µl culture media. 4.2.3.3 Cell Encapsulation To encapsulate the cells in hydrogel, each cell suspension and 40 µL of PBS buffer was first loaded into one well in a 384-high contrast imaging well-plate (Corning). Next, 6.5 µL of the premixed pre-polymer solution is pipetted gently with minimal mixing. The imaging well-plate was then centrifuged for 3 minutes at 3800 rpm (Accuspin 1R, Fisher scientific), followed by exposure to 375 nm high-power UV LED (Thor labs) for 5 seconds. 4.2.3.4 Immunocytochemistry Immunocytochemistry was performed by first fixing cells in 4% paraformaldehyde for 10 minutes, washing twice with PBS and permeabilizing the cells with 0.025% Tween-20 for 15 minutes. After washing the cells twice more with PBS, the cells were blocked with 3% bovine serum albumin BSA (30 min) and stained for one hour with DAPI (1 µM) for DNA, EpCAM-Alexa Fluor 488 (1:100 dilution) and Pan-Keratin-Alexa Fluor 647 (1:100 dilution). ICC was performed in parallel on matching samples that were either cytospun onto a glass slide, hydrogel-encapsulated in an imaging plate or non-encapsulated within an imaging plate. This protocol differed between cell specimens because washing of non-encapsulated cells involved adding 40 39 µl of PBS followed by centrifugation (3800 rpm, 3 min), washing of cytospin slides employed gentle PBS rinsing and the washing of hydrogel encapsulated cells involved adding PBS and mixing by pipette 10 times. Immunostained cells were directly imaged using both bright field and fluorescent microscopy, using a Nikon Ti-E inverted fluorescent microscope with 10x, 20x and 60x magnification with a high-resolution camera or a Zeiss laser scanning confocal microscope LSM 780 at 40x magnification. 4.2.3.5 Cell Counting and Statistical Analysis Both the initial (prior to plating) and final numbers of all 3 matching ICC samples were manually counted by two individuals from the obtained images using ImageJ software. Experiments were performed 3 times for each cell dilution. Results from the count were averaged and plotted using Graphpad Prism7. 4.2.4 Summary In this chapter, a permeable hydrogel thin-film developed for encapsulating cells during immunocytochemistry in order to eliminate cell loss resulting from washing and centrifugation. We show that this hydrogel thin-film is permeable to immunoglobulins, stable enough to withstand pipetting, and allows immunostaining to be performed directly on a standard imaging well plates. Compared to standard ICC and Cytospin methods that become highly lossy for small cell samples (<10,000 cells), this process is lossless and can be used to stain <10 cells in a well. Furthermore, this process requires only a single centrifugation step, compared >8 steps for standard ICC, which greatly improves compatibility with robotic systems. Ultimately, this simple 40 and novel application of hydrogels for ICC could greatly improve small cell sample biological assays, such as drug screening on primary cells and identification of circulating tumor cells. 4.3 Micropatterning hydrogel permeability 4.3.1 Overview This chapter describes the development of a micropatterning technique to create hydrogels with spatially varying permeability to biomolecules. This characteristic is realized by micropatterning a low-molecular weight hydrogel inside a high-molecular weight hydrogel. The high-molecular weight hydrogel is permeable to biomolecules, and is used to entrap a cell sample to the bottom surface of an imaging well-plate. The low-molecular weight hydrogel is impermeable to biomolecules, and is used to encapsulate non-target cells while selectively exposing target cells. To enable simultaneous photo-patterning and fluorescent imaging, the custom 405 nm UV laser system been developed that added on inverted microscope. The developed laser system enables focusing the laser beam in minimized diameter at different location inside the microscope field of view. By combining high speed adjust of laser focusing spot and intensity of the laser beam, the raster scanning for micropatterning can be achieved. Furthermore, spatial resolution and permeability altering by photo-patterning been validated and optimized. 41 4.3.2 Results 4.3.2.1 Laser system setup Figure 4.7 Design of microscopic laser scanning system. The laser scanning system is built on a Nikon Ti-E microscope with a stage-up kit to introduce additional optics into the infinity space to enable simultaneous UV laser scanning and fluorescence imaging (Figure 4.7). The custom laser scanner consists of a 405 nm laser beam coupled with FC fiber, collimated by 5-axis fiberport collimator. The beam enters the input port 42 of a 2D galvo-galvo scanner. The angled beam from scanner passes through a telecentric scan lens, whose entrance pupil is aligned at middle point of two mirrors inside the scanner (29.045 mm from housing). The output beam from scan lens is focused at the intermediate image plane, on which the entrance pupil of the telecentric tube lens was aligned. The collimated beam angled by the galvo-galvo scanner is directed to the scan lens, which is then focused at different locations proportional to incident angle. The beam then entered the upper turret of microscope by rear port to the infinite path. The dichroic mirror, placed inside upper turret, reflects the laser beam input, which is then focused on the sample by the objective lens. Each component was assembled through a 60 mm cage and SM2 tube and mounted at rear port of microscope. The laser beam focus and imaging focus is adjusted by changing and aligning distance between scan lens and tube lens. 4.3.2.2 Spatial resolution of Micropatterning To establish the spatial resolution of micropatterning impermeable hydrogels inside permeable hydrogels, we formed empty permeable hydrogel thin-film, and then micropatterned an impermeable hydrogel by laser raster scanning to create a circular void pattern. Using 10X objectives to focus the UV laser, one round of rater scanning is limited to patterning resolution of 50 µm due to propagation of the polymerization (B). When using 20X objectives, the minimum circular void pattern was 25 µm (D). 43 Figure 4.8 Resolution in photopatterning circular voids using 10X and 20X microscope objectives. (A-B) Micrographs of circular voids formed by photopatterning impermeable hydrogel using a 10x objective. Effective diameter of isolated area is 100 µm and 50 µm. (C-D) Micrographs of circular voids formed by photopatterning impermeable hydrogel photopolymerized using a 20x objective. Effective isolated diameter corresponded to 50 µm and 25 µm. Note the voids are patterned by raster scanning. Scale bar: 25 µm. 4.3.2.3 Improving spatial resolution by multistep patterning To improve the spatial resolution of micropatterning impermeable hydrogels inside permeable hydrogels, a multi-step patterning was considered. By multi-step raster scanning using a 10X objective to focus the UV laser, the 100 µm circular void pattern was micropatterned first. Then at same point, 25 µm was patterned (A). At 20X, we used 50 µm as larger pattern and 12.5 µm as 44 smaller pattern (B). This approach provides the time gap between polymerization, which limits the propagation during the polymerization. it is possible to reduce the minimum void size to 25 µm for the 10X objective, and 12 µm for the 20X objective, which Figure 4.9 Improvement of spatial resolution and cell isolation. (A) Micrographs of circular voids formed by multistep photopatterning impermeable hydrogel photopolymerized using a 10x objective. Effective isolated diameter corresponded to 25 µm. (B) Multistep photopatterning using a 20x objective. Effective isolated diameter corresponded to 12 µm. (C-E) Multistep micropatterning of impermeable hydrogel to progressively encapsulate all cells except one target cell in impermeable hydrogel. Scale bar: 50 µm. are sufficient to isolate single cells in suspension. To validate, we encapsulated a cell sample in a permeable hydrogel thin-film as shown in C. Then, the first circular void pattern of 100 µm in diameter was micropatterned by raster scanning as shown in D. At this step, multiple cell can be 45 located inside void pattern. Lastly, to isolate single cell in maximum resolution, second circular void pattern of 25 µm was micropatterned on same point as shown in E. 4.3.2.4 Validation of hydrogel permeability Figure 4.10 Micropatterning hydrogel permeability (A) Bright-field and fluorescence micrographs of a 50 µm circle of permeable hydrogel in a region of impermeable hydrogel. (B) Diffusion of FITC-Dextran (Mw 10kDa) into permeable hydrogel. Scale bar: 50 µm. To validate the ability to micropattern hydrogels with varying permeability to biomolecules, we first polymerized a permeable hydrogel thin-film and then patterned an impermeable hydrogel with a 50 µm diameter circular void as shown in Figure 4.10A. After removing the unpolymerized pre-polymer by washing, the pattern is incubated for 10 minutes with FITC-dextran (Mw = 10 kDa), and then washed with fresh PBS. The resulting pattern is only fluorescent in the circular void, where FITC-dextran has penetrated into the permeable hydrogel, but not in the impermeable hydrogel as shown in Figure 4.10B. 46 4.3.3 Experimental 4.3.3.1 Hydrogel preparation The permeable hydrogel that developed in chapter 4.2. Impermeable hydrogel was produced by combining 2% (w/v) MEHQ (MilliporeSigma, Cat# M81644) with 100% Polyethylene glycol diacrylate (Mn 250) (PEG250-DA, MilliporeSigma, Cat# 475629), and adding Irgacure 819 (MilliporeSigma, Cat# 511447) to 1% (w/v). Each solution was freshly prepared prior to each experiment. 4.3.3.2 Laser system setup Custom laser system consists of a 405 nm diode laser (Coherent, Cat# 1284369), fiberport collimator (Thorlabs, Cat# PAF2-A4A), 2-axis galvo-galvo scanner (Thorlabs, Cat# LSKGG4), scan lens (Thorlabs, Cat# CLS-SL), and tube lens (Thorlabs, Cat# TTL200MP). Each component was assembled through a 60 mm cage and SM2 tube mounted at the rear port of a Nikon Ti-E microscope. The input laser is reflected by a dichroic mirror at the filter turret to the objective lens. We developed a custom-made software using NI-DAQ to control the laser and galvo scanner. To control the microscope, we developed specific software using the Nikon software developing kit (SDK). 4.3.3.3 Cell culture and preparation Fluorescently tagged human bladder cancer cell lines, UC13-EGFP and UC13-mCherry, were maintained in Minimum Essential Medium (MEM) culture media supplemented with 10 % Fetal Bovine Serum (FBS, Gibco, catalog # 10082147) and 1% penicillin-streptomycin (v/v, Gibco, catalog # 10378016) in a humidified incubator at 37°C and 5% CO2. Cells were harvested using 47 0.25% Trypsin-EDTA (Gibco, catalog # 25200056), spun at 200 xg for 5 min, and resuspended in PBS to generate the serial dilution. 4.3.3.4 Prepare for micropatterning To encapsulate the cells in hydrogel, the 40 µl cell suspensions in PBS were added into a 384-well glass bottomed imaging plate (Greiner bio, Cat# M4437) with 6.5 µL of the permeable hydrogel pre-polymer. The 384 well-plate was centrifuged at 3300 xg for 3 min (Accuspin 1R, Fisher scientific), followed by exposure under 375 nm UV LED (Thorlabs, Cat# M375L3) for 5 seconds. After photo-polymerization, non-polymerized hydrogel mixture was carefully removed and washed with 50 µl of PBS three times. Encapsulated cells were than scanned using a Nikon Ti-E fluorescent microscope to identify the location of specific target cells. After identifying target cells, the impermeable mixture was added to the sample where it penetrates the first permeable hydrogel with encapsulated cells. First, each wells were washed ten times with 50 µl of 100% ethanol. Next, the 40 μL of impermeable mixture was added and centrifuged at 100 xg for 5 min, allowing it to penetrate the first permeable hydrogel. Based on the previously stored location of the target cells, the 405 nm laser is scanned to polymerize all regions except a void around the target cell. After photo-polymerization, the remaining impermeable polymer was removed by 100% ethanol by gentle pipetting. 4.3.3.5 Patterning resolution test The empty permeable hydrogel was created by adding 40 µL of PBS into a 384-well glass bottom imaging plate, with 6.5 µL of permeable hydrogel pre-polymer and prepared for 48 micropatterning as previously described. Each well was micropatterned using void pattern size of 200 µm, 150 µm, 100 µm, 50 µm, 25 µm, 12.5 µm in diameter. For multistep patterning, the UC-13-EGFP cells were encapsulated and prepared for micropatterning as previously described. The 100 µm diameter void was patterned first around the target cell by one-round trip of laser raster scanning. Then, the 2nd 25 µm diameter void was patterned again on same spot, by one-round trip of laser raster scanning. 4.3.3.6 Diffusion test The empty permeable hydrogel was prepared for micropatterning in 384-well glass bottom imaging plate. Then, 50 µm diameter void well was micropatterned by laser raster scanning using laser scanning system presented at chapter 4.3.2.1. Then the void pattern was washed once using ethanol by pipetting followed by one wash with PBS by pipetting. Then 5% w/v FITC-Dextran (Mw 10 kDa) in PBS was added to well, incubated 10 mins at R.T. The excessive FITC-Dextran solution was then removed from well, followed by 1 time washing with PBS. After removing supernatant, the well plate was observed under bright-field and fluorescent microscopy. 4.3.4 Summary In summary, the laser system attachment for inverted microscope been developed and optimized. The spatial resolution of this system been tested by micropatterning impermeable hydrogel prepolymer and optimized to increase spatial resolution by multistep patterning. Finally, the permeability altering by micropatterning been investigated by fluorescein conjugated macromolecule. 49 4.4 Selective single cell RNA extraction 4.4.1 Overview This chapter describes the development and testing of a workflow to extract RNA from targeted single cells. We evaluated this workflow by extracting RNA from transgenic cancer cells, and then confirming this process using qPCR. Furthermore, the gene detection using pre-amplification methods and WTA methods has been validated. 4.4.2 Results 4.4.2.1 Validation of selective single cell lysis A B C Figure 4.11 Selective cell lysis. (A-C) Selective single cell lysis using UM-UC13-mCherry (mCh) and UM-UC13-EGFP (EGFP) cells shown using overlaid bright-field and fluorescence micrographs. (A) mCh and EGFP cells encapsulated in permeable hydrogel. (B) All non-target cells are encapsulated in micropatterned impermeable hydrogel, while exposing a single target EGFP cell. (C) After addition of LiDS lysis buffer, only the target EGFP cell was lysed. Fluorescence of non-target cells is reduced due to dehydration. To validate the ability to use micropatterning of hydrogel permeability to selectively lyse target cells, we encapsulated a mixture of UM-UC13-mCherry and UM-UC13-EGFP cells in the permeable hydrogel as shown in Figure 4.11A. The encapsulated cells are examined under fluorescent microscope and single target cell been selected. We then micropatterned the 50 impermeable hydrogel to selectively expose a single target cell as shown in Figure 4.11B. The LiDS lysis buffer is then added to the sample. As a result, fluorescence is lost in the exposed cell, indicating the targeted cell has been lysed as shown in Figure 4.11C. Fluorescence is retained in the remaining cells encapsulated in the impermeable hydrogel. Although, fluorescence intensity of these cells is reduced due to dehydration, but not eliminated. 4.4.2.2 Validation of single cell RNA extraction by target gene amplification Figure 4.12 mRNA extraction from single cell. qPCR for RNA selectively extracted mixed mCh and EGFP cell samples. Samples include no-template control (NT), bulk mCh, bulk EGFP, single cell mCh, singe cell EGFP, as well as mCh and EGFP cell pair. Vertical black arrows indicate target cDNA undetected. Ct values are averaged from N=5. To validate RNA extraction from single cells, we used our process to isolate RNA from a single target cell from mixtures of UM-UC13-mCherry and UM-UC13-EGFP cells for analysis using qPCR. A single mCherry or EGFP cell was selected from each well and lysed as before. Extracted RNA was captured using oligo-dT beads, and then reverse transcribed on the beads. The cDNA was then pre-amplified for 3 targeted genes and then analyzed using qPCR. As 51 expected, mCherry single cells showed detectable mCherry signal with an absence of EGFP signal, while the converse was true for EGFP single cells. These results confirm RNA can be extract from single target cells without contamination from surrounding cells via optical micropatterning (Figure 4.12). 4.4.2.3 Validation of single cell RNA extraction by WTA Figure 4.13 mRNA extraction from single cell. qPCR for RNA selectively extracted mixed Jurkat and Raji cell samples. Samples include no-template control (NT), bulk Jurkat (Jb), bulk Raji (Rb), single cell Jurkat (Jsc), singe cell Raji (Rsc). Vertical black arrows indicate target cDNA undetected. Ct values are averaged from N=4. Error bar indicates range. To validate unbiased whole transcriptome amplification (WTA), a single target cell from mixtures of Jurkat and Raji cells were isolated and RNA were extracted using our process. A single Jurkat or Raji cell was selected from each well based on fluorescent imaging and lysed as before. Extracted RNA was captured using oligo-dT beads, and then washed, eluted from the beads. The eluted RNA was then reverse transcribed with UMI primer and template switching oligo. The cDNA was then whole transcriptome amplified by LD-PCR (long distance PCR) and 52 then analyzed using qPCR targeting CD3G, CD79B, as well as GAPDH. The Jurkat cells known to be highly expressing CD3 genes, while Raji cells known to be highly expressing CD79 gene. As expected, Jurkat single cells showed detectable CD3 signal with an absence of CD79 signal, while the converse was true for Raji single cells. These results confirm WTA of extracted RNA via optical micropatterning successfully performed (Figure 4.13). 4.4.3 Experimental The permeable and impermeable hydrogel prepolymer was prepared as described in chapter 4.3.2. 4.4.3.1 Cell culture and preparation Fluorescently tagged human bladder cancer cell lines, UC13-EGFP and UC13-mCherry, were maintained and prepared as described in chapter 4.3.2. Jurkat T cells (ATCC TIB-152) and Raji B cells (ATCC CCL-86) were maintained in RPMI-1640 with 10% FBS and 1% penicillin-streptomycin. Jurkat were labeled with Vybrant DiO (Peak emission: 501 nm) and Raji with Vybrant Dil (Peak emission: 565 nm), according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA). Stained cells were mixed and incubated in 40 μL culture media at 37°C for the duration of each experiment. 4.4.3.2 Cell encapsulation and target cell isolation by laser micropatterning Cell encapsulation and photo-patterning were performed as described in chapter 4.3.2. 53 4.4.3.3 RNA extraction, reverse transcription (RT) and qPCR After laser-micropatterning, cell was lysed using LiDS lysis buffer (Thermofisher, Cat# A33562) and RNA were extracted with Oligo dT beads (Dynabeads, Thermofisher, Cat#61002) according to the manufacturer's protocol. First, 20 μL of LiDS lysis buffer was added and incubated at room temperature for 5-10 seconds. Then, 10 μL of Oligo dT beads that suspended in LiDS lysis buffer were added and incubated for 10 mins at room temperature for sufficient mRNA binding. The beads were then transferred to 0.2 ml PCR tubes, then washed twice with Wash Buffer A (Thermofisher, Cat#A33565), followed by washing with Wash Buffer B (Thermofisher, Cat#11900D) as per the manufacturer’s protocol. The beads were finally resuspended in 9 μL of RT-PCR grade DI-water (ThermoFisher, Cat#AM9935) for downstream RT reactions. To validate single-cell extraction using UC13 bladder cancer cell lines, SuperScript III RT kits were used to synthesize cDNA directly on beads, followed by pre-amplification and targeted qPCR. First, 1 μL of 10nM dNTP was added to the 9 μL RNA-bound dT beads suspension, and incubated at 65 °C for 5 mins, followed by chilling on ice for 1 min. An equal volume of reaction buffer, consisting of 4 μL of 5x RT buffer, 2 μL of 50 nM MgCl2, 2μL of 0.1M DTT, 1 μL of RnaseOUT, and 1 μL of SuperScript III RT enzyme (ThermoFisher, Cat# 18080093) was added to the chilled beads. The samples were incubated in a thermocycler (Applied Biosystems) using the following settings: 1 cycle of 50 °C for 30 mins, followed by 10 cycles of 55 °C for 2 mins and 50 °C for 2 mins, and a final cycle of 85 °C for 5 mins to inactivate the enzyme. The tubes were held at 4 °C until the next step. Pre-amplification was performed by adding 5 μL of the cDNA bound bead suspension from the RT reaction to pool of custom designed probe-based primer assays for EGFP, mCherry, and commercially available GAPDH (IDT, Cat# 54 Hs.PT.39a.22214836) at final concentration of 0.2X each. IDT Gene Expression Master Mix (IDT, Cat# 1055770) was added to a final volume of 20 μL for each isolated sample and 12 cycles of pre-amplification was performed (3:00 @ 95 °C, 12 cycles of [0:15 @ 95 °C, 1:00 @ 60 °C]). The single cell selection was then validated by running qPCR in duplicate, targeting EGFP, mCherry and GAPDH, for each extracted single cell, by using 3 μL of the amplicon from the pre-amplification. To the amplicon, 10 μL of Fast advance Master Mix (Applied Biosystems), 1X concentration of primer, and qPCR was performed as follows: 2:00 @ 50 °C, 2:00 @ 95 °C, 45 cycles of [0:01 @ 95 °C, 0:20 @ 60 °C]. To validate the whole transcriptome amplification (WTA) using Jurkat and Raji cells, RNA was eluted from the dT beads, followed by RT reaction using Maxima H minus kit (Thermo, Cat#M1661) and long-distance PCR for cDNA amplification using Q5 Hotstart Hi-Fi master mix (NEB, Cat#M0494). Specifically, RNA was eluted from oligo dT beads by suspending in 10 µL of elution buffer and incubated at 80 °C for 2mins. The eluted RNAs were quickly transferred to new tube, mixed with 2 µL of 10 µM UMI (unique molecular index) dT primer (IDT), and incubated 2 mins at 85 °C followed by chill in the ice for 1min. For RT, reverse transcription enzyme Maxima H minus was used according to the manufacture’s protocol. Briefly, 4 μL of 5x buffer, 2 µL of dNTP, 1 μL of 50 µM of template switching oligo (IDT) and 1 μL of Maxima H- enzyme mix were added to the 12 µL of RNA samples, attached with UMI. The samples were incubated in thermocycler, 50 °C for 5 mins, 42 °C for 1.5 hours, 50 °C for 5mins, and then 85 °C for 5 mins to inactivate the enzyme. The samples were held at 4 °C until the next step. Synthesized cDNA was amplified by long-distance PCR using Q5 hotstart Hi-Fi master mix according to the manufacture’s protocol. For 50 µL reaction, mixture of 25 µL 55 of Q5 master mix and 5 µL of 10 µM Handle primer was added to 20 µL of cDNA. After gentle mixing by pipetting, tubes were incubated in thermocycler as follows: 98 °C for 30 sec, 30 cycle of [0:10 @ 98 °C, 0:25 @ 62 °C, 3:00 @ 72 °C] then 72 °C for 5mins. The tubes were held at 4 °C shortly for next step, or stored at -20 °C. qPCR was performed under the same condition above, targeting CD3 (Thermo, Hs00173941_m1) and GAPDH (Thermo, Hs99999905_m1). 4.4.4 Summary In summary, the single cell isolation and RNA extraction workflow has been developed and validated. The single cell isolation and lysis been demonstrated by selectively lysing fluorescent tagged single cell, along with RNA extraction was confirmed by qPCR through pre-amplification of target genes or WTA. Together, these results confirm that RNA can be selectively extracted from single cells, identified by imaging, via micropatterning hydrogel permeability without contamination from any surrounding cells. 56 4.5 Molecular workflow for sequencing library preparation 4.5.1 Overview This chapter describes the development of a customized molecular workflow for sequencing library preparation, including custom sequencing oligos specific the Illumina Next-seq. This molecular workflow provides unbiased WTA and sequencing read from custom site. 4.5.2 Molecular workflow 4.5.2.1 Process: from mRNA to sequencing library The sequencing library preparation process involves the following steps: First, RNA released from single cells are captured using oligo dT beads (A). Second, the captured mRNAs are washed and eluted from beads and annealed with UMI-dT custom primer, which PCR handle is encoded (B). Third, first-stand cDNA was synthesized with TSO for template switching (C). Fourth, the 2nd stand cDNA been synthesized by PCR handle during the first cycle of long distance PCR (LD-PCR) (D). Fifth, the synthesized cDNAs been amplified using PCR handle sequence at both end of cDNA (E). Sixth, LD-PCR amplicons are tagmented, which divide the cDNA stands to smaller fragments, using illumina DNA flex kit (F). The illumina’s READ1 and READ2 sequences are attached at the fragmented amplicons simultaneously. The resulting fragments consist of PCR handle, UMI, dT, cDNA fragment followed by inserted READ1 or READ2 sequence (G). Seventh, the custom sequencing adapter P5 and illumina’s standard 57 sequencing adapter P7 were attached to tagmented amplicon by PCR for sequencing read from PCR handle site (H). Eighth, the adapter tagged cDNA fragments then size selected, rGrGrGC C CC C CG G GC C CG G GP5 (29)TS Oligo*dT(25)Poly‐A mRNAPCRHandle (25) UMI (16) dT(30)TS Oligo (31)cDNAi5(8)i7(10)P7 (24)R2 (33)R2 (33) R1 (33)PCR HandleP5 i5 PCR Handle i7 P7R2UMI dT(30) cDNATransposomePCR Handle (25)PCRHandle* (25)UMI* (16)Template SwitchingUMI-annealingBead Capture2nd standcDNA SynthesisLD-PCRTagmentationTagmented FragmentsAdapter TaggingPrepared LibraryABCDEFGHIC C C Figure 4.14 Detailed molecular workflow for sequencing library preparation. (A) The extracted mRNAs are captured using oligo-dT beads. (B) The mRNAs are then eluted from the beads and annealed to 58 oligonucleotides consisting of PCR handle, unique molecular index (UMI), and oligo-dT. (C) Reverse transcription is performed with a template switching oligo, which provides a PCR handle. (D-E) The reverse transcribed cDNA is then amplified by long distance PCR (LD-PCR). (F-G) The amplified cDNA is tagmented using the Illumina Nextera DNA flex kit. (H) After tagmentation with read sequence insertion, the sequencing adapters P7 and P5 are attached by PCR. (I) To specifically sequence the UMI on READ1, a custom primer is used, which contains P5, Illumina’s i5 index and PCR handle. The prepared library sequenced using Illumina Next-seq 550. checked quality of library. The final sequencing library fragments consist of P5 adapter, i5 index, PCR handle, UMI, dT, cDNA, R2, i7 index followed by P7 adapter (I). Finally, samples been pooled to 6 µM and sequenced using illumina Next-seq 550, with pair-ended (25 bp at READ1, 125 bp at READ2). 4.5.2.2 Design of oligos To establish custom single cell RNA sequencing workflow, number of oligos been designed. First, the UMI-dT primer been designed for reverse transcription. We aimed to provide PCR handle and UMI in this primer. 25 bases of PCR-handle followed by 16 bases of UMI, which created by random nucleotide, followed by 30 bases of T. At 3’ end of primer, we designed 2 nucleotides as phosphorothioated DNA bases, in order to provide resistant to degrade and low melt temperature, which provides easy annealing. Last two nucleotides, VN, were used in order to improve chance to anneal at end of poly-A tail. Next, to attach PCR-handle by template switching, oligo for template switching has been designed. The oligo consists of PCR-handle, and 3 bases of ribosome G at 3’ end. rG nucleotides used because it provides superior template switching performance than DNA G nucleotides99. Next, custom adapter was designed. These adapters consist of standard illumina’s P5 adapter sequences, along with standard i5 indices and 59 PCR-handle sequence that anneals. We used 9 different i5 indices for multiplexing. Also, 12 bases of G or C inserted between index and PCR-handle, to provide higher melting temperature, since Next-seq equipment uses 60°C for annealing. To read from PCR-handle site, custom READ1 primer also designed, which consist of same sequence from adapter primer, between end of index to end of primer. Finally, custom index read primer has been developed. This primer is reverse compliment of custom READ1 primer. 4.5.3 Experimental All reagents including enzymes for reverse transcription and PCR, are described in chapter 4.4.2.4. 4.5.3.1 Primer sequences Following sequences are used for preparing sequencing library. Notation been used as follows: N presents random nucleotide from one of A,T,G,C. V presents random nucleotide from one of A,G,C. * represents phosphorothioated oligo. r represents RNA oligo. UMI-dT sequence : AAGCAGTGGTATCAACGCAGAGTACNNNNNNNNNNNNNNNNTTTTTTTTTTTTTTTTTTTTTTTTTTTT*V*N Template switching oligo : AAGCAGTGGTATCAACGCAGAGTGAATrGrGrG Primer for LD-PCR : AAGCAGTGGTATCAACGCAGAGT Custom index-P5 adapter: AATGATACGGCGACCACCGAGATCTACAC [i5] GCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGT*A*C 60 Name Sequence i501 TAGATCGC i502 CTCTCTAT i503 TATCCTCT i504 AGAGTAGA i505 GTAAGGAG i506 ACTGCATA i507 AAGGAGTA i508 CTAAGCCT i509 CGTCTAAT Table 4.1 Sequence of i5 indices. Custom READ 1: GCCTGTCCGCGGAAGCAGTGGTATCAACGCAGAGTAC Custom i5 index read primer : GTACTCTGCGTTGATACCACTGCTTCCGCGGACAGGC 4.5.4 Summary In summary, we designed custom oligo and developed molecular workflow for sequencing library preparation of single cell RNA sequencing. Custom dT primer and TSO providing unbiased WTA, by LD-PCR along with UMI. A custom read primers providing specific sequencing of UMI. Together, this process providing robust sequencing library preparation. 61 4.6 Single cell sequencing of Raji and Jurkat cells 4.6.1 Overview In previous chapters, the selective single RNA extraction and WTA workflow has been developed. In this chapter, the workflow for selective single cell RNA sequencing been developed. The selective single cell RNA sequencing has been demonstrated using Jurkat and Raji single cells. 4.6.2 Results To validate the single cell RNA sequencing pipeline, the sequencing data has been analyzed by differential gene expression analysis. We sequenced 3 replicates of bulk and single Jurkat cells, as well as 3 replicates of bulk and single Raji cells. The heatmap shows top 100 genes that highly expressed for Jurkat and Raji cells (). Both cell types are clustering distinctively. In Jurkat cells, the T-cell receptor (TCR) related genes such as CD3D, CD3G, ZAP70, shows high expression, with minimum or no expression detected on Raji cells. In contrast, the Raji cell cluster shows high expression of Raji cell specific genes such as CD19, CD79B, with minimum or no expression detected on Jurkat cells. We confirm that by performing simple gene expression analysis, the Jurkat single cell and Raji single cell samples shows different transcriptome profiles, and See-N-Seq able to perform selective RNA sequencing without contamination. 4.6.3 Experimental 4.6.3.1 Hydrogel preparation The permeable hydrogel prepolymer was prepared as described in chapter 4.2, as well as the impermeable hydrogel prepolymer was prepared as described in chapter 4.3. 62 3‐3Relative expression Figure 4.15 Comparison between Jurkat and Raji cell. Heatmap illustrates Top 100 genes of highly expressed in Jurkat bulk (Jb) and Raji bulk (Rb) samples with Jurkat single cell samples (Jsc) and Raji single cell samples (Rsc). 4.6.3.2 Cell preparation Jurkat T cells (ATCC TIB-152) and Raji B cells (ATCC CCL-86) were maintained in RPMI-1640 with 10% FBS and 1% penicillin-streptomycin. Jurkat were labeled with Vybrant DiO (Peak emission: 501 nm) and Raji with Vybrant Dil (Peak emission: 565 nm), according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA). Cells are mixed and resuspended in PBS. 63 4.6.3.3 Cell encapsulation and target cell isolation Cell encapsulation and photo-patterning were performed as described in chapter 4.3.2. The single cell or cell pair is targeted as follows: 1) single Jurkat cell, 2) single Raji cell, 3) bulk Jurkat cells, 4) bulk Raji cells, as well as no-template control. 4.6.3.4 RNA extraction and WTA The RNA extraction from single cell and WTA performed as described in chapter 4.4.2.4. 4.6.3.5 Amplicon purification To prepare the sequencing library, the LD-PCR amplicons are first purified with AMPure XP beads (Beckman Coulter) according to the manufacturer's protocol. Briefly, 35µL of PCR amplicon were transferred to 96 well plate, mixed with 63µL of beads and incubated for 5 min at room temperature. Beads were washed with 70% ethanol twice and resuspended in 40µL of PCR grade water. After incubation for 2 min, the purified-DNAs were eluted by magnet (DynaMag-96 side magnet, Thermofisher, Cat#12331D) and transferred to a new 96-well PCR plate for library preparation. 4.6.3.6 Library preparation Library was prepared as described in chapter 4.5. briefly, we used illumina DNA flex kit (Illumnia, Cat# 20018705), which performs the tagmentation, according to manufacture protocol by SBC (Sequencing and Bioinformatics Consortium) at UBC. Prepared library was sequenced using Nextseq 500 with Mid-throughput, 25 bp at READ1 by custom primer and 125 bp at 64 READ2 by illumina’s standard primer. Also, 8 bp of i5 index was sequenced using custom index read primer while 8 bp of i7 index was sequenced using illumina’s standard primer. 4.6.3.7 Sequencing data process pipeline After sequencing, the raw reads are first demultiplexed based on i5 and i7 indices. The read quality of the demultiplexed fastq files were then confirmed by FASTQC. The Q.C. passed fastq files were then fed into UMI-tools, which detects UMI sequences. Our criteria for consider as UMI was 16 bases from READ1, followed by poly-T sequence. The detected UMI were then trimmed and recorded at the end of flowcell ID on both read1 and read2. Then the poly-A tail at read2 was removed using Cutadapt. The trimmed fastq file was aligned and mapped using Hisat2 aligner using default options against GRChg38 from NCBI, along with RefSeq annotation. Aligned BAM file was fed into featureCounts in Subread package to count number of detected genes. At this point, the gene count is not deduplicated. Since featureCounts create BAM tags XT for each gene, and XS for assigned status, we used these tags in count function of UMI-tools to count and deduplicate the reads. Further statistic analyze and plotting were performed using edgeR package in R. 4.7 Conclusion In this chapter, the See-N-Seq workflow has been developed. First, we develop a cell encapsulating strategy using permeable hydrogel and characterized by the amount of cell loss during the immunocytochemistry process for cell phenotyping. We demonstrated that the cell encapsulation in permeable hydrogel compatible with ICC and no loss occurs, while cells are remaining in the same location. Next, the micropatterning of impermeable hydrogel was 65 developed, to form a hydrogel that has spatially varying permeability. The resolution of micropatterning was characterized and spatial permeability change was validated by diffusion of macromolecules. By combining cell encapsulation technique using permeable hydrogel and spatial permeability altering by micropatterning, the selective single cell RNA extraction has been developed and characterized by extracting RNA from a single transgenic cell. Also, the molecular workflow for preparing the sequencing library from extracted single cell RNA and single Jurkat and Raji cell has been sequenced. See-N-Seq provides a simple and effective method to isolate RNA from single cells identified by imaging, enabling the profiling of transcriptional programs. 66 Chapter 5: The Immunological Synapse 5.1 Introduction To demonstrate the ability to sequence RNA from specific transient events at the single cell level, we analyzed the transcriptional profiles following the formation of individual immunological synapses. The dendritic cells make contact with T cells for decision-making related to activation of the T cell. Once the immunological synapse engaged, the T cell must decide the best course of action. This process requires complicated sets of signals with information such as the nature, abundance or distribution to be transmitted back and forth between cells. Based on communicated information, T cell made cellular decisions whether they should mount a productive response, differentiate, abort, or proceed apoptosis. Since the immunological synapse is the first step of cellular decision-making process in immune response, understanding immunological synapse plays a key role in understanding the underlying mechanism from information exchange to final cellular decision. Immunological synapses are formed based on the classical two-signal hypothesis, the immunological synapse also includes numerous functionally diverse receptor-ligand interactions that initiate both co-stimulatory (e.g. CD28) and co-inhibitory (e.g. CTLA4, PD-1) signaling pathways1. Signaling from these co-regulatory receptors converge to activate transcription factors AP-1 (Fos/Jun), NFAT and NF-κB2, but the precise transcriptional program that results from this activation has not been clearly elucidated. 67 RajiJurkatγ εε δVα VβCβCαSEECD4MHC‐II α2 β2β1α1PTCRCD3 Figure 5.1 Schematic of immunological synapse. The schematic shows interaction between Jurkat cell and Raji cell. Jurkat cells are a model for helper T-cells that express CD4 and the T-cell receptor (TCR) on the cell surface. Raji cells are a model for B-cells that present antigens via major histocompatibility complex II (MHC-II). The staphylococcus aureus enterotoxin E (SEE) superantigen forms an immune synapse between Jurkat and Raji cells without matching the Jurkat TCR to antigens presented by the Raji MHC-II. We used an in vitro model of immunological synapses formed using Jurkat, Raji, and Staphylococcus aureus enterotoxin E (SEE) super-antigen. Jurkat cells are a model of CD4+ helper T-cells. Raji cells are a model of an antigen presenting cell with major histocompatibility complex II (MHC-II). The Jurkat cells first recognize MHC-II by CD4 molecule that presented on the surface. Then, T-cell receptor (TCR) approaches to MHC-II molecule for checking proper antigen exist. If proper antigen presents, TCR binds to the MHC-II and forming synapse. In our 68 in vitro model, the SEE super-antigen that binds to anywhere in TCR, which forms an immunological synapse between Jurkat and Raji cells without requiring a proper antigen match as shown in Figure 5.1. As for single cell RNA sequencing workflow, the cell pairs of the immunological synapse model at 0, 4, 24 hour post-synapse were selected and sequenced to investigate time dependent transcriptome profile changes. 5.2 Sequencing of immunological synapse 5.2.1 Selection of immunological synapse We prepared the sample by pre-staining Jurkat cells using Dil and an anti-CD3 antibody to identify the TCR. Raji cells were stained using DiO and treated using SEE. The Jurkat and Raji cells were then mixed, incubated, and observed using fluorescence microscopy. When an immune synapse is formed, the TCR localizes to form a clear line at the interface between Jurkat and Raji cells as shown in Figure 5.2. Interestingly, immune synapses do not form automatically. There are many instances of Jurkat and Raji cells in contact with one another without observed TCR localization. 69 A BDC Figure 5.2 Fluorescent micrograph of immune synapse. (A) 3-channel merged fluorescent micrograph of the immunological synapse formed between Jurkat cell and SEE treated-Raji cells. (B) Jurkat cell, CY3 channel (orange). (C) The interface of synapse has linear localization of TCR, which stained by anti-CD3. (D) Raji cells, in EGFP channel (green). Scale bar: 10 µm. JurkatRajiJurkatRajiA BDC Figure 5.3 Selection of immune synapse. (A) The image of cell pair that formed immune synapse. (B) Selection of this cell pair via micropatterning hydrogel permeability. (C) The images of non-synapse pair. This cell pair is physically separated. (D) Selection of this cell pair from different well. Scale bar: 50 µm. 70 The immune synapse formed cell pair was selected via micropatterning hydrogel permeability. Micropatterning produced a well of unpolymerized PEG250-DA with a 50 µm diameter, centered on the target cell (Figure 5.3A,B). In the same manner, the non-synapse pair was also selected as a control (Figure 5.3C,D). To demonstrate that we could consistently generate transcript sequencing libraries from these cell conjugates, we first generated libraries from bulk specimens (~1000) of Raji (Rb) and Jurkat (Jb) cells from wells without SEE-priming, as well as non-synapsing cells (NSb) or cell synapses sustained for 0, 4 or 24 hours (S0b, S4b, S24b) from wells with SEE-primed Raji. In parallel, we captured single Jurkat (Jsc), Raji (Rsc), non-synapsing (NS0sc, NS4sc, NS24sc) or individual synapsing cells at the three time intervals (S0sc, S4sc, S24sc). Together, isolation and sequence library preparation was performed for 56 independent specimens (Table 5.1). Abbreviations Sample Description Jb1 Jurkat cell Bulk replicate #1 Jb2 Jurkat cell Bulk replicate #2 Jb3 Jurkat cell Bulk replicate #3 Jsc1 Jurkat Single cell replicate #1 Jsc2 Jurkat Single cell replicate #2 Jsc3 Jurkat Single cell replicate #3 Rb1 Raji cell Bulk replicate #1 Rb2 Raji cell Bulk replicate #2 71 Rb3 Raji cell Bulk replicate #3 Rsc1 Raji Single cell replicate #1 Rsc2 Raji Single cell replicate #2 Rsc3 Raji Single cell replicate #3 S0b1 Immeadate after Synapse formed, bulk cells, replicate #1 S0b2 Immeadate after Synapse formed, bulk cells, replicate #2 S0b3 Immeadate after Synapse formed, bulk cells, replicate #3 S0sc1 Immeadate after Synapse formed, singel cell, replicate #1 S0sc2 Immeadate after Synapse formed, singel cell, replicate #2 S0sc3 Immeadate after Synapse formed, singel cell, replicate #3 S0sc4 Immeadate after Synapse formed, singel cell, replicate #4 S0sc5 Immeadate after Synapse formed, singel cell, replicate #5 S0sc6 Immeadate after Synapse formed, singel cell, replicate #6 S4b1 After 4 hours of Synapse formed, bulk cells, replicate #1 S4b2 After 4 hours of Synapse formed, bulk cells, replicate #2 S4b3 After 4 hours of Synapse formed, bulk cells, replicate #3 S4sc1 After 4 hours of Synapse formed, single cell, replicate #1 S4sc2 After 4 hours of Synapse formed, single cell, replicate #2 S4sc3 After 4 hours of Synapse formed, single cell, replicate #3 S4sc4 After 4 hours of Synapse formed, single cell, replicate #4 S4sc5 After 4 hours of Synapse formed, single cell, replicate #5 S4sc6 After 4 hours of Synapse formed, single cell, replicate #6 72 S4sc7 After 4 hours of Synapse formed, single cell, replicate #7 S24b1 After 24 hours of Synapse formed, bulk cells, replicate #1 S24b2 After 24 hours of Synapse formed, bulk cells, replicate #2 S24sc1 After 24 hours of Synapse formed, single cell, replicate #1 S24sc2 After 24 hours of Synapse formed, single cell, replicate #2 S24sc3 After 24 hours of Synapse formed, single cell, replicate #3 S24sc4 After 24 hours of Synapse formed, single cell, replicate #4 S24sc5 After 24 hours of Synapse formed, single cell, replicate #5 NS0b1 Cell pair without synapse forming, bulk cells, replicate #1 NS0b2 Cell pair without synapse forming, bulk cells, replicate #2 NS0sc1 Single cell pair without synapse forming, replicate #1 NS0sc2 Single cell pair without synapse forming, replicate #2 NS0sc3 Single cell pair without synapse forming, replicate #3 NS0sc4 Single cell pair without synapse forming, replicate #4 NS0sc5 Single cell pair without synapse forming, replicate #5 NS4sc1 Single cell pair without synapse forming, after 4 hours of incubation, replicate #1 NS4sc2 Single cell pair without synapse forming, after 4 hours of incubation, replicate #2 NS4sc3 Single cell pair without synapse forming, after 4 hours of incubation, replicate #3 NS4sc4 Single cell pair without synapse forming, after 4 hours of incubation, replicate #4 NS24sc1 Single cell pair without synapse forming, after 24 hours of incubation, replicate #1NS24sc2 Single cell pair without synapse forming, after 24 hours of incubation, replicate #2NS24sc3 Single cell pair without synapse forming, after 24 hours of incubation, replicate #373 NS24sc4 Single cell pair without synapse forming, after 24 hours of incubation, replicate #4NS24sc5 Single cell pair without synapse forming, after 24 hours of incubation, replicate #5NS24sc6 Single cell pair without synapse forming, after 24 hours of incubation, replicate #6NS24sc7 Single cell pair without synapse forming, after 24 hours of incubation, replicate #7 Table 5.1 List of samples and its abbreviations. 5.2.2 Read depth of sequencing Figure 5.4 The number of mapped UMI counts per each sample. The bulk samples show higher counts than single cell samples. Transcript libraries were generated for each condition and transcript sequencing was performed to assess gene expression. Paired end sequencing of transcript cDNA was performed, which captured the UMI on the transcript 3’ end and a transcript sequence on the 5’ end. Alignment of the 5’ read mapped >80% of transcripts to exonic sequence. Unique transcript copy number was 74 determined by UMI count, following deduplication, and this analysis indicated that 200,000 ~ 1,000,000 unique transcripts were sequenced for single cells, while 500,000 ~ 1,500,000 transcripts were sequenced from bulk specimens (Figure 5.4). 5.2.3 Distribution of UMI Figure 5.5 Nucleotide distribution in sequenced UMIs. The distribution of UMI was investigated for presents of bias on random generated UMI. The UMI located precisely at the beginning of the first read, invariably followed by a poly-T stretch. As shown in Figure 5.5, the UMI was evenly distributed across the nucleotides A, C, T, G without significant bias. Overall, nucleotide C has portion of 32.76%, nucleotide A has portion of 25.75%, nucleotide T has portion of 21.92%, while G has portion of 19.56%. The UMI from all samples were present, which confirmed the success of the individual single cell reverse transcription reaction. 75 5.2.4 Correlation analysis – Scatter plot To investigate homogeneity of single cell samples, the correlation between replicates in each sample group was tested. The UMI based exonic mapped counting results loaded to ‘edgeR’ and log2-CPM value was counted. Then samples were grouped, and correlation based on Pearson’s r value was calculated. These correlations were plotted in matrix form with scatter plot of log2-CPM as shown in . The bulk Jurkat samples and bulk Raji samples show very high correlation (Pearson’s r = 0.85 – 0.99), which implies these samples are highly homogeneous (A,B). Also, bulk samples of 0 hour post-synapse (mean Pearson’s r = 0.85), 4 hour post-synapse (mean Pearson’s r = 0.81), 24 hour synapse (mean Pearson’s r = 0.93) were highly correlated as shown in E-G. However, individual Jurkat cells had limited correlation with each other (mean Pearson’s r = 0.54, C), while individual Raji cells showed moderate correlation with each other (mean Pearson’s r = 0.76, D). When we compare single 0 hour post-synapse samples each other to investigate the repeatability, mean Pearson’s r was 0.74, which lower than bulk samples but shows moderate correlation (H). In case of single 4 hour post-synapse samples, mean Pearson’s r was 0.76, which also lower than bulk, but moderate correlation and deviation was very small (I). Interestingly, at 24 hour post-synapse, mean Pearson’s r was 0.76. However, the deviation was large as 0.16, which may indicate exist of large variants in biological replicates. Hence, we compared Pearson’s r between S24sc5 to other samples. The samples showed 2 distinctive group, which S24sc1,2 shows high correlation (0.93 and 0.96) while S24sc3,4 shows low correlation (0.64 and 0.68) (J). In contrast, the comparison between S24sc3 and S24sc4 showed high correlation (0.97). This represents S24sc1,2,5 and 76 A B CE F GH I JD Figure 5.6 Scatter and correlation between transcriptomes of bulk cell samples and between single cells. The Log2-CPM of each sample was plotted in matrix of scatter plot. The scatter plot in lower-left matrix position shows relationship between compared samples. Each dot represents one gene and each axis indicates Log2-CPM values, while number in the reflected upper-right matrix position indicates Pearson correlation coefficient for compared samples. Samples are grouped as follows: (A) Jurkat bulk (Jb), (B) Raji bulk (Rb), (C) Single Jurkat cells (Jsc), (D) Single Raji cells (Rb), (E) Bulk mixtures of Raji and Jurkat cells containing immune synapse at 0 hour (S0b), (F) Bulk mixtures of Raji and Jurkat cells containing immune synapse at 0 hour (S4b), (G) Bulk mixtures of Raji and Jurkat cells containing immune synapse at 0 hour (S24b), (H) Single Raji-Jurkat synapses at 0 hour (S0sc), (I) Single Raji-Jurkat synapses at 4 hours (S4sc), and (J) Single Raji-Jurkat synapses at 24 hour (S24sc). 77 S24sc3,4 has different transcriptome profiles. Based on this result, we divided S24sc samples to 2 subgroups, S24sc1,2,5 and S24sc3,4 for further analysis. The lower correlation in single cell samples compared to bulk samples were expected as single cell samples have more chance to explore heterogeneity. By analyze gene expression level across the single cell sample to corresponding bulk sample, 16,606 distinct RefSeq genes were detected for bulk samples, while 14,168 (85%) were also detected in at least one single cell inside correspond single cell group. 5.2.5 Correlation between bulk and single cells – Scatter plot The bulk and single cell sample of non-synapse cell pair (NS0b, NS0sc) were compared to investigate sequencing quality as well as variation between samples. Correlation between bulk samples are moderate (Pearson’s r = 0.67), and moderate correlation also observed in single cell samples (Pearson’s r = 0.58 - 0.78, mean = 0.688). However, low correlation observed between NS0b2 and NS0sc1. This may result of higher divergence of transcriptome in NS0sc1 since its cell pair. Overall, correlation between bulk sample and single cell pair samples show moderate as mean Pearson’s r is 0.68. 78 Figure 5.7 Scatter and correlation between transcriptomes of non-synapsing bulk cells and non-synapsing cell pairs. Non-synapsing bulk cells are abbreviated as NSb, while non-synapsing cell pairs are abbreviated as NS0sc. The scatter plots in lower-left matrix position shows relationship between compared samples. Each dot represents one gene and each axis indicates Log2-CPM values, while number in the reflected upper-right matrix position indicates Pearson correlation coefficient for compared samples. 79 5.3 Differential Gene Expression analysis Differential gene expression (DGE) analysis was performed in order to validate the RNA sequencing process as to compare gene expression between experimental conditions. The DGE analysis threshold was manually applied to genes that exhibited significance differential expression (p < 0.05). 5.3.1 Jurkat to Raji A BJb vs Rb Jsc vs Rsc Jsc vs RscC Figure 5.8 DGE analysis between Jurkat and Raji Cells. The volcano plot shows DGE analysis between sample group of (A) Jurkat bulk and Raji bulk, (B) Single Jurkat and single Raji. X-axis indicates log2-fold change, Y-axis indicates -log10 P-value. Top 5 genes that has lowest P-value highlighted as a gene name. (C) Mean-Difference (MD) plots of DGE analysis between single Jurkat and single Raji cell. The X-axis is average log-CPM (counts per million) and the Y-axis is log-fold-change. The red highlights indicate upregulation while the blue highlights indicate downregulation in comparison. The highlights are applied to adj P < 0.05. The transcriptomes of individual Raji and Jurkat cells exhibited a high degree of divergence (Pearson’s r=0.51). Differential gene expression (DGE) analysis visualized using volcano plots comparing Jurkat to Raji cell in bulk and single cells showed significantly different in gene expression (Figure 5.8A,B). DGE analysis can also be visualized using mean-divergence (MD) 80 plots. Comparing Jurkat to Raji single cells showed 424 upregulated and 890 downregulated genes (Figure 5.8C; p < 0.05, False discover rate (FDR) corrected). The most significant upregulated genes included those involved in T-cell signal transduction and cytokine signaling (CD3D, IFITM1, IL32), T-cell migration (FLNC, CAPG), resistance to apoptosis and cell stress (GSTP1, GPX1). Gene ontology analysis (http://www.geneontology.org/) of the 50 most over-expressed genes in Jurkat affirmed the T-lymphocyte origin of these cells, as 28 transcripts were associated with GO terms representing the immune system, of which 17 were involved in leukocyte activation, and positive thymic T-cell selection was the most highly enriched category (81.80-fold). Highly expressed transcripts included the ZAP-70 T-cell signaling receptor as well as the CD3 gamma and delta chains of the T-cell receptor complex, which is expected as these genes are significantly expressed in Jurkat cells100,101. 5.3.2 Jurkat and Raji to non-synapse pair Jsc+Rsc vs NS0sc Figure 5.9 MD plots of DGE analysis between conjugation of single Jurkat, single Raji cell and non-synapse at 0 hour (NS0sc). 81 We then used DGE analysis to evaluate the combination of independent Raji and Jurkat cell conjugates. In wells where synapse formation was induced by SEE, non-synapsing Raji and Jurkat cells were isolated. Gene expression in these cell pairs were compared with a composite of the independent Raji and Jurkat gene expression datasets (Figure 5.9). These gene expression profiles were generally consistent, with only 53 genes significantly upregulated and 51 significantly downregulated in the composite dataset, compared to the non-synapsing cell pair. 5.3.3 0 hour post-synapse S0sc vs NS0sc Figure 5.10 MD plots of DGE analysis between synapse at 0 hour (S0sc) and non-synapse at 0 hour (NS0sc). DGE analysis between Jurkat-Raji immune synapses at 0 hour and non-synapsing pairs showed highly consistent expression profiles (Figure 5.10). This result is expected since transcriptional programs often require hours to execute. PIK3IP1 downregulation been observed, which the negative regulator for activating PI3K. This may refers starting of TCR clustering by regulation 82 of PI3K-Akt pathway. Also related to activation of T-cell as the T-cell needs to be activated to form a synapse. 5.3.4 4 hour post-synapse S0b vs S4b S0sc vs S4scS0sc vs S4sc S4sc (1,3,6,7) vs S4sc (2,4,5)A BC D Figure 5.11 DGE analysis at 4 hour post-synapse. Volcano plots shows (A) bulk sample comparison at 4 hour post-synapse (S4b) to 0 hour post-synapse (S0b). (B) single cell sample comparison at 4 hour post-synapse 83 (S4sc) to 0 hour post-synapse (S0sc). (C) MD plot shows DGE analysis between S4sc and S0sc. (D) MD plot of DGE analysis between subgroup of 4 hour post-synapse (S4sc1,3,6,7 and S4sc2,4,5). At 4 hour post-synapse, gene expression profiles begin to diverge from the 0 hour profile. Single cell sample comparison shows more divergence than bulk samples as shown in Figure 5.11A,B. In single cell samples compare to 0 hour, 85 upregulated and 153 downregulated genes were observed (Figure 5.11C). Specifically, genes associated with intracellular signal transduction (CCL17, ADGRG1, ROR1, CCL21, GNA13), cell-cell junction (FBF1, LIMS2, STRN, GJB2), and synaptic transmission (SYNJ2BP, CHRNB1, P2RX1, UNC13B, APBA3) were significantly upregulated. Between subgroups of single cell samples, gene expression profile was highly consistent as expected by high correlation between samples and sample subgroup (Figure 5.11D). only 2 genes observed as upregulated, which KCNK6 and ZNF626. These genes are potassium channel proteins and zinc finger proteins. Variation in these genes considered as less significant since working as house-keeping rather than working for specific pathway or process. 5.3.5 24 hour post-synapse At 24 hour post-synapse, the gene expression for bulk sample has few significant upregulated genes, however, the single cell sample has large fold changes in gene expression (Figure 5.12A,B). In single cell samples, gene expression profiles Jurkat-Raji cell conjugates diverges into two distinct groups. One subgroup was S241,2,5, consisting of three cells (S24sc1, S24sc2, and S24sc5; Pearson’s r=0.83-0.96), and the other was S243,4, consisting of two cells (S24sc3 and S24sc4; Pearson’s r=0.94) both consisted of cells with highly consistent transcriptional 84 profiles. However, only a modest correlation was observed among cells between the two subgroups (Pearson’s r=0.60-0.68). Both subgroups were remarkable in that significantly more S0b vs S24b S0sc vs S24scS0sc vs S24sc (1,2,5) S0sc vs S24sc (3,4)A BC D Figure 5.12 DGE analysis at 24 hour post-synapse. Volcano plots shows (A) bulk sample comparison at 24 hour post-synapse (S24b) to 0 hour post-synapse (S0b). (B) single cell sample comparison at 24 hour post-synapse (S24sc) to 0 hour post-synapse (S0sc). (C) MD plot shows DGE analysis between subgroup of S241,2,5 (S24sc1, S24sc2, S24sc5) and S0sc. (D) MD plot of DGE analysis between subgroup of S43,4 (S24sc3, S24sc4) and S0sc. 85 transcripts were upregulated than downregulated, compared to Jurkat-Raji immune synapses at 0 hour (Figure 5.12C,D). Specifically, 308 transcripts were upregulated and 17 downregulated for subgroup S241,2,5 while 307 were upregulated and 5 downregulated for subgroup S243,4. Among the top five most differentially expressed transcripts in S241,2,5 were GZMB, XCL1, XCL2 and TCIRG1/TIRC7, which are each typically associated with Th1 polarization101–104. 5.3.6 Transcriptome divergence T‐cell activationTh1Th26‐6Relative expression Figure 5.13 The heatmap illustrates log-CPM of marker genes for T-cell activation, T helper type 1 (Th1) cells, and T helper type 2 (Th2) cells. Red indicates genes more expressed while blue indicates genes less expressed. The highlight box indicates high expression samples for marker genes. To further investigate whether the distinct subsets reflect phenotypic polarization of the activated Jurkat T-cells, we evaluated expression of a panel of T-cell activation genes, as well as those 86 known to be upregulated in Th1 and Th2 polarized subtypes (Figure 5.13). TBX21 is a master regulator for the Th1 subtype and was enhanced 2.7 and 1.5 fold in S24sc2 and S24sc5, compared to 0 hour post-synapse105. MAPK8/JNK-1, MAPK14/P38α, STAT5a, NFATc1, RUNX3, TNFRSF4 and IL21R are also important Th1 subtype markers that are upregulated in Jurkat cells105,106. The cells of the S243,4 subgroup expressed higher levels of the GATA3 Th2 master regulator than did cells at 0 hour post-synapse. RXRA, MAPK1/ERK-2, IL2RA and PRKCQ are previously established Th2 markers that were distinctly overexpressed in S243,4106–108. GATA3 and STAT6 were upregulated in both groups, which is not surprising since these genes are involved in T-cell development109,110. Together, these results confirm the potential for T-cells to adopt divergent transcriptional programs after synapsing. 5.4 Experimental The permeable hydrogel prepolymer was prepared as described in chapter 4.2, and the impermeable hydrogel prepolymer was prepared as described in chapter 4.3. Jurkat T cells (ATCC TIB-152) and Raji B cells (ATCC CCL-86) were maintained in RPMI-1640 with 10% FBS and 1% penicillin-streptomycin. To generate immunological synapse, 2×105 Raji B cells were pre-incubated 1.5 hrs with 2 μg/ml purified superantigen staphylococcal enterotoxin E (SEE, Toxin Technology. Cat# ET404 and washed twice in PBS with 1% bovine serum albumin (200 xg, 5 min). Jurkat were labeled with Vybrant DiO (Peak emission: 501 nm) and Raji with Vybrant Dil (Peak emission: 565 nm), according to the manufacturer’s instructions (Invitrogen, Carlsbad, CA). In addition, anti-CD3 antibody conjugated with BV421 (blue) was added to T cells to visualize the TCR localization during immunological synapse. Stained cells were mixed and incubated in 40 μL culture media at 37°C for the duration of each experiment. 87 Cell encapsulation and photo-patterning were performed as described in chapter 4.3.2. The single cell or cell pair is targeted as follows: 1) single Jurkat cell, 2) single Raji cell, 3) synapse formed cell pair at 0 hour, 4 hour, 24 hour post-synapse, 4) non-synapse formed cell pair at 0 hour, 4 hour, 24 hour. As a control, following bulk cells are also prepared: 1) bulk Jurkat cells, 2) bulk Raji cells, 3) bulk synapse formed cells at 0 hour, 4 hour, 24 hour post-synaspe, 4) non-synapse formed cells at 0 hour. RNA extraction and WTA from single cells was performed as described in chapter 4.4.2.4. To prepare the sequencing library, the LD-PCR amplicons are first purified with AMPure XP beads (Beckman Coulter) according to the manufacturer's protocol. Briefly, 35µL of PCR amplicon were transferred to 96 well plate, mixed with 63µL of beads and incubated for 5 min at room temperature. Beads were washed with 70% ethanol twice and resuspended in 40µL of PCR grade water. After incubation for 2 min, the purified-DNAs were eluted by magnet (DynaMag-96 side magnet, Thermofisher, Cat#12331D) and transferred to a new 96-well PCR plate for library preparation. The sequencing library was prepared as described in chapter 4.5. briefly, we used illumina DNA flex kit(Illumnia, Cat# 20018705), which performs the tagmentation, according to manufacture protocol by SBC (Sequencing and Bioinformatics Consortium) at UBC. Prepared library was sequenced using Nextseq 500 with Mid-throughput, 25 bp at READ1 by custom primer and 125 bp at READ2 by illumina’s standard primer. Also, 8 bp of i5 index was sequenced using custom index read primer while 8 bp of i7 index was sequenced using illumina’s standard primer. After sequencing, the raw reads are first demultiplexed based on i5 and i7 indices. The read quality of the demultiplexed fastq files were then confirmed by FASTQC. The Q.C. passed fastq 88 files were then fed into UMI-tools, which detects UMI sequences. Our criteria for consider as UMI was 16 bases from READ1, followed by poly-T sequence. The detected UMI were then trimmed and recorded at the end of flowcell ID on both read1 and read2. Then the poly-A tail at read2 was removed using Cutadapt. The trimmed fastq file was aligned and mapped using Hisat2 aligner using default options against GRChg38 from NCBI, along with RefSeq annotation. Aligned BAM file was fed into featureCounts in Subread package to count number of detected genes. At this point, the gene count is not deduplicated. Since featureCounts create BAM tags XT for each genes, and XS for assigned status, we used these tags in count function of UMI-tools to count and deduplicate the reads. Further statistic analyze was performed using edgeR package in R. For statistical analyze differential gene expression, the UMI-based count data been loaded to ‘edgeR’. The loaded data been grouped first by sample types, and then filtered to remove 0 count genes across the samples. If majority of gene were expressed as 0 count, it will be filtered out. After initial filtering, the library normalizing factor was calculated by provided function. This step is required for correcting bias occur from difference in read counts. The common dispersions were then calculated based on normalized data. Then the data was fitted using a quasi-likelihood negative binomial generalized log-linear model by provided function. The contrast, which defines group for comparison, was applied and gene-wise statistical tests were conducted with likelihood ratio tests replaced by empirical Bayes quasi-likelihood F-tests. The test results then filtered by P<0.05 with FDR corrected. These differential gene expression data was presented by volcano plots, MD plots, or heatmap. 89 5.5 Conclusion In this chapter, we demonstrate the utility of the single cell RNA sequencing workflow via micropatterning of hydrogel permeability. We sequenced and analyzed individual Jurkat-Raji cell conjugates at the time that they initially formed an immunological synapse as well as 4h and 24h post-synapse. This analysis captures transient transcriptional events following the formation of immunological synapses in order to elucidate the enacted transcriptional program. Furthermore, the observation that individual Jurkat cells adopt distinct transcriptional patterns at 24h demonstrates how this same approach could be used to assess heterogeneous outcomes of from cellular decision-making. The immunological synapse represents one example of broader biological phenomena, whereby cells accept external signals that are processed by its decision-making machinery resulting in the enactment of transcriptional programs. 90 Chapter 6: Conclusions 6.1 Summary of results This dissertation described the development of See-N-Seq, a technology to selectively sequence RNA from single cells via micropatterning hydrogel permeability, as well as its application to sequence transcriptome from transient cellular events. As the first step of See-N-Seq workflow, the cell capturing technique by encapsulating in permeable hydrogel thin-film has been developed. The various parameters including porosity, specific gravity, and mechanical strength of the permeable hydrogel have been optimized, as well as characterization by counting cell loss during the immunocytochemistry. Based on developed cell encapsulation by permeable hydrogel thin-film, a micropatterning process to modify spatial permeability of hydrogel has been developed as a second step of See-N-Seq workflow. The hydrogel that has spatially varying permeability achieved by photo-polymerizing of an impermeable hydrogel in the permeable hydrogel matrix. The spatial resolution of micropatterning has been optimized, as well as the permeability altering via micropatterning validated by imaging diffusion of macromolecules. Next, the third step of See-N-Seq workflow has been developed, based on spatially varying hydrogel permeability via micropatterning, which is the extract RNA from the targeted single cell that encapsulated in the permeable hydrogel. The RNA extraction characterized by demonstrating selective lysis of targeted single cell, as well as analyzed by qPCR. The molecular workflow also developed for preparing sequencing library from extracted single cell RNA and applied to Jurkat single cells and Raji single cells. The prepared single cell library was sequenced and shows two distinctive transcriptome profiles between Jurkat cell and Raji cell, which validate the single cell RNA extraction and sequencing by See-N-Seq. 91 Finally, the developed See-N-Seq workflow applied to sequence the specific transient cellular event, using an in vitro model of immunological synapse formed between Jurkat cell and SEE-treated Raji cell. Sequencing libraries were generated at 0, 4, 24 hours after forming immunological synapses, as well as non-synapse formed cell pair at same time points as a control. Our results showed that 24 hours after synapse, the transcriptome of Jurkat-Raji cell pairs diverged into two distinctive profiles, while 0 and 4 hours post-synapse shows similar transcriptome profiles. Specifically, at 24 hours post-synapse, one subgroup shows upregulation of gene sets such as TBX21, MAPK8 that related to differentiate to Th1 cell type, while other subgroup shows upregulation of gene sets such as RXRA, IL2RA that related to differentiate to Th2 cell type. Together, this work demonstrated the potential to extract and sequence RNA from single targeted cells in order to elucidate transcriptional programs enacted after transient events. The strength of See-N-Seq comes from the ability to select a single cell based on a micrograph image and sequence that specific cells RNA. Since the transcriptome profile during the cellular decision-making process occurs transiently, it is important to sequence select specific cells at specific time points, which is a unique feature of See-N-Seq. The throughput of See-N-Seq is limited compared to non-selective single cell RNA sequencing methods. However, non-selective single cell RNA sequencing is typically used for investigating cellular heterogeneity or cell type clustering and cannot identify cellular transient events. When compared to other selective single cell RNA sequencing methods, the throughput of See-N-Seq is likely be significantly greater with automation. We anticipate this capability being used to uncover cellular decision-making process that may confer health and disease at the level of tissues, organs, and organisms. 92 6.2 Future work Having developed a robust workflow for selective single cell RNA sequencing via micropatterning hydrogel permeability, a key goal toward applying this technology in research and translational settings is to refine the process to increase the throughput of the process. Currently, the most time-consuming step was cell imaging and manually investigate cells to find target cell. The image acquisition could be improved by imaging through high-content screening (HCA). Also, by using pre-defined criteria for specific cell phenotypes, the cell targeting speed can be improved. The throughput of micropatterning step also can be improved by full automation. The current See-N-Seq protocol uses custom software that creates mask for fine void pattern and encapsulate other cells in impermeable hydrogel at lower magnification. However, the micropatterning requires manual movement of stage to locate the well that contains target cell. This manual stage moving step can be eliminated by full automation of micropatterning step. Together, fully automated See-N-Seq protocol from cell imaging to micropatterning can improve throughput significantly. The throughput can be further improved by employing barcoded beads for RNA extraction. By embedding cells with barcoded beads, the target single cell can be associated with single barcoded beads. This can enable the RNA extraction from multiple single cell from one well. 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Figure 5.8 B,C Upregulated genes Downregulated genesSymbol logFC PValue Symbol logFC PValueCD3D 13.889 4.06E-08 IMP4 -10.737 4.77E-08IFITM1 11.393 3.05E-06 ISG20 -10.596 6.86E-08GSTP1 11.184 1.55E-07 MIR4435-2HG -10.339 1.33E-07IL32 11.111 4.96E-06 PTPMT1 -10.078 2.60E-07FLNC 10.776 8.82E-06 ZNF593 -10.002 3.11E-07FCGRT 10.711 9.87E-06 PHGDH -9.981 3.31E-07CAPG 10.681 1.04E-05 MCM5 -9.811 5.08E-07SOX4 10.410 2.20E-07 SNHG17 -9.719 6.43E-07MARCHF7 10.406 1.67E-05 DDX49 -9.691 6.94E-07GZMA 10.405 1.68E-05 CRYBB2P1 -9.659 7.49E-07MARCHF2 10.308 1.97E-05 CNPY3 -9.602 8.60E-07SMIM24 10.269 2.11E-05 PFKM -9.602 8.60E-07RBM41 10.265 2.12E-05 FXYD5 -9.520 1.06E-06KRT1 10.234 2.25E-05 RRP1 -9.513 1.08E-06CIDECP1 10.218 2.31E-05 E2F2 -9.489 1.15E-06C21orf91 10.180 2.46E-05 PFAS -9.344 1.66E-06CIR1 10.171 2.49E-05 UCHL1 -9.340 1.71E-06IFITM2 10.171 2.51E-05 DPM2 -9.325 1.75E-06GGPS1 10.159 2.56E-05 SYNCRIP -9.316 1.79E-06LCK 10.152 2.59E-05 RSPH3 -9.291 1.93E-06TMEM41A 10.120 2.74E-05 MS4A1 -9.278 1.98E-06COX11 10.065 3.00E-05 MRRF -9.272 2.03E-06FAM50A 10.057 3.04E-05 WDR25 -9.242 2.19E-06SELENOW 9.926 3.79E-05 MANBAL -9.214 2.31E-06PTPN6 9.915 3.88E-05 PDZD11 -9.153 2.71E-06PIP4K2A 9.900 3.99E-05 AKR1A1 -9.150 2.71E-06AGTRAP 9.891 4.05E-05 BIRC3 -9.116 2.95E-06SH2D1A 9.887 4.08E-05 NUDT22 -9.096 3.12E-06TAZ 9.872 4.18E-05 MINCR -9.084 3.21E-06GPX1 9.831 7.97E-07 JAGN1 -9.073 3.30E-06106 LPCAT2 9.827 4.51E-05 RFC2 -9.059 3.40E-06CD247 9.794 4.77E-05 MRPL4 -9.017 3.82E-06TMEM186 9.750 5.17E-05 WDR18 -9.014 3.82E-06TPST2 9.745 5.21E-05 NCOR1 -8.999 3.94E-06A1BG 9.720 5.38E-05 AP1G2 -8.997 4.06E-06TMEM245 9.682 5.76E-05 EIF4E -8.992 4.06E-06NUP50-DT 9.682 5.76E-05 SFT2D1 -8.970 4.32E-06IFT22 9.643 6.18E-05 SAFB -8.967 4.32E-06PLEKHG5 9.642 6.18E-05 CPNE5 -8.961 4.46E-06NCLN 9.627 6.34E-05 SLC2A5 -8.948 4.46E-06METTL22 9.616 6.46E-05 PEX16 -8.944 4.60E-06IGFBP6 9.613 6.52E-05 ZNF574 -8.920 4.90E-06ZNF561 9.601 6.64E-05 GLRX3 -8.851 5.78E-06DOLPP1 9.592 6.76E-05 BPTF -8.787 6.86E-06IRF3 9.584 6.82E-05 ATAD3A -8.749 7.63E-06SLC7A5P2 9.580 6.88E-05 CARD19 -8.748 7.63E-06INTS6-AS1 9.576 6.95E-05 CC2D1A -8.744 7.63E-06PRAF2 9.554 7.21E-05 MRPS33 -8.740 7.63E-06FLYWCH1 9.541 7.35E-05 KIAA2013 -8.712 8.20E-06CPNE7 9.538 7.42E-05 APTX -8.706 8.20E-06ANXA11 9.532 7.49E-05 LSM10 -8.696 8.51E-06TCIRG1 9.497 7.93E-05 LOC730202 -8.690 8.51E-06DBN1 9.494 8.01E-05 CHMP2A -8.673 9.18E-06LZTR1 9.484 8.17E-05 PPP2R5C -8.669 9.18E-06DGKA 9.479 8.25E-05 SQSTM1 -8.661 9.18E-06ERCC3 9.474 8.25E-05 PRPF4 -8.661 9.18E-06FAM91A1 9.442 8.76E-05 MCM4 -8.632 9.91E-06CTSW 9.439 8.76E-05 ARPC5L -8.631 9.91E-06ELMOD2 9.423 9.03E-05 LETMD1 -8.628 9.91E-06QRICH1 9.406 9.22E-05 NTHL1 -8.627 5.17E-07UBASH3B 9.392 9.52E-05 CDT1 -8.617 1.03E-05SFSWAP 9.376 9.72E-05 C1orf50 -8.602 1.07E-05ADA 9.342 0.0001036 DEF8 -8.588 1.12E-05IMPA2 9.336 0.0001048 HAUS8 -8.584 1.12E-05TPP1 9.334 0.0001048 MXD4 -8.568 1.16E-05STMN3 9.322 0.0001071 EBNA1BP2 -8.552 1.21E-05GCLM 9.317 0.0001083 NUP188 -8.545 1.26E-05RAD21 9.266 0.0001171 FASTKD1 -8.529 1.31E-05PPP1R12A 9.253 0.0001212 DCAF16 -8.529 1.31E-05CYTH1 9.244 0.0001226 FAAP20 -8.520 1.31E-05107 CEP250 9.226 0.0001255 ASPSCR1 -8.520 1.31E-05LYSMD4 9.189 0.0001348 TPD52L2 -8.520 1.31E-05HEXIM2 9.173 0.0001381 FNBP1 -8.506 1.37E-05TAL1 9.171 0.0001398 SLC3A2 -8.501 1.37E-05DCTN4 9.165 0.0001398 HMGB3 -8.499 1.37E-05MT1F 9.164 0.0001398 KIF5B -8.480 1.43E-05MFGE8 9.143 0.000145 THOP1 -8.443 1.63E-05YTHDF1 9.140 0.000145 RABGAP1L -8.437 1.63E-05MAST4 9.130 0.0001487 UAP1 -8.430 1.63E-05RTL8A 9.109 0.0001544 PPP2R2D -8.424 1.63E-05CCDC142 9.095 0.0001564 NOP16 -8.419 1.70E-05DHFR2 9.080 0.0001626 CCL22 -8.399 1.78E-05AGAP3 9.074 0.0001647 SUGP2 -8.383 1.86E-05RTEL1 9.067 0.0001647 MRPS35 -8.366 1.95E-05HAUS6 9.059 0.0001669 ZPR1 -8.338 2.04E-05ALKBH2 9.058 0.0001669 HLA-DQA1 -8.325 2.14E-05ID1 9.037 0.0001736 DYNLT1 -8.325 2.14E-05DNAJC18 9.026 0.0001784 SLC50A1 -8.322 2.14E-05S100A10 9.016 0.0001808 ARMC9 -8.316 2.14E-05UGP2 9.013 0.0001808 RBSN -8.308 2.25E-05NSD3 9.005 0.0001833 C19orf24 -8.302 2.25E-05DCTN6 9.002 0.0001833 STAT5B -8.278 2.36E-05SAT2 8.978 0.000191 GCAT -8.278 2.48E-05LAT 8.968 0.0001964 UBE2C -8.267 2.48E-05SNX10 8.953 0.0001991 DBF4 -8.261 2.48E-05BIN2 8.939 0.0002049 LARP4B -8.256 2.48E-05CELF2 8.936 0.0002049 ASAH1 -8.252 2.60E-05SSX2IP 8.926 0.0002108 ELAC2 -8.239 2.60E-05CD2 8.921 0.0002108 CCNA2 -8.233 2.74E-05SLC25A15 8.887 0.0002235 PCIF1 -8.209 2.88E-05UTP11 8.885 0.0002235 GNAI3 -8.180 3.04E-05IFT52 8.859 0.0002337 PMF1 -8.173 3.04E-05PAOX 8.850 0.0002409 CDC27 -8.156 3.20E-05MED9 8.850 0.0002372 IGLC5 -8.146 3.37E-05C20orf197 8.836 0.0002446 STK24 -8.145 3.37E-05AOC3 8.833 0.0002446 ODC1 -8.141 3.37E-05NOLC1 8.797 0.0002602 IFT57 -8.133 3.37E-05HOMER3 8.794 0.0002643 TMEM97 -8.128 3.37E-05C14orf119 8.790 0.0002643 PDE8A -8.125 3.56E-05SGF29 8.753 0.0002817 CLECL1 -8.115 3.56E-05108 UCP2 8.735 1.07E-05 WDR43 -8.103 3.76E-05ZUP1 8.732 0.0002911 MDM4 -8.097 3.76E-05SARS2 8.732 0.0002911 ZMYND19 -8.097 3.76E-05LOC100506990 8.720 0.0003008 UBP1 -8.081 3.98E-05ARFGAP1 8.719 0.0002959 SDHAF2 -8.059 4.21E-05EMID1 8.711 0.0003059 TNFRSF13B -8.055 4.21E-05NET1 8.707 0.0003059 IPO5 -8.049 4.21E-05APOBEC3F 8.686 0.0003164 RGS16 -8.040 4.21E-05SMARCA2 8.684 0.0003164 LITAF -8.036 4.46E-05SNX8 8.683 0.0003164 TCL1A -8.035 4.46E-05FAM173A 8.665 0.0003274 ST6GALNAC4 -8.026 4.46E-05TM9SF1 8.622 3.10E-05 PCGF5 -8.023 4.46E-05PCAT18 8.606 0.0003573 SPAG7 -8.021 4.46E-05KCNA6 8.585 0.0003771 HIRIP3 -8.020 4.46E-05IGLL1 8.583 0.0003771 EML4 -8.014 4.46E-05MAPRE2 8.567 0.000384 RNFT1-DT -8.013 4.73E-05THAP8 8.535 0.0004059 GATB -8.013 4.73E-05GTF3C3 8.520 0.0004137 RIPK2 -8.010 4.46E-05BAZ1B 8.514 0.0004216 RER1 -8.004 4.73E-05RPL32P3 8.501 0.0004297 ADD1 -7.989 5.01E-05ERCC2 8.499 0.000438 C6orf226 -7.989 5.01E-05PRR14L 8.494 0.000438 GUCD1 -7.985 5.01E-05CD8B 8.490 0.000438 CCR7 -7.976 5.01E-05ZDHHC4 8.486 0.000438 CAPN1 -7.960 5.32E-05DLX4 8.483 0.0004466 OIP5-AS1 -7.946 5.32E-05RNPEPL1 8.483 0.0004466 SNX22 -7.946 5.32E-05NOL4L 8.476 0.0004466 SNHG1 -7.946 5.32E-05ISCA2 8.473 0.0004554 PRKACB -7.916 5.66E-05BNIP1 8.427 0.0004932 INF2 -7.916 5.66E-05LINC00884 8.426 0.0004932 TCP11L1 -7.902 6.02E-05TIMP2 8.409 0.0005033 COASY -7.895 6.02E-05TFDP2 8.397 1.91E-05 PCED1B-AS1 -7.895 6.02E-05FAM24B 8.395 0.0005137 NARS -7.893 6.02E-05TBC1D32 8.390 0.0005137 SLC26A6 -7.890 6.02E-05CARHSP1 8.389 1.94E-05 SZRD1 -7.867 6.42E-05ATP13A2 8.381 0.0005245 RPUSD1 -7.826 7.30E-05FAH 8.364 0.0005355 YARS -7.812 7.30E-05TSPAN7 8.361 0.0005469 BCL2A1 -7.812 7.30E-05TSPAN5 8.341 0.0005706 IKZF3 -7.812 7.30E-05LRRC37B 8.319 0.0005831 PCK2 -7.812 7.30E-05109 RHOC 8.312 2.21E-05 XBP1 -7.803 7.81E-05KDM6A 8.310 0.0005959 MRPS27 -7.794 7.81E-05SAMD13 8.308 0.0005959 PLIN2 -7.788 7.81E-05ZNF256 8.308 0.0005959 KDELR2 -7.786 7.81E-05GINS4 8.307 0.0005959 NOTUM -7.785 7.81E-05ZNF282 8.289 0.0006091 HIST1H2AL -7.785 7.81E-05TWISTNB 8.280 2.19E-05 RAD50 -7.770 8.35E-05PLK3 8.272 0.0006368 IDNK -7.770 8.35E-05LIN37 8.271 0.0006368 UQCC3 -7.755 8.95E-05LINC01882 8.268 0.0006368 EXD3 -7.754 8.35E-05JARID2 8.268 0.0006368 PEX6 -7.738 8.95E-05IAH1 8.261 0.0006513 PGD -7.737 2.49E-06ZNF330 8.249 0.0006663 MPZL1 -7.731 8.95E-05CACNA2D4 8.247 0.0006513 CENPM -7.731 8.95E-05TMX4 8.247 0.0006513 CD83 -7.729 8.95E-05INKA1 8.238 0.0006663 CENPV -7.723 9.60E-05KIAA0930 8.236 0.0006663 NIP7 -7.722 8.95E-05HMGN5 8.215 0.0006977 SAPCD2 -7.722 8.95E-05LEF1-AS1 8.213 0.0006977 CDKN2A -7.719 9.60E-05ALG2 8.213 0.0006977 UTP18 -7.717 9.60E-05LZIC 8.204 0.0007142 BCAT1 -7.716 9.60E-05ANKRD13D 8.204 0.0007142 CTSS -7.696 9.60E-05RETREG1 8.204 0.0007142 CD79B -7.663 1.51E-06ANKRD50 8.201 0.0007142 TRAF1 -7.659 0.000111CST3 8.195 0.0007312 USP28 -7.656 0.000111SIX6 8.189 0.0007312 EIF4B -7.647 0.000111PMVK 8.183 0.0007312 PNKP -7.642 0.000111RCCD1 8.145 0.0007859 SNRNP40 -7.639 0.000111GNA15 8.136 0.0007859 MCRIP1 -7.634 0.000111FMNL3 8.113 0.0008256 IFI16 -7.633 0.0001195HMGN3 8.113 0.0008256 RABEP1 -7.629 0.0001195REEP1 8.100 0.0008465 DYNC1LI1 -7.629 0.0001195BEX3 8.099 0.0008465 ILVBL -7.627 0.0001195LPIN1 8.074 0.0008906 ECI1 -7.624 0.0001195NFATC1 8.073 0.0008906 ZNF18 -7.622 0.0001195RCBTB2 8.060 0.0008906 DALRD3 -7.622 0.0001195MYH9 8.057 3.41E-05 COPS4 -7.621 0.0001195ITM2A 8.049 0.0009138 PDLIM1 -7.618 0.0001195ACAP1 8.043 0.0009138 AARS -7.617 0.0001195SCARNA5 8.041 0.0009379 TEDC2 -7.611 0.0001195110 TPM2 8.041 0.0009379 C11orf24 -7.600 0.0001195F8 8.033 0.000963 VPREB3 -7.595 3.54E-06BBS12 8.019 0.000963 USP9X -7.587 0.000129LGALS9 8.016 0.000963 PMPCA -7.586 0.000129TLE2 8.016 0.000963 FCHSD2 -7.580 0.000129SLC9A5 8.005 0.0009889 EXOSC5 -7.580 0.000129SUGP1 8.005 0.0009889 SDHA -7.580 0.000129BTF3L4 7.991 0.0010159 ORC3 -7.569 0.000129PIBF1 7.991 0.0010159 RTF2 -7.566 0.0001394EFNA4 7.984 0.0010159 KLF6 -7.551 0.0001394CD3G 7.979 0.0010159 NOL6 -7.551 0.0001394PI16 7.977 0.0010438 C1orf159 -7.548 0.0001394BARD1 7.973 0.0010438 DDX1 -7.548 0.0001394SLC35B4 7.970 0.0010438 SMAP1 -7.548 0.0001394LZTFL1 7.967 0.0010438 TSG101 -7.548 0.0001394HPGD 7.965 0.0010438 XPO6 -7.538 0.0001394ARL2BP 7.962 9.60E-05 UFM1 -7.530 0.0001509ARHGEF18 7.959 0.0010729 NEK6 -7.523 0.0001509FHIT 7.954 0.0010729 SMIM4 -7.523 0.0001509UBASH3A 7.943 4.15E-05 GPSM2 -7.515 0.0001509TSPAN13 7.928 0.0011346 ZBTB45 -7.515 0.0001509SLC25A38 7.927 4.27E-05 CCDC22 -7.515 0.0001509ZNF576 7.916 0.0011346 OLMALINC -7.514 0.0001509PNPLA6 7.888 0.0012013 PIGU -7.508 0.0001509PCMTD1 7.888 0.0012013 TNFRSF9 -7.496 0.0001636DKFZP586I1420 7.885 0.0012013 GALE -7.495 0.0001636MFSD12 7.873 0.0012367 SMAP2 -7.482 0.0001636DUS2 7.873 0.0001124 PAFAH1B2 -7.482 0.0001636CD72 7.858 0.0012737 B9D1 -7.482 0.0001636SORT1 7.857 0.0012737 PRG2 -7.476 0.0001636CCDC82 7.850 0.0012737 HSH2D -7.476 0.0001636SNRNP35 7.842 0.0013121 MFSD4B -7.471 0.0001636FAM160A2 7.829 0.0013121 CENPA -7.469 0.0001777SMIM8 7.829 0.0013121 NADK -7.466 0.0001636VAT1 7.826 0.0013121 MKKS -7.464 0.0001777CRKL 7.825 0.0013121 TMED8 -7.460 0.0001777PBRM1 7.816 0.0013523 RMND5B -7.457 0.0001777CASP8AP2 7.806 0.0013941 RCL1 -7.457 0.0001777SLC25A26 7.804 3.79E-05 TAF1D -7.451 0.0001777ICAM4 7.800 0.0013941 EMC1 -7.448 0.0001777111 SRPK1 7.794 0.0013941 SLC25A12 -7.448 0.0001777DCAF7 7.777 0.0014378 RCC1L -7.448 0.0001777NAGK 7.777 0.0014378 RASSF2 -7.444 0.0001777CDCP1 7.771 0.0014378 MSTO1 -7.439 0.0001777AUTS2 7.754 0.0014835 CTC1 -7.437 0.0001777TRIM68 7.741 0.0015312 POC1A -7.437 0.0001777NEIL2 7.732 0.0015312 SURF1 -7.437 0.0001777DHFR 7.710 0.0016332 COG5 -7.432 0.0001777ARV1 7.688 0.0016878 NDUFAF6 -7.432 0.0001934DPF3 7.688 0.0016878 SND1 -7.414 0.0001934E4F1 7.683 6.52E-05 STX3 -7.413 0.0001934NREP 7.679 0.0016878 ELMO1 -7.413 0.0001934CPT2 7.675 0.0017451 SNAP29 -7.406 0.0001934PREP 7.675 0.0017451 TANK -7.397 0.0001934NLRC5 7.670 0.0017451 TLR10 -7.397 0.0001934HCFC1 7.658 0.0017451 PYCR1 -7.384 0.0002108SLC19A1 7.653 0.001805 TOP1 -7.383 0.0002108EFHD2 7.651 0.001805 TNIP1 -7.381 0.0002108TNFRSF1A 7.651 0.001805 NA -7.377 0.0002108ASB8 7.647 0.001805 FAM53B -7.377 0.0002108BCAP29 7.647 0.001805 SLC43A3 -7.377 0.0002108LRP12 7.640 0.001805 PPFIA1 -7.377 0.0002108IARS 7.625 0.0001711 DOCK6 -7.377 0.0002108ROBO3 7.623 0.0018679 MCFD2 -7.377 0.0002108TTPAL 7.623 0.0018679 ABCF2 -7.377 0.0002108SLA 7.623 0.0018679 PHF19 -7.377 0.0002108TRMT10B 7.614 0.0018679 IFNAR1 -7.356 0.0002108ACSF3 7.613 0.0018679 SCD -7.348 0.0002304HEXD 7.603 0.0019339 CHP1 -7.342 0.0002304CXXC1 7.595 0.0019339 TFB1M -7.337 0.0002304WASHC5 7.595 0.0019339 TSFM -7.336 0.0002304SH3BP2 7.581 0.0020032 ZNF37A -7.335 0.0002304ENKD1 7.546 0.0021527 SLC39A11 -7.332 0.0002304PHF7 7.546 0.0021527 REC8 -7.330 0.0002304REEP2 7.546 0.0020761 EBLN3P -7.330 0.0002304ACBD7 7.512 0.0022333 TK1 -7.327 0.0002304RFNG 7.507 0.0022333 TMC8 -7.315 0.0002522HOOK2 7.487 0.0023182 B3GAT3 -7.302 0.0002522TRIM56 7.475 0.0024078 DDX19B -7.302 0.0002522SLC2A8 7.475 0.0024078 HSPBP1 -7.302 0.0002522112 FAAH2 7.467 0.0024078 LYN -7.302 0.0002522ALDOC 7.464 0.0024078 ZNF782 -7.302 0.0002522ARL4C 7.463 0.0024078 ACLY -7.285 0.0002768RNF44 7.452 0.0024078 RB1CC1 -7.270 0.0002768HOMER1 7.447 0.0025022 CRYBB2 -7.269 0.0002768IFITM3 7.443 0.0025022 CD48 -7.268 0.0002768FNIP2 7.443 0.0025022 MFSD2A -7.264 0.0002768GLIPR2 7.426 0.0026019 SIAH2 -7.264 0.0002768MRPS5 7.425 0.0002413 FAM107B -7.260 1.49E-05SERINC3 7.424 0.0001014 IGHG3 -7.259 0.0002768GRINA 7.420 0.0026019 CCDC74A -7.254 0.0002768RAB30-DT 7.410 0.0026019 NAGLU -7.248 0.0002768USP48 7.405 0.0027072 ASXL1 -7.248 0.0002768HMCES 7.400 0.0027072 ANKLE2 -7.242 0.0003045RHBDD2 7.394 0.0027072 SRSF8 -7.226 0.0003045STK3 7.361 0.0029365 ORC1 -7.224 0.0003045CCNDBP1 7.361 0.0028186 NMB -7.224 0.0003045BOD1L1 7.361 0.0028186 RGS19 -7.224 0.0003045DNM1 7.361 0.0028186 CCDC86 -7.210 0.0003045RBM48 7.352 0.0029365 GSTK1 -7.203 0.0003045CD1C 7.351 0.0029365 LYRM2 -7.196 0.0003045NIPSNAP3A 7.348 0.0029365 TAX1BP1 -7.191 0.0003358MEX3A 7.345 0.0029365 SLC29A1 -7.189 0.0003358RALBP1 7.343 0.0029365 COA4 -7.183 0.0003358SLTM 7.341 0.0001094 CIC -7.183 0.0003358CNOT6 7.339 0.0029365 ABHD12 -7.183 0.0003358PLEKHA2 7.339 0.0029365 DDX10 -7.179 0.0003358MRPS36 7.328 0.0030615 KPNA3 -7.179 0.0003358SUOX 7.321 0.0030615 CLN6 -7.179 0.0003358ZNF101 7.321 0.0030615 SUV39H1 -7.179 0.0003358BEND6 7.321 0.0030615 UBE2L3 -7.165 0.0003358CCDC12 7.320 0.0030615 ZCCHC17 -7.155 0.0003358TGOLN2 7.319 0.0030615 SCAF8 -7.155 0.0003712LSM1 7.306 0.003194 WDR74 -7.148 1.99E-05GPX7 7.294 0.003194 HUS1 -7.142 0.0003712SART1 7.294 0.003194 LRCH1 -7.141 0.0003712ZFP36L2 7.289 0.003194 URI1 -7.141 0.0003712CWF19L1 7.280 0.003194 MAP3K4 -7.141 0.0003712FMR1 7.280 0.003194 MGLL -7.139 0.0003712SDHC 7.277 0.0033347 DCTN1 -7.131 0.0003712113 APC 7.273 0.0033347 PI4K2B -7.131 0.0003712EXOSC10 7.265 0.0033347 CHTOP -7.124 0.0003712NFU1 7.265 0.0033347 PDHA1 -7.123 0.0003712MBD3 7.252 0.0033347 PPP1R16A -7.097 0.0004116ZNF526 7.237 0.0034843 NA -7.097 0.0004116ORAI2 7.237 0.0034843 PNPLA4 -7.097 0.0004116PPP1R15A 7.211 0.0036434 FNBP4 -7.097 0.0004116TP53TG1 7.202 0.0036434 PEA15 -7.094 0.0004116DNAJB2 7.199 0.0038128 TMEM231 -7.082 0.0004116EIF4H 7.199 0.0038128 CRLS1 -7.082 0.0004116PROCR 7.199 0.0036434 RABL6 -7.068 0.0004577RGL4 7.198 0.0036434 RFXANK -7.060 0.0004577TXNDC16 7.194 0.0038128 KNSTRN -7.056 0.0004577MIEF1 7.194 0.0038128 EZR -7.056 2.73E-05GIMAP4 7.194 0.0038128 KTN1-AS1 -7.053 0.0004577RBM12 7.189 0.0038128 UBE3C -7.053 0.0004577FYB1 7.181 0.0038128 GART -7.049 0.0004577CCDC174 7.174 0.0039935 RNF219 -7.030 0.0004577WRB 7.161 0.0039935 BRPF1 -7.030 0.0004577RPL23AP82 7.161 0.0039935 UBE2G1 -7.022 0.0005105SIGIRR 7.149 0.0039935 NUDT8 -7.015 0.0005105ATP6V0A1 7.149 0.0039935 ZNF511 -7.011 0.0005105HAUS7 7.149 0.0039935 ASH1L-AS1 -7.006 0.0005105LSP1P4 7.129 0.0041865 RABL3 -7.006 0.0005105TRL-CAG2-1 7.124 0.0041865 PROB1 -7.006 0.0005105ABHD14A 7.124 0.0041865 TRMO -7.006 0.0005105PGGT1B 7.124 0.0041865 UROD -7.004 0.0005105RUNX2 7.124 0.0041865 ACTL10 -7.004 0.0005105IVNS1ABP 7.121 0.0041865 CASC11 -7.004 0.0005105TMEM168 7.121 0.0041865 CD320 -6.981 3.35E-05LRRC14 7.121 0.0041865 SMARCD2 -6.980 0.0005105ZAP70 7.120 0.0041865 GATC -6.980 0.0005105PHKB 7.110 0.0043927 MSRB2 -6.959 0.0005712ZNF107 7.103 0.0043927 IL7R -6.957 0.0005712MSL3 7.103 0.0043927 DMAC2L -6.956 0.0005712TMSB4Y 7.097 0.0043927 ACTR6 -6.950 0.0005712TRIM7 7.081 0.0043927 NIPA2 -6.950 0.0005712CHMP5 7.081 0.0043927 CD40 -6.950 0.0005712PFN2 7.063 0.0046134 GPATCH4 -6.948 0.0005712RNF26 7.055 0.0046134 TUBB3 -6.947 0.0005712114 RTL6 7.055 0.0046134 ESF1 -6.942 0.0005712DIAPH1 7.017 8.37E-05 ACTR1B -6.922 0.0005712PSMC4 6.987 0.0002139 PRPF4B -6.922 0.0005712METTL21A 6.951 0.0002639 PLOD1 -6.918 0.0006415EXOSC1 6.853 0.0002685 SLAMF7 -6.914 0.0006415SCAMP5 6.848 0.0002728 PSPH -6.911 0.0006415PHKG2 6.778 0.0001256 AP4B1 -6.909 0.0006415DDX51 6.606 0.0004059 COMTD1 -6.909 0.0006415MINDY2 6.588 0.0004137 SLC37A4 -6.909 0.0006415ZDHHC24 6.511 0.0001824 NXT1 -6.909 0.0006415DDX47 6.504 0.0011301 CEP63 -6.909 0.0006415TRAC 6.486 0.0001429 CDYL -6.909 0.0006415SPACA9 6.479 0.0005033 SEMA4A -6.902 0.0006415PDAP1 6.445 0.0003857 DEPDC1 -6.893 0.0006415EAPP 6.441 0.0001792 COPZ1 -6.893 0.0006415TMCO3 6.341 0.000737 SLC7A1 -6.893 0.0006415NFYC 6.295 0.0006817 MRPS26 -6.893 0.0006415TMA16 6.292 0.0006977 SFI1 -6.893 0.0006415NMRK1 6.289 0.0006977 MRPL50 -6.893 0.0006415MAPK12 6.281 0.0006611 PRPF38B -6.891 0.0006415CCDC115 6.254 0.0007312 POLR1E -6.886 0.0006415C12orf57 6.216 0.0002427 ANKLE1 -6.881 0.0006415TTC3 6.096 0.0004839 ATP5F1E -6.870 0.000723RRP36 6.008 0.0025708 ADAL -6.867 0.000723MTO1 6.005 0.0006751 DPH3 -6.865 0.000723UBE2Q2P1 5.994 0.0010742 PXN -6.864 0.000723SPDL1 5.980 0.0005154 FBLN5 -6.864 0.000723SREK1 5.974 0.0004904 DPH6-DT -6.864 0.000723TSNAX 5.969 0.0011673 NFE2L1 -6.864 0.000723PICK1 5.962 0.0011673 POLDIP3 -6.864 0.000723CFAP36 5.959 0.0007315 ELAC1 -6.858 0.000723FBXO11 5.902 0.0015334 GGCX -6.858 0.000723PSMC6 5.824 0.0006345 SFXN1 -6.846 0.000723CHD2 5.813 0.0014531 RIMKLA -6.835 0.000723ZFAND5 5.757 0.00056 RIMS3 -6.835 0.000723PIH1D1 5.748 0.0016878 ATE1 -6.835 0.000723TMEM9 5.710 0.004227 GOT1 -6.835 0.000723SEC13 5.706 0.0017329 ZNF431 -6.835 0.000723ETNK1 5.690 0.0010872 EMC3-AS1 -6.835 0.000723FIP1L1 5.643 0.001471 C11orf1 -6.826 0.000723115 GAMT 5.604 0.0025078 SMIM12 -6.815 0.0008181CMC4 5.568 0.0018682 DUSP2 -6.815 0.0008181POLE3 5.497 0.0021093 C12orf49 -6.805 0.0008181PLD3 5.490 0.0026019 IPO4 -6.805 0.0008181ANXA6 5.380 0.0030615 SIL1 -6.805 0.0008181GGA1 5.380 0.0013244 PAK1IP1 -6.805 0.0008181BTN3A2 5.364 0.003194 FLNA -6.805 0.0008181KNOP1 5.348 0.001694 ARNT -6.805 0.0008181MVB12A 5.316 0.0009603 ODR4 -6.805 0.0008181TAF7 5.316 0.0011334 ADGRB2 -6.805 0.0008181FARSB 5.307 0.0034843 TRPC6 -6.805 0.0008181ZNHIT1 5.261 0.0010225 INO80C -6.805 0.0008181SF3B1 5.258 0.002218 NOB1 -6.798 0.0008181CDK5RAP3 5.244 0.0022123 AP3D1 -6.786 0.0008181FANCI 5.244 0.0039935 STARD3 -6.777 0.0008181COMMD3 5.202 0.0014027 MAPKAP1 -6.776 0.0008181MR1 5.172 0.0043927 DTX1 -6.774 0.0008181TMEM242 5.120 0.0027795 TAF4B -6.765 0.0009297YJU2 5.116 0.0028185 WASH2P -6.760 0.0009297POLR3C 5.058 0.0027111 POLR3E -6.755 0.0009297MLLT11 4.928 0.0037258 ENO1-AS1 -6.753 0.0009297RAB12 4.923 0.0037669 ELP2 -6.750 0.0009297NDUFA5 4.876 0.0032306 WDR83 -6.750 0.0009297BRD8 4.792 0.0032004 SLC9A7 -6.750 0.0009297TMEM179B 4.791 0.0026918 DPH6 -6.742 0.0009297WSB1 4.706 0.0039919 NRTN -6.742 0.0009297MRPL46 4.706 0.0024749 BOD1 -6.742 0.0009297C4orf48 4.648 0.0029026 ACBD3 -6.740 0.0009297ZSWIM7 4.643 0.0035673 SRGN -6.737 0.0009297TNFAIP8 4.615 0.0038115 GGA2 -6.727 0.0009297AGPAT2 4.614 0.0037862 ST20 -6.693 0.0010613POLRMT 4.499 0.0039356 OLA1 -6.693 0.0010613TMED10 4.417 0.0044076 NCKIPSD -6.693 0.0010613PHIP 4.389 0.0040567 PDLIM5 -6.693 0.0010613 EXOSC2 -6.680 0.0010613 ODF3B -6.677 0.0010613 PURPL -6.677 0.0010613 SEC24C -6.677 0.0010613 POLR1C -6.669 0.0010613 PDCD6 -6.652 1.52E-05116 ZDHHC12 -6.646 0.0010613 USP33 -6.643 0.0010613 MFSD14A -6.643 0.0010613 ZNF83 -6.643 0.0010613 DGAT1 -6.643 0.0010613 VAMP8 -6.640 7.72E-05 CBWD2 -6.638 0.0012173 APEH -6.636 0.0012173 WDR45B -6.634 0.0012173 NECAB3 -6.634 0.0012173 UIMC1 -6.634 0.0012173 MZT1 -6.633 0.0012173 CHCHD3 -6.610 0.0012173 PELP1 -6.609 0.0012173 B4GAT1 -6.608 0.0012173 EMC9 -6.608 0.0012173 IDH1 -6.608 0.0012173 SELENOM -6.608 0.0012173 PLRG1 -6.608 0.0012173 RWDD2A -6.608 0.0012173 SLC44A1 -6.607 0.0012173 NQO1 -6.593 0.0012173 DLD -6.593 0.0012173 PM20D2 -6.583 0.0014037 KLHL21 -6.576 0.0014037 CENPJ -6.574 0.0014037 PRDM2 -6.573 0.0014037 FKBP4 -6.573 0.0014037 PPM1A -6.573 0.0014037 FNIP1 -6.573 0.0014037 LY96 -6.573 0.0014037 SIGMAR1 -6.573 0.0014037 TMEM203 -6.573 0.0014037 MYCBP2 -6.572 0.0014037 SGPP1 -6.572 0.0014037 BICRA -6.572 0.0014037 LMBRD2 -6.572 0.0014037 FOXM1 -6.546 0.0014037 MICU2 -6.537 0.0014037 ZDHHC8 -6.537 0.0014037117 LRP2BP -6.537 0.0014037 TOR2A -6.537 0.0014037 NPAT -6.534 0.0014037 NOC3L -6.519 0.0016277 EIF4A3 -6.517 0.0014037 RPS27L -6.516 4.96E-06 MIPEP -6.514 0.0014037 TELO2 -6.513 0.0016277 PSMF1 -6.509 0.0001055 TMEM18 -6.507 0.0016277 RTL10 -6.507 0.0016277 LOC100996447 -6.507 0.0016277 SNX11 -6.499 0.0016277 SHARPIN -6.499 0.0016277 G6PD -6.499 0.0016277 MYNN -6.492 0.0016277 RAB29 -6.490 0.0016277 HLA-DQB1 -6.489 0.0001094 GCSH -6.488 0.0016277 PLEKHM2 -6.478 0.0016277 PHB -6.478 1.71E-05 RPL22L1 -6.477 0.0016277 SRM -6.473 6.64E-06 SBNO1 -6.472 0.0016277 P2RX5 -6.472 0.0016277 SERPINB9 -6.472 0.0016277 SDF4 -6.464 0.0016277 TRMT6 -6.464 0.0016277 MOB2 -6.461 0.0016277 CIITA -6.461 0.0016277 TXNDC9 -6.461 0.0016277 LDAH -6.461 0.0016277 ZNF337 -6.461 0.0016277 STX16 -6.461 0.0016277 TENT2 -6.461 0.0016277 TSPAN17 -6.461 0.0016277 ILRUN -6.461 0.0016277 BLK -6.461 0.0016277 TBC1D25 -6.461 0.0016277 PLEK -6.453 0.0001223118 C2orf74 -6.449 0.0016277 LINC02245 -6.439 0.0018989 RNF114 -6.439 0.0018989 CRYZL1 -6.439 0.0018989 SLC7A11 -6.439 0.0018989 NA -6.439 0.0018989 SIRT5 -6.439 0.0018989 MEPCE -6.439 0.0018989 ERG28 -6.434 0.0018989 DENR -6.427 0.0018989 NUBP2 -6.422 0.0018989 P2RX5-TAX1BP3 -6.422 0.0018989 COQ4 -6.422 0.0018989 DNMT1 -6.409 0.0018989 ENTR1 -6.409 0.0018989 NUDT16 -6.396 0.0001371 HSD17B7 -6.381 0.0018989 PRKD2 -6.381 0.0018989 PTPN18 -6.381 0.0018989 NSD2 -6.381 0.0018989 NCF1C -6.381 0.0018989 UBXN2B -6.381 0.0018989 UBR5 -6.381 0.0018989 MTHFD1L -6.375 0.0022301 DUSP5 -6.368 0.0022301 JUP -6.368 0.0022301 EIF2A -6.368 0.0022301 VIRMA -6.368 0.0022301 LINC01278 -6.361 7.62E-05 BRIX1 -6.357 0.0022301 CBWD5 -6.348 0.0022301 SYK -6.342 0.0022301 DHRS4-AS1 -6.340 0.0022301 SNHG19 -6.340 0.0022301 NUP88 -6.340 0.0022301 TOMM34 -6.340 0.0022301 DNTTIP1 -6.340 0.0022301 UBE3D -6.340 0.0022301 SLC4A2 -6.340 0.0022301 DNLZ -6.340 0.0022301119 SAMD12 -6.338 0.0022301 GOT2 -6.334 0.0022301 MCRIP2 -6.328 0.0022301 G3BP1 -6.328 0.0022301 FCRLA -6.326 0.0022301 ARL2 -6.320 5.37E-05 TPX2 -6.318 0.0022301 PGAP2 -6.297 0.0022301 IL21R -6.297 0.0022301 GSTA4 -6.297 0.0022301 MMUT -6.297 0.0022301 NCF1 -6.297 0.0022301 PI4KB -6.293 0.0026381 RASGRP2 -6.293 0.0026381 LOC93622 -6.293 0.0026381 PPP3CC -6.293 0.0026381 ZNF7 -6.293 0.0026381 FPGS -6.291 0.0026381 ITGB3BP -6.288 0.0026381 SFXN4 -6.286 0.0026381 RSRP1 -6.277 0.0026381 OSBPL10 -6.275 0.0026381 C12orf73 -6.272 0.0026381 RBFA -6.270 0.0026381 NFKB1 -6.270 0.0026381 RIPOR2 -6.265 9.56E-05 LRRC8D -6.253 0.0026381 GDPD1 -6.253 0.0026381 DMAC2 -6.253 0.0026381 SPTBN1 -6.253 0.0026381 OSER1 -6.253 0.0026381 BFSP2 -6.253 0.0026381 BTK -6.253 0.0026381 CYP51A1 -6.244 0.0026381 SKA2 -6.229 0.0026381 MIF4GD -6.227 0.0026381 DAPP1 -6.223 5.64E-05 TOR1AIP1 -6.214 0.0026381 CDK11A -6.214 0.0026381 TCTN1 -6.214 0.0026381120 IFRD1 -6.214 0.0026381 ZBTB10 -6.214 0.0026381 FDPS -6.212 0.0031454 DDX19A -6.207 0.0031454 TUSC2 -6.207 0.0031454 SSBP2 -6.207 0.0031454 RSBN1L -6.207 0.0031454 CD274 -6.207 0.0031454 RANGAP1 -6.204 0.0001979 PLEKHJ1 -6.200 0.0001979 NCBP2 -6.197 0.0031454 AICDA -6.195 0.0031454 SNAP47 -6.192 0.0031454 VPS26A -6.192 0.0031454 PRKAR2B -6.192 0.0031454 SEL1L -6.181 0.0031454 CDCA8 -6.176 0.0031454 RTN3 -6.169 0.0002359 HNRNPDL -6.165 0.0002159 TAOK3 -6.164 0.0031454 MRPS10 -6.162 0.0031454 LYRM1 -6.161 0.0031454 RABGEF1 -6.161 0.0031454 MACO1 -6.160 0.0031454 SNORA23 -6.160 0.0031454 MAPK1IP1L -6.160 0.0031454 NDUFAF1 -6.160 0.0031454 MED29 -6.160 0.0031454 CAMLG -6.160 0.0031454 MED20 -6.160 0.0031454 RMDN1 -6.160 0.0031454 CYTOR -6.157 5.12E-05 BUD31 -6.147 0.0031454 IMPA1 -6.146 0.0031454 RNF144B -6.144 0.0031454 IDH3G -6.133 0.0002583 MED31 -6.131 0.0031454 FSTL3 -6.131 0.0031454 DTNB -6.131 0.0031454 RASL10A -6.131 0.0031454121 LRR1 -6.113 0.003783 RNF19B -6.112 0.003783 ATXN7L3B -6.111 0.003783 RPAP3 -6.111 0.003783 RAD51 -6.111 0.003783 GRB2 -6.111 0.003783 PTRH2 -6.111 0.003783 IL12RB1 -6.111 0.003783 ABHD14B -6.111 0.003783 SLBP -6.111 0.003783 ZNF277 -6.111 0.003783 TMEM65 -6.111 0.003783 RDH10 -6.111 0.003783 IL11RA -6.111 0.003783 PTPA -6.111 0.003783 GSTZ1 -6.097 0.0002583 NADK2 -6.096 0.003783 TGS1 -6.096 0.003783 ATP6AP1 -6.096 0.003783 ZC3H15 -6.095 0.0002835 DNAJC12 -6.095 0.003783 SLC35F2 -6.078 0.003783 CCNB1 -6.078 0.0001493 KCNN3 -6.070 0.0002973 ATRAID -6.065 4.97E-05 ISOC2 -6.064 2.24E-05 BRMS1 -6.061 0.003783 ZNF384 -6.061 0.003783 YARS2 -6.061 0.003783 CFL2 -6.061 0.003783 AKT1 -6.061 0.003783 SNUPN -6.061 0.003783 TRMT1 -6.061 0.003783 FAM98A -6.061 0.003783 THADA -6.061 0.003783 UBA6 -6.061 0.003783 TNIP2 -6.061 0.003783 VPS36 -6.061 0.003783 PSMC1 -6.061 0.003783 LAGE3 -6.054 0.0002835122 UTY -6.044 0.003783 SMS -6.043 0.0045936 SNHG9 -6.043 0.0045936 ALDH9A1 -6.043 0.0045936 OVGP1 -6.042 0.003783 ZNF267 -6.042 0.003783 TMEM230 -6.042 0.003783 GPCPD1 -6.042 0.003783 FKBP15 -6.042 0.003783 NT5C3A -6.025 0.0045936 JCHAIN -6.015 2.08E-05 LOC102724301 -6.009 0.0045936 LINC01481 -6.009 0.0045936 SRP54 -6.009 0.0045936 ATPAF2 -6.009 0.0045936 C2orf69 -6.009 0.0045936 TIMMDC1 -6.009 0.0045936 SH3KBP1 -6.009 0.0045936 PDK3 -6.009 0.0045936 ESRRA -6.008 0.0045936 APOO -6.008 0.0045936 TMED5 -6.005 0.0045936 CD81 -5.996 4.66E-05 RITA1 -5.994 0.0001855 CDC34 -5.990 0.0045936 LRBA -5.980 0.000344 RAB1A -5.972 0.0045936 RBBP8 -5.969 0.0045936 KCMF1 -5.969 0.0045936 PTRHD1 -5.968 8.25E-05 KLF10 -5.968 0.0045936 STK26 -5.959 0.0003616 USP22 -5.958 0.0004005 CTSH -5.958 6.56E-05 SH3GLB1 -5.954 0.0045936 DMAP1 -5.954 0.0045936 BTG2 -5.954 0.0045936 RPP30 -5.954 0.0045936 MTIF2 -5.954 0.0045936 DDX27 -5.954 0.0045936123 SLC25A17 -5.954 0.0045936 CCDC157 -5.954 0.0045936 EPRS -5.948 0.0045936 EFCAB2 -5.948 0.0045936 TYSND1 -5.948 0.0045936 WBP1L -5.948 0.0045936 MAPKAPK5-AS1 -5.948 0.0045936 NFKBIA -5.948 0.0045936 USP14 -5.948 0.0045936 PREB -5.948 0.0045936 ATG16L1 -5.948 0.0045936 EIF4G1 -5.948 0.0045936 RIDA -5.948 0.0045936 CXorf38 -5.948 0.0045936 WBP11 -5.946 0.0003616 NAPSB -5.925 0.0004219 EI24 -5.912 2.26E-05 RABIF -5.893 0.0004692 PITHD1 -5.885 0.0001288 MFSD5 -5.884 0.0002335 HNRNPAB -5.880 0.0004219 C8orf33 -5.875 0.0004953 GCHFR -5.860 7.92E-05 DCAF10 -5.829 0.0004953 BASP1 -5.816 0.0005535 PRDX6 -5.809 0.0005234 SYNGR2 -5.789 8.16E-05 SCARB1 -5.787 0.0005858 ECE2 -5.775 0.0001661 CCDC85B -5.764 9.06E-05 MZB1 -5.763 5.06E-05 GABARAPL2 -5.750 0.000658 NOL3 -5.731 0.0006206 MECR -5.728 0.0003394 GRN -5.720 4.87E-05 LINC00926 -5.709 0.0001966 SRPRB -5.663 0.0007888 RNF7 -5.620 0.0002262 SNHG8 -5.609 5.64E-05 CLEC2D -5.591 0.0008396124 ANXA7 -5.583 0.0008396 SPCS3 -5.546 0.0002431 TPGS2 -5.525 0.0010191 ASL -5.518 0.0001615 CPSF6 -5.511 0.0001269 ATG12 -5.474 0.0004208 C7orf50 -5.443 0.0013416 ATOX1 -5.418 6.85E-05 PTPRA -5.418 0.0004035 BAG1 -5.409 0.0003715 CD70 -5.407 0.0007559 CD47 -5.406 0.0003275 BCAS4 -5.392 8.50E-05 FGFR1OP -5.374 0.0014417 MRPL43 -5.373 0.000439 CUL1 -5.361 0.0015515 SAMM50 -5.351 0.0014417 YBX3 -5.338 0.0002184 PBDC1 -5.323 0.0015515 KATNB1 -5.320 0.0008923 SAE1 -5.312 0.0004005 CMPK1 -5.302 0.0018049 AKR1B1 -5.302 0.000439 NDUFAF7 -5.295 0.0009722 NOL10 -5.286 0.0016721 PEMT -5.274 0.0009722 CCDC69 -5.259 0.0019512 HLA-DRB1 -5.252 0.0004692 KIF22 -5.245 0.0010614 SAT1 -5.239 0.0003616 RAB8A -5.229 0.002113 PSMD1 -5.189 0.0003567 ZBTB1 -5.184 0.002113 RPP25L -5.180 0.0012733 POLR2B -5.179 0.002292 SLC1A5 -5.136 0.0013994 UBE2J1 -5.130 0.0006582 RBM25 -5.119 0.0006582 STK40 -5.085 0.0027118 TFAM -5.073 0.0027118125 MRPS17 -5.056 0.0005506 TMEM14C -5.044 0.0017028 RCC1 -4.978 0.0009324 ARMC6 -4.971 0.0004826 SNHG3 -4.956 0.0006986 LRRC58 -4.948 0.0035427 PTRH1 -4.947 0.0020947 EIF3J -4.936 0.0038898 UBE2A -4.930 0.0020947 FKBP11 -4.927 0.0042812 NGDN -4.923 0.0042812 CINP -4.921 0.0007888 MAP4K2 -4.920 0.0020947 HSPA5 -4.897 0.0005313 ARG2 -4.890 0.0023335 EIF4E2 -4.881 0.0009683 JPT2 -4.881 0.0011555 DAPK3 -4.868 0.0042812 AP1S1 -4.850 0.0042812 PPIG -4.840 0.002608 EXOSC7 -4.814 0.0018483 GHITM -4.770 0.0004243 SNRNP25 -4.725 0.0004278 EIF1B -4.718 0.0015515 VMA21 -4.707 0.0032916 HCLS1 -4.705 0.0004346 SSR3 -4.663 0.0012546 TUBB4B -4.661 0.001197 PIM2 -4.648 0.0018049 POP4 -4.639 0.0027446 ACTR10 -4.636 0.0042188 PA2G4 -4.633 0.0014841 NADSYN1 -4.611 0.002562 POLE4 -4.608 0.0017972 PLA2G12A -4.604 0.0013687 MRPL12 -4.602 0.0005564 FOXK1 -4.572 0.0014498 NDUFB5 -4.570 0.0023945 DMAC1 -4.564 0.0009893 NOL7 -4.561 0.0008039126 CDCA3 -4.552 0.0023945 ZCCHC7 -4.526 0.003162 ZMAT3 -4.523 0.002562 TTF2 -4.521 0.0013807 ZC3HAV1 -4.519 0.0021596 EIF3E -4.518 0.0008795 SMIM14 -4.502 0.0024908 SPG21 -4.487 0.0014507 YIF1A -4.479 0.0009276 HAGHL -4.469 0.0015748 ALKBH4 -4.468 0.0007759 METAP2 -4.449 0.0009836 PIGT -4.441 0.0007991 SNAPC4 -4.389 0.0025397 ACSL3 -4.381 0.0023147 SREBF2 -4.367 0.0036632 MTERF4 -4.366 0.0036632 UBE2O -4.351 0.001144 PDXP -4.349 0.0014522 CBX5 -4.348 0.0042693 COX14 -4.344 0.0012584 SERBP1 -4.342 0.0010001 ARL3 -4.336 0.0018232 MPST -4.333 0.0020797 PNP -4.327 0.001078 TUBA1C -4.311 0.0013858 AP5B1 -4.285 0.0037692 RPA3 -4.282 0.0010025 NGRN -4.276 0.00462 GOLT1B -4.265 0.002455 BZW2 -4.240 0.0035427 COA3 -4.201 0.0017604 TAGLN2 -4.196 0.0026502 B4GALT3 -4.195 0.0021962 COX19 -4.186 0.0038898 SF3B5 -4.148 0.002051 DCAF11 -4.143 0.0026399 MVD -4.136 0.0039747 PPP1CB -4.125 0.003401 AHCY -4.115 0.0021091127 MTCH2 -4.112 0.0020941 ORMDL2 -4.096 0.0029721 EBAG9 -4.063 0.00462 BCAP31 -4.057 0.0028499 SLC39A4 -4.041 0.0021475 WARS -4.041 0.0035704 DNAJC8 -4.011 0.0042408 TONSL -4.001 0.004099 BUB3 -3.981 0.0033869 ATXN10 -3.976 0.0028376 PDRG1 -3.971 0.0024939 IGKC -3.904 0.0019297 ZNRD2 -3.872 0.0029696 CHID1 -3.850 0.0032111 KTN1 -3.847 0.0043814 DNPEP -3.762 0.0039822 SMUG1 -3.712 0.0038441 LAMTOR2 -3.680 0.0040295 FKBP2 -3.669 0.0046217 XRCC6 -3.587 0.0040684 Figure 5.9 Upregulated genes Downregulated genesSymbol logFC PValue Symbol logFC PValueFLNC 10.462 5.76E-06 NAT10 -11.010 2.43E-06KRT1 9.919 1.35E-05 ZDHHC6 -10.597 4.65E-06UBASH3A 9.563 2.36E-05 TST -10.411 6.22E-06LPCAT2 9.512 2.56E-05 SWI5 -10.371 6.63E-06NUP50-DT 9.368 3.20E-05 MMGT1 -10.109 9.98E-06PLEKHG5 9.328 3.41E-05 MON1A -9.891 1.41E-05SLC7A5P2 9.264 3.76E-05 SAC3D1 -9.643 2.08E-05INTS6-AS1 9.257 3.81E-05 MIR181A2HG -9.614 2.18E-05CPNE7 9.219 4.05E-05 ZHX3 -9.515 2.53E-05TCIRG1 9.183 4.28E-05 MGMT -9.378 3.15E-05RTL8A 8.794 7.88E-05 RNF122 -9.327 3.41E-05DHFR2 8.759 8.34E-05 SUMO3 -9.100 4.84E-05RTEL1 8.746 8.50E-05 RRP12 -9.061 5.18E-05DNAJC18 8.705 9.09E-05 LINC00476 -9.040 5.35E-05MED9 8.536 0.0001178 SMYD5 -8.990 5.79E-05PAOX 8.528 0.0001191 DDX50 -8.718 8.83E-05128 AOC3 8.517 0.0001218 MYL5 -8.682 9.36E-05HOMER3 8.472 0.0001303 COQ9 -8.613 0.0001036ZNF668 8.424 0.0001398 TDG -8.441 0.0001349EMID1 8.388 0.0001484 GNB5 -8.426 0.0001398DDX47 8.345 0.0001598 CDAN1 -8.420 0.0001398PCAT18 8.292 0.0001726 RMRP -8.326 0.0001619MVB12A 8.277 3.21E-05 MAP1A -8.306 0.0001682KCNA6 8.262 0.0001819 TBC1D20 -8.287 0.0001726TAZ 8.232 7.74E-05 SGCB -8.229 0.0001894RPL32P3 8.188 0.0002028 TMEM8B -8.214 0.0001919CD8B 8.176 0.0002057 RFC5 -8.195 0.0002DLX4 8.170 0.0002086 DKC1 -8.182 0.0002028NOL4L 8.152 0.0002146 OGFOD2 -8.148 0.0002146RPARP-AS1 8.148 0.0002146 ALDH5A1 -8.112 0.0002274LINC00884 8.109 0.0002308 PHF6 -8.092 0.0002342TBC1D32 8.076 0.0002413 ACAP3 -8.068 0.0002413KDM6A 7.997 0.0002732 TRAF3 -8.038 0.0002526LRRC37B 7.993 0.0002732 OGFOD3 -8.038 0.0002526SAMD13 7.991 0.0002775 NUDT9 -8.026 0.0002606ZNF256 7.991 0.0002775 ZNF580 -8.019 0.0002606ZNF282 7.976 0.000282 CASTOR1 -8.010 0.0002647KIAA0930 7.919 0.0003114 CTCF -7.936 0.0002962ANKRD50 7.888 0.0003221 LRTOMT -7.934 0.0002962TBPL1 7.737 9.17E-05 ELP5 -7.915 0.0003062MFGE8 7.657 0.0002413 ZNF142 -7.864 0.0003334NBR1 7.621 9.36E-05 CCDC43 -7.859 0.0003334GGPS1 7.472 0.0001047 LOC100507600 -7.850 0.0003393TMEM186 7.209 0.0001988 PIK3IP1 -7.843 0.0003453YTHDF1 7.199 0.0002434 TENT5C -7.821 0.0003578DOLPP1 7.063 0.0002532 SPRYD4 -7.812 0.0003578RBM41 6.977 0.0001549 EPHX2 -7.676 0.0001773CLDND1 6.952 0.0002744 MLST8 -7.342 0.0002456UBASH3B 6.857 0.0003469 ASNSD1 -7.277 0.0001813ALKBH4 6.776 0.0003514 BOP1 -7.090 0.0002777TPST2 6.723 0.0003486 UBL4A -6.918 0.0003309DCTD 6.626 0.0003065 Figure 5.10 Upregulated genes Downregulated genesSymbol logFC PValue Symbol logFC PValue129 THEM6 9.060 7.12E-06 MIR181A2HG -9.925 1.12E-07MLXIP 8.553 1.88E-05 RNF122 -9.638 2.17E-07P2RX1 8.462 2.23E-05 TMEM8B -8.527 2.75E-06PRKD2 7.924 6.12E-05 MAGEF1 -8.387 3.84E-06NPRL3 7.799 7.72E-05 ACAP3 -8.383 3.78E-06FCRL5 7.628 0.0001072 TENT5C -8.129 6.89E-06NCK1-DT 7.486 0.0001397 MYL4 -8.069 7.78E-06CEP68 7.430 0.0001557 ACSF2 -7.940 1.05E-05ZNF747 7.358 0.0001785 KANK2 -7.680 1.91E-05ZNF696 7.307 0.0001962 CACFD1 -7.636 2.07E-05HYAL2 7.157 0.0002593 ROCK2 -7.610 5.77E-06IRF2 7.155 0.0002593 FAM193A -7.541 2.53E-05AP4B1 7.133 0.0002737 RNFT1-DT -7.517 2.77E-05STAM2 6.857 0.0001584 SPRY2 -7.517 2.77E-05MTMR3 6.790 0.0001799 PSMC3IP -7.469 3.04E-05HHIP 6.639 0.0002403 CEP19 -7.447 3.34E-05ZNF165 6.001 0.0002482 C9orf116 -7.421 3.34E-05 PROB1 -7.400 3.57E-05 MOGS -7.390 3.57E-05 TBC1D20 -7.371 1.01E-05 CTC1 -7.307 3.26E-06 ACOX3 -7.289 4.52E-05 KCNMB4 -7.287 4.52E-05 LOC101928068 -7.211 5.62E-05 WFS1 -7.211 5.41E-05 LGALS9 -7.149 1.08E-05 PHTF2 -7.141 1.20E-05 SPAG8 -7.122 6.82E-05 FANCM -7.081 7.09E-05 TSPAN17 -7.060 1.83E-05 LIPT2 -7.009 8.36E-05 SNX21 -6.980 8.73E-05 FAM86C2P -6.877 0.0001194 ZNF143 -6.845 0.0001194 AOC2 -6.831 0.0001252 USP44 -6.737 0.0001524 LINC01521 -6.668 0.000178 NFATC2 -6.665 0.000178 ZHX3 -6.663 1.54E-05 CRACR2B -6.645 0.0001877130 CCDC93 -6.598 0.0002094 FAIM -6.567 0.0002215 ABCA7 -6.562 0.0002215 HIST1H3H -6.486 0.0002636 SHOX2 -6.465 0.00028 GOT1 -6.441 7.97E-05 SAMD10 -6.430 0.0003168 SAMD8 -6.407 0.0003168 RFNG -6.396 0.0003168 R3HCC1L -6.337 0.0001186 CDAN1 -6.193 4.03E-05 SBF2 -6.142 0.0001741 MAP3K14 -6.014 0.000231 DCTN1 -6.011 0.0001439 ZNF142 -5.968 6.83E-05 LMF1 -5.871 0.0001665 FGR -5.725 0.0002341 EPHX2 -5.657 9.55E-05 FZD3 -5.622 0.0003122 ARFGAP1 -5.612 0.0001486 SAC3D1 -5.595 0.0001071 RAE1 -5.559 9.81E-05 TMPO-AS1 -5.524 0.000174 SNX4 -5.489 0.0001768 USP28 -5.399 0.0002341 NHLRC3 -5.378 0.0001837 PIK3IP1 -5.368 0.0002652 TWNK -5.353 0.0002122 MICAL3 -5.294 0.0003146 IVD -5.263 0.0002856 RFC5 -5.260 0.0002977 FAM111A -5.215 0.0002147 TRAF3 -5.135 0.0002763 Figure 5.11D Upregulated genes Symbol LogFC PValue KCNK6 9.56 9.18E-07 ZNF626 8.712 6.30E-06 131 Figure 5.11B,C Upregulated genes Downregulated genesSymbol logFC PValue Symbol logFC PValueKAT8 7.969 6.39E-06 P2RX1 -8.704 1.55E-07MOGS 7.464 2.00E-05 HHIP -8.134 7.08E-07EEPD1 7.457 2.05E-05 SNORD3C -7.912 1.27E-06PYGB 7.454 2.05E-05 FCRL5 -7.870 1.45E-06SAMD10 7.368 2.50E-05 ZSCAN32 -7.811 1.71E-06ZNF626 7.310 2.82E-05 CEP68 -7.672 2.46E-06ZNF608 7.212 3.52E-05 CCL17 -7.448 4.41E-06SLC44A1 6.967 6.13E-05 INPP4A -7.400 4.98E-06CTPS2 6.905 6.93E-05 AP4B1 -7.373 5.30E-06C16orf74 6.829 8.41E-05 CTSF -7.245 7.61E-06SEC16A 6.643 0.0001284 JADE3 -7.117 3.32E-06ORMDL3 6.641 0.0001284 RIOK2 -7.085 1.13E-05ABHD17B 6.589 0.0001441 RNVU1-15 -7.033 1.32E-05COL4A3BP 6.546 0.0001559 PYGO2 -7.008 1.43E-05LINC00893 6.543 0.0001559 PHKA1 -6.999 1.43E-05RHOBTB2 6.526 0.0001623 ROGDI -6.933 1.69E-05TBX19 6.501 0.000169 TMEM98 -6.824 2.30E-05MYLK-AS1 6.465 0.0001835 SNX24 -6.824 2.30E-05ARHGEF37 6.402 0.0002179 PINLYP -6.824 1.39E-06TMC3 6.399 0.0002179 SPART -6.788 2.52E-05SLAIN2 6.376 0.0002278 STX18-AS1 -6.765 2.64E-05ZNF837 6.373 0.0002278 FBF1 -6.703 3.05E-05LOXL1 6.347 0.0002383 GNS -6.605 4.14E-05SH3BP2 6.325 0.0002494 DHRS4-AS1 -6.590 4.14E-05COG4 6.313 0.0002612 TOM1L2 -6.565 4.36E-05ENOX2 6.302 0.0002612 LSP1P4 -6.551 4.61E-05EGR4 6.264 0.000287 ZFP41 -6.531 4.87E-05KLF7 6.256 0.000287 RNVU1-19 -6.504 5.14E-05GPAT2 6.213 0.0003163 TRIM21 -6.498 5.14E-05KATNAL2 6.177 0.0003495 EVA1B -6.474 5.76E-05FAM219B 6.168 0.0003495 TSPAN15 -6.448 6.11E-05CKB 6.167 0.0003495 PRRT3 -6.435 6.11E-05LRP2BP 6.152 0.0003679 PQLC3 -6.392 6.88E-05WWP2 6.149 0.0003679 FCER2 -6.388 6.88E-05SNORD63 6.142 0.0003875 UCP3 -6.343 7.78E-05MOSPD2 6.118 0.0003875 APBA3 -6.297 8.83E-05TRIB3 6.115 0.0004085 BCL11B -6.288 8.83E-05132 LONRF1 6.101 0.0004085 LTBP3 -6.258 9.42E-05OSCP1 6.099 0.0004085 EME1 -6.256 0.0001006TCTA 6.082 0.0004309 CCDC15 -6.248 0.0001006SIPA1 6.042 0.0004809 TCHP -6.223 1.04E-05KCNMB4 6.014 0.0005088 FUOM -6.204 1.73E-05DGKQ 5.999 0.0005088 LINC00649 -6.184 0.0001152HBBP1 5.977 0.0005387 LOC101927989 -6.169 0.0001235GPER1 5.974 0.0001098 SUV39H1 -6.117 0.0001325PC 5.973 0.0005387 KLHL35 -6.117 0.0001325ZCWPW1 5.966 0.0005387 DUXAP8 -6.117 0.0001325MIR3936HG 5.957 0.000571 LOC100289230 -6.107 0.0001424HIP1 5.938 0.000571 AZGP1 -6.085 4.37E-05RFNG 5.937 0.000571 DAGLB -6.078 1.43E-05TP53BP1 5.933 0.0006059 VPS13B -6.066 1.43E-05ASPN 5.898 0.0006436 TSPAN4 -6.045 0.0001652IFT140 5.896 0.0006436 SYNJ2BP -6.021 0.0001783GRXCR2 5.888 0.0006436 LINC01607 -6.008 0.0001783PACS2 5.886 0.0006436 SEPSECS-AS1 -5.964 0.0002086PPP1R32 5.879 0.0006436 ABHD16B -5.955 0.0002086ARID3B 5.871 0.0006843 EPM2A -5.943 0.0002086PROCA1 5.869 0.0006843 ZNF564 -5.933 0.0002262NA 5.867 0.0006843 MYBBP1A -5.890 0.0002457IL10RA 5.851 0.0007285 DBNDD1 -5.889 0.0002457ZNF347 5.822 0.0007285 C3orf62 -5.883 0.0002457TNFSF14 5.818 0.0007285 OSGEPL1-AS1 -5.875 0.0002457LOC652276 5.806 0.0007765 ZNF333 -5.869 0.0002674ZNF282 5.801 0.0007765 ZSWIM9 -5.847 0.0002674TNNI2 5.797 0.0007765 HIST1H4I -5.822 0.0002915ITPRIPL2 5.777 0.0008287 BEX3 -5.805 1.99E-05PLOD3 5.775 0.0008287 MIR3928 -5.800 0.0003184NBPF3 5.764 0.0008287 PRORSD1P -5.797 0.0003184RNF170 5.754 0.0004058 LINC00174 -5.786 0.0003184DCTN1 5.749 0.0001171 PDCD6IPP2 -5.766 0.0003485ZNF408 5.693 0.0002015 RABL2A -5.744 0.0003485DST 5.689 0.0002391 QSOX1 -5.741 3.93E-05CDKN1A 5.636 0.0001634 ODF2L -5.724 3.54E-05SART3 5.479 0.0003697 CRIP3 -5.720 0.0001265TAZ 5.474 0.0001996 NEK1 -5.717 0.0003823ZSWIM1 5.452 0.0001996 JAM3 -5.711 0.0003823GUCD1 5.343 0.0004877 SORT1 -5.692 0.0003823133 UBE2E3 5.272 0.000263 B4GAT1 -5.689 0.0004203CCDC47 5.204 0.0004608 ANXA2R -5.687 0.0004203MDN1 5.162 0.0007445 DSCR9 -5.686 0.0004203CD80 5.131 0.0007445 DYNC2H1 -5.683 0.0004203VPS16 4.974 0.0007103 ZBTB21 -5.678 0.0004203ATAD2 4.957 0.0006646 SMPD1 -5.669 0.0004203CHRNB1 4.855 0.0008226 PTCD2 -5.647 0.0004632RELB 4.641 0.0007872 SCNN1D -5.626 0.0004632 ST3GAL6 -5.622 0.0005117 COL6A1 -5.620 0.0004632 ZBTB4 -5.618 0.0005117 LIMS2 -5.596 0.0001534 DPH6-DT -5.573 0.0005117 KRT1 -5.566 0.0005667 SENP7 -5.554 0.0005667 ADGRG1 -5.553 0.0005667 NUDT11 -5.553 0.0005667 ERVK13-1 -5.549 0.0005667 SNORA44 -5.536 0.0005667 ADCK2 -5.528 3.05E-05 HECW2 -5.523 0.0006294 RIPK4 -5.523 0.0006294 TULP3 -5.518 0.0006294 ADAMTSL2 -5.518 0.0006294 MAP2K5 -5.499 0.0006294 GJB2 -5.471 0.0007011 LOC102724064 -5.469 0.0007011 HNRNPA1L2 -5.441 0.0001582 CEP70 -5.437 6.83E-05 EEF1AKMT1 -5.435 0.0002324 FAAH -5.421 0.0007833 IQCG -5.420 0.0007833 SLC11A1 -5.418 0.0007833 ARHGEF12 -5.413 9.23E-05 HYAL3 -5.373 0.0002078 STAM2 -5.363 6.74E-05 STRN -5.359 8.47E-05 SNORD17 -5.348 0.0001053 THEM6 -5.282 5.61E-05 C11orf98 -5.225 0.0002911134 PEX1 -5.199 0.0001534 KIAA0040 -5.195 0.0004777 HBQ1 -5.192 6.71E-05 C14orf119 -5.186 7.31E-05 FRG1CP -5.151 9.03E-05 HACD1 -5.107 0.0006276 AGGF1 -5.106 0.0007247 ARID5A -5.065 0.0006276 TEFM -5.036 0.0001342 ARHGAP19-SLIT1 -5.029 0.0007247 PRKCA -5.021 0.0002832 HYAL2 -5.011 0.0002832 C16orf95 -5.001 0.0001451 APOBEC3D -5.000 0.0003574 SLC35D1 -4.963 0.0001233 CYB5D1 -4.899 0.0002773 ITPR2 -4.889 0.0001739 PI16 -4.870 0.000398 IRF2 -4.852 0.0002749 FOXN2 -4.806 0.000177 SEC22B -4.778 0.0002369 UBASH3B -4.770 0.0002917 EIF3J-DT -4.745 0.0003651 ZNF574 -4.643 0.0002749 AASDH -4.604 0.0003899 SCFD2 -4.558 0.0005299 CDK16 -4.496 0.0003569 LARP1B -4.478 0.0003645 TMEM39A -4.448 0.0006268 STAT1 -4.423 0.000644 DHRS1 -4.396 0.0007952 CASP6 -4.384 0.0005603 STAG3 -4.374 0.0004439 METTL15 -4.264 0.0006808 MELK -4.240 0.000828 FADS1 -4.219 0.0007695 Figure 5.12C Upregulated genes Downregulated genesSymbol logFC PValue Symbol logFC PValue135 GZMB 9.198 5.92E-09 P2RX1 -8.724 9.31E-05NLRX1 8.549 5.33E-08 HBQ1 -8.538 0.0001281XCL2 8.238 1.50E-07 STAM2 -8.374 0.0001698XCL1 8.220 1.56E-07 SNORD3C -7.936 0.000355TCIRG1 8.069 2.61E-07 HES4 -7.771 0.0004687SLC45A3 7.721 8.33E-07 CEP70 -7.720 0.0005069P3H4 7.668 1.01E-06 HIST1H1D -7.714 0.0005151PLEKHN1 7.598 1.23E-06 AAMDC -7.605 0.0006186ASB6 7.584 2.65E-08 HIST1H1B -7.479 0.0007538IRGQ 7.437 2.01E-06 ZNF574 -7.418 0.0008454TRIM56 7.413 2.17E-06 INPP4A -7.414 0.0008454FGD3 7.396 2.34E-06 NCK1-DT -6.196 0.0004911HDC 7.143 5.36E-06 GPN3 -6.023 0.0006446FBLIM1 7.137 5.36E-06 UBE2D1 -5.975 0.0007131KMT5C 7.107 5.87E-06 MAP3K2-DT -5.925 0.000696IL2RA 7.079 6.43E-06 JADE3 -5.885 0.0008251CD69 7.053 7.07E-06 LY86 -5.847 0.0008794BEND5 6.960 9.49E-06 SLC29A2 6.949 9.49E-06 ULK1 6.925 1.05E-05 CD40LG 6.912 1.05E-05 PLEKHH3 6.900 1.17E-05 SSR4P1 6.898 1.17E-05 LRP5L 6.858 1.30E-05 FAM227A 6.809 1.45E-05 PPARA 6.805 1.62E-05 OSBP 6.802 1.62E-05 SLX4 6.801 1.62E-05 DDX19B 6.785 1.62E-05 GPR22 6.775 1.62E-05 ORMDL3 6.768 1.81E-05 LUCAT1 6.761 1.81E-05 BBS2 6.750 1.81E-05 CNOT6 6.750 1.81E-05 TSNARE1 6.719 2.04E-05 NPHP4 6.681 2.29E-05 FRMD8 6.651 2.59E-05 LMBR1L 6.624 2.59E-05 SLC44A1 6.622 2.59E-05 OGFR 6.613 2.94E-05 136 ACVR1 6.595 2.94E-05 CPLANE1 6.586 2.94E-05 TYROBP 6.580 2.94E-05 SIPA1 6.575 3.34E-05 IFNG-AS1 6.566 3.34E-05 SIPA1L1 6.517 3.81E-05 PAPOLG 6.500 3.81E-05 STK10 6.489 4.36E-05 IBA57 6.464 4.36E-05 MLLT10 6.457 4.36E-05 TSPYL4 6.451 4.36E-05 ZNF93 6.439 5.01E-05 MAGED2 6.436 5.01E-05 GUSBP11 6.411 5.01E-05 PORCN 6.403 5.01E-05 ZNF548 6.400 5.01E-05 P2RX5-TAX1BP3 6.387 5.78E-05 SPTY2D1 6.373 5.78E-05 VKORC1L1 6.371 5.78E-05 ZBED6CL 6.335 6.70E-05 SH2B3 6.327 6.70E-05 USP20 6.324 6.70E-05 TECPR1 6.306 6.70E-05 CEP19 6.299 7.80E-05 ITPKB 6.276 7.80E-05 SLC27A4 6.274 7.80E-05 TRMT2B 6.271 7.80E-05 SLC27A2 6.260 7.80E-05 OGDHL 6.255 7.80E-05 ACTN1 6.249 7.80E-05 NFATC1 6.247 9.12E-05 ST14 6.221 9.12E-05 NA 6.207 9.12E-05 CD276 6.200 9.12E-05 PYGB 6.196 9.12E-05 C17orf99 6.196 9.12E-05 RPL32P3 6.195 9.12E-05 SH2D2A 6.185 0.0001072 SPPL2B 6.179 8.88E-06 TRIO 6.177 0.0001072 137 RNF19A 6.171 0.0001072 CAMSAP1 6.169 0.0001072 PRKD3 6.165 3.43E-06 ARHGAP9 6.163 0.0001072 LINC01281 6.162 0.0001072 SENCR 6.162 0.0001072 CACFD1 6.153 0.0001072 ANKS1A 6.153 0.0001072 STAT5A 6.147 0.0001072 CDC42EP3 6.145 0.0001072 BACH2 6.121 0.0001267 KCNMB4 6.119 0.0001267 ZNF248 6.115 8.88E-06 KYAT1 6.114 0.0001267 GPAT2 6.101 0.0001267 HIPK1 6.058 0.0001507 LIPT2 6.053 0.0001507 RPGR 6.042 1.37E-05 COQ5 6.015 0.0001802 RBM5-AS1 6.001 0.0001802 BRPF1 5.996 1.60E-05 NRIP2 5.995 0.0001802 GGACT 5.966 0.0001802 DUSP4 5.966 0.0001802 PUDP 5.959 0.000217 MACC1 5.939 0.000217 ARHGEF9 5.938 0.000217 TMEM25 5.930 0.000217 TLR5 5.921 0.000217 IQCD 5.911 0.000217 LINC00667 5.899 5.09E-05 SH3BP2 5.891 0.0002631 ASPHD2 5.885 0.0002631 MAFG 5.851 0.0002631 MEGF8 5.849 0.0002631 TARSL2 5.837 0.0002631 BABAM2 5.829 0.0002631 CYHR1 5.828 5.77E-06 SERPINB10 5.826 6.44E-05 HLA-DPA1 5.815 0.0002631 138 CLUHP3 5.809 0.0003216 PITX1 5.809 0.0003216 MOGS 5.797 0.0003216 ADGRL1 5.793 0.0003216 FLVCR1-DT 5.787 0.0003216 LGALS17A 5.786 0.0003216 ETV4 5.775 0.0003216 TNFAIP8L1 5.772 0.0003216 UBTD1 5.771 9.36E-06 ZNF654 5.769 0.0003216 PNPLA2 5.769 0.0003216 ABCA7 5.764 0.0003216 GAREM1 5.744 0.0003964 PIH1D2 5.738 0.0003964 ZC3H12A 5.733 0.0003964 RETN 5.732 0.0003964 C19orf44 5.732 0.0003964 SHPRH 5.732 0.0003964 BTBD6 5.731 1.26E-05 SAMD10 5.728 0.0003964 MYO1C 5.715 3.43E-05 SLC2A4RG 5.707 0.0003964 CYGB 5.697 0.0003964 AP3B1 5.691 0.0003964 NBPF15 5.688 0.0003964 LOC105371184 5.679 0.0003964 LOC652276 5.677 0.0003964 PIP4P1 5.657 0.0004931 KIAA1109 5.654 0.0004931 LRRC41 5.644 0.0004931 ZBTB40 5.640 4.55E-05 RETSAT 5.632 0.0004931 NMNAT1 5.629 0.0004931 ZBTB5 5.627 0.0004931 SCAMP4 5.623 0.0004931 MINDY1 5.619 0.0004931 METTL8 5.617 0.0004931 RPL13P5 5.597 0.0004931 SLC35A3 5.593 0.0006195 FMNL3 5.590 0.0006195 139 COLQ 5.587 0.0006195 PRKCE 5.587 0.0006195 NDST1 5.584 0.0006195 LINC01882 5.576 0.0006195 ZNF444 5.573 0.0006195 MAP3K13 5.571 0.0006195 CYP2R1 5.570 0.0006195 ZNF143 5.570 0.0006195 MR1 5.569 0.0006195 BEST4 5.565 0.0006195 KHNYN 5.565 5.02E-05 ALKBH4 5.560 3.93E-05 MED22 5.559 0.0006195 AKAP5 5.559 0.0006195 BTG2 5.557 0.0006195 FOXP4-AS1 5.550 0.0006195 ZBTB43 5.544 6.13E-05 CDKN2B-AS1 5.542 0.0006195 JAK3 5.529 0.0006195 MB 5.526 0.0006195 TCTA 5.526 0.0006195 ZBTB16 5.524 0.0006195 C14orf93 5.517 0.0006195 PANO1 5.515 0.0006195 GLB1L 5.513 0.0006195 NRCAM 5.506 0.0007869 EXO5 5.497 0.0007869 DCAF5 5.494 1.81E-05 ROCK2 5.494 6.13E-05 ARID3B 5.493 0.0007869 SETD3 5.490 0.0007869 PHLDA3 5.487 0.0007869 SLC39A13 5.480 0.0007869 DST 5.465 8.43E-05 MAGEF1 5.456 0.0007869 ATP10D 5.456 0.0007869 GTSF1 5.454 0.0007869 PLD2 5.451 0.0007869 VIPAS39 5.432 0.0007869 RGS18 5.432 0.0007869 140 TTC31 5.429 0.0007869 ADCY7 5.428 3.51E-05 L3MBTL2 5.426 0.0007869 NA 5.421 0.0007869 TTC24 5.417 0.0010118 PHLDA1 5.401 0.0010118 C7orf26 5.398 0.0010118 SMIM3 5.392 0.0010118 C11orf68 5.392 0.0010118 JAK1 5.392 0.0010118 TYMP 5.392 0.0010118 ACOX3 5.391 0.0010118 ZNF213 5.391 0.0010118 CCDC71 5.367 0.0010118 LOC101927692 5.367 0.0010118 ESYT2 5.365 0.0001054 ARMC10 5.356 0.0010118 USF3 5.355 0.0010118 NCR3LG1 5.352 4.71E-05 FAM219B 5.351 0.0010118 ANKMY2 5.351 0.0010118 PDIK1L 5.348 0.0010118 OCEL1 5.331 5.09E-05 HRH2 5.330 0.0010118 NAGPA 5.328 0.0010118 SLC29A3 5.325 0.0010118 UBXN2B 5.313 3.11E-05 TUT1 5.303 8.56E-05 PICALM 5.302 5.50E-05 IST1 5.290 0.0001183 FCHSD2 5.283 0.0001332 SMARCD3 5.260 0.0003572 TTLL12 5.253 0.0001061 GZMA 5.227 2.47E-05 THRIL 5.202 0.0001702 SRSF4 5.056 4.06E-05 MT1E 5.049 0.000252 ARFGAP1 5.043 5.83E-05 FADD 5.032 0.0002892 DENND4B 5.028 0.0002341 141 DCTN1 5.020 0.0001172 NA 5.002 0.0001172 CAMK4 4.972 0.0008481 CD28 4.933 0.0003857 ZNF320 4.915 0.0003333 GNG4 4.915 0.0003857 DSTYK 4.891 0.0003857 SPTLC1 4.878 0.0004482 CCDC18-AS1 4.859 0.0001887 PPFIA1 4.854 0.0001045 LTO1 4.849 0.0004482 SHMT1 4.844 0.0005234 ZNF518A 4.841 0.0004482 TBC1D4 4.840 0.0002177 BTN2A2 4.827 0.0004482 ANKRD28 4.818 0.0005234 TRAC 4.806 4.31E-05 ZNF443 4.796 0.0004026 ATAD2 4.783 0.0002313 TRIM25 4.774 0.0006142 CHRNB1 4.754 0.000257 FBXO41 4.750 0.0006142 SAAL1 4.740 0.0007246 KLHL5 4.736 0.0001587 PALD1 4.728 0.000168 FBXL19-AS1 4.723 0.0006142 RGS3 4.712 0.0007246 TBC1D25 4.692 0.0007246 SART3 4.667 0.0008596 GAK 4.647 0.0008596 MDN1 4.609 0.001026 CDK13 4.594 0.0004506 TMEM250 4.582 0.001026 CCDC82 4.576 0.0008596 SNX22 4.575 0.000193 RUSC1 4.558 0.0002055 GIGYF2 4.554 0.0003939 PARPBP 4.542 0.0004006 HYOU1 4.534 0.0001002 POLR3F 4.525 0.0005082 142 FGR 4.502 0.0010331 TSEN54 4.489 0.0005601 CHTF18 4.445 0.0004584 FBH1 4.423 0.0002351 SIK3 4.353 0.0008481 NAPA-AS1 4.345 0.0008813 ZMYND8 4.323 0.0005989 SLC4A7 4.321 0.0007315 AGAP6 4.289 0.0009723 NUP214 4.272 0.0007827 WASH7P 4.270 0.0006252 PFKL 4.257 0.0001931 CAMKK2 4.243 0.0003313 DYM 4.229 0.0007396 UNC13D 4.228 0.001119 XRCC2 4.227 0.0006659 FAAP100 4.214 0.0003515 KRBA1 4.185 0.0008248 CDIPT 4.180 0.0006113 ARIH1 4.125 0.0009219 TRRAP 4.122 0.0008022 ZNF3 4.112 0.000512 WDR62 4.058 0.0009698 SETD5 4.031 0.0004573 STAT6 4.017 0.0009766 STX12 3.977 0.0005968 MAPKAPK5-AS1 3.935 0.000928 C6orf89 3.902 0.0008825 Figure 5.12D Upregulated genes Downregulated genesSymbol logFC PValue Symbol logFC PValueZNF500 13.271 2.23E-14 PQBP1 -10.743 0.0007502PRDM4 12.152 1.09E-12 POP5 -10.734 0.0007571CITED4 11.986 1.92E-12 BTF3 -10.518 0.0009707IL2RA 11.846 3.13E-12 LAMTOR2 -10.488 0.0010046DSTYK 11.652 1.60E-13 DANCR -10.449 0.0010502C9orf116 11.457 1.21E-11 USP50 11.124 3.85E-11 F11R 11.077 1.36E-12 143 TBC1D20 11.064 1.23E-12 CISH 10.788 1.25E-10 ZNF266 10.646 1.23E-12 RFX2 10.536 1.62E-12 N4BP2L1 10.284 7.30E-10 OAZ3 9.987 2.03E-09 CRYL1 9.975 4.01E-11 CHPF2 9.956 5.82E-11 PRKCQ 9.922 5.61E-11 PIP4P1 9.902 2.72E-09 RNF121 9.784 6.18E-11 LMBR1L 9.502 1.11E-08 NAA60 9.454 3.17E-11 RAD51D 9.318 2.10E-08 TAPT1 9.251 2.75E-08 CDK13 9.136 9.46E-10 MED9 9.107 4.54E-08 XCL2 9.107 4.54E-08 TMEM9 9.092 7.94E-11 ZNF655 8.965 1.16E-10 HMGCS1 8.891 1.02E-10 LYRM9 8.859 1.08E-07 DOCK11 8.859 1.08E-07 DMAC2 8.802 1.42E-10 EPAS1 8.748 1.57E-07 HJURP 8.743 8.80E-10 VPS4B 8.644 2.69E-10 MAP7D2 8.618 4.83E-09 SCARNA21 8.617 1.71E-09 KIF1C 8.569 8.25E-10 PYGB 8.525 3.72E-07 RBM22 8.475 5.18E-10 SLC29A2 8.445 4.56E-07 RBM19 8.421 4.63E-10 NDUFAF1 8.421 5.35E-10 C1orf53 8.371 7.42E-10 RICTOR 8.352 9.38E-10 DNAJC21 8.348 6.33E-10 MEX3A 8.183 1.14E-06 TNFRSF11A 8.183 1.14E-06 144 EFHD1 8.183 1.14E-06 MAF1 8.124 2.02E-09 CMTM6 8.117 1.33E-09 SLC48A1 8.109 1.92E-09 NCOA4 8.082 3.56E-09 TM9SF3 8.023 2.48E-09 PRPS2 7.950 2.21E-09 TMEM39B 7.943 3.08E-09 E2F6 7.937 7.94E-08 SHC1 7.896 7.94E-08 MFSD3 7.871 3.31E-09 ADH5 7.864 3.38E-09 UCK1 7.862 3.90E-06 LGALS1 7.856 4.21E-09 DDX49 7.794 4.00E-09 NAGA 7.687 2.25E-08 CHAF1A 7.603 6.32E-09 MAPRE2 7.558 2.01E-07 TRAC 7.534 1.24E-08 KDM5A 7.527 1.07E-08 SERINC3 7.462 2.64E-08 ARID3B 7.448 1.73E-05 SYNE1 7.448 1.73E-05 EGLN1 7.363 3.61E-08 DCAF13 7.343 1.99E-08 USP13 7.297 2.16E-08 PUDP 7.187 4.48E-05 PNPLA2 7.187 4.48E-05 ANKS1A 7.187 4.48E-05 RSRC1 7.174 2.75E-08 FDX1 7.166 2.01E-07 CNOT2 7.153 4.53E-08 SH2B1 7.100 9.30E-08 CABIN1 7.099 5.33E-08 MICAL1 7.076 1.45E-07 LRMP 7.068 4.41E-08 ABCC4 7.049 1.63E-07 ABCB7 7.022 7.80E-05 OSGIN1 6.995 7.80E-05 TRIM59 6.969 4.51E-07 145 FBXO34 6.953 1.71E-06 GCSAM 6.946 2.09E-07 UNK 6.921 4.19E-07 TNFAIP1 6.911 0.0001171 GTF2H1 6.902 9.68E-08 DGCR6 6.898 4.05E-07 SPIB 6.887 8.46E-08 FXR2 6.883 1.13E-07 IER3 6.867 0.0001171 ANKRD33B 6.867 0.0001171 USH2A 6.867 0.0001171 LOC101929532 6.867 0.0001171 ARFIP2 6.865 1.24E-07 RMND5B 6.841 1.44E-07 NCKAP1L 6.827 1.18E-07 BMS1 6.825 1.16E-07 IL10RB 6.774 2.99E-07 DNAJA1 6.766 1.14E-07 TXNDC9 6.733 2.21E-07 LPGAT1 6.704 1.96E-07 ASH2L 6.666 8.06E-07 FCGRT 6.656 2.48E-07 MAGED1 6.643 1.61E-07 CNIH1 6.625 2.09E-07 MYB 6.612 3.50E-07 FAM111A 6.582 4.38E-07 TRL-TAG3-1 6.581 6.88E-06 COPS5 6.577 3.66E-07 MIR142 6.570 3.36E-07 MFF 6.533 2.69E-07 STARD3 6.529 5.89E-07 WAS 6.455 0.000509 ZDHHC17 6.455 0.000509 RYBP 6.455 0.000509 SNORD42A 6.455 0.000509 CD96 6.455 0.000509 STARD5 6.455 0.000509 PIMREG 6.455 0.000509 TNFAIP8L1 6.455 0.000509 SLC4A4 6.455 0.000509 146 KCTD1 6.455 0.000509 SHQ1 6.453 1.13E-06 DDX27 6.451 3.56E-07 FGD1 6.439 1.50E-05 ZNF410 6.418 5.99E-07 MYADM 6.418 0.000509 TYMP 6.418 0.000509 OPTN 6.397 1.50E-05 CEBPG 6.397 5.16E-07 ARIH2OS 6.382 3.87E-06 MTHFD2 6.231 6.62E-07 MYL12B 6.190 7.40E-07 FAM216A 6.181 8.25E-07 NUP58 6.181 1.49E-06 ITM2A 6.178 7.63E-07 CCDC91 6.177 3.77E-05 ZMIZ2 6.176 1.47E-06 MAPK1IP1L 6.144 6.24E-06 SUPV3L1 6.136 3.77E-05 POLA1 6.130 1.22E-06 ANAPC7 6.093 1.67E-06 EML6 6.062 3.62E-05 UCHL1 6.044 1.13E-06 KMT2E-AS1 6.035 1.44E-06 ZFP36L1 5.998 2.83E-06 RABEP1 5.981 1.54E-06 ANKH 5.977 2.79E-06 GAA 5.957 2.82E-06 KDM5B 5.858 0.0001171 DNAJB2 5.816 0.0001171 CNOT9 5.787 3.21E-06 THAP7 5.777 5.04E-06 TERF2IP 5.756 4.95E-06 ARL4C 5.696 4.64E-06 TMEM8A 5.695 8.10E-06 SLC25A17 5.646 2.04E-05 SLC25A33 5.635 6.34E-06 PARP2 5.626 1.07E-05 ERBIN 5.574 1.22E-05 KIAA0319L 5.559 1.58E-05 147 NREP 5.549 1.30E-05 SNAPC4 5.539 8.06E-06 MLLT3 5.539 0.0001878 C16orf72 5.528 9.71E-06 FBRS 5.525 0.0001878 DOT1L 5.520 0.0001878 SUGP1 5.519 8.04E-06 HADHB 5.517 8.85E-06 MECR 5.503 8.02E-06 TPM3 5.483 7.18E-06 RNH1 5.470 2.26E-05 PARVG 5.463 9.46E-06 SART3 5.446 0.000509 SORD 5.446 0.000509 H1FX 5.429 0.000509 PLIN3 5.368 1.51E-05 AK2 5.358 1.06E-05 RB1 5.296 2.82E-05 GARS 5.290 1.36E-05 PPP1R1C 5.269 0.0001001 TP53RK 5.259 2.80E-05 GCLM 5.252 2.90E-05 COPS2 5.210 1.96E-05 ZC3H8 5.194 3.85E-05 MIS12 5.190 0.0001397 ATG3 5.148 2.15E-05 UBE2V2 5.144 2.48E-05 SLC25A1 5.128 2.24E-05 VEGFB 5.117 2.99E-05 PGAM1 5.113 3.64E-05 NUBP1 5.102 0.0002319 ALG5 5.083 2.63E-05 SMARCC2 5.079 4.80E-05 NUP214 5.069 0.0002319 ECHS1 5.067 2.75E-05 SLA 5.062 3.59E-05 PPP1R14B 5.060 2.73E-05 KRT81 5.058 0.0001381 SRGN 5.052 5.20E-05 LRPAP1 5.047 3.12E-05 148 LAMP1 5.044 0.0010621 ZNF37A 5.042 5.83E-05 SNAPIN 4.998 3.81E-05 CYFIP2 4.997 3.82E-05 EIF2A 4.996 4.57E-05 NDUFV1 4.979 3.54E-05 EXOC7 4.973 0.0002922 LOC730202 4.967 3.76E-05 XPO6 4.957 0.0001009 FBLN2 4.956 6.46E-05 PPDPF 4.944 0.0001131 RNF19B 4.908 0.0002819 COPG2 4.899 0.0005193 MORF4L2 4.869 6.38E-05 CTBP1-DT 4.857 8.28E-05 NACA2 4.836 0.000549 MIR1244-3 4.825 8.34E-05 DKC1 4.799 6.27E-05 ARL6IP5 4.798 6.93E-05 TCHP 4.785 6.64E-05 AK4 4.773 8.68E-05 TMEM138 4.756 9.28E-05 JUND 4.754 8.26E-05 UPF3B 4.728 8.71E-05 TSEN15 4.718 0.0001009 PPP1R35 4.705 0.000202 TMEM191A 4.696 0.000248 RHOH 4.692 9.72E-05 HNRNPUL1 4.690 0.0001005 SERP1 4.667 9.70E-05 PARD6B 4.664 0.0001237 PSMD2 4.654 9.71E-05 GRN 4.643 0.0001036 TMEM43 4.635 0.0008997 BATF3 4.621 0.0005066 STT3A 4.612 0.0001407 LIMK2 4.586 0.0003197 RNF141 4.578 0.0003089 MPI 4.558 0.0002261 GRK3 4.553 0.000236 149 RBM15B 4.493 0.0001764 TRIR 4.483 0.0001612 CD164 4.474 0.0002172 C11orf80 4.472 0.0001992 NSA2 4.457 0.0001748 SLC25A46 4.450 0.0004483 SLC35F2 4.436 0.000325 OCIAD1 4.432 0.0002255 PRKCSH 4.428 0.0007265 ALG12 4.425 0.0002866 MRPL37 4.408 0.0002056 GRPEL1 4.407 0.0002057 ACTR1B 4.393 0.0002882 CDC42SE2 4.392 0.0003371 LINC01934 4.389 0.0009202 ZFP91 4.389 0.0004415 DUS1L 4.379 0.0003003 EMC10 4.375 0.0002704 RHOT2 4.372 0.0003436 ATF5 4.370 0.000436 CDK11B 4.358 0.0002651 MRPS9 4.358 0.0003067 BPTF 4.352 0.000274 DRAM2 4.352 0.000352 GGH 4.347 0.0003468 WDR18 4.345 0.0002487 SFXN1 4.314 0.000283 LIME1 4.298 0.0003037 EIF4G2 4.284 0.0002968 RAB1B 4.266 0.0005488 ATP6V1G1 4.258 0.000358 UBXN1 4.252 0.000316 CCNI 4.252 0.0003281 EMC9 4.246 0.0003822 CARS 4.236 0.0009029 NUDT1 4.217 0.0003525 ADRM1 4.201 0.0003676 RSU1 4.201 0.0004861 ITFG2 4.199 0.0004735 TMPO 4.188 0.0004581 150 NDUFB5 4.149 0.0009722 PGK1 4.137 0.0004568 LAMTOR1 4.127 0.0004647 DCTN5 4.111 0.0005984 CD27 4.104 0.0006688 EPM2AIP1 4.062 0.0010467 FGFR1OP2 4.058 0.0006734 SMARCA4 4.050 0.0005877 TSR2 4.039 0.0009308 RAD21 4.037 0.0006497 KIF2A 4.012 0.0009581 RPS4Y1 4.009 0.0006296 PNN 4.001 0.0007158 SMARCD2 3.996 0.0008618 TMEM38A 3.984 0.0010923 KIAA1143 3.961 0.0009709 NCL 3.960 0.0007287 HSPA8 3.908 0.0008497 PPM1G 3.905 0.0008709
Thesis/Dissertation
eng
Mechanical Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Platform technology for selective single cell RNA sequencing of transient events
Text