Medicine, Faculty of
DSpace
UBCV
Chu, Chieh Min
Doctor of Philosophy - PhD
University of British Columbia
Diabetes affects around 500 million people worldwide. Diabetes is marked by raised blood sugar levels owing to a relative or absolute lack of insulin, which is produced in pancreatic islet beta-cells. Pancreatic beta-cells are heterogeneous, with many studies reporting functional and morphological differences. However, it is unclear whether insulin gene activity is also heterogeneous in beta-cells and whether this heterogeneity is static or dynamic. We utilized a mouse model in which the endogenous Ins2 gene has been replaced with GFP (Ins2GFP), to study endogenous insulin gene activity. We found that beta-cells can be found in two states, Ins2(GFP)HIGH and Ins2(GFP)LOW. We also found that gene activity at the Ins2 loci was dynamic, and that cells could transition between the two beta-cell states over time. We profiled the two cell states at the transcriptome and proteome levels, and perturbed beta-cell functions using a small molecule screen to determine the mechanisms behind Ins2 dynamics and cell state transitions. We found that the Ins2(GFP)HIGH state was associated with increased beta-cell maturity factors and increased fragility, and cAMP pathways were the strongest effectors of Ins2 gene activity. We next investigated INS gene expression using an INS-EGFP stem cell-derived beta-cell line. Similar to the studies using beta-cells from Ins2GFP mice, we found that INS gene expression was bimodal and dynamic over time in stem cell-derived beta-like cells. We interrogated the two INS gene expression states with a combination of transcriptomics and proteomic analyses. We found that cells with high EGFP expression had increased mRNA expression and protein abundance in the endoplasmic reticulum (ER) and Golgi. To understand the relationship between INS gene dynamics and beta-cell survival, we also performed a high-throughput screen of prioritized ligands, curated from a combination of proteomic and transcriptomic data. We found that several factors, namely FGF5, FGF19 and FGF8F had protective effects on SC beta-cells. Overall, our studies defined the heterogeneity in insulin gene activity in both mice and SC beta-cell models and reveal pathways which modulate insulin production. These observations may inform new cell therapies for diabetes.
DYNAMIC INSULIN GENE ACTIVITY STATES IN INSULIN PRODUCING CELLS by Chieh Min Chu B.Sc., National Central University, 2019 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Cell and Developmental Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) August 2024 © Chieh Min Chu, 2024 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Dynamic insulin gene activity states in insulin producing cells submitted by Chieh Min Chu in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Cell and Developmental Biology Examining Committee: Dr. James D. Johnson, Professor, Department of Cellular and Physiological Sciences, UBC Supervisor Dr. Sheila J.S. Teves, Assistant Professor, Department of Biochemistry, UBC Supervisory Committee Member Dr. Wyeth Wasserman, Professor, Department of Medical Genetics, UBC University Examiner Dr. Louis Lefebevre, Associate Professor, Department of Medical Genetics, UBC University Examiner Additional Supervisory Committee Members: Dr. Francis C. Lynn, Associate Professor, Department of Surgery, UBC Supervisory Committee Member Dr. Eric Jan, Professor, Department of Biochemistry and Molecular Biology, UBC Supervisory Committee Member iii Abstract Diabetes affects around 500 million people worldwide. Diabetes is marked by raised blood sugar levels owing to a relative or absolute lack of insulin, which is produced in pancreatic islet beta-cells. Pancreatic beta-cells are heterogeneous, with many studies reporting functional and morphological differences. However, it is unclear whether insulin gene activity is also heterogeneous in beta-cells and whether this heterogeneity is static or dynamic. We utilized a mouse model in which the endogenous Ins2 gene has been replaced with GFP (Ins2GFP), to study endogenous insulin gene activity. We found that beta-cells can be found in two states, Ins2(GFP)HIGH and Ins2(GFP)LOW. We also found that gene activity at the Ins2 loci was dynamic, and that cells could transition between the two beta-cell states over time. We profiled the two cell states at the transcriptome and proteome levels, and perturbed beta-cell functions using a small molecule screen to determine the mechanisms behind Ins2 dynamics and cell state transitions. We found that the Ins2(GFP)HIGH state was associated with increased beta-cell maturity factors and increased fragility, and cAMP pathways were the strongest effectors of Ins2 gene activity. We next investigated INS gene expression using an INS-EGFP stem cell-derived beta-cell line. Similar to the studies using beta-cells from Ins2GFP mice, we found that INS gene expression was bimodal and dynamic over time in stem cell-derived beta-like cells. We interrogated the two INS gene expression states with a combination of transcriptomics and proteomic analyses. We found that cells with high EGFP expression had increased mRNA expression and protein abundance in the endoplasmic reticulum (ER) and Golgi. To understand the relationship between INS gene dynamics and beta-cell survival, we also performed a high-throughput screen of prioritized ligands, curated from a combination of proteomic and transcriptomic data. We found that several factors, namely FGF5, FGF19 and FGF8F had iv protective effects on SC beta-cells. Overall, our studies defined the heterogeneity in insulin gene activity in both mice and SC beta-cell models and reveal pathways which modulate insulin production. These observations may inform new cell therapies for diabetes. v Lay Summary Diabetes is a disease that affects up to 50% of the population and is the result of raised blood sugar levels. Blood sugar levels are controlled by insulin, and insulin is produced by  cells in the pancreas. Not all  cells are the same and many researchers have tried to understand their differences. Using a protein that glows when insulin is made, we found that there are two major states a  cell can be in: one with higher insulin production and one with lower. We also found that it was possible for  cells to cycle between these two states. We treated  cells with various drugs and proteins to understand what controls insulin production and  cell health. Our studies improve our understanding of insulin production and  cells. This work is necessary for the development of therapeutics with a focus on  cell replacement. vi Preface Studies presented in this dissertation were conceived, designed, performed and analyzed by the author CM Chu, with the exceptions described below. This thesis was written by J Chu with editing provided by JD Johnson, SJ Teves, F Lynn and E Jan. A version of Chapter 3 has been published. CMJ Chu, H Modi, C Ellis, NAJ Krentz, S Skovsø, YB Zhao, H Cen, N Noursadeghi, E Panzhinskiy, X Hu, DA Dionne, YH Xia, S Xuan, MO Huising, TJ Kieferr, FC Lynn, and JD Johnson. (2022) Dynamic Ins2 Gene Activity Defines β Cell Maturity States. Diabetes. Dec 1;71(12):2612-2631. CMJ Chu designed and performed experiments (Ins2GFP live cell imaging experiments, FACS, staining for DEGs of interest), analyzed/interpreted data (scRNAseq, HPAP data, RNA velocity), wrote the revised manuscript. H Modi designed studies, performed experiments (qPCR, Ins1-mCherry live cell imaging experiments, FACS), analyzed/interpreted data, and wrote the original version of the manuscript. C Ellis constructed custom R scripts for analysis. NAJ Krentz analyzed/interpreted scRNAseq data. S Skovsø helped maintain the Ins2GFP mouse colony and analyzed/interpreted data. YB Zhao analyzed/interpreted the scRNAseq data. H Cen performed experiments, analyzed/interpreted data. N Noursadeghi worked on staining for insulin and GFP. E Panzhinskiy supervised work and analyzed/interpreted data. X Hu performed islet isolations, GTT, ITT assays. DA Dionne performed experiments, analyzed/interpreted data. YH Xia provided custom R scripts for analyzing data. S Xuan provided the Ins2GFP mouse model. MO Huising provided a key reagent, designed studies, and analyzed/interpreted data. TJ Kieffer supervised work. FC Lynn supervised work, analyzed/interpreted data, and edited the manuscript. JD Johnson conceived the project, designed studies, interpreted data, edited the manuscript, and is the guarantor of this work. vii A version of Chapter 4 will be submitted for peer review. CMJ Chu, B Sabbineni, HH Cen, X Hu, WQG Sun, GP Brownrigg, YH Xia, J Rogalski, JD Johnson. (2024) Signal transduction pathways controlling Ins2 gene activity and β cell state transitions. Unless otherwise stated, CMJ Chu designed and performed all experiments, analyzed/interpreted data, wrote the manuscript. B Sabbineni performed experiments (3D live cell imaging), analyzed/interpreted data. HH Cen analyzed/interpreted data. X Hu performed experiments. WQG Sun performed experiments. GP Brownrigg analyzed/interpreted data. YH Xia performed analyzed/interpreted data. J Rogalski performed experiments and analyzed/interpreted data. JD Johnson conceived the project, designed studies, interpreted data, edited the manuscript, and is the guarantor of this work. A version of Chapter 5 will be submitted for peer reviews. CMJ Chu, ME Omur, B Sabbineni, SY Chen, J Kolic, YH Xia, S Mar, LT Huang, J Rogalski, W Wasserman, FC Lynn, JD Johnson. (2024) Insulin gene expression states in stem cell-derived β cells. CMJ Chu designed and performed experiments (FACS, 3D live cell imaging, OPP, reaggregation experiments, live cell imaging, cell death assays, human viral transductions), analyzed/interpreted data (FACS, proteomics, 3D live cell imaging, OPP, reaggregation experiments, live cell imaging, cell death assays, human viral transductions), wrote the manuscript. ME Omur analyzed data (scRNAseq), analyzed/interpreted data (scRNAseq). B Sabbineni performed experiments (3D live cell imaging), analyzed/interpreted data (3D live cell imaging). SY Chen performed experiments (OPP assay). J Kolic performed experiments. YH Xia analyzed/interpreted data (proteomics). S Mar performed experiments (cell culture for scRNAseq, reaggregation experiments). LT Huang performed experiments (cell culture for scRNAseq, live cell imaging, cell death assay). J Rogalski analyzed/interpreted data viii (proteomics). W Wasserman supervised work. FC Lynn performed experiments, supervised work, analyzed/interpreted data. JD Johnson conceived the project, designed studies, interpreted data, edited the manuscript, and is the guarantor of this work. A version of Chapter 6 will be submitted for peer review. CMJ Chu, LT Huang, S Kamal, WW Wasserman, FC Lynn, JD Johnson. (2024) A prioritized medium-throughput screen on human stem cell-derived insulin secreting beta cells identifies FGF5, FGF8F, FGF6, FGF4, and FGF19 as protective factors. CMJ Chu designed studies, performed experiments (live cell imaging, prioritized ligand screen), analyzed/interpreted data (prioritized list of ligands, live cell imaging, prioritized ligand screen), wrote the manuscript. LT Huang performed experiments (live cell imaging, prioritized ligand screen). S Kamal analyzed/interpreted data (prioritized list of ligands). WW Wasserman supervised work. FC Lynn conceived the project, designed studies, obtained funding, supervised work, performed experiments, and analyzed/interpreted data. JD Johnson conceived the project, designed studies, interpreted data, edited the manuscript, and is the guarantor of this work. Animal protocols were approved by the University of British Columbia Animal Care Committee (certificate # A16–0022, # A19-0267, and # A21-0135). ix Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ......................................................................................................................... ix List of Tables .............................................................................................................................. xiii List of Figures ............................................................................................................................. xiv List of Symbols ......................................................................................................................... xviii List of Abbreviations ................................................................................................................. xix Acknowledgements .................................................................................................................... xxi Chapter 1: Introduction .......................................................................................................... 1 1.1 Diabetes......................................................................................................................... 1 1.1.1 Type 1 diabetes ......................................................................................................... 3 1.1.2 Type 2 diabetes ......................................................................................................... 5 1.2 Pancreatic β cells in the islet ......................................................................................... 8 1.2.2 Regulation of insulin transcription.......................................................................... 10 1.2.3 Regulation of insulin translation ............................................................................. 13 1.2.4 Post translational processing of insulin in β cells ................................................... 14 1.2.5 Insulin secretion in β cells ...................................................................................... 16 1.2.6 Stress and resilience in β cells ................................................................................ 18 1.3 β cell heterogeneity ..................................................................................................... 20 1.3.1 Morphological heterogeneity of pancreatic β cells ................................................. 21 1.3.2 Molecular heterogeneity of pancreatic β cells ........................................................ 21 x 1.3.3 Functional heterogeneity of pancreatic β cells........................................................ 23 1.4 Therapies for diabetes ................................................................................................. 24 1.4.1 Insulin based therapies ............................................................................................ 24 1.4.2 Cell replacement therapies ...................................................................................... 25 1.4.3 Drug therapies ......................................................................................................... 26 1.4.4 Lifestyle management ............................................................................................. 28 1.5 Thesis overview .......................................................................................................... 29 Chapter 2: Methods and Materials ...................................................................................... 31 2.1 Animals and in vivo physiology .................................................................................. 31 2.2 Immunostaining and RT-PCR..................................................................................... 31 2.3 Differentiation of stem cell-derived spheroids ........................................................... 33 2.4 Stem cell-derived spheroid dissociation and culture .................................................. 34 2.5 Islet isolation, dissociation and culture ....................................................................... 34 2.6 Fluorescence-activated cell sorting ............................................................................. 35 2.7 Live cell imaging ........................................................................................................ 36 2.8 Live-cell imaging analysis .......................................................................................... 38 2.9 Protein synthesis assay ................................................................................................ 40 2.10 Single cell transcriptomics .......................................................................................... 40 2.11 Mass Spectrometry / Proteomics ................................................................................ 42 2.12 Statistics and data visualization .................................................................................. 44 Chapter 3: Dynamic Ins2 gene activity defines β cell maturity states ............................... 46 3.1 Introduction ................................................................................................................. 46 3.2 Results ......................................................................................................................... 47 xi 3.2.1 In vivo heterogeneity of insulin content in human β cell and insulin gene activity in Ins2GFP mice......................................................................................................................... 47 3.2.2 Live cell imaging of insulin gene activity in Ins2WT/GFP mice ................................ 49 3.2.3 Autocorrelation analysis of fluctuations in Ins2 gene dynamics ............................ 51 3.2.4 Ins2 gene dynamics under various glucose and serum conditions ......................... 51 3.2.5 Profiling β cell states with single cell RNA sequencing ......................................... 53 3.3 Discussion ................................................................................................................... 58 Chapter 4: Signal transduction pathways controlling Ins2 gene activity and  cell state transitions ................................................................................................................................ 90 4.1 Introduction ................................................................................................................. 90 4.2 Results ......................................................................................................................... 92 4.2.1 Live 3D imaging of intact islets from Ins2GFP mice ............................................... 92 4.2.2 Identification of  cell selective islet proteins ........................................................ 92 4.2.3 Proteomic profiles of  cells in Ins2(GFP)HIGH and Ins2(GFP)LOW states .............. 94 4.2.4 Mechanistic interrogation of Ins2 gene activity regulation .................................... 99 4.2.5 Cell state transitions in the context of media conditions and perturbations ......... 101 4.3 Discussion ................................................................................................................. 103 Chapter 5: Insulin gene expression states in stem cell-derived β cells ............................ 123 5.1 Introduction ............................................................................................................... 123 5.2 Results ....................................................................................................................... 124 5.2.1 Heterogeneity in INS gene activity in stem cell-derived spheroids ...................... 124 5.2.2 Transcriptomic analysis of the INS(EGFP)HIGH and INS(EGFP)LOW states .......... 125 5.2.3 Proteomic analysis of the INS(EGFP)HIGH and INS(EGFP)LOW states .................. 126 xii 5.2.4 Cellular context-aware protein-protein interaction networks ............................... 129 5.2.5 Protein synthesis of the INS(EGFP)HIGH and INS(EGFP)LOW states ..................... 131 5.2.6 Stability and resilience of the INS(EGFP)HIGH and INS(EGFP)LOW states ........... 132 5.2.7 INS gene expression states in human donor β cells .............................................. 133 5.3 Discussion ................................................................................................................. 134 Chapter 6: A prioritized medium-throughput screen on human stem cell-derived insulin secreting beta cells identifies FGF5, FGF8F, FGF6, FGF4, and FGF19 as protective factors .................................................................................................................................... 151 6.1 Introduction ............................................................................................................... 151 6.2 Results ....................................................................................................................... 152 6.2.1 Live imaging of SCβ cell response to various stressors ....................................... 152 6.2.2 A high-throughput screen of 148 ligands for mediators of cell survival .............. 153 6.3 Discussion ................................................................................................................. 156 Chapter 7: Conclusion ......................................................................................................... 165 7.1 Summary and overall discussion .............................................................................. 165 7.2 Limitations ................................................................................................................ 169 7.3 Future directions ....................................................................................................... 171 References ...................................................................................................................................174 Appendices ..................................................................................................................................201 xiii List of Tables Table 2.1. Primary antibodies used for staining. ..................................................................... 32 Table 2.2 Primers used for qPCR. ............................................................................................. 33 Table 2.3 Drugs and small molecules used in targeted  cell Ins2 gene activity screen. ...... 36 xiv List of Figures Figure 1.1. Overview of the pathology of type 1 diabetes and type 2 diabetes. ....................... 8 Figure 1.2. Islet cell compositions in rodents and humans. ..................................................... 10 Figure 1.3. Overview of possible pathways which regulate insulin gene transcription. ....... 13 Figure 1.4. Human insulin precursor sequence. ...................................................................... 15 Figure 1.5. Regulation of insulin secretion via glucose. .............................................................. 17 Figure 3.1. In vivo heterogeneity of insulin content and gene activity in human and mouse islets. ............................................................................................................................................. 66 Figure 3.2. Identifying high and low GFP expressing cells in Ins2WT/GFP knock-in mice. .... 67 Figure 3.3. qPCR results from Ins2GFP mice and Ins2WT/GFP:Ins1-mCherry mice. .............. 68 Figure 3.4. Tracking β cell heterogeneity with Ins1-mCherry;Ins2WT/GFP knock-in mice. .. 70 Figure 3.5. Quantification of long term live cell imaging of Ins2WT/GFP:Ins1-mCherry. ...... 71 Figure 3.6. Live cell imaging of Ins2 gene activity reveals bursting behavior of the endogenous Ins2 gene locus. ....................................................................................................... 73 Figure 3.7. Quantification of long term live cell imaging of Ins2WT/GFP cells. ........................ 74 Figure 3.8. Autocorrelation analysis of cells with fluctuating behavior reveals possible connections to circadian rhythm. .............................................................................................. 75 Figure 3.9. Effects of media glucose concentration on Ins2 gene activity. ............................. 77 Figure 3.10. GFP fluorescence over time. ................................................................................. 78 Figure 3.11. Quantification of long term live cell imaging of Ins2WT/GFP cells in various culture conditions. ....................................................................................................................... 79 xv Figure 3.12. Autocorrelation and clustering of long term live cell imaging of Ins2WT/GFP cells in various culture conditions. ..................................................................................................... 80 Figure 3.13. Single cell RNA sequencing analysis of Ins2(GFP)LOW and Ins2(GFP)HIGH β cells in young Ins2WT/GFP heterozygous mice. ........................................................................... 82 Figure 3.14. Single cell RNA sequencing analysis of Ins2(GFP)LOW and Ins2(GFP)HIGH β cells in 60-week-old mice. ........................................................................................................... 83 Figure 3.15. Single cell RNA sequencing analysis of Ins2(GFP)LOW and Ins2(GFP)HIGH β cells in old Ins2GFP/GFP homozygous mice. ................................................................................. 84 Figure 3.16. Differential gene expression in Ins2(GFP)LOW and Ins2(GFP)HIGH -cells. ...... 85 Figure 3.17. Differential gene expression according to gfp mRNA levels in the combined samples of old Ins2WT/GFP heterozygous and Ins2GFP/GFP homozygous mice. ......................... 86 Figure 3.18. Pathway enrichment analysis of differentially expressed genes in the combined samples of young and old Ins2GFP knock-in mice. .................................................................... 87 Figure 3.19. Ins2(GFP)HIGH β cells show increased markers of maturity and increased cell death across multiple conditions. .............................................................................................. 89 Figure 4.1. Live 3D imaging of intact Ins2GFP mouse islets. .................................................. 108 Figure 4.2. Proteomic analysis of FACS purified Ins2GFP β cells. ........................................ 110 Figure 4.3. Integration of STRING networks and protein expression data in Cytoscape reveals differences in protein production and export between GFP positive and GFP negative cells. ............................................................................................................................. 112 Figure 4.4. Integration of STRING networks and protein expression data in Cytoscape reveals differences in transcription and mRNA processing between GFP expression states...................................................................................................................................................... 114 xvi Figure 4.5. OPP protein synthesis assay of Ins2(GFP)LOW and Ins2(GFP)HIGH cells. ........ 115 Figure 4.6. OPP protein synthesis assay on small molecules of interest from the screen. . 116 Figure 4.7. A small molecule screen for effectors of Ins2 gene activity. .............................. 118 Figure 4.8. Hierarchical clustering of cell behavior traits from small molecule screen. .... 119 Figure 4.9. Cell state transitions in the context of various media conditions and perturbations. ............................................................................................................................ 121 Figure 4.10. Integration of STRING protein networks and a small molecule screen on Cytoscape. .................................................................................................................................. 122 Figure 5.1. Heterogeneity of INS gene activity in stem cell-derived insulin producing cells...................................................................................................................................................... 137 Figure 5.2. Single cell RNA sequencing analysis of the two INS gene activity states. ........ 139 Figure 5.3. Proteomic analysis of FACS purified INS-EGFP SCβ cells. ............................. 141 Figure 5.4. Integration of STRING networks and protein expression data of the two INS gene activity states in Cytoscape. ............................................................................................. 143 Figure 5.5. FACS plots of purified populations of INS(EGFP)HIGH and INS(EGFP)LOW cells. ............................................................................................................................................ 144 Figure 5.6. OPP protein synthesis assay of INS(EGFP)HIGH and INS(EGFP)LOW cells. ..... 145 Figure 5.7. Investigating dynamics and stability in dispersed INS(EGFP)HIGH and INS(EGFP)LOW cells . ................................................................................................................ 146 Figure 5.8. Reaggregation of FACS purified SCβ cells. ........................................................ 147 Figure 5.9. Live cell imaging experiments investigating differential cell death in INS(EGFP)HIGH and INS(EGFP)LOW cells . ............................................................................ 148 xvii Figure 5.10. FACS purification of INS gene expression states in human primary islets using a RIP-GFP adenovirus. ............................................................................................................ 149 Figure 5.11. Protein abundance of major UPR markers in INS(EGFP)HIGH and INS(EGFP)LOW cells. ................................................................................................................. 150 Figure 6.1. Stress assay on SCβ cells using live cell imaging. ............................................... 160 Figure 6.2. INS gene activity in the context of various stress conditions. ............................ 161 Figure 6.3. Screening for effectors of SCβ cell survival against cytokine induced cell death with a prioritized list of 148 ligands. ....................................................................................... 162 Figure 6.4. Analysis of INS gene activity in the context of cytokine cocktail and ligands. 163 Figure 6.5. Analysis of cell death assay in the context of INS(EGFP)HIGH and INS(EGFP)LOW states. .............................................................................................................. 164 xviii List of Symbols α Alpha β Beta δ Delta xix List of Abbreviations BSA - Bovine serum albumin CMRL media - Connaught Medical Research Laboratories DAPI - 4’,6-diamidino-2-phenylindole EGFP - Enhanced green fluorescent protein FACS - Fluorescence activated cell sorting FBS - Fetal bovine serum GFP - Green fluorescent protein GSIS – Glucose stimulated insulin secretion HbA1c - Glycated haemoglobin A1c HEPES - 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid HPAP - Human pancreas analysis program ITS - Insulin-transferrin-selenium MEM - Minimum essential medium NA - Numerical aperture NEAA - Non-essential amino acids solution OPP - O-propargyl-puromycin PBS - Phosphate-buffered saline PCA - Principle component analysis ROCKi - Rho-kinase inhibitor SCβ - Stem cell-derived β-like cells scRNA-seq - Single cell RNA-sequencing STRING - Search Tool for Retrieval of Interacting Genes/Proteins TCEP - Tris(2-carboxyethyl)phosphine xx TFE - Trifluoroethanol UMAP - Uniform manifold approximation and projection xxi Acknowledgements I am eternally grateful to my supervisor, Dr. James Johnson for his support and guidance during my time working with him. His teachings of patience, interactions with people, and generosity have helped me grow as a person. His mentorship on designing and conducting experiments, data representation, knowledge gathering, and critical thinking are all skills which have helped me through my degree and will help me in future endeavors. His leadership in the lab, and in the field of diabetes, has fostered a strong teamwork focused attitude which provided a collaborative and trusting environment for conducting research. I will always be grateful for the role he played in allowing me to grow and learn in his lab. I would also like to thank members of my supervisory committee, Dr. Francis Lynn, Dr. Sheila Teves and Dr. Eric Jan for their advice and support over the years. Their guidance has served as fantastic checkpoints for my growth and have ensured a smooth progression through my degree. I would like to thank all of the labs I have worked and collaborated in, in particular the Francis Lynn lab, whose members taught me all matters related to stem cell differentiation, and whose leader and team have always been welcoming and kind to me. Also a big thank you to the Timothy Kieffer lab, which has generously provided me with support, guidance, and equipment for conducting experiments. I want to express my greatest appreciation to those who have collaborated with me on my projects, including members of the Johnson lab, Lynn lab, Kieffer lab, Wasserman lab, Foster lab, and McDonald lab. These collaborations have taught me much through the years, and I’m very grateful to have had these opportunities. xxii Finally, I would like to express my enduring gratitude to my parents and Karen, who have supported me through my degree and were always there for me in my darkest hours. Without them, nothing would have been possible. xxiii Dedication To my parents and Karen for their unwavering support and love. 1 Chapter 1: Introduction 1.1 Diabetes Diabetes mellitus is a debilitating disease effecting millions of people worldwide. In 2023, it is estimated that up to half a billion people are living with diabetes, with that number projected to increase to 1.3 billion by 2050 (1). It has been estimated that 2 million deaths were a result of diabetes per year (2). Worldwide, it is estimated that there are 17.7 million more males than females with diabetes (3). In Canada, 30% of all people live with diabetes or prediabetes, 10% of which is diagnosed (4). Diabetes is characterized by raised blood sugar levels, often stemming from a relative or absolute lack of the body’s main anabolic hormone, insulin (5). Insulin is primarily produced in pancreatic β cells, and the death or dysfunction of β cells is a key step in diabetes pathology. There are 4 methods of diagnosis of diabetes in Canada: 1) fasting plasma glucose (fast at least 8 hours prior to test) ≥ 7 mmol/L, 2) 2 hour plasma glucose in a 75 g oral glucose tolerance test (OGTT) ≥ 11.1 mmol/L, 3) glycated haemoglobin A1c (HbA1c) ≥ 6.5%, or 4) random plasma glucose (any time of day) ≥11.1 mmol/L (6). Common symptoms of diabetes include the classic polyuria, polydipsia, and polyphagia; these are associated with hyperglycemia (7). Other symptoms include unexplained weight loss, fatigue, and body pain (7). If left untreated, diabetes can result in serious complications (2). Complications from diabetes can be categorized as microvascular or macrovascular (8). Microvascular complications include neuropathy, nephropathy, and retinopathy; macrovascular complications include cardiovascular related diseases, stroke, as well as peripheral vascular diseases (8). Afflicting up to 30-50% of patients, diabetic peripheral neuropathy is one of the more common complications and results in a slow buildup of sense impairment and neuropathic pain (9). Diabetes related nephropathy 2 accounts for 44% of new end-stage renal disease cases, and can increase the risk of other diabetic complications such as cardiovascular diseases (10). Diabetic retinopathy is a common complication of diabetes that results in 10,000 new cases of blindness per year in the United States alone and is associated with prolonged hyperglycemia (11). Cardiovascular related diseases results in up to 65% of deaths related to diabetes, with heart diseases and stroke being the most common factors to result in death (12). Peripheral arterial disease is caused by narrowing of blood vessels, and constitutes a major risk factor for lower-extremity amputations (13). There are multiple forms of diabetes including type 1 diabetes, type 2 diabetes, gestational diabetes, neonatal diabetes and maturity onset diabetes of the young (14). Type 1 diabetes is believed to be caused by the destruction of pancreatic β cells via the patient’s own immune system (15). Type 2 diabetes is caused by a lack of adequate insulin production from pancreatic β cells and insulin resistance in peripheral tissue (16). Gestational diabetes is a result of reduced insulin production and insulin resistance during pregnancy (17). Gestational diabetes impacts 3-9% of pregnancies, and primarily occurs during the late second or early third trimester (18). Children birthed from gestational diabetes patients have increased risk of obesity and development of type 2 diabetes (19). Neonatal diabetes is a rare form of diabetes which occurs in young children between the neonatal period and infancy, and has two types: permanent and transient neonatal diabetes (20). Permanent neonatal diabetes requires lifelong treatment, while transient neonatal diabetes can be off treatments anywhere between a few weeks to the first few years of life (21). Both forms of neonatal diabetes generally arise from abnormal pancreas or pancreatic β cell function due to monogenetic mutations, with said mutations not being exclusive to either; for instance, mutations in INS have been observed to both permanent neonatal diabetes 3 and transient neonatal diabetes (22,23). Mature onset diabetes of the young is a monogenetic form of diabetes observed in non-obese patients that account for less than 5% of all diabetes cases (24). Types of mature onset diabetes of the young are classified by the genes involved, with at least 14 known mutations (24,25). The mutated genes are often associated with β cell function and insulin production, with the most common types of mature onset diabetes of the young impacting the genes HNF4α, GCK, HNF1α, and HNF1β (25). Due to similar clinical characteristics, up to 80% of mature onset diabetes of the young cases are often misdiagnosed as type 1 diabetes or type 2 diabetes (26). Below is further discussion of the two most common types of diabetes, type 1 diabetes and type 2 diabetes. 1.1.1 Type 1 diabetes Type 1 diabetes is the result of the autoimmune destruction of a majority of pancreatic β cells, resulting in loss of insulin production (27). Type 1 diabetes diagnoses often occurs in youth and young adults (most commonly between age 10-14), though it can occur at any age (28). Type 1 diabetes accounts for between 0.1-17.8% of all diabetes cases in different countries (29). It has been hypothesized that type 1 diabetes is initiated by the recognition of β cell peptides as autoantigens by autoreactive CD4+ T lymphocytes (30,31). The most common autoantigens include the insulin B chain peptide, glutamic acid decarboxylase 65 (GAD65), insulinoma-associated protein 2 (IA-2), and the transmembrane zinc transporter 8 (ZnT8) (32). Once islet autoantigens have been identified as a possible threat, immune cells may infiltrate the islet and result in inflammation, a process called insulitis (33). In type 1 diabetes pathogenesis, what follows is the destruction of up to 70-99% of β cell mass due to the activation and expansion of CD8+ T cells, the dominant lymphocyte which targets and kills β cells (34). Recent studies have 4 shown that this process can take up to several years, with type 1 diabetes patients reporting cells which stained positive for insulin many years after initial diagnosis (35). Some studies have shown that patients who have lived with type 1 diabetes for decades may still secrete low amounts of c-peptide (cleaved from and secreted alongside insulin) and possess insulin secreting β cells (36,37). These findings suggest that type 1 diabetes progression is chronic, and dispels the initial belief that the majority of β cell mass is lost at type 1 diabetes diagnosis. Thus, it is imperative to find methods to slow down or halt the development of the autoimmune response in type 1 diabetes and improve surviving β cell function. The process of type 1 diabetes progression is often thought to be a combination of environmental and genetic factors (28). While the environmental factors triggering type 1 diabetes have yet to be fully elucidated, known factors include nutritional factors, viral infections, intestinal microbiota, and even certain vaccines (38). Many susceptibility genes have been identified as type 1 diabetes risk factors. Of note, the human leukocyte antigen (HLA) genes are the most prominent loci associated with type 1 diabetes and play a role in antigen presentation. In particular, two susceptibility haplotypes in the HLA class II region are the major genetic risk factors for type 1 diabetes, with up to 90-95% of children with type 1 diabetes carrying either one or both susceptibility haplotypes (39). Another critical gene associated with type 1 diabetes risk is the INS gene itself. The 5’untranslated region of the INS gene houses a variable number of tandem repeats (VNTR) which are 14-bp in length, with three classes of alleles categorized by the length: class I (26-63 repeats), class II (64-143 repeats), and class III (140-210 repeats) (40). Of these three classes, class I has been associated with type 1 diabetes, while class III has been considered protective against type 1 diabetes (41,42). Other genes identified to be associated with type 1 diabetes include protein tyrosine phosphatase non-receptor 5 type 22 (PTPN22), a suppressor of T lymphocyte activation (43); cytotoxic T-lymphocyte antigen (CTLA4), a negative regulator of cytotoxic T cells (44); and Gli-similar 3 protein (GLIS3), a gene which may play a role in regulating insulin expression and secretion (45). Overall, the majority of type 1 diabetes risk factors seem to be either primarily related to immune activation or insulin production. However, more research is needed to fully understand the causes and triggers of type 1 diabetes. 1.1.2 Type 2 diabetes Type 2 diabetes is the most common type of diabetes, accounting for up to 90% of all diabetes cases (46). The largest population of people living with type 2 diabetes are aged 40-59 years old, though an increasing number of younger diagnoses are appearing (47). Multiple models of type 2 diabetes pathogenesis have been proposed (48). The first, and more commonly cited model, proposes that insulin resistance first occurs, possibly due to environmental factors such as obesity (49). To deal with the reduced insulin sensitivity and increased circulating glucose, compensatory insulin secretion occurs to maintain glucose homeostasis (16). This may result in hyperinsulinemia, which is a key predictor for type 2 diabetes pathogenesis (50). After prolonged hypersecretion of insulin, β cells may start to die due to a combination of the stress of excessive insulin production, lipotoxicity, and unresolved hyperglycemia (51). Another model proposes how hyperinsulinemia may occur prior to insulin resistance (52). This model emphasizes the importance of insulin in multiple metabolic processes, and that excess insulin may be a driving force in obesity and type 2 diabetes (48). For instance, it has been shown that insulin plays a key role in regulating white adipocyte lipid accumulation, and that reduction of insulin production via gene deletion can be protective against diet induced obesity and improve 6 insulin sensitivity (53-55). Overall, while more studies are needed to clarify the exact sequence of events leading up to type 2 diabetes, it has been generally accepted that the primary hallmarks of type 2 diabetes include insufficient insulin secretion as a result of β cell dysfunction and peripheral tissue insulin resistance (56). Multiple pathways can contribute to type 2 diabetes progression, such as β cell dysfunction and death, insulin resistance, and glucagon effects; type 2 diabetes patients may present with a mix of any of the above as well as other factors, such as fat distribution or reduced incretin effects (57). This “mixing and matching” of contributing factors results in strong heterogeneity between individuals living with type 2 diabetes, making direct correlation of risk factors and disease difficult. Recently, a review by McCarthy has proposed a “palette” model, which utilizes different colors to represent different pathways which contribute to diabetes development; by mixing and matching different pathways (“colors” in the “palette”), the review paints a picture of how type 2 diabetes occurs in a spectrum (58). Attempts to untangle these factors and segregate type 2 diabetes into various “subtypes” according to disease heterogeneity include a study by Ahlqvist et al, which utilized hierarchical and k-means clustering on different patient traits, such as BMI, age of diagnosis, β cell function, and insulin sensitivity; they identified 5 diabetes subtypes: severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD) (59). These subtypes had different characteristics, such as differing response to insulin treatments and greater chance of developing certain complications. Dennis et al also performed a clustering of a different group of patients and found similar results, supporting the robustness of this type of analysis (60). 7 While the heterogeneity of type 2 diabetes makes it challenging to identify risk factors, there are still trends that can be followed. Factors believed to contribute to type 2 diabetes risk include genetic factors, obesity, sedentary lifestyles, and unhealthy eating habits (61). Obesity is often thought to have been a major cause of type 2 diabetes due to ~90% of patients in western countries being obese or overweight (62). However, contrary to findings in western countries, a study in China found that 50.3% of type 2 diabetes patients were not overweight (63).On the genetics side, hundreds of type 2 diabetes risk factors in the context of genetics have been identified using genome wide association studies, with more than 534 associated loci identified (GWAS) (64). In particular, proliferator-activated receptor gamma 2 (PPARG), ATP-dependent potassium channel Kir6.2 (KCNJ11), solute carrier family 30 member 8 (SLC30A8), adenylate cyclase 5 (ADCY5), hematopoietically-expressed homeobox (HHEX), and transcription factor 7-like 2 (TCF7L2) have been identified as genes whose variants may increase the risk of type 2 diabetes, and have strong statistical association with type 2 diabetes across multiple studies (65,66). Other genes that were found to be associated with type 2 diabetes include genes implicated in MODY, including HNF4α, GCK, HNF1α, and PDX1 among many others. However, it should be noted that the majority of type 2 diabetes risk variants are located near genes whose function are yet unknown and were not expected to play a role in type 2 diabetes pathology. 8 Figure 1.1. Overview of the pathology of type 1 diabetes and type 2 diabetes. Modified from Khawandanah et al (2019) (67). 1.2 Pancreatic β cells in the islet Pancreatic β cells are located in the islet of Langerhans, which are clusters of endocrine cells responsible for maintaining glucose homeostasis (68). An adult human pancreas is estimated to contain 3.6-14.8 million islets of different sizes (69). Adult human islet composition is usually 50-60% β cells, 30-50% α cells, with the remaining 10% comprised of δ cells and other cell-types (70). In regards to architecture of human islets, current studies have mostly given up on the search for a “stereotypical” human islet due to heterogeneity between islets; for instance, in smaller islets, it has been proposed that β cells reside in the core, while in larger islets it is 9 believed that β and α cells are dispersed throughout the islet (71). In mice, islets are composed of 65-80% β cells, 10-20% α cells, with the remaining 10% comprised of δ cells and other cell-types (72). The stereotypic mouse islet consists of a core of mostly β cells, while α cells, δ cells, and PP cells are primarily located in the periphery (73). β cells have been shown to have an average diameter of 10 µm, with around 20 pg of insulin content per cell (74). The molecular identity of β cells has been heavily researched. During development of the human pancreas, the first insulin expressing endocrine cells appear at around 7.5-8 weeks of gestation (75). This is followed by cells expressing glucagon and somatostatin at gestation week 8, and ghrelin expressing cells at gestation week 9 (75,76). Between gestation weeks 8-11, cells co-expressing NGN3, PDX1, and insulin appear, with known endocrine cell identity markers PAX6, NKX2.2, NKX6.1, and PAX4 also appearing at around the same period (77,78). In mature islets, maintenance of β cell identity is often associated with key transcription factors, such as PDX1, MAFA, NEUROD1, FOXO1, and NKX6.1 (79-81). Other markers often associated with β cell identity include markers of β cell function such as SLC2A2 and GCK, as well as other markers that have been shown to correlate with β cell maturity such as UCN3 and IAPP (82-84). The primary function of β cells is to sense and maintain glucose homeostasis via the production and secretion of insulin, which is the primary anabolic hormone in our body. Below we will discuss the production, secretion, and consequences of insulin in pancreatic β cells. 10 Figure 1.2. Islet cell compositions in rodents and humans. Figure not to scale. Modified from Campbell et al 2021 (85). 1.2.2 Regulation of insulin transcription Humans and most mammals have a single insulin gene (INS), with the human gene possessing 1425 base pairs (including exons and introns) and being located on chromosome 11 (86). It has three exons separated by two introns; the first exon comprising entirely of the 5’untranslated region (UTR); the second exon which encodes the B-chain, signal peptide, and part of the C-peptide; and the third exon which possesses the A-chain and the rest of the C-peptide (87). Mice have two insulin genes (Ins1 and Ins2). The Ins2 gene is the mouse equivalent of the human INS gene, and also possesses three exons and two introns. The Ins1 gene only has two 11 exons, and is believed to have been partially duplicated of Ins2 via reverse transcription of the Ins2 mRNA transcript and reinserted into the mouse genome (88). Both genes are predominantly transcribed in the islet, though while Ins1 is β cell specific, Ins2 expression appears in the thymus and brain (53). While some studies have reported that the two genes are equally expressed in mice (89), others have shown that Ins2 may be more strongly expressed (90). In terms of external stimuli, insulin biosynthesis is controlled chiefly by glucose metabolism, which stimulates both insulin gene transcription and mRNA translation (91). Glucose has also been shown to help maintain stability of insulin mRNA (92). In regards to insulin itself, there have been reports of negative feedback between insulin expression and secretion (93). More recently, there have been reports of positive regulation of insulin expression by insulin via the phosphatidylinositol 3-kinase (PI3-K) pathway (94). Another report suggested that islet cell autoantigen 512 (ICA512) is a major regulator of feedback control for insulin secretion (95). Insulin granules are anchored to actin via ICA512, and upon release of insulin a fragment of ICA512 is cleaved off and relocates to the nucleus, where it prevents STAT5 dephosphorylation, which in turn upregulates insulin transcription (96). Fatty acids are important for β cell function, and it has been shown that high concentrations of the saturated fatty acid palmitate can reduce insulin transcription in high glucose conditions (97). The incretin GLP-1 has been shown to upregulate insulin transcription and increase insulin mRNA stability, possibly through cAMP mediated pathways (98). In the context of the insulin gene itself, elements in the insulin gene promoter serve as key binding sites for major transcription factors which regulate insulin gene activity. The A elements are A/T rich elements with a TAAT core, and is a binding site for the transcription factor duodenal homeobox-1 (PDX-1), which is a major regulator of insulin gene activity and an islet 12 cell marker (99). In the human insulin promoter, there are four A elements: A1, A2, A3, A5. The mouse Ins2 promoter has A1, A2, and A3, while the Ins1 promoter has the unique A4 alongside A1, A2, and A3 (100). The C1 element in the insulin promoter have been shown to be a binding site for MAF bZIP transcription factor A (MAFA), another key regulator for insulin transcription and β cell marker (101). The human insulin promoter and both mouse insulin promoters possess C1 elements (99). E elements have been shown to be major binding sites for BETA2/NeuroD1, a transcription factor important in regulation of insulin transcription and β cell survival (102). The human insulin gene and mouse Ins2 gene each possess one E element, while the mouse Ins1 gene possesses two (103). The Z element is unique to the human insulin promoter, and has been suggested to be activated by PDX-1 and MAFA (104). Cyclic AMP response elements (CRE) have been shown to be key regulators of insulin transcription, with a variety of transcription factors, particularly those in the CRE binding protein (CREB)/ATF family, binding to them and inducing insulin transcription (105). The human insulin gene possesses four CRE sites, while other non-primate mammals have one (103). 13 Figure 1.3. Overview of possible pathways which regulate insulin gene transcription. Adapted from Melloul et al (2002) (100). 1.2.3 Regulation of insulin translation β cells are able to upregulate protein synthesis, including insulin translation, in response to nutrients such as glucose. This is done, at least partially, via dephosphorylation of eukaryotic initiation factor 2alpha (eIF2α) (106). It has been shown that the 5’ and 3’ UTRs of insulin mRNA are necessary for insulin translation regulation and that removal of these regions drastically reduces glucose stimulated insulin protein synthesis (107). Glucose can upregulate factors, such as protein-disulfide isomerase (PDI), which in turn binds to the 5’UTR of insulin mRNA and stimulates insulin translation (107). Polymyrimidine tract binding proteins (PTBPs), specifically PTBP1, are also known to improve insulin mRNA stability and initiate translation by 14 binding to the 3’UTR of insulin mRNA (108). Compared to the more global eIF2α mediated upregulation of translation, glucose induced translational regulation via the UTRs of insulin mRNA seems more specific. Negative regulation of insulin translation is necessary to ensure β cells are not overcome by stress or do not produce too much insulin and result in hypoglycemia. The unfolded protein response (UPR) pathways play key roles in regulating β cell translation. Phosphorylation of eIF2α via the ER resident sensor PERK is key for maintaining β cell health, as mutation of PERK results in Wolcott-Rallison syndrome associated with permanent neonatal diabetes in patients (109). In mice reduced in PERK expression (PERK (-/-) knockout), insulin production is severely impaired, leading to permanent neonatal diabetes (110). Another UPR protein, inositol-requiring protein 1A (IRE1A), has been shown to stimulate insulin production early during exposure in high glucose, but reduces insulin synthesis after prolonged exposure (111). Overall, both positive and negative regulation of insulin translation has been shown to be critical in β cell function. 1.2.4 Post translational processing of insulin in β cells Insulin consists of 51 amino acids and is a 5.8 kDa peptide hormone responsible for maintaining glucose homeostasis and regulating metabolism (112). The A-chain and B-chain are linked together by two disulfide bonds. In pancreatic β cells, insulin is first transcribed from the INS gene into mRNA in the rough endoplasmic reticulum and then translated into the precursor preproinsulin (113). Preproinsulin possesses a signal peptide, which interacts with signal recognition particles (SRP), which moves preproinsulin through the rough endoplasmic reticulum and into the lumen (113,114). While the SRP complex is necessary for co-translational translocation, preproinsulin may also move to the ER via SRP independent post-translational 15 translocation mediated by the SEC61 translocon and SEC62 (115). Once in the ER, preproinsulin is cleaved by a signal peptidase into proinsulin, then undergoes folding mediated via ER chaperone proteins and forms 3 disulfide bonds (116,117). The folded proinsulin is then transported to Golgi apparatus to undergo sorting (118). Sorting of proinsulin involves either sending for constitutive secretion, or regulated secretion (119). In constitutive secretion, proinsulin is packaged into transport vesicles that are directly trafficked to and can fuse with the plasma membrane to be released (120). In regulated secretion, proinsulin is packaged into immature clathrin coated secretory granules (119). As these vesicles mature, the clathrin coating is removed and the pH inside the vesicle declines from around 6.5 to 5.5 via the granule luminal ATP-dependent proton pump (121). This decrease in pH increases the cleaving efficiency of prohormone convertases 1/3 (PC1/3), PC2, and carboxypeptidase E (CPE) (122,123). These enzymes then cleave proinsulin into the active insulin hormone and connecting peptide (C-peptide) (112). Insulin, along with C-peptide and islet amyloid polypeptide (IAPP) are stored in secretory granules (112). Figure 1.4. Human insulin precursor sequence. Adapted from Ward et al (2011) (124). 16 1.2.5 Insulin secretion in β cells Insulin secretion is primarily triggered by glucose, which is the best substrate for inducing this phenomenon. In mice, β cells sense and acquire glucose via glucose transporter 2 (GLUT2), while in humans GLUT1 and GLUT3 are the primary glucose transporters (83). Glucose transporters in β cells have relatively low affinity towards glucose, thus preventing sensing and induction of glucose resulting in insulin secretion at low glucose levels (83). Glucose is then phosphorylated by the hexokinase glucokinase into glucose-6-phosphate (G6P) (125). Glucokinase is considered the rate limiting factor in β cell glucose metabolism, due to it 1) also having a lower affinity for glucose compared to other hexokinases, and 2) is not inhibited by its own product, thus ensuring high activity in high glucose situations (125). G6P then engages in glycolysis and generates pyruvate, which then enters the tricarboxylic acid cycle (TCA) and generates ATP (126). The increase in ATP increases the ATP/ADP ratio in β cells, leading to closure of ATP-sensitive potassium (KATP) channels and depolarization of the cell membrane (127). This in turn leads to the opening of voltage-dependent calcium channels and an influx of calcium, which then induces exocytosis of insulin granules (128). 17 Figure 1.5. Regulation of insulin secretion via glucose. Adapted from Fu et al (2014) (112). While the core product of glycolysis is ATP, which stimulates insulin secretion, other byproducts of pyruvate metabolism, such as NADPH and glutamate generated via anaplerosis, can also stimulate or amplify insulin secretion (129). After glucose is phosphorylated into G6P, G6P can also be metabolized to provide glycerol-3-phosphate (Gly3P), which is important for generating acyl-CoA and diacylglycerol, which in turn can enhance insulin secretion (130). Amino acids, in particular a combination of leucine and glutamine, can also induce insulin secretion. This is done via leucine-induced activation of glutamate dehydrogenase, which converts glutamate into α-ketoglutarate (131). Glutamine can then enter the TCA cycle via α-18 ketoglutarate and produce ATP. Although calcium is the primary second messenger which triggers insulin secretion, other signaling factors, such as cyclic AMP (cAMP), are also capable of inducing insulin release (132). cAMP can induce insulin production and secretion, with a study suggesting that the cAMP binding protein cAMP-GEF can potentiate insulin secretion in a PKA independent manner (133,134). Other hormones have also been reported to regulate insulin release. Estrogen receptors are present in β cells, and 17β-estradiol is known to enhance insulin secretion (135). The incretins GLP-1 and GIP are well known to increase insulin secretion via cAMP signaling pathways (136). Leptin has also been shown to have an inhibitory effect on insulin release, and is hypothesized to exert its inhibitory effect by antagonizing cAMP, possibly through paracrine effects (137). Growth hormone (GH) is also shown to reduce insulin secretion, with the mechanism believed to be mediated through activation of phosphodiesterases (PDE), which break down cAMP (138). Overall, there are many pathways and factors that contribute to insulin secretion, and more are being discovered which helps with therapies targeting insulin release to alleviate diabetes. 1.2.6 Stress and resilience in β cells β cells may encounter many different sources of stress in their role of maintaining glucose homeostasis, and many studies have investigated the many mechanisms with which β cells encounter and deal with stress (111,139-145). The most common source of stress is ER stress, which generally occurs when the demand for protein folding surpasses folding capacity (141). While β cells are generally equipped to handle large insulin production outputs, this balance may shift in the context of insulin resistance in type 2 diabetes, or reduced numbers of β cells in type 1 diabetes (142). As discussed in chapter 1.2.2, the UPR pathway is the primary mechanism of β 19 cell response to ER stress, in which the three arms in the UPR pathway (IRE1A, ATF6, PERK) help to alleviate stress from excessive protein production (140). IRE1A primarily acts through the splicing of X-box-binding protein 1 (XBP1), which then promotes the expression of chaperone and ER associated degradation (ERAD) proteins (146). ATF6 can be cleaved by site 1/2 proteases, which then allows it to function as a transcription factor that also upregulates chaperone and ERAD proteins (147). PERK mediates attenuation of global translation and can induce apoptosis via CHOP in chronic stress situations. Deletion of any of the three arms in the UPR pathway can impair glucose metabolism and result in diabetes (148). Failure to properly manage ER stress may result in β cell death and dysfunction via C/EBP-homologous protein (CHOP) induced apoptosis (149). Cellular stress can also occur from infections and inflammation. β cells respond to that with the integrated stress response (ISR), in which four protein kinases reduce protein synthesis. The protein kinases are: 1) PERK, which detects ER stress; 2) protein kinase R (PKR), which is mainly activated by viral infections; 3) general control nonderepressible 2 (GCN2), which activates during amino acid depletion; and 4) heme-regulated inhibitor (HRI), which is not linked to β cells but more to erythrocytes and detects heme levels (150). All four kinases phosphorylate eIF2α, reducing global translation while paradoxically promoting translation of a subset of mRNAs, which encode proteins that help alleviate stress (111,150). However, chronic, unrelieved ISR can result in cell death (151). Oxidative stress occurs when build up of reactive oxygen species (ROS) overwhelms the antioxidant capacity of a cell (152). Exogenous factors which induce ROS include cytokines and glucose, while endogenous factors include by-products of mitochondria activity and peroxisomes (152,153). Due to the high production of insulin, NADPH generation is often a cause of 20 oxidative stress in β cells (154). Under oxidative stress, the transcription factor nuclear-factor-erythroid 2-related factor 2 (NFE2L2) can translocate to the nucleus and induce expression of antioxidant genes, such as glutathinone peroxidase (GPX) and glutathione-S-transferase (GST) (155,156). It has been found that inducing NFE2L2 expression can suppress the onset of hyperglycemia in non-obese diabetic (NOD) mice (157). It is important to understand how β cells deal with stress for the development of therapeutics which seek to improve β cell function. 1.3 β cell heterogeneity Numerous studies have found that β cells are heterogeneous. Morphological, functional, and molecular differences have been found in studies on β cell subpopulations (158,159). Many factors contribute to the generation of the many β cell subpopulations. For instance, vascularization of islets developed unequally, with peripheral cells mostly interacting with capillaries while central cells interact with main vessels, resulting in varied distribution of nutrients and other factors (160). Larger islets also have more vascularization compared to smaller islets (160). Innervation of islets is also not equal, as only a small number of cells in the islet interact with axon terminals (161). It was found that neurotransmitters such as GABA and serotonin increase proliferation, thus highlighting the impact of unequal innervation (162). The development of the islet is also a factor; during pancreas development, the dorsal and ventral buds form the head and tail of the pancreas, resulting in islets that have different origins (163) . More studies are needed to understand the generation and development of β cell subpopulations. Below, we will discuss examples of β cell heterogeneity in the context of morphology, molecular markers, and function. 21 1.3.1 Morphological heterogeneity of pancreatic β cells Some β cells have larger sizes and larger nuclei compared with others, and are often located near the center of the islet (164). Some of this increased size has been attributed to polyploidy, possibly as a result of metabolic stress (165). β cells near the core of the islet have larger ERs and Golgi apparatuses (166). It was also observed that β cells have heterogeneity in polarity; while the majority of β cells have polarity, some were found to be nonpolar (167). β cells have been found to have heterogeneity in gap junctions, which are important for β cell coupling. One study observed twice as many gap junctions in the periphery of the islet compared to the core under basal conditions (168). 1.3.2 Molecular heterogeneity of pancreatic β cells Many β cell subpopulations have been identified based on molecular markers and gene expression. For instance, variability in key factors regulating β cell insulin secretion were identified, such as with the main β cell glucose sensors GLUT2 and GCK (167,169). Other factors with an indirect connection to insulin secretion include sorting for polysialylated-neural cell adhesion molecule (PSA-NCAM), and it was found that expression of this gene positively correlated with glucose stimulated insulin secretion (170). Studies on heterogeneity based on promoter activity have been undertaken. Using a transgenic mice expressing GFP under control of the Ins1 gene promoter, it was found that cells with different levels of GFP expression also differed in size and insulin secretion, with low GFP expressing cells also secreting less insulin (171). A PDX1 and INS dual reporter construct was also used to identify β cell subpopulations in human, mouse, and MIN6 cells (172). Interestingly, using time lapse imaging, it was shown that cells with low INS expression could transition to 22 high INS expression without cell division, bringing into question the stability of some of these subpopulations (172). Single cell RNA sequencing (scRNAseq) opened the door to considerably more β cell subpopulations based on gene expression. Variability in ER stress markers such as HPSA5 and CHOP were used to identify multiple β cell subpopulations in nondiabetic human donors (173). Another study used cell surface markers and combined FACS and scRNAseq to profile four β cell subpopulations (174). A study utilizing pseudotime analyses on human β cell scRNAseq data found populations of β cells with high and low INS expression with varying levels of UPR marker expression (175). One of their populations had low INS expression with increased UPR marker expression, and it was hypothesized that cells with high INS may transition to this low INS subpopulation in an effort to relieve the stress from high insulin production (175). Another β cell heterogeneity phenomenon that was identified were the hub cells, a group of highly connected cells representing 1-10% of all β cells that would coordinate calcium signals via gap junctions (176). These cells had high GCK expression with low levels of insulin, supporting their roles as sensors and coordinators (176). Other examples of heterogeneity studies include using CD63 as heterogeneity markers, leader cells, and first responder cells (177-179). However, as the field of studying β cell heterogeneity grew, a few concerns arose with using global gene expression to identify β cell heterogeneity (180). For instance, with the ever increasing number of cells scRNAseq techniques can analyze, statistical significance is all but guaranteed based on p-values. A meta-analysis examining five scRNAseq studies found very few β cell subpopulation markers that corresponded to past scRNAseq heterogeneity studies (180). These issues will need to be addressed as the field progresses, and brings into question whether some of these differences are biologically meaningful. 23 1.3.3 Functional heterogeneity of pancreatic β cells As insulin secretion is the primary role of β cells, the majority of examples of functional β cell heterogeneity is regarding insulin secretion (128). Earlier studies on β cell heterogeneity grouped them into more responsive or less responsive populations to glucose (181). β cells could also be sorted based on NADPH autofluorescence in response to glucose, a readout for metabolic activity (182). The morphological differences in β cells can translate to functional differences. For instance, the aforementioned larger β cells in the core were found to de-granulate faster when presented with glucose, while smaller cells in the periphery only de-granulated upon longer exposure to glucose (166). These larger cells secreted considerably more insulin; in dispersed human β cells, 20% of the cells were responsible for secreting 70-90% more insulin (183). Similar to morphological differences, molecular heterogeneity may also translate into functional heterogeneity. For instance, cells with increased GCK and GLUT2 were also more found to be more responsive to glucose (167,169). The study which identified four β cell subpopulations with cell surface markers also found that two of their four subpopulations had increased glucose stimulated insulin secretion, while the other two had increased basal insulin secretion (174). One method which directly linked functional differences to molecular differences was patch-seq, which utilizes a pipeline that performs patch-clamp techniques and scRNAseq on the same cell to correlate human β cell exocytosis and gene expression (184). 24 1.4 Therapies for diabetes Most forms of diabetes occur primarily due to a relative or absolute lack of insulin. In the case of type 1 diabetes, owing to a lack of insulin producing cells, most therapies aim to replenish insulin, either by directly providing it (insulin based therapies) or providing the ability to produce more (cell replacement therapies)(185). For type 2 diabetes, especially in its earlier stages, there are often still insulin producing cells in the patient, though these cells may be dysfunctional (145). Therefore, therapies for type 2 diabetes aim to reduce symptoms of diabetes via management of insulin resistance and hyperglycemia (drug therapies, lifestyle changes), and sometimes with a combination of direct replenishment of insulin (186). 1.4.1 Insulin based therapies The seminal discovery of insulin by Frederick Banting and Charles Best in 1922 allowed the development of insulin based therapies for diabetic patients (187). Prior to that, children with type 1 diabetes diagnoses often only had a 1-2 year life expectancy post diagnosis. Direct injection of insulin is the primary therapeutic method for treatment of type 1 diabetes, and is often combined with lifestyle or drug treatments for type 2 diabetes (188). Development of insulin therapies are ongoing, with newer products developed which increase efficiency and accuracy of delivery insulin. These products include insulin pumps, glucose monitors, and different insulin isoforms which have either shorter or longer lifespans (189). However, despite significant progress, to the point that diabetes is often considered a more manageable condition than other diseases, the lifelong maintenance and multiple insulin injections daily needed for insulin therapies make it fall short of being a true cure (190). 25 1.4.2 Cell replacement therapies Pancreatic β cells are the primary cell type responsible for producing insulin, and death and dysfunction of β cells is a key step in diabetes pathology in both type 1 diabetes and type 2 diabetes (51,191). Therefore, replacement of dysfunctional or lost β cells has always been considered an important aspect of finding a cure for diabetes. The Edmonton protocol was developed in the 1990s at the University of Alberta, and is a technique for isolating islets from a cadaveric organ donor and transplanting them into diabetic patients (192). The protocol was shown to improve glycemic control and greatly reduce complications related to diabetes compared to insulin therapies (193). Around 50-70% of transplant recipients are insulin independent five years post-transplant (194). However, the requirement of consistent and expensive immunosuppressant applications and lack of organ donors make this method of diabetes treatment unattainable for the majority of diabetes patients. Stem cell-derived insulin producing cells have emerged as a promising and scalable alternative to cadaveric donor islets (195,196). The idea of having a potentially unlimited source of β cells, and with the possibility of obtaining patient derived induced pluripotent stem cells (IPSCs) which can bypass the need for immunosuppressants make stem cell-derived β cells (SCβ) a particularly enticing direction for finding a cure (195). However, development of SCβ cells as a cure is still undergoing progress, and has faced numerous challenges in the field. For instance, while SCβ cells have been shown to respond to glucose and secrete insulin, we are still far away from creating true β cells in vitro (195). SCβ cells do not respond to glucose with efficiencies comparable to human islets, and the lack of vascularization, other cell types, islet architecture are all issues that need to be addressed before we can truly recreate the islet (197). 26 While truly recreating an islet is still a difficult task to achieve, it has been shown that SCβ cells are capable of regulating glucose homeostasis. In 2014, glucose–responsive insulin production was observed in SCβ cells developed using a seven-stage differentiation protocol (196). They also showed that stage 7 (S7) SCβ cells could reverse diabetes in streptozotocin-treated mice more efficiently than S4 cells. Recent phase I/II clinical trials undertaken by Viacyte showed that implanting encapsulated insulin producing cells derived from stem cells could help reduce the need for daily insulin doses in type 1 diabetes by up to 44% (198). However, in the same clinical trial it was found that after 6 months, encapsulated β cell mass was only 4% of the initial mass, suggesting that cell survival is still an issue (198). Overall, while the field of developing stem cells into β cells is promising, there are still hurdles that need to be overcome to develop this idea into a full fledge cure. 1.4.3 Drug therapies Drug therapies are primarily aimed at treating type 2 diabetes, as opposed to the insulin replacement based therapies for type 1 diabetes. Due to type 2 diabetes patients often still possessing insulin producing cells, type 2 diabetes drugs often aim to increase insulin secretion or insulin sensitivity to help manage and maximize efficiency of these cells. Drugs for treating type 2 diabetes include insulin secretagogues, biguanides, insulin sensitizers, incretin mimetics, alpha glucosidase inhibitors, amylin agonists and sodium-glucose co-transporter-2 (SGLT2) inhibitors (199). Insulin secretagogues (tolbutamide, acetohexamide, glibenclamide, glipizide) act by inducing secretion of insulin, and usually act via binding to sulfonylurea receptors in ATP sensitive potassium channels in β cells (200). Biguanides (Metformin, Phenformin, Buformin) improve insulin sensitivity by increasing insulin receptor activity (201). Insulin sensitizers 27 (Pioglitazone, Rosiglitazone, Ciglitazone) are primarily peroxisome proliferator activated receptor (PPAR) agonists, which also increase insulin sensitivity (202). Incretin mimetics (Exenatide, Lixisenatide, Dulaglutide) are usually glucagon-like peptide 1 (GLP-1) agonists or DPP-IV inhibitors, which increase insulin secretion and reduce glucagon secretion (203). Alpha glucosidase inhibitors (Acarbose, Voglibose, Miiglitol) are oral drugs which delay carbohydrate absorption and help reduce postprandial hyperglycaemia (204). Amylin agonists (Pramlintide) imitate the action of amylin, which delays stomach emptying and reduces glucagon secretion after a meal (205). SGLT2 inhibitors (Canagliflozin, Dapagliflozin, Luseogliflozin) acts on SGLT2, which promotes reabsorption of glucose in the kidney, to prevent reabsorption of glucose into the blood stream and helps reduce glucose levels in blood (206). Recent developments in drug therapy include a number of drugs which seek to delay type 1 diabetes onset via affecting the immune system, or to assist in β cell survival. Teplizumab, a humanized immunoglobulin G1 monoclonal antibody that binds to the CD3ε chain and may improve immune tolerance, has been approved to delay the onset of stage 3 type 1 diabetes (207). Ustekinumab, a drug which blocks IL12 and IL23 action and thus help reduce autoimmune cytokine pathway impact on β cells, has also entered phase 1/2 clinical trials (208). Verapamil, a calcium channel blocker used to treat heart rhythm problems, has been shown to reduce glucotoxicity-induced β cell death via reduction of thioredoxin-interacting protein (209). This drug has recently passed a phase 3 clinical trial, and may soon be approved for type 1 diabetes and type 2 diabetes treatment (210,211). While the majority of drug therapies for diabetes have focused on enhancing insulin secretion and sensitivity, Verapamil represents a new type of treatment focused specifically on β cell survival. Survival of the patient’s own β cells can help maintain long-term glucose homeostasis without worrying about β cell exhaustion (as in 28 secretagogues) and immune suppression (cell replacement therapies).- Discovery of the most efficient drugs with the least side effects is a priority in identifying drug candidates for diabetes treatments. 1.4.4 Lifestyle management Lifestyle management is often viewed as the most optimal treatment for type 2 diabetes. It has been proposed that hepatic insulin resistance as a result of increasing liver fat induces further insulin secretion, which in turn increases lipogenesis and thus triggers a vicious cycle (212,213). The excess fat can also be cycled to the islets, which may blunt acute insulin secretion in response to food, resulting in hyperglycemia and inducing even more insulin secretion. The hypersecretion of insulin and high free fatty acid levels may then lead to eventual β cell exhaustion and failure (214,215). This phenomenon, termed the twin cycles hypothesis, if true, suggests that reduction of caloric intake and fat tissue may return normoglycemia in diabetic and prediabetic patients (212). Thus, recent therapies involving lifestyle modification methods with the aim of treating diabetes primarily include exercise and changes in diet with the aim of managing obesity and caloric intake (216). Studies on the effects of exercise and diet on diabetes include the United States Diabetes Prevention Program (DPP), which assigned 3235 overweight adults with impaired glucose tolerance to specific treatment groups, including a lifestyle intervention group which put participants on 150 minutes of moderate physical exercise per week with low fat and calorie diets (217). This treatment group saw a reduction of 7% body weight per year, and after 4 years had one-third the diabetes incidence rate as the control group. Another study done in Finland called the Diabetes Prevention Study (DPS) conducted a similar test, with 522 overweight adults with impaired glucose tolerance (56). Lifestyle intervention successfully 29 reduced diabetes incidence by 58% by the end of four years. Recently, the Diabetes Remission Clinical Trial (DiRECT), a trial aimed at using a combination of exercise and diet control to achieve type 2 diabetes diabetes remission without diabetic drugs, was conducted with promising results (218). DiRECT found that it was possible to achieve remission in type 2 diabetes which had a duration of up to 6 years (218). The reduction of blood glucose level may also reduce insulin hypersecretion, thus providing the β cells ‘rest’. This ‘rest’ may help to improve long term β cell function, which is reflected in the restoration of first phase insulin secretion and reduced insulin levels in blood (219,220). Overall, while lifestyle management is often considered the most effective treatment method and has the least side-effects, maintaining such regimens are often difficult, especially with the more sedentary modern day lifestyles. 1.5 Thesis overview Our lab previously used lentiviral reporter constructs on mice and human primary β cells and showed β cells transitioning between expression states over time, suggesting that some of the heterogeneity in β cells is dynamic. There are currently no studies on β cell heterogeneity in the context of insulin gene activity at the endogenous insulin gene loci. It is also unknown whether heterogeneity and dynamics in primary cells and insulin producing cell lines can be recapitulated in stem cell-derived insulin producing cells. I investigated heterogeneity in insulin gene activity at the endogenous gene loci in both mouse primary cells and SCβ cells, as well as sought to understand the mechanisms behind dynamics in insulin gene activity. The first aim of this project was to identify heterogeneity in Ins2 gene activity using an Ins2GFP knock-in mouse model. Because the WT Ins2 gene has been replaced with GFP, this allows me to monitor endogenous Ins2 gene activity. I FACS purified  cells isolated and 30 dispersed from Ins2GFP mice and found two populations, Ins2(GFP)HIGH and Ins2(GFP)LOW. I then performed live cell imaging to track insulin gene activity over time to confirm the stability of this heterogeneity. We then followed up with scRNAseq analyses and cell death assays to profile the two cellular expression states. The second aim was to understand the mechanisms behind cell state dynamics using a screen of drugs and small molecules which perturbed β cell functions. In collaboration with the Leonard Foster lab, I performed FACS purified proteomics to profile pure β cells at the protein level, and compare the Ins2(GFP)HIGH and Ins2(GFP)LOW expression states. The third aim was to examine whether SCβ cells had similar dynamics and cellular expression states. I used a stem cell line with EGFP knocked-in to the INS gene sequence to monitor endogenous insulin gene activity. Similar to the analyses in primary mice β cells, I also performed scRNAseq analyses, FACS purified proteomics analyses, and cell death assays to profile heterogeneity in INS gene activity. The fourth aim was to uncover pathways which may aid in SCβ cell survival and impact INS gene activity. A prioritized list of 148 ligands was designed based on proteomics, bulk RNAseq, and scRNAseq databases. I then performed image based screening on INS-EGFP SCβ cells using propidium iodide (PI) as a cell death marker and EGFP as a reporter for INS gene activity. Taken together, my studies provide insights on heterogeneity at the endogenous insulin gene loci, and uncover the mechanisms behind dynamics in insulin gene activity. 31 Chapter 2: Methods and Materials 2.1 Animals and in vivo physiology Animals were housed and studied in the Modified Barrier Facility using protocols approved by the UBC Animal Care Committee in accordance with international guidelines. Ins2WT/GFP knock-in mice were obtained from Shouhong Xuan (221). These mice were crossed with transgenic mice where the Ins1 promoter drives a mCherry:H2B fluorescent fusion protein (222). Glucose tolerance and insulin secretion were assessed in both male and female mice (12-14 weeks) injected intraperitoneally with 2g/kg (20%) glucose after a 5 hr fast. Insulin from in vivo samples was measured using ELISA kits (Alpco, Salem, USA). Insulin tolerance was assessed after injection of 0.75 U insulin per kg body weight after a 5 hr fast. 2.2 Immunostaining and RT-PCR Pancreata from PBS perfused mice were harvested and fixed in 4% paraformaldehyde for 24 hr before being washed and stored in 70% ethanol, prior to paraffin embedding. Pancreatic sections (5 μm) were taken from at least three different regions of the pancreas 100 μm apart. Sections were deparaffinized, hydrated with decreasing concentrations of ethanol, and rinsed with PBS. Sections were subjected to 15 min of heat-induced epitope retrieval at 95°C using a 10 mM citrate buffer, pH 6.0. Sections were blocked then incubated with primary antibodies overnight in a humid chamber at 4°C. A list of primary antibodies can be found in Antibodies section and Table 2.1. Primary antibodies were visualized following incubation with secondary antibodies conjugated to AlexaFluor 488, 555, 594, or 647 as required (1:1,000; Invitrogen, Waltham, USA). Counter staining was done by Vectorshield mounting media with DAPI (H-1200). Images for β cell and α cell area were taken on ImageXpressMICRO using a 10× (NA 0.3) 32 objective and analyzed using the MetaXpress software (Molecular Devices, San Jose, USA). All other images were taken on a Zeiss 200M microscope using 20× air (NA 0.75), 40× oil (NA 1.3), and/or 100× oil (NA 1.45) objectives and analyzed using Slidebook software (Intelligent Imaging Innovations, Denver, USA). For quantification of immunofluorescence, we used the segment masking function of Slidebook, generating a GFP high mask and a GFP low mask and comparing the two masks (Fig. 3.6C). For human pancreas sections stained for C-peptide and PDX1, we obtained images from the HPAP database (223) and performed analysis with custom scripts in CellProfiler. Real-time RT-PCR was conducted as described previously (1). A list of primers used can be found in Table 2.2. Table 2.1. Primary antibodies used for staining. Antibody Name Source Species Company RRID Dilution IHC Insulin Guinea Pig Abcam Cat# Ab7842 AB_306130 1/100 GFP Rabbit Thermo Fisher Cat# A11122 AB_221569 1/1000 PDX1 Rabbit Abcam Cat# Ab47267 AB_777179 1/2000 GFP Chicken USbiological life sciences Cat#168231 AB_2307313 1/200 CHGA Sheep Biomol International Cat#CA1128 AB_2291864 1:200 33 CLOCK Rabbit Thermo Scientific Cat#MA5-32713 AB_1809990 1/50 GRP78 Rabbit Abcam Cat# Ab21685 AB_2119834 1/500 Proinsulin Human R&D systems Cat#MAB13361 AB_2126534 1/200 Table 2.2 Primers used for qPCR. Forward Reverse Ins1 5'TAGTGACCAGCTATAATCAGAG3' 5'ACGCCAAGGTCTGAAGGTCC3' Ins2 5'CCCTGCTGGCCCTGCTCTT3' 5'AGGTCTGAAGGTCACCTGCT3' Pre-Ins1 5'TGGCTTCTTCTACACACCCAAG3' 5'ACAATGCCACGCTTCTGCC3' Pre-Ins2 5'GGGGAGCGTGGCTTCTTCTA3' 5'GGGGACAGAATTCAGTGGCA3' gfp 5'AGAACGGCATCAAGGTGAAC 5'TGCTCAGGTAGTGGTTGTC3' 2.3 Differentiation of stem cell-derived spheroids Human embryonic stem cells (WiCell WA01 line) that contain EGFP knocked-in and added-on downstream of the insulin coding sequence were differentiated to SCβ cells using a 6-stage protocol as described (224). After the initial 21 days of differentiation, SCβ cells were cultured in CMRL with 5.6 mM glucose (Thermofisher, Waltham, USA) containing 1% fatty acid-free BSA (Proliant), 1:100 Glutamax, 1:100 NEAA, 1 mM pyruvate, 10 mM HEPES, 1:100 ITS (Thermofisher, Waltham, USA), 10 μg/ml of heparin sulfate, 1 mM N-acetyl cysteine, 10 μM zinc sulfate, 1.75 μL 2-mercaptoethanol, 2 μM T3 and 0.155 mM ascorbic acid (Sigma, Burlington, USA) until Day 27. Between Day 27-35, aggregates were grown in CMRL with 5.6 34 mM glucose containing 1% fatty acid-free BSA, 1:100 Glutamax, 1:100 NEAA, 1 mM Pyruvate, 10 mM HEPES, 1:100 ITS, 10 μg/ml heparin sulfate, 1 mM N-acetyl cysteine, 10 μM zinc sulfate, 1.75 μL 2-mercaptoethanol, 10 nM T3, 1:2000 Trace elements A (Cellgro, Nebraska, USA), 1:2000 trace elements B (Cellgro, Nebraska, USA), 1:2000 Lipid Concentrate (Thermofisher, Waltham, USA) and 0.5 µM ZM447439 (Selleckchem, Houston, USA) (225). 2.4 Stem cell-derived spheroid dissociation and culture For dispersion of stem cell-derived spheroids into individual SC β cells, spheroids were hand-picked and washed 2 time with PBS. Spheroids were then immersed for 8 minutes in Accumax (STEMCELL technologies, Vancouver, Canada), with gentle flicking every 2 minutes. Four times the amount of PBS compared to Accutase was then added to halt Accutase activity, and the cells were spun down at 200 x g, 5 minutes. Liquid was aspirated, and CMRL with 5.6 mM glucose containing 1% fatty acid-free BSA, 1:100 Glutamax, 1:100 NEAA, 1 mM Pyruvate, 10 mM HEPES, 1:100 ITS, 10 μg/ml heparin sulfate, 1 mM N-acetyl cysteine, 10 μM zinc sulfate, 1.75 μL 2-mercaptoethanol, 10 nM T3, 1:2000 Trace elements A, 1:2000 trace elements B, 1:2000 Lipid Concentrate and 0.5 µM ZM447439 with 10 uM ROCK inhibitor Y-27632 (STEMCELL technologies, Vancouver, Canada) was added according to experiment requirements . 2.5 Islet isolation, dissociation and culture Pancreatic islets were isolated using collagenase, filtered, and hand-picked as previously described (226). Islets were cultured overnight (37°C, 5% CO2) in RPMI1640 medium (Thermofisher, Waltham, USA) with 11 mM glucose (Sigma, Burlington, USA), 100 units/ml 35 penicillin, 100 μg/ml streptomycin (Thermofisher, Waltham, USA), and 10% vol/vol FBS (Thermofisher, Waltham, USA). For islet dissociation in preparation for imaging experiments, islets were washed 4 times with MEM (Corning, New York, USA) and immersed for 5 minutes in 0.05% trypsin at 37°C. Cells were then resuspended in RPMI1640 medium and given 24 hours of rest (37°C, 5% CO2) before imaging. For experiments involving various culture conditions, cells were incubated overnight in serum-free media for serum-free and serum-starved conditions, and media with 10% FBS for serum conditions. Prior to imaging, cells were given a media change and stained with Hoechst cell nuclei marker and propidium iodide cell death marker for 2 hours. Serum-free condition cells were given media with no serum, while serum-starved (rescue) and serum conditions were given media with 10% FBS. Cells were then treated with various glucose concentrations immediately prior to imaging. 2.6 Fluorescence-activated cell sorting Pancreatic islets were dispersed using 0.05% trypsin and resuspended in 1xPBS with 0.005% FBS. Dispersed islets were then filtered into 5 ml polypropylene tubes. FACS was conducted on a Cytopeia Influx sorter (Becton Dickinson, Franklin Lakes, NJ, USA) at the Life Sciences Institute core facility. Cells were excited with a 488 nm laser (530/40 emission) and a 561 nm laser (610/20 emission). For FACS of SCβ spheroids, SCβ spheroids were dispersed as described in section 2.4. Dispersed cells were then filtered into 5 ml polypropylene tubes. FACS was conducted on a Cytopeia Influx sorter (Becton Dickinson, Franklin Lakes, NJ, USA) at the Life Sciences Institute core facility. Cells were excited with a 488 nm laser (530/40 emission) and a 561 nm laser (610/20 emission). 36 For reaggregation, SCβ spheroids were dispersed as described above. Dispersed cells were then filtered into 5 mL polypropylene tubes. Cells were FACS purified as described above. Sorted cells were then transferred to Aggrewell 800 plates (STEMCELL Technologies, Vancouver, Canada) to generate clusters containing 1,000 cells/cluster. After 60 hours, the reaggregated clusters were transferred into 6-well plates containing CMRL with 5.6 mM glucose containing 1% fatty acid-free BSA, 1:100 Glutamax, 1:100 NEAA, 1 mM Pyruvate, 10 mM HEPES, 1:100 ITS, 10 μg/ml heparin sulfate, 1 mM N-acetyl cysteine, 10 μM zinc sulfate, 1.75 μL 2-mercaptoethanol, 10 nM T3, 1:2000 Trace elements A, 1:2000 trace elements B, 1:2000 Lipid Concentrate and 0.5 µM ZM447439 for further culture. 2.7 Live cell imaging To define the incidence and kinetics of the transitions in Ins2 and INS gene activity, we cultured dispersed mouse islet cells or SCβ cells on 96-well or 384-well glass bottom plates, and imaged them every 5 or 30 minutes for up to 96 hours through a 10x air objective using a ImageXpressMICRO environmentally-controlled, robotic imaging system (Molecular Devices, San Jose, USA) employing a 300 W Xenon lamp (227). Cells were exposed to 359 nm light for 110 ms, 491 nm light for 15 ms, 561 nm for 75 ms. Treatments were applied immediately prior to imaging. Treatments and concentrations used in Chapter 4 were decided based on online literature search. The list of treatments can be found in Table 2.3. Table 2.3 Drugs and small molecules used in targeted  cell Ins2 gene activity screen. Drug Concentration Vendor Catalogue # Reference PMID Cyclohexamide 355 μM Sigma Aldrich C7698 32844746 Sal003 5 μM Sigma Aldrich S4451 28009255 37 CCT020312 0.2 μM Calbiochem 324879 28148553 DHBDC 20 μM Calbiochem 5315510001 23784735 ISRIB 0.2 μM Sigma Aldrich SML0843 25858979 GSK2606414 / Perk inhibitor 1 0.03 μM Sigma Aldrich 516535 22827572 PKRi/C16 2 μM Sigma Aldrich I9785 31723021 Thapsigargin 1 μM Millipore 586005 36690328 Tunicamycin 0.1 μM Sigma Aldrich T7765 32268491 Eeyarestatin I 10 μM Sigma Aldrich E1286 21124757 APY 29 20 μM Sigma Aldrich SML2381 25018104 Sunitinib 5 μM Sigma Aldrich PZ0012 28449948 Azoramide 20 μM Sigma Aldrich SML1560 26084805 Tacrolimus / FK506 0.03 μM Selleck Chem S5003 12761338 Cylcosporin A 5 μM Selleck Chem S2286 12761338 LY 294002 50 μM Sigma Aldrich L9908 18202127 Wortmannin 0.1 μM Sigma Aldrich W1628 8666133 Sodium butyrate 1000 μM Sigma Aldrich B5887 2843409 Trichostatin A (TSA) 0.625 μM Sigma Aldrich T1952 25305500 MGCD0103/Mocetinostat 25 μM Selleck Chem S1122 18413790 Somatostatin (SST) 1 μM Sigma Aldrich S9129 7907998 Epinephrine 50 μM Sigma Aldrich E4250 7907998 GLP-1 amide 0.01 μM R&D systems 2082 10909973 Rosiglitazone 10 μM Sigma Aldrich R2408 19338578 Staurosporine 0.1 μM Sigma Aldrich S4400 14510500 Okadaic 0.1 μM Sigma Aldrich O9381 8183910 Palmitate 0.5 μM Sigma Aldrich P9767 15944145 Metformin 0.5 μM Sigma Aldrich PHR1084 27662780 Dexamethasone (DEX) 0.1 μM Sigma Aldrich D4902 28406939 Isoxazole 9 (ISX-9) 40 μM Selleck Chem S7914 22143803 AS1842856 1 μM Calbiochem 344355 25483084 Diazoxide 100 μM Sigma Aldrich D9035 17229937 IBMX 100 μM Sigma Aldrich I5879 6201163 Arginine 10000 μM Sigma Aldrich A8094 25205820 Verapamil 2.7 μM Sigma Aldrich V4629 1095438 Ryanodine 10 μM Millipore 559276 15033925 38 5-amino-imidazole carboxamide riboside (AICAR) 300 μM Sigma Aldrich A9978 15823553 Pramlintide 1 μM Sigma Aldrich SML2523 1311671 Tolbutamide 0.37 μM Sigma Aldrich T0891 6988275 HNMPA 100 μM Abcam ab141566 18202127 S961 0.1 μM Phoenix Peptide 051-86 26740601 Bortezomib 0.004 μM Sigma Aldrich 5043140001 19190324 MG132 0.4 μM Sigma Aldrich M8699 19190324 xestospongin C 2 μM Sigma Aldrich X2628 23302634 Actinomycin D 2 μM Sigma Aldrich A9415 21922053 α-amanitin 2 μM Sigma Aldrich A2263 24213132 Triptolide 0.1 μM Sigma Aldrich T3652 24213132 5,6-Dichloro-1-beta-Ribo- furanosyl Benzimidazole (DRB) 50 μM Sigma Aldrich D1916 24213132 For 3D live imaging, intact mouse islets were incubated overnight in appropriate culture media with Hoechst 33342 (0.05 μg/mL) and propidium iodide (0.5 μg/mL). After 24 hours, cells were transferred into 2 well or 8 well µ-Slides (Ibidi, Wisconsin, USA) with fresh culture media. Images were then acquired using a LEICA THUNDER Imager Live Cell & 3D assay microscope at 25x water objective (numerical aperture 0.95). 2.8 Live-cell imaging analysis Analysis of Ins2GFP cells and INS-EGFP SCβ cells were done using MetaXpress analysis software and custom R scripts. Hierarchical clustering was done with various cellular behavior traits as features, and a heatmap was constructed using the pheatmap function in R. Features include ACF time (calculates when the autocorrelation function equals 0 in a time series), FWHM (full width at half maximum), difference (calculates the range of fluorescent intensity change), deviation (mean average deviation), mean (average lifetime relative fluorescent 39 intensity), and sharp (a function which detects sharp peaks). Principle component analysis was done using the same cellular behavior traits as variables. We performed K-means clustering using the kmeans function in R, and projected the cells onto the first two principle components. Categorization of cells into high and low GFP expressing states in live cell imaging and single cell RNA sequencing data was done using model based clustering (228). Autocorrelation analysis of single mutant Ins2WT/GFP was done using the acf function in R (229). To calculate the oscillation periods of GFP, we first selected for cells with at least one oscillation. We calculated the autocorrelation function of all cells for all time-points, and filtered for cells with at least two significantly positive peaks (autocorrelation function >0.05) using a find peaks function in R (230), indicating an oscillation. We calculated oscillation periods by finding the distance of the two positive peaks to get a full wavelength period. Oscillation periods of oscillating cells with more than one oscillation were then binned in 4-hour intervals and plotted into a barchart using R scripts. Analysis of 3D live cell imaging experiments was done using Imaris Microscopy Image Analysis software with the FIJI Labkit module and custom R scripts. Image processing, including smoothing and background subtraction, was performed using Imaris. Labkit facilitated image labeling and pixel classification for identification of GFP fluorescence in individual β cells. Nearest neighbor analysis was done using Imaris Python XTensions. Distance-to-core analysis was conducted on Imaris, using a reference frame positioned at the islet's center to measure distance of each cell from the origin reference frame. Plots were generated for visualization using Graphpad Prism or custom R scripts. 40 2.9 Protein synthesis assay One day after isolation, dispersed islets or SCβ cells were seeded into an optical 96-well or 384-well plate (Perkin Elmer, Waltham, USA) at a density of approximately 50,000 cells per cm2 in appropriate culture media. Treatments were applied 3 hours after seeding. After 24 hours of incubation, fresh culture media was applied, then supplemented with 20 μM OPP (Invitrogen, Waltham, USA). The assay was performed according to instructions provided by the manufacturer. Cells were imaged at 10x, numerical aperture 0.3, with an ImageXpressMicro high-content imager and analyzed with MetaXpress to quantify the integrated staining intensity of OPP-Alexa Fluor 594 in cells identified by NuclearMask Blue Stain. 2.10 Single cell transcriptomics For scRNAseq of Ins2GFP cells, islets were isolated, hand-picked, dispersed, and subjected to FACS as described above into Ins2(GFP)HIGH and Ins2(GFP)LOW cells. Single Cell Suspension was loaded on the 10x Genomics Chromium Controller for capture in droplet emulsion. The 60-week old mouse islet cell libraries were prepared using the Chromium Single Cell 3’ Reagent v2 Chemistry kit (10x Genomics, Pleasanton, CA, USA) and the standard protocol was followed for all steps. The 8-week old mouse islet cell libraries were prepared using the Chromium Single Cell 3’ Reagent v3 Chemistry kit (10x Genomics). Libraries were then sequenced on a Nextseq500 (Illumina). Cell Ranger 3.0 (10x Genomics) was used to process raw fastq data. The results were visualized using 10x Genomics Loupe Cell Browser. Cells with high mitochondria gene ratios (>0.1), low numbers of genes (<500), and low UMI counts (<1000) were excluded prior to analysis. This was done for both 60-week-old mouse data and 8-week-old mouse data respectively. 41 Seurat R package (version 4.0.4) was used to perform analysis (231). Data were normalized, scaled and analyzed with PCA. PCs generated were used to build a UMAP, with which we used to identify populations of cells using known cellular markers (Ins1 for β cells, Gcg for α-cells, Sst for δ cells, Ppy for pancreatic polypeptide/ γ cells, Ghrl for ε cells, and Pcam1 for endothelial cells). Cells expressing Ins1 were selected, and the rest were used as a base expression level for differential expression analysis using the FindMarkers function in Seurat. Differential expression analysis between the subsets of cells was performed using the Wilcoxon rank sum test option in Seurat. Differential expression analysis over GFP gradient was performed by first calculating correlation functions for each gene with GFP, then selecting for the genes that had the highest values. Top hits were used for pathway enrichment analyses and heatmaps. Pathway enrichment analysis was performed using custom R scripts with data from Gene Ontology Resource database. RNA velocity analysis was performed by mapping reads to exons and introns with velocyto, while RNA velocity vectors were inferred and overlaid onto UMAPs using ‘dynamical’ mode in scVelo. For SCβ cells, multi-omics sequenced files were processed and analyzed using Cell Ranger version 8. Genes were mapped and referenced using the human reference genome GRCh38. RNA modality from integrated dataset is used for downstream analysis. Low-quality cells were removed by filtering out cells with low RNA counts (nCount_RNA <300). Cell type annotation was performed manually by the expression of specific markers. SCβ cells were subsetted and categorized based on mean expression levels of insulin and EGFP. 344 cells exhibiting higher expression levels of both INS and EGFP than the mean values were annotated as INS(EGFP)HIGH, while 482 cells with expression levels lower than the mean values for both INS and eGFP were classified as INS(EGFP)LOW. Cells that do not fall into either of these categories are excluded 42 from analysis. ‘CorrelatePairs’ function in R is used to calculate gene pairs correlation. Pheatmap package and ‘DoHeatmap’ from Seurat package were used for plotting heatmaps. ‘DotPlot’ and ‘VlnPlt’ functions from the Seurat package are used to plot expression of genes. Pathway analysis was performed with ‘EnrichGo’ function with ‘org.Hs.eg.db’ as an organism database from ClusterProfiler 4.2.2 and org.Hs.eg.db 3.13.0 packages. ‘barplot’ function in R is used to visualize pathway results. 2.11 Mass Spectrometry / Proteomics For proteomics sample preparation, between 2000 and 10,000 cells from Ins2GFP knock-in mice or INS-EGFP SCβ cells were diluted with ultrapure water, then lysed by adding cold trifluoroethanol (TFE) (final 50% TFE) to the suspension. Samples were then chilled on ice for 10 min, vortexed for 1 min and further sonicated for 5-10 min in ice bath. Tris (pH 8-8.5) was added to adjust the pH and concentration to final 100 mM Tris at pH 8-8.5. Total protein mass was estimated 0.3 ng/cell. Samples were reduced with tris(2-carboxyethyl)phosphine (1 g TCEP: 50 g protein) incubated for 20 min at room temperature, then alkalized with chloroacetamide (5 g CAA:50 g protein) incubated at 95°C for 10 min. Then samples were diluted with 50 mM Ammonium bicarbonate (final TFE < 10%). Proteins were digested with LysC/Trypsin (1 g LysC/Trypsin: 50 g protein) for 2 hours at 37°C, followed by digestion with trypsin (1 g trypsin: 50 g protein) and incubated overnight at 37°C. Samples were further digested with trypsin (1 g trypsin: 125 g protein) for 5 hours at 37°C. Digestion was stopped by acidification. Samples were desalted with 6 mM C18; briefly, stage tips were conditioned with 100% methanol, equilibrated with 0.2% TFA, loaded with samples, then washed with 0.2% 43 TFA, and finally eluted with 2, 80 L volumes of 40% ACN, 0.1% formic acid. Eluate was dried in vacuum then kept in -20°C. Before instrumental analysis, samples were reconstituted in in 0.5% acetonitrile, 0.1% formic acid and concentration was measured using NanoDrop One (ThermoFisher, Waltham, USA) with the A205 Scopes method (absorbance at 205 nm, baseline correction at 340 nm). For liquid chromatography steps, 75 ng of peptides were injected and separated on-line using NanoElute UHPLC system (Bruker Daltonics Billerica, USA) with Aurora Series Gen2 (CSI) analytical column, (25 cm x 75 μm 1.6μm FSC C18, with Gen2 nanoZero and CSI fitting; Ion Opticks, Parkville, Australia) heated to 50°C and coupled to timsTOF Pro (Bruker Daltonics, Billerica, USA). A standard 30 min gradient was run from 2% B to 12% B over 15 min, then to 33% B from 15 to 30 min, then to 95% B over 0.5 min, and held at 95% B for 7.72 min. Before each run, the analytical column was conditioned with 4 column volumes of buffer A. Where buffer A consisted of 0.1% aqueous formic acid and 0.5 % acetonitrile in water, and buffer B consisted of 0.1% formic acid in 99.4% acetonitrile. The NanoElute thermostat temperature was set at 7°C. The analysis was performed at 0.3 μL/min flow rate. For mass spectrometry steps, the Trapped Ion Mobility – Time of Flight Mass Spectrometer (TimsTOF Pro; Bruker Daltonics, Billerica, USA) was set to Parallel Accumulation-Serial Fragmentation (PASEF) scan mode for DIA acquisition scanning 100 – 1700 m/z. The capillary voltage was set to 1800V, drying gas to 3 L/min, and drying temperature to 180°C. The MS1 scan was followed by 17 consecutive PASEF ramps containing 22 non-overlapping 35 m/z isolation windows, covering the m/z range 319.5 – 1089.5 (more information in DIA windows). As for TIMS setting, ion mobility range (1/k0) was set to 0.70 – 1.35 V·s/cm2, 100 ms ramp time and accumulation time (100% duty cycle), and ramp rate of 9.42 Hz; this resulted in 1.91s of 44 total cycle time. The collision energy was ramped linearly as a function of mobility from 27 eV at 1/k0 = 0.7 V·s/cm2 to 55 eV at 1/k0 = 1.35 V·s/cm2. Mass accuracy: error of mass measurement is typically within 3 ppm and is not allowed to exceed 7 ppm. TimsTOF Pro was run with timsControl v. 4.1.12 (Bruker, Billerica, USA). LC and MS were controlled with HyStar 6.0 (6.2.1.13, Bruker, Billerica, United States). For the final search and quantification of the results, acquired diaPASEF data were then searched using DIA-NN (v. 1.8.1) to obtain DIA quantification, with the spectral library generated from the canonical proteome for Mus musculus and Homo sapiens from Uniprot. Quantification mode was set to “Any LC (high precision) and a two-pass search was completed. All other settings were left default. Samples with a GFP or EGFP protein ratio of less than 3 when comparing their respective high and low activity state samples were excluded. All proteomics data was made publicly available on ProteomExchange. 2.12 Statistics and data visualization Statistics and data representation for nearest neighbor analyses, distance to core analyses, and OPP translation assays, were conducted using GraphPad Prism 9 (Graphpad Software, San Diego, USA). Statistics and data representation for live cell imaging experiments employed custom R scripts. Student’s t-test and one-way ANOVAs were used for parametric data as indicated in figure legends. For all statistical analyses, differences were considered significant if the p value was less than 0.05. Error bars were presented as ± standard error of the mean (SEM). For proteomics analyses, proteins that were present in less than 80% of samples were removed from analysis. Significantly differential proteins were defined with a threshold of p value < 0.05 and graphs were generated using custom R scripts. For STRING and Cytoscape analyses, significantly differentially abundant proteins were entered into the STRING website, 45 where nodes and networks were generated. For STRING settings, only ‘Experiments’ and ‘Databases’ interactions were included, with medium confidence interaction scores. STRING networks and protein expression data were then imported into Cytoscape and reordered according to their cellular location. Background images were generated with Biorender. For STRING and Cytoscape analyses in our small molecule screen, the primary binding proteins for small molecules were determined using online literature search. Proteins were then entered into STRING, where nodes and networks were rendered. For settings on STRING, all interactions were included, with medium confidence interaction scores. STRING networks, GFP fluorescence, cell death, and cell state transition data were then imported and finalized in Cytoscape. Background graphs were generated using Biorender. 46 Chapter 3: Dynamic Ins2 gene activity defines β cell maturity states 3.1 Introduction Pancreatic β cells in the islets of Langerhans are the only source of circulating insulin, a conserved and essential hormone that is required for nutrient homeostasis and life (215). Insulin production is demanding, as insulin mRNA can account for roughly half of all β cell mRNA and its synthesis, folding and processing require semi-specialized transcription factors, enzymes and cellular conditions (42,232). However, not all β cells appear the same. Indeed, functional β cell heterogeneity is well established (233), including cellular specialization for islet cell synchronization, insulin secretion, insulin production, and marker gene expression (174-176,234-237). For example, the in vivo existence of extreme β cells, defined as having >2-fold Ins2 mRNA than the median expression, was revealed with single-molecule fluorescence in situ hybridization in pancreas sections (233,238). Single cell RNA sequencing has shown that human β cells also express INS over a similarly wide range (175). However, it remains unclear whether this variation is the hallmark of distinct stable populations of β cells, or whether it is indicative of transitions between more labile β cell states. To date, the vast majority of islet cell ‘sub-populations’ have been defined by single time-point snapshots, making it impossible to know to what extent observed β cell heterogeneity represents distinct cell ‘fates’ or variations in cell ‘states’. This information is essential for interpreting existing and future data. Using a dual Ins1 and Pdx1 promoter reporter construct and live cell imaging, we previously demonstrated that mouse and human β cells can transition between less and more differentiated states over a time scale of ~24 hours (172,226,239). However, the artificial promoter constructs in these early studies may not reflect endogenous 47 gene activity, leaving open the question of whether endogenous insulin gene activity is similarly dynamic. In this study, we measured endogenous insulin gene activity using an Ins2GFP knock-in/knockout mouse line in which the coding sequencing of the evolutionarily conserved Ins2 gene has been replaced with green fluorescent protein (gfp) (221). Live cell imaging of dispersed cells from these mice revealed that GFP fluorescence changed over time in a subset of cells, suggesting that variation in Ins2 levels results from dynamic transcriptional activity at the Ins2 gene locus rather than stable heterogeneity. Single cell RNA sequencing was used to characterize the Ins2(GFP)HIGH cellular state in an unbiased way, revealing increased markers of β cell maturity as well as alterations in protein synthesis machinery and cellular stress response networks. Pancreatic β cells in the Ins2(GFP)HIGH cellular state were also more fragile across a range of stress conditions. To the best of our knowledge, our observations are the first to define the temporal kinetics of endogenous insulin gene activity, which represents a previously uncharacterized form of β cell plasticity. Understanding the dynamics of insulin production has relevance for understanding the pathobiology of diabetes and for regenerative therapy research (240). 3.2 Results 3.2.1 In vivo heterogeneity of insulin content in human β cell and insulin gene activity in Ins2GFP mice Heterogeneity of insulin production is an established phenomenon. We started our study by investigating human pancreases from online databases (223) that were stained with antibodies to C-peptide and PDX1, a key transcription factor for β cell survival and function (241,242). As 48 expected, based on single cell RNA sequencing data (175) and previous single cell imaging of human β cells (243),we identified β cells with both high and low C-peptide and PDX1 protein levels in human pancreas (Fig. 3.1A, B, C). We have previously shown limited correlations between insulin content and PDX1 immunofluorescence or nuclear localization in human β cells (243). Analysis of single cell RNA sequencing data previously compiled by our lab (42) showed that there was a bimodal distribution in insulin gene expression in β cells from both type 2 diabetic and non-diabetic donors, with a left-shift in high insulin expressing cells in patients with type 2 diabetes (Fig 3.1D). In order to study insulin gene activity in living cells, we examined islets from mice in which the coding sequencing of the evolutionarily conserved Ins2 gene has been replaced with GFP (221) (Fig. 3.1E). Mice lacking 1 or 2 wildtype functional Ins2 alleles had normal glucose homeostasis (Fig. 3.1F), consistent with our previous studies of Ins2 knockout mice (53) and the ability of Ins1 to compensate (244). Immunofluorescence staining of pancreata from Ins2GFP mice revealed a possible bimodal distribution of endogenous insulin production in vivo, similar to our single cell analyses (Fig. 3.2A). Hand counting 1879 cells across 11 randomly selected islets showed that 38.7% of β cells had substantially higher GFP immunofluorescence above an arbitrary, but consistently applied, threshold (Fig. 3.2A). The percentage of cells with high GFP did not appear to vary as a function of islet size. We have observed similar heterogeneity when examining the -galactosidase knock-in in to the Ins2 locus (53), meaning that this observation is not unique to this knock-in line or to GFP as a reporter. FACS confirmed this bimodal distribution and that less than half of all β cells engage in high Ins2 gene transcription at a given time (Fig. 3.2B). FACS analysis also validated that gfp mRNA, Ins2 mRNA, and pre-mRNA were significantly increased in high GFP-expressing cells compared 49 to cells expressing less GFP (Fig. 3.3A), strongly suggesting that GFP protein levels accurately reflect Ins2 mRNA in this system. We did not expect there to be a perfect correlation due to the different predicted transcription-to-protein time courses for GFP and insulin (Fig. 3.2C). Nevertheless, these data demonstrate that GFP production, reflecting the activity of the endogenous Ins2 gene locus, is bimodal in vivo and ex vivo, with 35% of β cells showing significantly higher activity. Hereafter, we refer to cells with high GFP abundance as Ins2(GFP)HIGH. 3.2.2 Live cell imaging of insulin gene activity in Ins2WT/GFP mice We next generated a mouse model to examine endogenous Ins2 activity dynamics relative to a stable background β cell marker. We crossed the Ins2GFP knock-in line with transgenic mice with an allele wherein histone-fused mCherry is driven by the less complex Ins1 promoter that is known to have relatively stable red fluorescence in virtually all β cells (222), allowing us to track Ins2 gene activity in real-time (GFP), while observing all β cells (mCherry). As expected, immunofluorescence of intact pancreatic sections and FACS analysis of dispersed islets from Ins2WT/GFP:Ins1-mCherry mice showed that mCherry labelled virtually all β cells, while GFP was robustly expressed in a clearly separated subset of β cells we deemed Ins2(GFP)HIGH (Fig. 3.4A,B,C). qPCR of these FACS purified cells confirmed the expected elevated expression of GFP, Ins2, pre-Ins2, Ins1, and pre-Ins1 mRNA in Ins2(GFP)HIGH cells (Fig. 3.4D, Fig. 3.3B). Ins2 pre-mRNA would be expected to precede GFP fluorescence by at least 1.3 hours, as per estimations in Fig. 3.2C, which provides a possible explanation for the elevated pre-Ins2 in the mCherry positive, but GFP low β cells. Both intact islets and dispersed islet cells isolated from Ins2WT/GFP:Ins1-mCherry mice showed a similar proportion of Ins2(GFP)HIGH and Ins2(GFP)LOW 50 cells to that we observed in vivo, demonstrating that this heterogeneity was not altered by isolation or dispersion/culture. A high-throughput, live cell imaging system with environmental control was used to study dispersed islet cells from Ins2WT/GFP:Ins1-mCherry mice. Remarkably, live cell imaging over ~3 days identified a subset of β cells that transitioned in and out of Ins2(GFP)HIGH activity state over the course of 36-hour long recordings (Fig. 3.4E, Fig. 3.5A, B). Analysis of 547 cells with various cellular behavior metrics as variables identified 3 distinct clusters of Ins2 gene activity (Fig. 3.5B, C). While testing for viability of the Ins2WT/GFP β cells, we noticed GFP fluorescence in Ins2WT/GFP cells sometimes rapidly decreases just prior to cell death. Therefore, in a larger study, we used propidium iodide, a dye which permits the real-time detection of the terminal stage of cell death, to identify changes in GFP fluorescence that are not associated with cell death (Fig. 3.6A, B, C). As propidium iodide emits red fluorescence at 636 nm, we used Ins2WT/GFP mice without Ins1-mCherry for these experiments. We chose the heterozygous Ins2WT/GFP genotype to keep the experiment as close as possible to wild-type insulin levels. Dispersed islets from Ins2WT/GFP mice were studied for up to 4 days, imaging cells at 5-minute intervals. Similar to our pilot study, we identified β cells that transitioned between high and low fluorescence states over the course of recordings (Fig. 3.6A, B). We tracked and quantified fluorescence intensities of Hoechst, GFP, propidium iodide in 2115 individual cells, confirming a bimodal distribution of mean GFP fluorescence in the cellular population (Fig. 3.6D, Fig. 3.7A) we found in our FACS analysis. Quantification of other GFP wavelength parameters, including autocorrelation function (ACF) and full width at half maximum (FWHM) revealed groups of cells with fluctuating Ins2 gene activity (Fig. 3.6E). We combined K-means clustering with principal component analysis (PCA), to reveal 4 clusters displaying distinct cellular behaviors (Fig. 3.7B, C). Collectively, our 51 long-term live cell imaging demonstrates significant dynamic fluctuations in the activity of the endogenous Ins2 locus in primary β cells, demonstrating that the apparently heterogeneity observed in vivo is not necessarily stable. 3.2.3 Autocorrelation analysis of fluctuations in Ins2 gene dynamics The observation of fluctuations in Ins2 gene activity in some β cells within our 4-day time window prompted us to determine the most common oscillation frequencies in Ins2WT/GFP β cells. We performed autocorrelation analysis on GFP fluorescent intensity over time in Ins2WT/GFP β cells that had at least one oscillation during the imaging period (Fig. 3.8A) and found that 357 (17%) out of 2115 cells had at least one oscillation, of which 226 had oscillation periods of 8-20 hours. The most common frequency fell within the 12-16 hour bin, with an average of ~17 hours across all oscillating cells (Fig. 3.8B), suggesting this oscillatory behavior may be diurnal in nature. Interestingly, we also found that cells which oscillated had significantly higher GFP fluorescent intensity in comparison to non-oscillating cells (Fig. 3.8B). Together, our long-term live cell imaging data suggest that circadian-influenced oscillations of Ins2 gene activity persist in culture. 3.2.4 Ins2 gene dynamics under various glucose and serum conditions Glucose is a primary driver of insulin production, including insulin gene transcription (108,232). Therefore, we asked if altering glucose concentrations would impact endogenous Ins2 gene activity in our knock-in model. We treated isolated islet cells from Ins2WT/GFP mice with multiple culture conditions, including without serum (serum-free), with serum, starvation from serum for 9 hours prior to imaging (serum-starved), and an array of glucose concentrations (0, 5, 52 10, 15, 20 mM). The time required for mature Ins2 mRNA production in response to elevated glucose is still under debate, with various studies describing an increase in mature Ins2 mRNA being detected at a range of several hours after high glucose treatments, up to ~48 hours (245,246). We found significantly higher Ins2 gene activity (inferred by GFP fluorescence) in higher glucose concentrations under serum-containing conditions at the 48-hour mark (Fig. 3.9A, B). We did not find significantly higher Ins2 gene activity in serum starved conditions, but there was still a trend towards higher gene activity in higher glucose concentrations compared to 5 mM and 10 mM glucose. In serum-free and all 0 mM glucose conditions, cells died rapidly, confounding analysis of Ins2 gene activity (data not shown). After de-trending our data as described in the methods, a rise in Ins2 gene activity was observed around 12 hours into the experiment in cells cultured in higher glucose concentrations in both serum and serum-starved conditions (Fig. 3.9A, Fig 3.10A). Interestingly, we found more cells transitioning from the Ins2(GFP)LOW state to the Ins2(GFP)HIGH state, as well as fewer cells transitioning from the Ins2(GFP)HIGH state to the Ins2(GFP)LOW state in higher glucose concentrations, which may explain the increase in overall Ins2 gene activity in those conditions (Fig. 3.9C). We also investigated the range in which GFP fluorescence can fluctuate, and found cells in higher glucose conditions tended to have a greater increase in GFP fluorescence compared to lower glucose conditions (Fig. 3.12A). We performed hierarchical clustering with heatmap construction and extracted individual variables and compared the treatments (Fig. 3.11A-I). We found a significant decrease in higher versus lower glucose concentrations in the ACF time variable (Fig. 3.11H). PCA with K-means clustering analyses of all measurements did not reveal any distinct clusters (Fig. 3.12B, C). 53 Autocorrelation analyses showed considerable difference in populations of oscillating versus non-oscillating cells in the various treatments. Of particular interest was the higher ratio of oscillating cells in higher glucose concentrations, suggesting that glucose may also influence Ins2 gene expression volatility (Fig. 3.8B). Combined with data in our initial Ins2WT/GFP imaging study in basal 10 mM glucose conditions, this suggests that oscillating cells may be a contributor to the increase in Ins2 gene activity in response to high glucose (Fig. 3.4). Oscillation periods between treatments were not significantly different, but there was a trend towards higher oscillation periods in higher glucose concentrations (Fig. 3.11C). We also looked at the amplitude of the oscillations by comparing the range of GFP fluorescence in oscillating cells, but we did not find any differences (Fig. 3.11B). Collectively, these results show that various glucose concentrations under serum conditions may influence Ins2 gene activity and behavior. 3.2.5 Profiling β cell states with single cell RNA sequencing To characterize the Ins2(GFP)HIGH state in a comprehensive and unbiased way, we performed single cell RNA sequencing on FACS purified Ins2(GFP)HIGH and Ins2(GFP)LOW cells from islets pooled from 8-week old Ins2WT/GFP mice, 60-week old Ins2WT/GFP mice, and 60-week old Ins2GFP/GFP mice (Fig. 3.13A-E, Fig. 3.14A-E, Fig. 3.15A-D). We focused on β cells by including only those cells expressing Ins1 for downstream analysis (Fig. 13AB, Fig. 3.13A). Examination of the distribution of Ins2 gene expression in these Ins1-expressing cells further confirmed our previous qPCR data (Fig. 3.4A) that gfp can accurately represent Ins2 expression levels (Fig. 3.14C, Fig. 3.13B). We also found that gfp mRNA is expressed in a bimodal distribution, consistent with our FACS and live cell imaging results (Fig. 3.14C, Fig. 3.13B). We then examined differential gene expression as a function of gfp mRNA (Fig. 3.14DE, Fig. 3.13C, D). 54 We also considered Ins2(GFP)HIGH and Ins2(GFP)LOW as binary categories and examined differential gene expression (Fig. 3.16 A, B). In 8-week-old Ins2WT/GFP mice, pathway enrichment analysis of cells expressing high gfp showed that the Ins2(GFP)HIGH cells had significant alterations in genes involved in hormone regulation, hormone secretion, hormone transport, protein synthesis, and protein cleavage (Fig. 3.13C). Cluster of genes ordered by correlation with gfp in heterozygous Ins2WT/GFP β cells showed that Sema6a, and the antioxidant metallothionein genes Mt1 and Mt2, were most closely correlated to gfp mRNA at the single cell level. Classical genes related to β cell function and maturity that were positively correlated with gfp include Ins2, Ero1lb, Chromogranin A (Chga), the Glut2 glucose transporter Slc2a2, Iapp, Nkx6-1, and Pdx1 (222,247,248) (Fig. 3.13D). The gfp-high state was also characterized by increased mRNA expression of genes encoding signal recognition particle 9 (Srp9), G-protein subunit gamma 12 (Gng12), nucleotide pyrophosphatase/phosphodiesterase 2 (Enpp2), aldehyde dehydrogenase 5 family member A1 (Aldh5a1), and tetraspanin-28 (Cd81). Stress response genes, such as Sec61b, Sec61g and Sec23b were also highly expressed (249,250) (Fig. 3.13D). The gfp-high state was associated with decreased expression of genes including soluble factors such as peptide YY (Pyy), somatostatin (Sst), glucagon (Gcg) pancreatic polypeptide (Ppy), suggesting in aggregate a less ‘poly-hormonal’ and therefore, more mature, gene expression profile (240,251) (Fig. 3.13D). In agreement, gfp-high β cells had lower expression of pappalysin 2 (Pappa2), an  cell-selective regulator of insulin-like growth factor bioavailability, and the epsilon cell marker (Etv1) (252) (Fig. 3.13D). Gfp-high cells also had reduced expression of several genes linked to insulin production and secretion, such as multiple subunits of the 40S and 60S ribosomes, eukaryotic translation initiation factor 3 subunit C (Eif3c), Eukaryotic translation initiation factor 2 subunit 2 55 (Eif2s2), and heat shock 70kDa protein 9 (Hspa9; Mortalin) (Fig. 3.13D). Interestingly, a decrease in the expression of certain ER stress genes was found, including Prkcsh, Nucb2, and Fam129a (253-255) (Fig. 3.13D). Thus, in young mice, the Ins2(GFP)HIGH cell state is associated with a mature single cell gene expression profile, optimal insulin secretion, and a reorganization of protein synthesis machinery. Age is known to significantly alter the properties of pancreatic β cells, including their function and ability to enter the cell cycle (256). Thus, we conducted an additional similar study in three 60-week-old mice. In this experiment, we studied cells from both Ins2WT/GFP (Fig. 3.14A-E) and Ins2GFP/GFP islets (Fig. 3.15A-C). Pathway enrichment analysis of cells expressing high gfp showed that the Ins2(GFP)HIGH cells had significant alterations in genes involved in vasculature development, metabolism, and ion homeostasis (Fig. 3.14D). Cluster analysis of genes ordered by gfp gradient in old heterozygous Ins2WT/GFP β cells revealed that Ins2 expression most closely matches gfp mRNA (Fig. 3.14E). Similar to the younger mice, genes that increased with gfp expression included genes related to optimal insulin secretion such as Chga and Slc2a2, as well as key β cell transcription factors and maturity markers Nkx6-1 and Pdx1 (Fig. 3.14E). Other genes related to insulin production and secretion that were upregulated include Ins1, secretogranin 3 (Scg3), proprotein convertase subtilisin/kexin type 1 (Pcsk1), ubiquinol-cytochrome c reductase complex assembly factor 2 (Uqcc2), synaptotagmin-like 4 (Sytl4) (241,257,258) (Fig. 3.14E). There was also an enrichment in key β cell transcription factors and maturity markers Ucn3 and Neurod1 (82,112) (Fig. 3.14E). Other notable genes that were upregulated in gfp-high cells were Gng12, Enpp2, Ppp1r1a, and metabolism-regulating genes, such as glucose-6-phosphatase catalytic subunit 2 (G6pc2) and hydroxyacyl-CoA dehydrogenase (Hadh) (Fig. 3.14E). ER stress-related gene Neat1 was also increased (259). Also 56 in agreement with the analysis of young islets, gfp-high β cells from old homozygous mice had decreased expression of Pappa2, Etv1, and Sst, as well as other markers of β cell immaturity (Gcg, Ppy, Pyy). Ldha, another de-differentiation marker, was also negatively correlated with gfp (Fig. 3.14E). Several ER stress genes were also anti-correlated with gfp, including Eef2 and Spp1 (260,261) (Fig. 3.14E). Thus, in older mice, the Ins2(GFP)HIGH cell state is overall similar to younger mice, with a more mature single cell expression profile and association with genes related to metabolism. Separately, we analyzed genes ordered by gfp mRNA gradient in old homozygous Ins2GFP/GFP β cells (Fig. 3.15). Many of the same genes that we observed in the heterozygous samples had similar expression patterns in the GFP homozygous samples, including maturity markers Chga, Neurod1, Pdx1, Ucn3, Slc2a2, Gng12, and Ppp1r1a, and de-differentiation markers Sst, Pyy, Ppy, Gcg. Nuclear protein 1, transcriptional regulator (Nupr1), a stress adaptation gene, was reduced in homozygous Ins2GFP/GFP β cells with high gfp expression. When we combined data from old Ins2WT/GFP and Ins2GFP/GFP cells, pathway enrichment analysis of high gfp-expressing cells in older mice showed that the Ins2(GFP)HIGH cells had significant alterations in genes involved in protein translation, RNA splicing, mRNA processing (Fig. 3.15B). With this combined analysis, gfp correlation with many of the same genes found by analysing Ins2WT/GFP and Ins2GFP/GFP cells separately were also identified (Fig. 3.17). Pathway enrichment analysis of differential expression in all Ins1 positive cells revealed upregulation in genes related to translation machinery in younger mice, while in older mice there was upregulation in genes related to cellular respiration (Fig. 3.18A, B). The single cell RNA sequencing studies identified enrichment in genes that control β cell stress responses and markers of β cell maturity. We followed this up by performing RNA 57 velocity analysis on older Ins2WT/GFP and Ins2GFP/GFP mice. We found that the direction of the velocity vectors, which point towards clusters of β cells higher gfp expression, indicate β cells of older Ins2WT/GFP and Ins2GFP/GFP with higher levels of gfp mRNA were more mature than β cells with lower levels of gfp mRNA (Fig 3.15A, Fig. 3.19A). Due to high dropout rates of gfp mRNA in the younger mice, we were unable to perform RNA velocity analysis on those mice. Our single cell analyses also showed correlation of gfp with various gene products related to optimal insulin secretion and processing. Immunofluorescent staining for insulin and proinsulin indicated increased abundance of both proteins in cells with high GFP, consistent with a possible difference in functional maturity between high and low GFP cell states (Fig. 3.19 D, E, Fig. 3.18C, D). We found that PDX1 levels were significantly elevated in cells with in high GFP staining compared to those in the same islet with lower GFP (Fig. 3.19F). However, we did not find a significant difference in CHGA, another putative maturity marker (Fig. 3.18C). We next selected individual gene products from the list of significantly altered mRNAs in high gfp-expressing cells (Fig. 3.18). We have previously shown that insulin production itself is a significant stress under basal conditions in β cells (215) and we therefore predicted that cells with increased Ins2 gene activity and GFP production would be more sensitive to stress. Interestingly, Hspa5 (GRP78), a major ER stress regulator, was downregulated at the mRNA level in high gfp-expressing cells (Fig. 3.19G), but increased at the protein level in high cells with higher GFP staining in pancreatic sections obtained from Ins2WT/GFP mice. The autocorrelation analyses revealed possible connections to circadian cycles. Indeed, high gfp-expressing cells had increased Clock gene expression (Fig. 3.19B). Immunofluorescent staining showed that cells with higher GFP-abundance had higher levels of CLOCK protein than cells with lower GFP, confirming our single cell analyses (Fig. 3.19H). Thus, the states marked by high gene activity at the endogenous 58 Ins2 locus likely possess critical functional differences. Taking the immunofluorescence data together with the single cell RNA sequencing analysis, we conclude that β cells with high gfp expression have a more mature β cell profile. Finally, we asked if high insulin production was associated with increased -cell fragility. Indeed, using live cell imaging, we found that Ins2(GFP)HIGH cells were >10 fold more sensitive to apoptosis at multiple glucose concentrations and in the presence of the model ER-stressor thapsigargin, when compared with Ins2(GFP)LOW cells in the same cultures (Fig. 3.19I). Collectively, we have shown that insulin gene activity marks a range of  cell maturity states, with consequences for function and survival. 3.3 Discussion The goal of the present study was to determine the nature of Ins2 gene expression heterogeneity. Analysis of pancreatic tissue sections from Ins2GFP knock-in mice showed that, at any given time, roughly 40% of all β cells exist in a GFP-high state, suggesting that not all β cells have simultaneously high levels of active transcription at the Ins2 locus in vivo. Over the course of multi-day in vitro imaging experiments, we observed clear transitions between Ins2(GFP)HIGH and Ins2(GFP)LOW states in single β cells, possibly linked to the circadian clock. However, Ins2 gene activity was stable for the duration of these studies in the majority of cells. We used single cell RNA sequencing to characterize the Ins2(GFP)HIGH cellular state and found that Ins2(GFP)HIGH were significantly more sensitive under all stress conditions examined. Together with previous live cell imaging data, the results of the present study demonstrate that a substantial component of β cell heterogeneity in relation to insulin gene expression is dynamic in time (Fig. 3.8J). 59 The dynamics of GFP fluorescence revealed by live cell imaging of dispersed islet cells from Ins2GFP mice provided an unprecedented look at insulin gene activity in populations of single β cells. Many of the Ins2(GFP)LOW and Ins2(GFP)HIGH β cell states were maintained over at least 24 hours. Autocorrelation analysis showed that of the cells which displayed at least one oscillation, a significant proportion have oscillation periods clustered at 8-12 hours. There were significantly more GFPHIGH cells with oscillations compared with GFPLOW cells. We also found a significant increase of Clock gene and protein levels in cells with high GFP staining. Circadian control of islet function is essential for maintaining normal glucose homeostasis, and circadian-based variations on the transcriptional level have been described in diverse cell types and shown to be critical for optimization of cellular function (262). Circadian influences are known to persist in cell culture in β cells, and insulin is both a target of circadian regulation, and a regulator of the circadian machinery in many cell types (263-265). Diurnal rhythms of β cell function and metabolism has long been recognized (266,267), and our study extends this to activity at the insulin gene locus. The study of pancreatic islet cell heterogeneity is currently experiencing resurgence, in part due to the application of single cell sequencing and optogenetic technologies to answer islet biology questions. There are many examples of β cell heterogeneity and these were reviewed recently (233). Insulin gene expression is a cardinal feature of pancreatic β cells, but cell-by-cell variability in insulin production has remained under-appreciated despite published evidence (237). For example, there are reports of significant β cell heterogeneity in transgenic mice expressing GFP under the Ins2 promoter (171). Similarly, we have shown variation in fluorescent protein expression under the control of Ins1 promoters in vivo and in vitro (172,226,239). However, a limitation in these studies is that artificial promoter constructs may 60 not recapitulate the more complex and long-range regulation available at the endogenous gene locus. Notwithstanding, cell-by cell analysis of insulin mRNA, either by single-molecule fluorescent in situ hybridization or single cell RNA sequencing, also showed a 2- to 10-fold range in native gene expression from endogenous insulin gene loci (175,238). The Ins2(GFP)HIGH β cells we identified in our study are possibly a temporal manifestation of the extreme β cells reported by Farack et al (238). For instance, their extreme β cells had significantly elevated Chga, Pdx1, Slc2a2, and Ucn3 mRNA expression (238), which is consistent with our single cell RNA sequencing data. It is possible that the dynamic nature of insulin transcription is an adaptation to the extremely high demands of producing and maintaining adequate stores of insulin in β cells. INS expression levels between β cell subpopulations identified by Dorrell et al (174) did not show significant variation, but certain subpopulations had differential expression in genes related to optimal insulin production and secretion (Neurod1, Ppp1r1a, Slc2a2), similar to the Ins2(GFP)HIGH cells. Single nucleus assay for transposase-accessible chromatin sequencing analyses (snATAC-seq) on the insulin gene loci in human islets also revealed the existence of β cells with two states of chromatin accessibility, further supporting our bimodal expression model (268). Assuming there are homeostatic mechanisms to maintain stable insulin protein stores, it is unclear how the need for a burst of insulin gene activity would be sensed by individual β cells, and future studies should attempt to define these mechanisms. It will be interesting to perform fluorescent tracking experiments on stem-cell lines with INS-GFP knock-ins or maturity markers undergoing directed differentiation towards a -cell fate to see if dynamics in fluorescence can be observed as cells mature (269); it is likely that in vitro differentiation protocols will need to be optimized to produce a full range of dynamic β cell characteristics. 61 The relationship between β cell states demarked by insulin expression and disease pathogenesis remains unclear. Type 1 diabetes risk is associated with an increase in INS mRNA in whole human fetal pancreas (270) and single adult β cells (271). Isolated islets and single human β cells from people with type 2 diabetes were reported to have reduced INS expression on average (252,272), although our recent meta-analysis of single cell RNA sequencing data sets did not find a statistically significant reduction in INS mRNA levels in -cells from donors with type 2 diabetes (271). Extreme β cells were significantly more common in diabetic db/db mice (238). We found that the Ins2(GFP)HIGH state was associated with significant vulnerability to cellular stress, so having an excess number of Ins2(GFP)HIGH β cells at a given time may negatively affect islet health and robustness. These results are consistent with our previous data defining the interrelationships between maximal insulin production, ER stress and β cell proliferation (215). We also note that in more hyperglycemic conditions, more cells may start to transfer towards the Ins2(GFP)HIGH state, but due to the small timeframe that the imaging allows, we were unable to consistently observe cells transition from Ins2(GFP)LOW to the higher end of Ins2(GFP)HIGH states. It has been shown that rest may be important for β cells, as dysfunctional cells that were removed from diabetic mice were shown to recover functionality when cultured at normal glucose levels (232). Thus, the ability to transition back to Ins2(GFP)LOW states may also be critical, as it may represent a form of rest for the β cells, with failure in doing so possibly resulting in chronic stress and dysfunction. It is also likely that stress may modulate the frequency of β cell state transitions, although we did not test this directly. The presence of a proportion of β cells in the Ins2(GFP)LOW state may be essential for islet function, with recent studies showing that loss of less mature β cells impacts overall islet function (273). Specifically, so called ‘hub β cells’ that help synchronize islets were reported to 62 have lower insulin content compared with typical β cells (176), and we have speculated that this represents a trade-off needed for their synchronizing function (234). Rodent and human β cells are long-lived (274,275), and perhaps β cells cycle through multiple states during their existence, including taking turns supporting the oscillatory coupling of the islet. Many of the genes that are differentially expressed in Ins2(GFP)HIGH β cells are known to play roles in type 2 diabetes susceptibility, including common alleles of the MODY/neonatal diabetes genes Pdx1, Neurod1, Nkx6.1, Abcc8, Slc2a2, as well as Slc30a8 and Pam previously identified by genome-wide association (276). It will also be interesting to examine the frequency of β cell states in the context of type 1 diabetes, given that pro-insulin, Pdx1, Iapp, and Chga are auto-antigens (277-279). Indeed, β cells undergoing proliferation or with lower insulin/maturity are protected in the NOD mouse model of type 1 diabetes (280,281). Collectively, these observations suggest that modulation of β cell states could be a therapeutic and/or prevention target for both type 1 diabetes and type 2 diabetes. Temporal transcriptional plasticity and gene expression bursting on a similar time scale as to what we have observed have been documented in bacteria, yeast and other mammalian cell types (282-284). For example, bursting gene expression patterns have been observed in pituitary cells (285-287). Interestingly, LH transcription in pituitary gonadotrophs is directly linked to proteasome activity (286), suggesting a possible mechanism for coupling protein loads and transcription in secretory cell types. Many cell-extrinsic and cell-intrinsic factors have been implicated in the modulation of transcriptional burst frequency, including histone modifications and chromatin topology (288-292). Future studies will be required to determine the molecular mechanisms mediating transcriptional bursting at the insulin gene locus in β cells. Future studies 63 should also seek to directly measure Ins2 mRNA transcription, perhaps using new CRISPR-based probes or other mRNA tagging systems (293-295). In this study, we identified a subpopulation of cells that had relatively rapid transitions between Ins2(GFP)LOW and Ins2(GFP)HIGH β cell states suggesting dynamic transcriptional activity at the Ins2 gene locus rather than stable heterogeneity. However, our study has several limitations. For example, our measurements of GFP fluorescence originating from the Ins2 locus-mediated transcription cannot distinguish the relative contribution in changes in GFP mRNA transcription/stability or GFP protein translation/degradation. The half-life of unmodified GFP is ~26 hours (296) and the changes in GFP fluorescence in some cells suggest coordinated increases of protein synthesis and coupled protein degradation, which may also explain the slow decrease observed in our over time analyses. Also, oscillations detected in the in vitro live cell imaging experiments cannot be used to determine the ratio of oscillating cells in vivo, due to the possible loss of intercellular signals and additional stress in imaging dispersed cells. While some intercellular signals could be missing in our analyses of dispersed islet cells (297,298), our in vivo analysis revealed a similar proportion of Ins2(GFP)LOW and Ins2(GFP)HIGH giving us confidence that the two-population phenomenon we measured in vitro is generalizable to in vivo conditions. Future experiments should involve more powerful live cell imaging techniques, including live tissue slices and intravitreal imaging, ideally with techniques that allow long-term cell tracking without excess phototoxicity or photo-bleaching (299,300). In conclusion, our data demonstrate that single β cells can switch between states marked by high and low activity of the phylogenetically conserved, endogenous insulin gene locus. This newly discovered phenomenon may account for much of the observed heterogeneity in β cell insulin gene expression measured at single time points and needs to be comprehensively studied 64 and leveraged in efforts to protect β cells in the context of diabetes and to generate new β cells from stem cells (240,301). A ND T2DEIns2GFP knock-in GFP Ins2 Ins2gfp/gfp Ins1+/+ e1GFP5’…CAGGTTATTGTTTCAACATG…3’5’…CAGGTTATTGTTTCAACTCGAG…3’Ins2wt/wtIns2gfp/gfpXhoI5’…ACCCATGTCCCGCCGTGAAGTGGAGGACCCACAAGGTG…3’e2 e3e3e15’…CGTACGATGCATGTCCCGCCGTGAAGTGGAGGACCCACAAGGTG…3’BsiWIFFemale GTTMale GTT0 30 60 90 1200102030Time (min)Blood glucose (mM)15 0 30 60 90 1200246810Time (min)Blood glucose (mM)150 30 60 90 1200102030Time (min)Blood glucose (mM)15 0 30 60 90 1200246810Time (min)Blood glucose (mM)15D0 30 60 90 1200246810Time (min)Blood glucose (mM)Ins2WT/WTIns2WT/GFPIns2GFP/GFP0 30 60 90Density0.0750.0250.0000.050120C-peptide intensity (au) PDX1 intensity (au)0 20 40 60 80NDT2DB C0.20.10.00.0 2.5 5.0 7.5INS expressionDensityscRNAseq0.1000.0750.0250.0000.0500.100Female ITTMale ITT6566 Figure 3.1. In vivo heterogeneity of insulin content and gene activity in human and mouse islets. (A) Variability in C-peptide (green stain) and PDX1 (red stain) protein levels in pancreatic sections from non-diabetic and type-2 diabetic donors. Orange arrows indicate low PDX1 expressing cells, red arrows indicate high PDX1 expressing cells, blue arrows indicate low C-peptide expressing cells, and green arrows indicate high C-peptide expressing cells. Scale bar is 100 m. (B, C) Quantification of staining for C-peptide and PDX1 in pancreatic sections from non-diabetic and type-2 diabetic donors, displayed as a histogram. (D) Comparison of insulin mRNA distribution in non-diabetic and type-2 diabetic human β cell single cell RNA sequencing data. (E)The Ins2GFP/GFP knock-in mouse model. Illustration shows, with scale, the replacement of most of the second exon with wildtype GFP. The wildtype Ins2 locus is shown for comparison, including RNA-seq exon coverage, aggregate (filtered) from NCBI Mus musculus Annotation release 106 (log 2 base scaled). (F) Normal glucose homeostasis in Ins2WT/GFP, Ins2GFP/GFP mice and WT (Male n=4-8, Female n=4-5). Density: probability density function, represents the probability a data is located within the defined range (i.e. ratio of the total) Insulin GFPABInsulin GFP% high GFP cells/islet104103102101100RFP4001002003000CountDeceased cellsGFP negative cellsGFP low cellsGFP high cells100 101 102 103 104GFP100 101 102 103 104GFP100 101 102 103 104GFPC gfpIns2pre-mRNA mRNA mature protein27h 0.7h~54hdegradation~1.3 h~24h-72hWT Ins2WT/GFP MergedFigure 3.2. Identifying high and low GFP expressing cells in Ins2WT/GFP knock-in mice. (A) GFP from the endogenousIns2 gene locus is high in a subset of mouse pancreatic β cells. Scale bar is 20 m. (B) Detection of Ins2(GFP)LOW andIns2(GFP)HIGH populations of β cells by FACS. Includes β cells isolated from a WT mouse for comparison. (C) Presumedtranscription-to-maturation times of GFP and insulin.67Figure 3.3. qPCR results from Ins2GFP mice and Ins2WT/GFP:Ins1-mCherry mice. (A) mRNA of Ins1, Ins2, and gfp in variousphenotypes of Ins2GFP mice. Units are relative expression calculated from ΔCT, then normalized to wild-type for Ins1 and Ins2,and normalized to Ins2GFP/GFP mice for gfp. (n=1 mouse) (B) mRNA of pre-Ins1 and pre-Ins2 in Ins2WT/GFP:Ins1-mCherry mice.Units are relative expression calculated from ΔCT, then normalized to wild-type (n=3 mice). One-way analysis of variance. * p <0.05, ** p < 0.01, *** p < 0.001Ins1Ins2-/- -/- WT/- WT/WT WT/WT-/-WT/- WT/WT -/-WT/WT WT/WT WT/WTWT/GFP(low)WT/GFP(high)GFP/GFP(high)GFP/GFP(low)Ins1-/- ,Ins2WT/--Ins1-/- ,Ins2WT/WTIns1WT/- ,Ins2-/-Ins1WT/WT ,Ins2-/-Ins1WT/WT , Ins2WT/GFP (low)Ins1WT/WT , Ins2WT/GFP (high)Ins1WT/WT , Ins2GFP/GFP (low)Ins1WT/WT , Ins2GFP/GFP (high)01234Normalized ΔCTIns1-/- ,Ins2+/-Ins1-/- ,Ins2+/+Ins1+/- ,Ins2-/-Ins1+/+ ,Ins2-/-Ins2WT/GFP (low)Ins2WT/GFP (high)Ins2GFP/GFP (low)Ins2GFP/GFP (high)01234Normalized ΔCTIns1-/- ,Ins2+/-Ins1-/- ,Ins2+/+Ins1+/- ,Ins2-/-Ins1+/+ ,Ins2-/-Ins2WT/GFP (low)Ins2WT/GFP (high)Ins2GFP/GFP (low)Ins2GFP/GFP (high)01234Normalized ΔCTIns1Ins2-/- -/- WT/- WT/WT WT/WT-/-WT/- WT/WT -/-WT/WT WT/WT WT/WTWT/GFP(low)WT/GFP(high)GFP/GFP(high)GFP/GFP(low)Ins1Ins2-/- -/- WT/- WT/WT WT/WT-/-WT/- WT/WT -/-WT/WT WT/WT WT/WTWT/GFP(low)WT/GFP(high)GFP/GFP(high)GFP/GFP(low)CH-GFCHGFNeg01234Normalized ΔCT✱✱✱✱CH-GFCHGFNeg051015Normalized ΔCTnsnsIns1Ins2mcherry(positive)WT/WT WT/WT WT/WTWT/GFP(high)GFP/GFP(low)mcherry(positive)mcherry(negative)mcherry(negative)WT/GFP(high)WT/GFP(high)WT/GFP(low)WT/GFP(low)WT/WTIns1Ins2mcherry(positive)WT/WT WT/WT WT/WTWT/GFP(high)GFP/GFP(low)mcherry(positive)mcherry(negative)mcherry(negative)WT/GFP(high)WT/GFP(high)WT/GFP(low)WT/GFP(low)WT/WTPre-In 2 Pre-Ins1Ins1 Ins2GfpAB68Ins1-mCherry Ins2GFP/GFP MergeDAPI+Ins2GFP knock-in GFP Ins2 Ins2gfp/gfp Ins1+/+ A B=Ins1-mCherry Ins2GFP/GFPDCIns1mCherry Tg mCherry Ins1 Ins2+/+ Ins1Tg/+ I s +/+Ins1+/+Ins1-mCherry tg8h 12h 22h 28hGFPmCherryHoechstMergeE Ins1-mCherry Ins2WT/GFPIns1Ins2mcherry(positive)WT/WTWT/WTWT/WTWT/GFP(high)GFP/GFP(low)mcherry(positive)mcherry(negative)mcherry(negative)WT/GFP(high)WT/GFP(high)WT/GFP(low)WT/GFP(low)WT/WTmcherry(positive)WT/WTWT/WTWT/WTWT/GFP(high)GFP/GFP(low)mcherry(positive)mcherry(negative)mcherry(negative)WT/GFP(high)WT/GFP(high)WT/GFP(low)WT/GFP(low)WT/WTmcherry(positive)WT/WTWT/WTWT/WTWT/GFP(high)GFP/GFP(low)mcherry(positive)mcherry(negative)mcherry(negative)WT/GFP(high)WT/GFP(high)WT/GFP(low)WT/GFP(low)WT/WTNegative9.46GFP+0.44Cherry+ GFP+ 34.4Low GFP16.3High GFP8.53Cherry+40.4RFPGFPIns1-mCherry Ins2WT/GFP 1234CH-GFCHGFNegGFP mouseGFP mouse neg WT01234Ins1 mRNA (normalized ΔCT)✱✱✱✱CH-GFCHGFNegGFP mouseGFP mouse neg WT01234Ins2 mRNA (normalized ΔCT)✱✱✱✱✱✱CH-GFCHGFNeg01234gfp mRNA (normalized ΔCT)✱✱✱✱GFP6970 Figure 3.4. Tracking β cell heterogeneity with Ins1-mCherry;Ins2WT/GFP knock-in mice. (A) Experimental model for tracking activity at the endogenous Ins2 locus (GFP) in β cells marked with mCherry driven by an Ins1 promoter, which constitutively marks 98% of β cells. Scale bar is 50 m. (B) Live cell imaging of islet cells isolated and dispersed from Ins1-mCherry;Ins2WT/GFP mice. Scale bar is 50 m. (C,D) Distinct populations of Ins2(GFP)LOW β cells and Ins2(GFP)HIGH β cells were FACS purified and examined by qPCR for gfp, Ins2, and Ins1 mRNA (n=3 mice). Units are relative expression calculated from ΔCT, then normalized to wild-type. One-way analysis of variance. (E) Dynamic imaging, over 30 hours in culture, of dispersed islet cells from Ins1-mCherry;Ins2WT/GFP mice labelled with Hoechst vital nuclear dye. Note the examples of cells with bursting GFP fluorescence. Scale bar is 50 μm. * p < 0.05, ** p < 0.01, *** p < 0.001 BTime (hours)CFWHMSharpMeanMeanAUCAUCOscillationOscillation-2 -1 0 1 2 Time (hr) A Group 1 Group 2 Group 3Time (hr) Time (hr) 0 10 20 30 40 50GFPmCherryHoechst0 10 20 30 40 500 10 20 30 40 50Relative fluorescent intensityFigure 3.5. Quantification of long term live cell imaging of Ins2WT/GFP:Ins1-mCherry. (A) Samples from three major groupsidentified from PCA analysis. (B) Heatmap with various calculations based on the red (mCherry) and green (GFP) wavelengths.Calculations include FWHM (full width at half maximum), Oscillation (mean average deviation), Mean, AUC (area under curve),and Sharp (number of sharp peaks over a cell’s lifetime). (C) Principle component analysis based on calculations on the red andgreen wavelengths.71CGFPPIHoechstRelative fluorescent intensityA 0h 16h 48h 84hPIHoechstGFPMergeBSegmentationOriginal imageDHoechstGFP2 1 0 -1 -25025010075GFP WT0 10 20 30 40 50MergeE5025010075502501007550250100755.02.50107.5Relative fluorescent intensityDensity0.000.010.020 25 50 75Relative fluorescent intensity10072Time (hours)73 Figure 3.6. Live cell imaging of Ins2 gene activity reveals bursting behavior of the endogenous Ins2 gene locus. (A) Dynamic imaging, of up to 96 hours in culture, of dispersed islet cells from Ins2WT/GFP mice. Cells were labelled with Hoechst vital nuclear dye and propidium iodide cell death marker. Note the examples of cells with fluctuating GFP fluorescence, indicated by white arrows. Changes in cell distribution in the red and blue wavelength can be attributed to deceased cells detaching from the bottom of the well. Scale bar is 40 m. (B) Examples of cells with dynamics in relative fluorescent intensity over time. (C) Segmentation of cells for analysis. Cells with over 100 relative fluorescent intensity in the green wavelength compared to background were selected, comprising of ~70% of all cells imaged. This method of segmentation yields a clear cut off of cells expressing GFP compared to WT cells. Line indicates the split between high and low GFP fluorescence states. Scale bar is 40 m. (D) Lifetime average GFP fluorescence of all cells imaged. (E) Hierarchical clustering of cellular dynamics of Ins2 gene activity using 6 parameters calculated from intensity over time of GFP. Parameters include deviation (mean average deviation), FWHM (full width at half maximum), mean (lifetime average GFP intensity), ACF time (function which calculates when the autocorrelation function equals 0 in a time series), difference (maximum value minus minimum value) and sharp (counts number of sharp peaks with increases greater than 10% of the mean in a cell’s lifetime). The above parameters were calculated using custom R functions. Relative fluorescent intensity: intensity relative to background, scaled 0-100. Density: probability density function, represents the probability a data is located within the defined range (i.e. ratio of the total). Group 1:Oscillations: yesGFP intensity: highGFP intensity range: highOther: Most cells with oscillations hereCells: 486Group 2:Oscillations: noGFP intensity: mediumGFP intensity range: lowOther: Mostly non-oscillating mid level GFP expressing cellsCells: 520Group 3:Oscillations: noGFP intensity: lowGFP intensity range: lowOther: Most low GFP expressing cells hereCells: 330Group 4:Oscillations: fewGFP intensity range: highestGFP intensity: highOther: Characterized by cells with greatest GFP intensity rangeCells: 7790 12 3624 48Time (hours)0 12 3624 48Time (hours)0 12 3624 48Time (hours)0 12 3624 48Time (hours)ACFigure 3.7. Quantification of long term live cell imaging of Ins2WT/GFP cells. (A) Lifetime fluorescent intensities of the blue andred wavelengths. (B) Principle component analysis on 2115 cells, based off cellular traits in the green wavelength (GFP).Principle components (eigenvectors) were calculated from calculations in Fig. 3E (C) Examples and observed traits from the fourmajor populations of cells identified in PCA. Relative fluorescence intensity: fluorescence intensity relative to background, scaled0-100.Relative fluorescent intensityRelative fluorescent intensityRelative fluorescent intensityRelative fluorescent intensity10075502501007550250Relative fluorescent intensityHoechstPropidium iodideB7550250502501550107550250Non-oscillating Oscillating100ABAutocorrelation functionAutocorrelation functionRelative fluorescent intensityRelative fluorescent intensity0 12 3624 480 12 3624 480 12 3624 48Time (hours)Time (hours)Time (hours)1.00.50.0-0.51.00.50.0-0.5806004020Oscillation period (hours)Time (hours)0-44-88-1212-1616-2020-2424-2828-3232-3636-4040-4444-4810075502500 12 3624 480255075Relative fluorescent intensity***Oscillating17%, 357 cellsNon-oscillating83%, 1758 cellsNumber of Cells1007550250Figure 3.8. Autocorrelation analysis of cells with fluctuating behavior reveals possible connections to circadian rhythm.(A) Ratio of cells with at least one and no oscillations. Autocorrelation functions of cells across all time-points were calculated,and oscillation detection was done using a findpeaks function in R. For line plots, units are relative fluorescent intensity tobackground. For autocorrelagrams, units are autocorrelation function, calculated with the acf function in R. (B) Traits ofoscillating cells. Average life-time GFP fluorescence intensity relative to background of oscillating versus non-oscillating cellsand bar-chart of cell oscillation periods binned in 4-hour intervals. For the bar-chart, only cells with at least one oscillation wereplotted (357 out of 2115 cells), and oscillation periods were calculated by finding the distance between two peaks inautocorrelograms. Relative fluorescent intensity: intensity relative to background, scaled 0-100. Student’s T-test. * p < 0.05, **p < 0.01, *** p < 0.00175AB1007550250Relative fluorescent intensitySerum starvedOscillating cellsNon-oscillating cells20%80%19%81%27%73%28%72%20%80%14%86%18%82%28%72%SerumANormalized GFP intensity12 24 36Time (hours)0.40.30.20.10Time (hours)48012 24 360 48AUC5 mM glucose10 mM glucose15 mM glucose20 mM glucoseSerum Serum starvedAUC**********CSerum Serum starved% change after 48 hours5 7.52.5055 mM glucose10 mM glucose15 mM glucose20 mM glucose10Serum Serum starvedLow to high transitionsHigh to low transitions5 mM glucose10 mM glucose15 mM glucose20 mM glucose7677 Figure 3.9. Effects of media glucose concentration on Ins2 gene activity. (A) Changes in GFP fluorescence over time. Calculated by normalizing to first two hours of imaging to remove initial variation, followed by de-trending to control for photobleaching. AUC was calculated using this modified data. (B) Quantification and comparison of raw GFP fluorescence and ratios of oscillating versus non-oscillating cells at the 48-hour mark across all treatments. Line indicates the split between GFP high and low fluorescence states. (C) Percent change in ratio of Ins2(GFP)high cells after 48 hours in various culture conditions. Units are relative fluorescent intensity to background. Relative fluorescent intensity: intensity relative to background, scaled 0-100. Kruskal–Wallis one-way analysis of variance. * p < 0.05, ** p < 0.01, *** p < 0.001 AFigure 3.10. GFP fluorescence over time. (A) AverageGFP fluorescent intensity over time of cells. Separatedinto all cells, high GFP expressing cells, and low GFPexpressing cells and including raw and normalized data.Normalized data is normalized to the first two hours ofimaging. (B) Experiment testing for effects of photobleaching on GFP fluorescence over time. Relativefluorescent intensity: fluorescent intensity relative tobackground, scaled from 0-100.10075502502 hour avgFBS positive, low GFP cells, normalizedFBS positive, all cells, rawFBS positive, high GFP cells, rawFBS positive, low GFP cells, rawFBS positive, all cells, normalizedFBS positive, high GFP cells, normalized5 mM glucose10 mM glucose15 mM glucose20 mM glucose1007550250100755025010075502501007550250100755025010075502500 12 3624 48Time (hours)0 12 3624 48Time (hours)B0 12 3624 48Time (hours)Relative fluorescent intensityRelative fluorescent intensity78JAB EDCF G IHFWHMMeanSharpDeviationACF timeDifferenceTreatmentSerum, 5 mM glucoseSerum, 10 mM glucoseSerum, 15 mM glucoseSerum, 20 mM glucoseSerum starved, 5 mM glucoseSerum starved, 10 mM glucoseSerum starved, 15 mM glucoseSerum starved, 20 mM glucoseRelative fluorescence intensitySerum Serum starved Serum Serum starvedHoursRelative fluorescence intensityMean average deviationNumber of sharp peaksFWHMHoursDifferenceFigure 3.11. Quantification of long term live cellimaging of Ins2WT/GFP cells in various cultureconditions. (A) Heatmap constructed with variouscalculations from the green (GFP) wavelength. (B-I)Visualization and statistical analyses on the variouscalculations from the green (GFP) wavelength. (J)Examples of cells transitioning to high and low GFPfluorescent states. Relative fluorescent intensity:fluorescent intensity relative to background, scaled from0-100. Kruskal–Wallis one-way analysis of variance. *p < 0.05, ** p < 0.01, *** p < 0.0010 10 20 30 40 5050250100750 10 20 30 40 50Relative fluorescent intensitySerum Serum starved Serum Serum starvedSerum Serum starved Serum Serum starved Serum Serum starved Serum Serum starved75250507525050100402010305075250501007525050Difference (oscillating cells)Oscillation period Lifetime mean DeviationSharp FWHM ACF time Difference(all cells)200407525050100301002040Time (hours)Time (hours)79-3 -2 -1 0 1 2 3BCFigure 3.12. Quantification of long term live cell imaging of Ins2WT/GFP cells in various culture conditions. (A) Histogramshowing range of GFP fluorescence change of all cells in 10 mM and 20 mM glucose, with serum, culture conditions. Calculatedby finding the highest and lowest fluorescence intensities measured in a cell during imaging, and calculating the difference.Negative values indicate the maximum value occurred before the minimum value, while positive values indicate the maximumvalue occurred after the minimum value during live cell imaging. (A) Principle component analysis on cells in various cultureconditions, with principle components (eigenvectors) based off cellular traits across all three wavelengths. (B) Heatmapconstructed with various calculations from the blue (Hoechst cell nuclei dye), red (propidium iodide cell death marker) and green(GFP) wavelengths. Relative fluorescent intensity: fluorescent intensity relative to background, scaled from 0-100.β-cell subpopulation 1β-cell subpopulation 2β-cell subpopulation 3β-cell subpopulation 4ACells Cells25-25-50050Relative fluorescence intensity80ADCIns2GWT/GFP (+)Ins2GFP/GFP (-)Ins2GFP/GFP (+)Ins2WT/GFP (-)Ins2WT/GFP (+)Ins2GFP/GFP (-)Ins2GFP/GFP (+)Ins2WT/GFP (-)Ins2WT/GFP (+)Ins2GFP/GFP (-)Ins2GFP/GFP (+)Ins2WT/GFP(-)Ins1Ins2gfpE Positively correlatedNegatively correlatedGene ontology, correlation with GFPBeta cellsAlpha cellsDelta cellsEndothelial cellsBAngiogenesisPositive regulation of cell migrationAmeboidal-type cell migrationATP metabolic processCalcium ion homeostasisRegulation of vasculature developmentRegulation of angiogenesisGeneration of precursor metabolites and energyPositive regulation of MAPK cascadeDivalent inorganic cation homeostasisEndocytosisT cell activationangiogenesisCellular calcium ion homeostasisSignal releaseCellular metal ion homeostasisCellular divalent inorganic cation homeostasissLeukocyte differentiationPositive regulation of cell adhesionRegulation of protein secretion0 5 10 15-log10(FDR)UMAP 2UMAP 1Ins1 SstGcg Ppy-5 0 5 -5 0 5024680-55100-55108183 Figure 3.14. Single cell RNA sequencing analysis of Ins2(GFP)LOW and Ins2(GFP)HIGH β cells in 60-week-old mice. (A) UMAP plot of all cells isolated and dispersed from Ins2WT/GFP or Ins2GFP/GFP mice and FACS purified into either GFP-negative (-) or GFP-positive (+) groups. (B) UMAPs projections with known markers for major cell populations in islets. Cells were selected for analysis based on high Ins1 expression and low expression of other cell population markers. (C) Log-normalized counts of Ins1, Ins2, and gfp mRNA quantification distributions from GFP-negative or GFP-positive single β cells. (D) Gene Ontology categories that are driven by genes differentially expressed in Ins2(GFP)LOW versus Ins2(GFP)HIGH β cells from old heterozygous Ins2WT/GFP mice. (E) Heatmap showing individual genes that are differentially expressed as a function of gfp mRNA expression in old heterozygous Ins2WT/GFP mice. Beta cellsAlpha cellsDelta cellsEndothelial cellsABIns2WT/GFP(-)Ins2WT/GFP (+)Ins2WT/GFP(-)Ins2WT/GFP (+)Ins2WT/GFP(-)Ins2WT/GFP (+)Ins1Ins2gfpNegatively correlatedGene ontology, correlation with GFPCPositively correlatedDFigure 3.14. Single cell RNA sequencing analysis of Ins2(GFP)LOWand Ins2(GFP)HIGH β-cells in young Ins2WT/GFP heterozygous mice. (A)UMAP plot of all cells isolated and dispersed from Ins2WT/GFPmice andFACS purified into either GFP-negative (-) or GFP-positive (+) groups.(B) Log-normalized counts of Ins1, Ins2, and gfp mRNA quantificationdistributions from GFP-negative or GFP-positive single β-cells. (C)Gene Ontology Categories that are driven by genes differentiallyexpressed in Ins2(GFP)LOW versus Ins2(GFP)HIGH β-cells. (D) Individualgenes that are differentially expressed as a function of gfp mRNAexpression.83APositively correlatedNegatively correlatedBCGene ontology, correlation with GFPFigure 3.15. Single cell RNA sequencing analysis of Ins2(GFP)LOW and Ins2(GFP)HIGH β-cells in old Ins2GFP/GFP homozygousmice. (A) UMAP projection of β-cells isolated and dispersed from Ins2GFP/GFP homozygous mice with RNA velocity vectorsoverlaid. (B) Gene Ontology Categories that are driven by genes differentially expressed in Ins2(GFP)LOW versus Ins2(GFP)HIGH β-cells. (C) Individual genes that are differentially expressed as a function of gfp mRNA expression.84Young miceAFigure 3.16. Differential gene expression in Ins2(GFP)LOW and Ins2(GFP)HIGH -cells. Differentialexpression of Ins1+/GFPLOW vs Ins1+/GFPHIGH in (A) young Ins2WT/GFP (B) Old Ins2WT/GFP, Ins2GFP/GFP andpooled genotypes. Genes are selected with 0.25 log2FC and expressed in at least 10% of cells in either group.Ins2WT/GFPIns1+Ins2WT/GFPIns1-Ins2WT/GFP GFP(-) Ins2WT/GFP GFP(+) Ins2WT/GFPIns1- Ins2WT/GFPIns1+Ins1-Ins1+Ins2GFP/GFPIns1+Ins2GFP/GFPIns1-85Old miceBFigure 3.17. Differential gene expression according to gfp mRNA levels in the combined samples ofold Ins2WT/GFP heterozygous and Ins2GFP/GFP homozygous mice. (A) Single-cell RNA sequencing in Ins1expressing β-cells from Ins2GFP knock-in mice showing differentially expressed genes in heatmap form.Positively correlatedNegatively correlated86AYoung mice all differentially expressedOld mice combinedall differentially expressedFigure 3.18. Pathway enrichment analysis of differentially expressed genes in the combined samples of young and oldIns2GFP knock-in mice. (A) Single-cell RNA sequencing in Ins1 expressing β-cells from young 8-week old Ins2GFP knock-in miceshowing differentially expressed pathways. (B) Single-cell RNA sequencing in Ins1 expressing β-cells from combined samples ofold Ins2WT/GFP heterozygous and Ins2GFP/GFP homozygous mice and knock-in mice showing differentially expressed pathways. (C)Quantification of staining for CHGA protein in pancreas sections from Ins2WT/GFP heterozygous mice. (D) Quantification of GFPintensity, comparing high GFP expressing regions and low GFP expressing regions, used in immunofluorescent staining forproteins of interest. Paired t-test. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.A BCIns2(GFP)HIGHIns2(GFP)LOW0100002000030000CLOCK-GFPGFP intensity (au)✱✱✱✱Ins2(GFP)HIGHIns2(GFP)LOW0500010000150002000025000GRP78-GFPGFP intensity (au)✱✱✱✱Ins2(GFP)HIGHIns2(GFP)LOW05001000150020002500PDX1-GFPGFP intensity (au)✱✱✱✱Ins2(GFP)HIGHIns2(GFP)LOW0200400600800CHGACHGA intensity (au)nsIns2(GFP)HIGHIns2(GFP)LOW0100020003000CHGA/INSULIN-GFPGFP intensity (au)✱✱✱✱Ins2(GFP)HIGHIns2(GFP)LOW050010001500Proinsulin-GFPGFP intensity (au)✱✱✱D87Ins2(GFP)HIGHIns2(GFP)LOWMaximal insulin production -cell maturity Protein synthesis/processing machinery UPR resolutionOpportunity to proliferate?Opportunity to coordinate synchronization?Maximum fragilityJI0 2 5 10 15 20 25Glucose (mM) 5Thapsigargin (nM) 1000 0 0 00 000100000200000300000400000AUC (Pi fold induction)✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱✱Ins2(GFP)LOW cellsIns2(GFP)HIGH cellsAGFP Low GFP mask High GFP maskCLOCK DAPI MergeCIns2(GFP)HIGHIns2(GFP)LOW0500010000150002000025000GRP78GRP78 intensity (au)✱✱✱✱Ins2(GFP)HIGHIns2(GFP)LOW0500010000150002000025000CLOCKCLOCK intensity (au)✱✱✱✱Ins2(GFP)HIGHIns2(GFP)LOW01000200030004000PDX1PDX1 intensity (au)✱✱Ins2(GFP)HIGHIns2(GFP)LOW02004006008001000ProinsulinProinsulin intensity (au)✱✱Ins2(GFP)HIGHIns2(GFP)LOW010002000300040005000InsulinInsulin intensity (au)✱✱✱✱D HE F GBUMAP 1-4 0 4 -4 0 4UMAP 20-55100-551036129Pdx1 Ucn3Clock Hspa5GFP highGFP lowGFP neg89 Figure 3.19. Ins2(GFP)HIGH β cells show increased markers of maturity and increased cell death across multiple conditions. (A) UMAP projection of β cells with RNA velocity vectors overlaid. (B) Expression levels of genes of interest. Genes selected include genes which serve as indicators of β cell maturity, the circadian cycle, and ER stress. (C) Immunofluorescent staining of pancreatic sections from Ins2WT/GFP mice reveals increased expression in markers of maturity, circadian cycle, and ER stress in high GFP expressing cells. Proteins stained were chosen based on expression in single cell RNA sequencing data. Quantification was done by arbitrarily selecting for regions with high GFP fluorescence and comparing to regions with low GFP fluorescence. (D-H) Quantification of immunofluorescent staining for insulin, proinsulin, PDX1, GRP78, and CLOCK. (I) Cells with high GFP intensities show increased apoptosis susceptibility at all tested glucose concentrations and in the presence of thapsigargin (>1000 cells/condition, repeated from 2 mice). (J) Hypothetical working model of transitions between insulin production states in β cells. 90 Chapter 4: Signal transduction pathways controlling Ins2 gene activity and  cell state transitions 4.1 Introduction Diabetes is a debilitating disease with rapidly increasing prevalence alongside lifestyle and environmental changes of industrialized societies (1,302). Diabetes is characterized by dysfunctional glucose homeostasis which is caused by a relative (type 2 diabetes) or near-absolute (type 1 diabetes) deficiency in insulin, the body’s main anabolic hormone (15). Insulin is produced by pancreatic islet β cells, whose dysfunction and/or death are prerequisites for diabetes progression (191,232,303). Not all β cells are the same (159,174,304). Several studies have shown that insulin content and mRNA can vary greatly between individual  cells (175,238). Previously, live cell imaging of a dual Ins1 and Pdx1 promoter reporter showed that mouse and human β cell lines could transition from less to more mature states (172,239) and identified endogenous and chemical regulators of this process (226,305). However, transgenic reporter constructs may not accurately report endogenous gene activity. Therefore, in Chapter 3, we used Ins2GFP knock-in mice (221) to investigate endogenous Ins2 gene activity and identified two distinct β cell states, one with high insulin gene activity, Ins2(GFP)HIGH, and one with lower insulin gene activity, Ins2(GFP)LOW (306). Live cell imaging revealed that  cells had the capacity to transition between the high and low GFP states in both directions. Single cell RNA sequencing (scRNAseq) demonstrated Ins2(GFP)HIGH cells had a more mature β cell transcriptome profile, including upregulation of factors related to optimal insulin secretion. However, it has been shown that the transcriptome and proteome have weak correlation in islets (307), so it is critical to understand the proteomic profile of these states. It is also important to 91 understand the molecular mechanisms that control  cell state transitions. ER stress and other cellular perturbations are also known to impact gene transcription (308,309). For example, stresses associated with the high Ins2 state lead to β cell fragility (306) and lower proliferation capacity (215), but the effects of stress itself on Ins2 gene activity remains unclear. With robotic imaging technology and automated analysis, it is possible to study dozens of signaling pathways at once. Advances in live imaging of  cell states present the opportunity for fluorescence activated cell sorting (FACS) purification of selected cell populations, while advances in mass spectroscopy permit the identification and cataloguing of thousands of cell-selective proteins that could be effects markers or drug targets. Previous proteomic studies purified β cells using insulin immunofluorescence staining or FAD autofluorescence (310,311), but these methods for enriching β cells have caveats such as the reliance on antibody specificity and that FAD autofluorescence may not identify all  cells. Thus, there is need to revisit this approach with updated and more sensitive proteomics equipment and analysis pipelines. Here, I define the proteomic profiles of the two Ins2 gene activity states using Ins2GFP knock-in mice and systematically tested the effects of 48 drugs known to affect selected signaling pathways and cellular functions on Ins2 gene activity and  cell state transitions. I present the first comprehensive comparison of the two Ins2 expression states at the protein and functional level, and reveal multiple genes implicated in mechanisms related to Ins2 gene activity and β cell state transitions. 92 4.2 Results 4.2.1 Live 3D imaging of intact islets from Ins2GFP mice Our previous analyses of Ins2 gene activity in dispersed mouse islets revealed a bimodal distribution of GFP fluorescence from a reporter construct knocked into the endogenous Ins2 gene locus (306) (Fig. 4.1A, B). In this study, we used live 3D imaging to define the prevalence and spatial relationship of cells in the two  cell insulin gene activity states in whole intact islets from Ins2GFP knock-in mice (Fig. 4.1C). Consistent with dispersed cells, Ins2 gene activity in intact islets also had a bimodal distribution (Fig. 4.1D). We did not find significant sex differences when comparing the ratios of Ins2(GFP)HIGH and Ins2(GFP)LOW cells in intact male and female islets (Fig. 4.1E), consistent with our previous work in dispersed  cells (312). Nearest neighbor analysis showed that cells in the same Ins2 activity state were more likely to be found in proximity of each other (Fig. 4.1F). We found no significant difference between the cell states and their distance to the core of the islet (Fig. 4.1G). Overall, live cell 3D imaging analysis revealed the that the Ins2(GFP)HIGH and Ins2(GFP)LOW expression states exist in intact islets, with cells in the same expression states being more likely to be found next to each other. 4.2.2 Identification of  cell selective islet proteins Proteins that are selective to  cells have potential as biomarkers and anchor targets for imaging and drug delivery (313). Previous studies have identified  cell-specific mRNAs using single cell RNA sequencing (314). However, mRNA and proteins in isolated islets only have a correlation coefficient of ~0.5 (307), so there is an unmet need for minimally biased, high-throughput characterization of  cell selective gene products at the protein level. Therefore, as a 93 step towards identifying  cell specific proteins, we compared the proteomes of GFP positive cells to GFP negative cells from dispersed and sorted mouse islets to a depth of 5555 proteins (Fig. 4.2A). As expected, GFP-negative, non- cells were enriched for known markers of other islet cell types, such as GCG, PYY, and SST (Fig. 4.2A). To rank the proteins, we multiplied the p value by the fold change (Appendix A.). The top novel non- cell proteins were the cell surface protein tetraspanin 8 (TSPAN8), arginase 1 (ARG1), and clusterin (CLU) (Fig. 4.2A). The top 5 most significantly enriched  cell selective proteins based on the p value multiplied by fold change method were: 1) interferon regulatory factor (IRF3), a key transcription factor of type 1 interferon-dependent immunity; 2) CAP-Gly domain containing linker protein 1 (CLIP1), which links cytoplasmic vesicles to microtubules; 3) 3'-phosphoadenosine 5'-phosphosulfate synthase 2 (PAPSS2), which mediates two steps in the sulfate activation pathway; 4) tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (YWHAZ), also known as 14-3-3, a scaffolding protein which we showed plays important survival and signaling roles in  cells (315); and 5) apoptosis inducing factor mitochondria associated 1 (AIFM1) (Fig. 4.2A). As expected, INS1 and IAPP proteins were also significantly enriched in pure  cells. The G protein-coupled receptor 158 (GPR158), a metabotropic glycine receptor (316) which may play a role in autocrine feedback (317), was highly enriched in  cells. Notably, proteins involved in ER homeostasis such as HSPA5 were increased in GFP positive  cells, suggesting higher ER processing, and possibly stress, in β cells compared to other islet cells (Fig. 4.2A). We next undertook pathway analysis to identify enriched pathways in our proteomic data from non- cells and pure  cells (Fig. 4.2C). Gene set enrichment analyses revealed that non- cells are enriched for proteins that mediate metabolic and catabolic processes including ‘cellular 94 modified amino acid metabolic process’ and ‘carbohydrate derivative metabolic process’. In pure  cells, there was enrichment of pathways entitled ‘skin development’ and ‘supramolecular fiber organization’, which was driven by the relative abundance of multiple keratin family proteins and are known to modulate  cell signalling and play roles in stress responses (318). Enrichment of ‘regulation of cellular component biogenesis’ and ‘regulation of protein-containing complex assembly’ pathways suggest  cells may exist in a more anabolic state than non- cells. Pathways involving ‘protein phosphorylation’ and ‘IB/NFB signalling’ were also enriched in pure GFP-positive  cells (Fig. 4.2C). NFB signalling has been implicated in a  cell subpopulation with lower PDX1 expression, which has similarities with our Ins2(GFP)LOW cells (319). Overall, these results suggest that differences between Ins2 expressing cells and other islet cells are primarily associated with protein production and signalling. 4.2.3 Proteomic profiles of  cells in Ins2(GFP)HIGH and Ins2(GFP)LOW states We next compared the proteomic profiles of the two distinct Ins2 gene activity (Ins2(GFP)LOW and Ins2(GFP)HIGH) states in FACS purified  cells (Fig. 4.2B). Using p value times fold change as a criteria again, the top 5 most enriched proteins in Ins2(GFP)LOW cells were: 1) glucagon (GCG), a marker for alpha cells; 2) pancreatic polypeptide (PPY) a marker for PP cells; 3) purkinje cell protein 4 (PCP4), a calmodulin regulator involved in calcium binding and signalling; 4) aldehyde dehydrogenase 1 family member L2 (ALDH1L2), a mitochondrial enzyme implicated in  cell protection and secretory dysfunction (320); and 5) adenine phosphoribosyltransferase (APRT), an enzyme involved in the nucleotide salvage pathway (Fig. 4.2B). These results suggest that cells in the less mature Ins2(GFP)LOW state was enriched in 95 markers of ‘multi-hormonal’ cells, consistent with our previous scRNAseq analysis (306). We also found enrichment in proteins in endoplasmic reticulum processing, including HSPA5, HSP90B1, and ERO1L. This corroborates our prior scRNAseq analyses and cell death assays, which also revealed increased ER stress factors at the mRNA level and a marked resiliency to ER stress in the Ins2(GFP)LOW state (17).The top 5 most enriched proteins (other than GFP) in Ins2(GFP)HIGH cells were: 1) zinc finger AN1-type containing 2B (ZFAND2B), a protein associated with the proteasome for degradation of misfolded proteins; 2) zinc finger RANBP2-type containing 2 (ZRANB2), a protein involved in RNA splicing and mRNA processing 3) myocyte enhancer factor 2D (MEF2D), a calcium regulated transcription factor; 4) SWI/SNF related, matrix associated, actin dependent regulator Of chromatin subfamily A, member 2 (SMARCA2), a protein which regulates transcription via altering chromatin structure; and 5) phosphofurin acidic cluster sorting protein 2 (PACS2), a protein involved in ER calcium ion homeostasis. The protein poly(A) polymerase alpha (PAPOLA), a protein required for creation of the 3’-poly(A) tail of mRNAs, was also upregulated in Ins2(GFP)HIGH cells (Fig. 4.2B). The pathways significantly enriched in the Ins2(GFP)LOW state relative to the Ins2(GFP)HIGH state included: ‘organic acid metabolic process’, ‘cellular respiration’, ‘generation of precursor metabolites and energy’, and ‘oxidative phosphorylation’, suggesting the less mature cells may have more energic capacity. They are also enriched in proteins involved in ‘fatty acid metabolic processes’ and ‘lipid metabolic processes’, suggesting the possibility of metabolic substrate preference and switching between these cell states (Fig. 4.2D). A subpopulation of human  cells identified by Xin et al in 2018 with low INS mRNA and high UPR related factors also had upregulation in oxidative phosphorylation pathways, and it was suggested that upregulation of this pathway may be a form of protection against oxidative stress (175). The Ins2(GFP)LOW state 96 was enriched in ‘hormone metabolic process’ which was also enriched in non- cells relative to  cells, including GCG, PYY, and SST. Gene set enrichment analysis revealed that the pathways most enriched in the Ins2(GFP)HIGH state were related to transcriptional upregulation and mRNA processing (Fig. 4.2D). The upregulation of ‘DNA-templated transcription’ and ‘RNA biosynthetic process’ pathways may suggest that the Ins2(GFP)HIGH state is more transcriptionally active. The Ins2(GFP)HIGH state also enriched for ‘RNA processing’, a pathway which involves mRNA capping and addition of the 3’poly(A) tail. Regulation and processing of the 5’ and 3’ UTRs of insulin mRNA is known for their importance in maintaining mRNA stability, and our prior scRNAseq and qPCR data indeed showed that the Ins2(GFP)HIGH state had more Ins2 mRNA (306). ‘Chromosome organization’ and ‘cell cycle’ pathway upregulation is also indicative of regulation of the transcriptional landscape. Overall, this data suggests that differences between the Ins2(GFP)HIGH and Ins2(GFP)LOW states were related to transcription mechanisms and processes. We next analyzed the relationship between our differentially abundant proteins using STRING and mapped them to a cell diagram in Cytoscape to visualize protein-protein networks in their most common cellular locations (Fig. 4.3A, B). Protein-protein interaction networks enriched in GFP negative cells, beyond the known markers from other islet cell types (GCG, PYY, PPY, SST), included protein networks related to lipid metabolism (GPX3, HEXB) and factors related to amino acid synthesis and transport (SLC7A8, SLC3A2, ARG1) (Fig. 4.3A). Protein-protein interaction networks enriched in GFP positive cells, compared with non-GFP, included those involved in GPCR and cAMP signalling, glycolysis, and the secretory pathway (ER, Golgi, granules, cytoskeleton). Specifically, pure  cells showed enrichment for multiple cytoskeletal factors, including CLASP2, a protein which associates with LL5, which in turn is needed for 97 clustering of insulin secretory machinery; VIL1, a member of calcium regulated actin binding proteins and is known plays a role in insulin exocytosis regulation; and CLIP1, a protein associated with granule trafficking (321-323). These data provide data on  cell specific protein networks at unprecedented detail. Next, we examined network differences between the Ins2(GFP)HIGH state and the Ins2(GFP)LOW state. The Ins2(GFP)LOW state was enriched for networks related to mitochondria function and cell respiration, including members of the pyrroline-5-carboxylate reductase family (PYCR1, PYCR2), and subunits of the NADH dehydrogenase (ubiquinone) complex (NDUFA6, NDUFB6, NDUFB10) (Fig. 4.4A). We also noted a number of factors with links to proteasome function (PSMA3, PSMB3, PSMB8), ER stress factors (HSPA5, HSP90B1, PDIA4, PDIA6), and translation factors (EIF3B, EIF2S1, EEF2) (Fig. 4.4A). These results suggested that cells with low Ins2 gene activity may be recovering from various sources of stress and may also be in the process of regulating translation machinery. RHOA, an F-actin regulator which reduces insulin secretion when activated, was upregulated in the Ins2(GFP)LOW state (324). Another actin protein, ARPC2, which was downregulated in human islets when treated with insulin, was also enriched in Ins2(GFP)LOW cells (243). On the other hand, many of the enriched proteins in the Ins2(GFP)HIGH state are typically localized to the nucleus and are linked to transcriptional regulation, including prominent transcription factors TOP1, CDK9, KAT7 and INTS1 (Fig. 4.4A). Several nuclear proteins are associated with mRNA processing, such as ZRANB2, SRPK1, and PAPOLA (325-327) (Fig. 4.4B). We also noted a number of factors in the Golgi, including: HOOK2, a component of the FTS/Hook/FHIP (FHF) complex which promotes vesicle trafficking; STRIP1, a protein involved in the localization of the Golgi; and VCPIP1, a protein involved in Golgi reassembly. There were also a fair number of proteins in the ER, including the 98 aforementioned ZFAND2B and PACS2; DNAJA1, a protein which stimulates ATP hydrolysis; and NGLY1, an enzyme involved in the degradation of misfolded proteins. Proteins related to the cell cytoskeleton and protein trafficking were also upregulated, including CLIP1, CLASP2, and protein transport regulator KIF3A, suggesting upregulation in protein export in the Ins2(GFP)HIGH state. Taken together, the Ins2(GFP)HIGH state was primarily associated with the regulation of transcription, mRNA processing, and protein trafficking, while the Ins2(GFP)LOW state may be in the process of recovering from stress associated with high Ins2 gene activity and a shift in protein translation machinery. Our proteomics data revealed enrichment of ‘regulation of cellular component biogenesis’ and ‘regulation of protein-containing complex assembly’ pathways in the Ins2(GFP)HIGH state, so we tested whether β cell translation capacity was different compared to other islet cell types using the O-propargyl-puromycin (OPP) assay, which incorporates a fluorescent marker into nascent proteins (Fig. 4.5A). Using GFP as a marker for β cells, we found that β cells had higher translational capacity compared to other islet cells (Fig. 4.5B). The upregulation of DNA template transcriptional regulation and RNA processing pathways in Ins2(GFP)HIGH cells relative to Ins2(GFP)LOW cells is suggestive of increased downstream translational capacity. We found that cells in the Ins2(GFP)HIGH state had significantly higher translational capacity compared to cells in the Ins2(GFP)LOW state in both male and female mice (Fig. 4.5C, D). We did not note any significant differences when comparing the translational capacity of the two Ins2 expression states in the context of sex differences (Fig. 4.6A). Overall, these data revealed differences between the Ins2(GFP)HIGH and Ins2(GFP)LOW expression states at the protein level, and showed how some of these differences can translate to differences in β cell function. 99 4.2.4 Mechanistic interrogation of Ins2 gene activity regulation The molecular mechanisms regulating Ins2 gene activity are not clear. We chose 48 drugs known to perturb key  cell signaling pathways to study their regulation of  cell states (Table 2.3)(Fig. 4.7A). Concentrations for the various compounds were decided based on documented doses used in in vitro assays. Dispersed islet cells from Ins2GFP mice were treated with compounds and imaged in a high-throughput format (Fig. 4.7B-L). Although all of the experiments were conducted in parallel using a robotic imaging system, we visualized the results from each family of compounds separately. Compounds that activate cAMP signalling, namely the PDE inhibitor 3-isobutyl-1-methylxanthine (IBMX) and the cAMP inducer GLP-1, significantly increased Ins2 gene activity over time (Fig. 4.7B). The rate of increase for GFP fluorescence was considerably greater in Ins2(GFP)HIGH cells in response to cAMP pathway activators compared to Ins2(GFP)LOW cells (Fig. 4.8B, C). Compared to DMSO control, the calcineurin pathway inhibitors cyclosporine A, okadaic acid, and tacrolimus reduced Ins2 gene activity (Fig. 4.7C). Inhibitors of the endonuclease IRE1A caused a steep drop in Ins2 gene activity after prolonged treatment, with APY29 inducing a strong increase prior to the drop (Fig. 4.7D). Effectors of ER pumps and channels had relatively few effects on Ins2 gene activity, with ATP2A2 inhibitor thapsigargin slightly increasing Ins2 activity (Fig. 4.7E). Compounds that impacted insulin signalling also had some interesting effects, in particular the protein kinase inhibitor staurosporine, which significantly increased Ins2 gene activity, and PI3K inhibitor LY294002, which caused a spike in expression at 48 hours followed by a steep decline (Fig. 4.7F). The glucocorticoid agonist dexamethasone reduced Ins2 activity, while PPARG agonist rosiglitazone slightly increased it (Fig. 4.7G). The three nutrients we tested all increased Ins2 gene activity, with arginine and sodium butyrate having a more significant increase over time 100 compared to palmitic acid (Fig. 4.7H). Compounds that affected cell membrane excitability also had interesting effects, with KATP channel (ABCC8 subunit) activator diazoxide and CACNA1C inhibitor verapamil decreasing Ins2 gene activity, while KATP channel (ABCC8 subunit) inhibitor tolbutamide modestly increasing near the end of the experiment (Fig. 4.7I). Perturbing protein degradation factors also yielded effects, in particular ERAD inhibitor eeyarestatin I, which decreased Ins2 gene activity rapidly after treatment (Fig. 4.7J). As expected, many compounds that affected transcriptional machinery also impacted Ins2 gene activity (Fig. 4.7K). TFIIH inhibitor Triptolide and RNA polymerase II inhibitor alpha amanitin resulted in an increase in Ins2 activity, peaking at around 48 hours and rapidly declining afterwards (Fig.6K). CDK9 inhibitor 5, 6-dichloro-1-β-D-ribofuranosylbenzimidazole (DRB) also increased Ins2 gene activity (Fig. 4.7K). TOP1 inhibitor actinomycin D led to a steep decline in Ins2 gene activity 24 hours after treatment (Fig. 4.7K). HDAC inhibitors MGCD0103 and trichostatin A both reduced Ins2 gene activity, but not as abruptly as triptolide and alpha amanitin (Fig. 4.7K). Compounds that affected translation that had an effect on Ins2 gene activity include EEF2 inhibitor cyclohexamide, which increased Ins2 activity followed by a steep decline after 48 hours, PKR inhibitor, which increased Ins2 gene activity, and PERK activator CCT020312 and EIF2S1 inhibitor sal003, which reduced Ins2 gene activity (Fig. 4.7L). We next performed hierarchical clustering using various cell behavior traits in the context of comparing cells from male and female mice (Fig. 4.8AB). Cell activity traits we used included mean GFP (average GFP fluorescence over time), max GFP (peak GFP fluorescence), and mean cell death (average PI intensity). Overall, the strongest effectors of GFP fluorescence did not show any significant sex differences (Fig. 4.8A). 101 Rapid drops in GFP fluorescence are indicative of cell death, so we explored the relationship between GFP expression and cell viability by plotting fluorescence in the green wavelength (GFP) against the red wavelength (PI) (Fig. 4.7M) (306). We note that the cAMP agonists IBMX and GLP-1 amide had the highest GFP fluorescence with relatively low cell death (Fig. 4.7M). We found that okadaic acid and trichostatin A had the greatest decrease in GFP fluorescence without significant cell death (Fig. 4.7M). The subset of drugs which had increasing GFP fluorescence followed by a rapid drop (APY 29, triptolide, alpha amanitin, cyclohexamide, DRB, LY294002) had significant cell death, indicating the drugs were toxic after prolonged treatment (Fig. 4.7M). This is supported by an OPP assay done in the context of several small molecules of interest (GLP-1 amide, IBMX, APY 29, triptolide), in which a significant drop in translational capacity can be observed after 24 hours of treatment, well before the drop in GFP fluorescence and noticeable cell death (Fig. 4.6B). Overall, our small molecule screen shows how various drugs and small molecules may impact Ins2 gene activity and how it may correlate to β cell health. 4.2.5 Cell state transitions in the context of media conditions and perturbations Various culture conditions can impact cell state transitions, with higher glucose concentrations and the addition of FBS inducing a greater rate of Ins2(GFP)LOW to Ins2(GFP)HIGH transitions (Fig. 4.9A). We next analyzed the rate of transitions for cells from Ins2GFP knock-in mice in the context of our small molecule screen (Fig. 4.9D). In general, most small molecules had higher Ins2(GFP)HIGH to Ins2(GFP)LOW transitions rates compared to Ins2(GFP)LOW to Ins2(GFP)HIGH, with our DMSO control at possessing a ~3:1 ratio (9.1% Ins2(GFP)HIGH to Ins2(GFP)LOW, 3.2% Ins2(GFP)LOW to Ins2(GFP)HIGH). Compared to our DMSO control, we found that cAMP 102 agonists GLP-1 amide and IBMX had the greatest increase in Ins2(GFP)LOW to Ins2(GFP)HIGH transitions (19% for GLP-1 amide, 14% for IBMX), with relatively low levels of Ins2(GFP)HIGH to Ins2(GFP)LOW transitions (Fig. 4.9B-D). Okadaic acid and cycplosporin A induced the highest rate of Ins2(GFP)HIGH to Ins2(GFP)LOW transitions (Fig. 4.9D). Interestingly, we found that several drugs with high GFP, including triptolide, APY 29, and alpha amanitin, had very few transitions of either type (Fig. 4.9D). These results showed that the increase in GFP fluorescence observed in these treatments was primarily driven by increasing GFP fluorescence in the Ins2(GFP)HIGH cells, and not in the Ins2(GFP)LOW cells. STRING/Cytoscape analyses helped contextualize the major binding partners of our small molecule screen (Fig. 4.10A). Several of the strongest effectors of GFP fluorescence were found to impact genes located within the nucleus, including CDK9, TOP1, POL2A, ERCC3, HDAC1, HDAC2, SP1, and PAK1. Many of these proteins have known roles in transcriptional mechanisms (328-333). On the cell membrane, GLP1R and NOS1 were receptors that had positive effects on GFP fluorescence. In the ER, perturbing ERN1, a major factor in UPR response, impacted GFP fluorescence and cell health (334). In the cytosol, perturbation of VCP, a major component of ERAD, resulted in significant cell death, while PIK3CA, PDE3A, EEF2 influenced GFP fluorescence. In the context of cell state transitions, perturbing PDE3A and GLP1R induced the highest rate of Ins2(GFP)LOW to Ins2(GFP)HIGH transitions, while perturbing PPP3CC and PPP2CA resulted in the highest rate of Ins2(GFP)HIGH to Ins2(GFP)LOW transitions. Overall, these results showed how perturbing various β cell signalling pathways may impact Ins2 gene activity and cell state transitions. 103 4.3 Discussion This study aimed to further our understanding of dynamic Ins2 gene activity states, their proteomic profiles, and their regulation by key cellular processes. Our 3D live cell imaging experiments showed how the Ins2(GFP)HIGH and Ins2(GFP)LOW expression states existed in live intact islets. Our study characterized the Ins2(GFP)HIGH and Ins2(GFP)LOW expression states at the proteome level and revealed mechanisms governing Ins2 gene activity and cell state transitions. The two Ins2 gene expression states were present in intact islets (Fig 4.1C). Cells in the same expression states were more likely to be next to each other. We did not find any correlation for either cell population to the islet periphery or core. This is similar to what was observed by Johnston et al, in which hub cells, a β cell subset with similarities to Ins2(GFP)LOW cells, did not show any clear preference for the islet center or periphery (176). A caveat of our 3D imaging was the lack of penetrance into the center of larger islets. Future studies could employ two-photon microscopy to resolve this issue (335). Minimally biased interrogation of proteins and protein networks selectively present in β cells and  cell states builds on past efforts to map the  cell proteome. Previously established FACS purifying mouse  cell populations based on insulin immunoreactivity yielded 6955 proteins across 36 islet samples with various diets and islet cell types (310), and led to the identification of a subpopulation of β cells with low CD81 expression which had enrichment for factors in spliceosome and RNA processing pathways, but reduced oxidative phosphorylation factors, similar to our Ins2(GFP)HIGH cells (310). Two β cell subpopulations identified in a study on human β cell scRNAseq data had low INS expression, and while one high UPR marker expression, the other had enrichment in pathways related to oxidative stress (175). These two β 104 cell subpopulations possess similarities with our Ins2(GFP)LOW cells, and it was proposed that these low INS expressing cells were in a state of recovery from the various stresses of high insulin production (175). Overall, our study identifies novel and previously established β cell markers at the protein level, and presented results showing how the cells in a high Ins2 gene expression state had enrichment for factors related to RNA processing and transcription, with reduced oxidative phosphorylation and ER stress markers. Our targeted perturbation study revealed pathways which impact Ins2 gene activity and cell state transitions. The calcineurin inhibitors tacrolimus, cyclosporine A, and okadaic acid reduced Ins2 gene activity (Fig. 4.7C), consistent with known roles for calcineurin on insulin mRNA in transgenic mice with a human insulin promoter (336). Okadaic acid and cyclosporine A are the two top inducers of Ins2(GFP)HIGH to Ins2(GFP)LOW transitions. NFAT, a calcineurin target, has been reported to be required for cAMP-mediated upregulation of insulin transcription in INS-1E cells (337). Interestingly, IRS2, a reported target of calcineurin for regulation of human β cell survival, is enriched in Ins2(GFP)HIGH cells, further supporting a role for calcineurin in cell state regulation (338). cAMP dynamics are an integral part of insulin production and secretion (133). GLP-1 amide and IBMX robustly increased Ins2 activity over time, peaking at around 25 hours for GLP-1 amide and 55 hours for IBMX (Fig. 4.7B). Interestingly, GLP-1 and IBMX, also had the highest rates of Ins2(GFP)LOW to Ins2(GFP)HIGH transitions (Fig. 4.9D). Our proteomics analyses of Ins2(GFP)HIGH cells showed upregulation of two phosphodiesterase (PDE) isoforms, PDE1C and PDE8A, both of which are known to play major roles in regulating cAMP and GSIS in rodent β cells (339,340). Okadaic acid, the strongest inducer of Ins2(GFP)HIGH to Ins2(GFP)LOW transitions, is known to increase PDE expression in rat adipocytes (341). It is also worth noting that despite strongly upregulating GFP expression, many treatments including 105 APY29, triptolide, alpha amanitin, staurosporine, sodium butyrate, and arginine, did not induce nearly as many cell state transitions as IBMX and GLP-1 amide. Taken together, these results may suggest that the dynamics between calcineurin, cAMP, and PDE concentrations play a major role in regulating Ins2 gene activity and cell state transitions. Many drugs and small molecules had effects on GFP fluorescence, several of which were directly correlated to cell health. Drugs such as actinomycin D and eeyarestatin I killed cells rapidly upon treatment, resulting in a rapid collapse of GFP fluorescence. Interestingly, a subset of drugs including APY29 (IRE-1A inhibitor), triptolide (TFIIH inhibitor), alpha amanitin (RNA polymerase II inhibitor), cyclohexamide (EEF2 inhibitor), DRB (CDK9 inhibitor), and LY294002 (PI3K inhibitor) induced a strong increase in GFP fluorescence followed by rapid cell death after prolonged treatment. The endonuclease IRE-1A is an important factor in  cell stress regulation (334). Inhibition of general transcriptional machinery such as TFIIH and RNA polymerase II is highly detrimental to cell health after prolonged periods of time (342,343). Inhibition of translation and cell cycle factors are also known to be toxic to cells (344,345). The PI3K signalling pathway is linked to cell survival and resistance mechanisms (346). The toxicity of these drugs may result in a buildup of stress cumulating in eventual cell death. It is interesting to note that the increase in GFP fluorescence in these drugs only occurs in Ins2(GFP)HIGH cells, but not Ins2(GFP)LOW cells. This may be due to the inherent fragility in Ins2(GFP)HIGH cells, which may make them more vulnerable to the effects of stress (306). It is also worth noting that the two small molecules which induced the greatest increase in GFP fluorescence, GLP-1 amide and IBMX, are also well known to have protective effects on β cells (347,348). The OPP assay done in the context of several small molecules of interest also supports this, with cells treated to 24 hours of triptolide and APY 29 displaying reduced translational capacity prior to cell death 106 and reduction in GFP fluorescence. These findings may suggest links between cell health and Ins2 gene activity. In summary, we provide data which further reveals the differences between the Ins2(GFP)HIGH and Ins2(GFP)LOW expression states beyond our previous study. Our live cell 3D imaging captures the two cell states in intact living islets, and our proteomic and translational assays characterizes them at the protein and functional level. The small molecule screen reveals pathways which may impact Ins2 gene activity and cell state transitions, including prominent effects by effectors of cAMP signalling. Our study highlights the importance of cell expression states and cell state transitions in the islet, and how targeting factors which influence cell state transitions may help manage β cell function in diabetes. DAGC GFPHoechst Low GFP High GFPMerge050100150Average distance of individualbeta cell to core (μm)Ins2(GFP)HIGH-Ins2(GFP)HIGHIns2(GFP)LOW-Ins2(GFP)LOWIns2(GFP)HIGH-Ins2(GFP)LOWBEMale Female02468High GFP: Low GFP (cell count)*F01020304050Average distance tonearest neighbors (μm)GFP Ins2 Exon 2Murine Ins2 gene locusIns2(GFP)HIGHIns2(GFP)LOW5004003002001000100 102 104100101102103104Ins2WT/WT Ins2WT/GFP Merge60% 23% 17%100 102 104100 102 104GFP GFPGFPcountSSCIns2(GFP)HIGHIns2(GFP)LOW107108 Figure 4.1. Live 3D imaging of intact Ins2GFP mouse islets. (A) Schematic of the Ins2GFP knock-in mouse model, in which the second exon of the wildtype Ins2 gene has been partially replaced with GFP. (B) FACS purification of Ins2(GFP)LOW and Ins2(GFP)HIGH populations of β cells, with β cells isolated from a WT mouse for comparison. (C) Example images of 3D live cell imaging of intact islets from Ins2GFP knock-in mice. Hoechst is a cell nuclei dye. Scale bar is 30 μm. (D) Density plot of cells from Ins2GFP islets. Each distribution represents an islet. Blue distributions are small islets with less than 150 counted GFP positive cells, while red islets are large islets with over 150 counted GFP positive cells (n = 40 islets). (E) Comparison of ratios of high GFP cells to low GFP cells in male and female mice. Each dot represents an islet (n = 20 male and 20 female islets from 3 male and 3 female mice). (F) Nearest neighbor analysis of cells from Ins2GFP knock-in mice (n = 40 islets). One-way ANOVA. (G) Distance to core of cells from Ins2GFP knock-in mice. Student’s T-test. Density: probability density function, represents the probability a data is located within the defined range (i.e. ratio of the total). * p < 0.05 B GFPAD10-1-2NES 0.20.30.40.50.62cellular respirationorganic acid metabolic processgeneration of precursor metabolites and energyoxidative phosphorylationfatty acid metabolic processhormone metabolic processlipid metabolic processregulation of blood pressureregulation of secretionfeeding behaviorskin developmentsupramolecular fiber organizationregulation of cellular component biogenesisI-kappaB kinase/NF-kappaB signalingprotein phosphorylationprotein polymerizationregulation of blood vessel endothelial cell migrationregulation of protein modification processregulation of protein-containing complex assemblyCsmall molecule catabolic processhormone metabolic processcellular modified amino acid metabolic processorganic acid catabolic processglutathione metabolic processregulation of cytokine production in immune responsealpha-amino acid catabolic processcarbohydrate derivative metabolic processL-amino acid transport0 2 4 6 80.20.30.40.50.6210-1-2NES-log10(FDR)0 2 4transcription, DNA-templatedRNA processingRNA biosynthetic processnegative regulation of cellular metabolic processchromosome organizationregulation of macromolecule biosynthetic processactin filament bundle assemblycell cyclecellular response to DNA damage stimulus6 842 6 842-log10(FDR)GFPGene ratioGene ratioRNA splicing110 Figure 4.2. Proteomic analysis of FACS purified Ins2GFP β cells. (A) Volcano plot of differentially expressed proteins comparing FACS purified GFP positive and GFP negative cells (n = 5 mice). (B) Volcano plot of differentially expressed proteins comparing FACS purified Ins2(GFP)HIGH and Ins2(GFP)LOW cells (n = 5 mice). (C) Gene set enrichment analysis of pathways enriched in GFP positive and GFP negative cells (n = 5 mice). (D) Gene set enrichment analysis of pathways enriched in Ins2(GFP)HIGH and Ins2(GFP)LOW cells (n = 5 mice). AABNode color:Log2 (GFP+/GFP-)Node border thickness :-Log10 (p-value)-3.3 -1.6 0.0 1.6 3.31.3 1.9 3.72.5 3.1142111112 Figure 4.3. Integration of STRING networks and protein expression data in Cytoscape reveals differences in protein production and export between GFP positive and GFP negative cells. (A) Integration of STRING networks and protein expression data of differentially abundant proteins (top 100 with p value lower than 0.05) in GFP negative cells in Cytoscape. Deeper color of the nodes represents the fold change (GFP negative/GFP negative), while the thickness of the line around the node represents p value. (B) Integration of STRING networks and protein expression data of differentially abundant proteins (top 100 with p value lower than 0.05) in GFP positive cells in Cytoscape. Deeper color of the nodes represents the fold change (GFP positive/GFP negative), while the thickness of the line around the node represents p value. AABNode color: Log2 (Ins2(GFP)HIGH/Ins2(GFP)LOW)Node border thickness :-Log10 (p-value)-3.3 -1.6 0.0 1.6 3.31.3 1.9 3.72.5 3.1142113114 Figure 4.4. Integration of STRING networks and protein expression data in Cytoscape reveals differences in transcription and mRNA processing between GFP expression states. (A) Integration of STRING networks and protein expression data of differentially abundant proteins (p value lower than 0.05) in Ins2(GFP)HIGH cells in Cytoscape. Deeper color of the nodes represents the fold change (Ins2(GFP)LOW/Ins2(GFP)HIGH), while the thickness of the line around the node represents p value (B) Integration of STRING networks and protein expression data of differentially abundant proteins (p value lower than 0.05) in Ins2(GFP)LOW cells in Cytoscape. Deeper color of the nodes represents the fold change (Ins2(GFP)HIGH/Ins2(GFP)LOW), while the thickness of the line around the node represents p value. B CDGFPNuclearMaskOPP MergeAGFP+GFP-Ins2(GFP)HIGHIns2(GFP)LOW* * *Figure 4.5. OPP protein synthesis assay of Ins2(GFP)LOW and Ins2(GFP)HIGH cells.(A) Representative images of OPP protein synthesis assay on Ins2WT/GFP mice. The blue wavelength is theNuclearmask cell nuclei stain, the green wavelength is GFP, and the red wavelength is the OPP marker for nascentpeptide formation. Scale bar is 40 μm. (B) Comparison of translational capacity of GFP positive β cells and GFPnegative cells (n = 6 mice). Student’s t-test. (C) Comparison of translational capacity of Ins2(GFP)HIGH andIns2(GFP)LOW cells (n = 6 mice). Student’s t-test. (D) GFP fluorescence of Ins2(GFP)LOW β cells andIns2(GFP)HIGH β cells. Student’s t-test (n=6 mice).0204060Translation0204060Translation0510152025GFP fluorescenceA ND T2DAFigure 4.6. OPP protein synthesis assay on small molecules of interest from the screen. (A) Translational capacity in Ins2(GFP)HIGH and Ins2(GFP)LOW cells in the context of sex. (B) GFP fluorescence and translational capacity in cells treated for 24 hours by APY 29, triptolide, GLP-1 amide, and IBMX. TriptolideGLP-1 amide05101520GFP fluorescenceIBMXControlAPY29 Ins2(GFP)HIGHIns2(GFP)LOWBFemale Male0204060Translation* *020406080Translation116Protein degradationRelative GFP fluorescence intensityILCalcineurin signalingCtacrolimusokadaic acidcyclosporin AcAMP signalingGLP-1 IBMXepinephrineSSTpramlinitideEndonucleasesAPY29sunitinibER pumps & channelsryanodinethapsigarginxestosponginInsulin signalingstaurosporineLY294002AICARmetforminS961HNMPAwortmanninAS1842856Nuclear receptorsdexamethasonerosiglitazoneNutrientssodium butyratepalmitic acidExcitabilitytolbutamideverapamildiazoxideTranscriptiontriptolideDRBalpha amanitinactinomycin Dtrichostatin AMGCD0103Translation factorscycloheximideCCT020312PKR inhibitorDHBDCsal003isribPERK inhibitor IazoramidePathway modulating drugsFcontrolcalcineurincAMP signallingendonucleasesER channelsinsulin / other signallingnuclear receptorsnutrientsplasma membrane excitabilityprotein degradationtranscriptiontranslation factorsTreatment category -log10 (p-value)0123GFP fluorescenceIBMX APY 29triptolidealpha amanitincyclohexamideLY294002DRBMGCD0103staurosporine sodium butyrateargininepalmitic acidPKR inhibitorthapsigarginETOHDMSOverapamiltunicamycinrosiglitazoneAICARDHDBCazoramidebortezomibsunitinibwortmanninPERKi ItolbutamideAS1842856metforminMG132pramlinitideISRIB sal003 CCT020312cyclosporin AxestosponginS961HNMPASSTtacrolimusdiazoxidetrichostatin Aokadaic acidepinephrineactinomycin Deeyarestatin IGLP1ryanodineCell deathdexamethasoneADGJMBEHKTime (hours)eeyarestatin ItunicamycinbortezomibMG132argininer t i r ti117118 Figure 4.7. A small molecule screen for effectors of Ins2 gene activity. (A) Experiment pipeline. Islets were isolated and dispersed from Ins2GFP knock-in mice. Cells were then stained with Hoechst cell nuclei dye and propidium iodide (PI) cell death marker for two hours, then subjected to treatments. Live cell imaging was then performed, and fluorescence for all three wavelengths was tracked across 96 hours of imaging. (B-L) Average GFP fluorescence of cells, across all treatments, over the period of 96 hours. Treatments were categorized based on their primary binding partners. Deceased cells were removed by thresholding for cells with high PI fluorescence. Dotted black lines indicate DMSO controls, dashed lines indicate inhibitors of their respective categories, and solid lines indicate activators of their respective categories (n = 6 mice). (M) Analysis of GFP fluorescence against cell death. GFP fluorescence is scaled GFP fluorescence relative to background after 48 hours of treatment. Cell death is scaled PI fluorescence relative to background after 72 hours of treatment. Size of dots represents p value (adjusted). Colors of dots represents categories, which were decided based on their primary binding partners (n = 6 mice). Two-way ANOVA, adjusted p value indicated by size of dots. A ND T2DGFP (AUC)BMaleFemaleMaleFemaleFigure 4.8. Hierarchical clustering of cell behavior traits from small molecule screen. (A) Hierarchical clustering of cell behavior traits in the context of sex. (B) Average GFP fluorescence for each treatment across all six individual mice. 119A Serum Serum starvedIns2(GFP)LOW to Ins2(GFP)HIGHIns2(GFP)HIGH to Ins2(GFP)LOWIBMXGFPGFP0h 72hBDMSOC IBMXGFPHoechstPropidium iodideTime (hours)Intensity50000400003000020000100005000040000300002000010000000 25 50 75 100200001500010000500000 25 50 75 10020000150001000050000GLP-1IBMXAPY 29triptolidealpha amanitincyclohexamideLY294002MGCD0103staurosporinesodium butyrateargininepalmitic acidPKR inhibitorETOHDMSOtunicamycinrosiglitazoneAICARDHDBCwortmanninAS1842856metforminMG132ISRIBisoxazole 9HNMPAtacrolimusokadaic acidepinephrineGLP1DRBthapsigarginverapamilazoramidebortezomibtolbutamidepramlinitideCCT020312cyclosporin AxestosponginS961SSTdiazoxidedexamethasonetrichostatin APERK inhibitor Isal003 ryanodineHigh to low transition %Low to high transition %-log10 (p-value)012340-1-2123-3GFP fluorescenceD120121 Figure 4.9. Cell state transitions in the context of various media conditions and perturbations. (A) Transition rates between the Ins2(GFP)HIGH and Ins2(GFP)LOW cell states in the context of various media conditions. % of cells transitioning: percent of subpopulation transitioning to other cell state after 48 hours of culture. (B) Example images of increasing GFP fluorescence in response to IBMX. (C) Example traces of GFP upregulation and cell state transitions in response to IBMX and GLP-1 amide. (D) Comparison of Ins2(GFP)HIGH to Ins2(GFP)LOW and Ins2(GFP)LOW to Ins2(GFP)HIGH transition rates in the context of the small molecule screen. Data shown is average transition rate by the end of 48 hours of treatment (n = 6 mice). Size of the dots denotes p value (adjusted). Color of the dots denotes scaled GFP fluorescence. Two-way ANOVA. NDAFigure 4.10. Integration of STRING protein networks and a small molecule screen on Cytoscape.(A) Primary binding partners for small molecules used in the screen localized in Cytoscape. Binding partners (bold)are chosen based on their affinity to the small molecule. Color of the nodes represents GFP fluorescence (scaled)after 48 hours of treatment. Borders of the nodes represents cell death (scaled PI fluorescence) after 72 hours oftreatment. Height of green triangles represents Ins2(GFP)LOW to Ins2(GFP)HIGH transition rates. Height of orangetriangles represents Ins2(GFP)HIGH to Ins2(GFP)LOW transition rates. Blue lines represent STRING interactions.Pointed arrows with grey lines represents upregulation effect. Inhibitor arrows with grey lines representsdownregulation effect.Node color: Relative GFP fluorescenceNode border thickness :% cell death-5.0 -2.5 0.0 2.0 4.11 25 10050 75160.8122123 Chapter 5: Insulin gene expression states in stem cell-derived β cells 5.1 Introduction Type 1 diabetes is caused by deficiency of insulin-producing pancreatic β cells, whose dysfunction and then death are essential for disease pathology (15,27,28,349). Patients with type 1 diabetes require insulin, and are thus completely dependent on insulin-based therapies (185). The emergence of techniques involving transplant of cadaveric donor islets provides the possibility for patients to be independent of insulin injections (192). However, due to the lack of readily available donors, the majority of type 1 diabetes patients will not have access to this therapy. Stem cell-derived β cells (SCβ cells) have emerged as a promising alternative to cadaveric donor islets due to their ability to infinitely replicate (195,196,350). Protocols for producing SCβ cells have matured over the past few years. It is now possible to make cells that are consistently glucose responsive, although not to the extent of high-quality primary human islets (225). Clinical trials are underway and some have reported (351). However, a number of issues need to be addressed before SCβ cells are ready for broad application. Currently none of the reported SC cells have been convincingly shown to have comparable glucose stimulated insulin secretion capacity relatively to high-quality primary β cells (197,225,307,352). The study of β cell heterogeneity has been enabled by advances in single cell transcriptomics, complementing the characterization of morphological and functional heterogeneity (174,181,273,353). Multiple SCβ cell states also exist (354,355). In the previous chapter, I recently used an Ins2GFP knock-in mouse model to investigate heterogeneity and dynamics in insulin gene activity at the endogenous Ins2 gene locus; I found that Ins2 gene activity was bimodal with two major insulin gene expression states, and that cells could transition between 124 these states over time (306). However, it is currently unknown whether these cell states and gene expression dynamics can be recapitulated in the considerably less mature SCβ cells. It is known that in islets, mRNA levels do not correlate very well with protein expression (307). With advances in the field of mass-spec based proteomics, we can now identify thousands of proteins with relatively little tissue sample (356). Combined with new FACS technologies that can sort cells at high rates with high accuracy, we can now FACS purify numerous cell states and subpopulations and analyze their proteomes (310). Currently, few studies have investigated the proteomes of SCβ cells, and none have done so in the capacity of FACS purified cells. In the present study, I investigated INS gene dynamics with a stem cell line that has EGFP knocked-in and added on to the endogenous INS gene loci (357). I characterized INS gene expression states in late stage SCβ cells with FACS, scRNAseq, and FACS purified mass-spec proteomics. I present a comprehensive comparison of INS gene expression states at the RNA, protein, and functional level. 5.2 Results 5.2.1 Heterogeneity in INS gene activity in stem cell-derived spheroids We initiated our investigation by performing FACS on dispersed day 35 INS-EGFP SCβ cells to determine the distribution of insulin gene activity states (Fig. 5.1A). After controlling for autofluorescence using unmodified H1 cells as a negative control, we found that EGFP positive cells can be split into two populations - one with higher EGFP and another with lower EGFP (Fig. 5.1B). From this point onwards, we designated cells with high EGFP expression INS(EGFP)HIGH, and cells with low EGFP expression INS(EGFP)LOW. We further investigated INS gene activity in the context of intact stem cell-derived spheroids using 3D live cell imaging 125 (Fig. 5.1C). Similar to our FACS experiments, we found that EGFP positive cells had a bimodal distribution in INS gene activity (Fig. 5.1D). We also found that cells in the same INS activity state tended to cluster closer to one another, and that cells in the INS(EGFP)HIGH state tended to be closer to the core of the spheroid. Overall, these data revealed that INS gene activity in SCβ cells is bimodal in distribution, and that the high and low activity states can be identified in intact stem cell-derived spheroids. 5.2.2 Transcriptomic analysis of the INS(EGFP)HIGH and INS(EGFP)LOW states To identify mRNA expression profiles of the two INS gene expression states, we reanalyzed a previously generated a single cell RNA sequencing (scRNAseq) dataset of SCβ cells (unpublished). We found that mRNA levels of EGFP had a bimodal distribution (Fig. 5.2A), consistent with our imaging data (Fig. 5.1). INS expression was not bimodal, similar to what we previously found in mice scRNAseq data (Fig. 5.2A) (306). Differential gene analysis of the two cell states (Fig. 5.2C), confirmed that INS was upregulated in the INS(EGFP)HIGH state. In addition, we found significant enrichment in the ribosomal proteins RPL3 and RPL27A, the positive regulator of β cell stimulus secretion coupling transthyretin (TTR) (358), and the endoplasmic reticulum chaperone protein heat shock protein family A member 5 (HSPA5, also known as BIP). Genes upregulated in the INS(EGFP)LOW cell state include the growth factor FGF13, the histone protein HDAC9, and the glutamate inotropic receptor AMPA type subunit 1 (GRIA1). Gene set enrichment pathway analysis comparing INS(EGFP)HIGH with INS(EGFP)LOW revealed that the two most significantly enriched pathways in INS(EGFP)HIGH were ‘cytoplasmic translation’, ‘protein folding in endoplasmic reticulum’, and ‘hormone transport’ (Fig. 5.2D). 126 These results suggested that cells with high INS gene expression may also be under more ER stress due to high translational output. We next investigated which genes had the highest positive correlation with EGFP mRNA, analyzed as a continuous variable (Fig. 5.2E, F). As expected, INS had the highest correlation to EGFP, followed by TTR (Fig. 5.2E). Other genes of interest included the aforementioned HSPA5; another ER protein folding gene heat shock protein 90 beta family member 1 (HSP90B1); the β cell maturity marker chromogranin A (CHGA); the insulin secretion regulator glucose-6-phosphatase catalytic subunit 2 (G6PC2); and protocadherin 7 (PCDH7), a gene recently shown to select for SCβ cells with enhanced insulin secretion (359,360) (Fig. 5.2E). Also upregulated were a significant number of ribosome components and mitochondrial genes (Fig. 5.2E). Genes negatively correlated with EGFP included the INS gene pioneering factor GLIS family zinc finger 3 (GLIS3); the N-type calcium voltage-gated channel subunit alpha1 B (CACNA1B); glutamate receptors GRIA1 and GRIA2; and neural cell adhesion molecule (NCAM), a molecule whose polysialylated form has been shown to be a marker for β cell functionality (Fig. 5.2F) (170). Taken together, our analyses comparing the transcriptomes of the two SCβ cell expression states showed that cells in the INS(EGFP)HIGH state have enrichment for markers related to protein folding, translational capacity, and β cell function. 5.2.3 Proteomic analysis of the INS(EGFP)HIGH and INS(EGFP)LOW states To further characterize the two SCβ cell gene activity states, we performed mass-spec based proteomics on FACS purified SCβ cells differentiated in a 35-day process (224,225). Single-cell proteomics is not yet feasible at the scale of scRNAseq, so we sorted cells into high EGFP, low 127 EGFP, and negative EGFP populations (Fig. 5.3A). As expected, the INS protein was significantly enriched in the INS(EGFP)HIGH population (Fig. 5.3B). We first performed differential protein abundance comparing EGFP positive -like cells EGFP negative non--like cells showed that the top three genes enriched in EGFP negative cells, based on fold change times FDR, were: anterior gradient 2 (AGR2), which plays a role in transmembrane receptor folding; annexin A2 (ANXA2), a protein which plays a role in cell growth and signal transduction; and glutathione S-transferase A1 (GSTA1), which plays a role in detoxification of electrophilic compounds (Fig. 5.3C). The top three proteins enriched in EGFP positive cells were: cryptic, EGF-CFC family member 1 (CFC1), a marker for endocrine cells (361); clusterin (CLU), a protein implicated in β cell protection against lipotoxicity induced apoptosis (362); and phospholipase D family member 3 (PLD3), a protein that regulates processing of amyloid-beta precursor proteins (Fig. 5.3C). Other notable proteins that were upregulated included the hormones glucagon (GCG) and somatostatin (SST), as well as the prohormone processing factor carboxypeptidase E (CPE), the ER folding protein endoplasmic reticulum oxidoreductase 1 beta (ERO1B), and paired box 6 (PAX6), a regulator of β cell survival and maintainer of β cell identity (363) (Fig. 5.3C). These data illustrate that while EGFP is a marker for insulin, EGFP positive cells contain a mix of polyhormonal cells, with markers for various endocrine cells. We next performed differential analyses comparing the proteomes of INS(EGFP)LOW and INS(EGFP)HIGH cells (Fig. 5.3D). The top genes upregulated in the INS(EGFP)LOW state were: vimentin (VIM), a mesenchymal marker that was upregulated in human α and β cells in the context of type 2 diabetes (364); creatine kinase B (CKB), an enzyme involved in energy homeostasis; and the amino acid transporter solute carrier family 7 member 14 (SLC7A14) (Fig. 5.3D). Other proteins enriched in INS(EGFP)LOW cells include the α cell markers aristaless 128 related homeobox (ARX) and GCG and protein C receptor (PROCR), a cell surface marker that was suggested to mark for endocrine lineages (365). Interestingly, CHGA was upregulated in INS(EGFP)LOW cells, in contrast to our scRNAseq data (Fig. 5.3D). The top proteins besides EGFP that were upregulated in INS(EGFP)HIGH were: protein disulfide isomerase family A member 4 (PDIA4), a protein suggested to play a role in β cell dysfunction and death in diabetes (366); the ER chaperone protein HSP90B1; and ankyrin repeat and SOCS box containing 9 (ASB9), a gene that has been implied to be upregulated in more mature β cells (367) (Fig. 5.3D). Other markers of interest included: the β cell marker pancreatic and duodenal homeobox 1 (PDX1); the prohormone processing factor proprotein convertase subtilisin/kexin type 1 (PCSK1); and the SCβ cell maturity marker ectonucleoside triphosphate diphosphohydrolase 3 (ENTPD3). Additional ER protein folding factors such as ERO1B, mesencephalic astrocyte derived neurotrophic factor (MANF), HSPA5, dnaJ heat shock protein family member B9 (DNAJB9), and dnaJ heat shock protein family member C3 (DNAJC3); and the mitochondrial pyrroline-5-carboxylate reductase 1 (PYCR1) were also upregulated. (Fig. 5.3D). Gene set enrichment pathway analyses (Fig. 5.3E) showed that low EGFP expressing cells are enriched for pathways related to transcriptional regulation, including ‘mRNA splicing, via spliceosome’, ‘mRNA processing’, ‘DNA conformation change’, ‘histone modification’, and ‘regulation of gene silencing’. Combined with our differential gene analyses, these results suggested that the INS(EGFP)LOW cell state is associated with more polyhormonal cells, and might be indicating cells which are still in the process of adjusting their transcriptional machinery. Pathways enriched in INS(EGFP)HIGH cells included several linked to the ER and Golgi apparatus, including ‘response to endoplasmic reticulum stress’, ‘ER to Golgi vesicle-129 mediated transport’, ’glycosylation’, and ‘Golgi organization’. This result may suggest an enrichment in protein production and processing along the ER-Golgi axis. The upregulation of ‘vesicle coating’ and ‘vesicle budding from the membrane’ is suggestive of increased secretion of ligands from these cells. Also upregulated were ‘organic acid catabolic process’ and ‘fatty acid catabolic process’, which may indicate responsiveness to certain nutrients. ‘ERAD pathway’ along with ‘response to endoplasmic reticulum stress’ may be indicative that the cells are experiencing stress due to upregulated insulin production. ‘Cellular respiration’ was also upregulated, indicative of increased mitochondrial function. Overall, these results suggested that the high INS state is associated with β cell maturity, as well as the stress that results from high insulin production. 5.2.4 Cellular context-aware protein-protein interaction networks We generated protein-protein interaction networks using the online analysis tool, STRING, to define the interrelationships between the differentially abundant proteins and mapped each protein to its most likely sub-cellular location (Fig. 5.4AB). In INS(EGFP)LOW cells, there were a large number of transcription factors in the nucleus, as was suggested by the pathway enrichment analyses (Fig. 5.4A). Also enriched were a large number of Serpin proteins, whose family has previously been implicated to play roles in β cell biology (368). Secreted proteins include α markers GCG, dipeptidyl peptidase 6 (DPP6) and dipeptidyl peptidase 4 (DPP4) (Fig. 5.4A). We also found upregulation in the protein ghrelin (GHRL), a hormone primarily produced in the gut but also in small amounts in the pancreas (Fig. 5.4A). Also of note are several proteins associated with extracellular matrix and cell adhesion, including fibronectin (FN1), fibulin 7 (FBLN7), and collagen type V alpha 2 chain (COL5A2) (Fig. 5.4A). INS(EGFP)HIGH cells exhibited enrichment 130 in proteins located in the ER, Golgi apparatus, and mitochondria (Fig. 5.4B). Enriched ER proteins primarily consisted of chaperone and protein folding factors, including members of the heat shock protein family (HSPA5, HSP90B1), J protein family (DNAJB9, DNAJB11, DNAJC3, DNAJC10), immunophilin family (FKBP2, FKBP9, FKBP10), and thioredoxin family (TXNDC5, TXNDC12, TXNDC16) (Fig. 5.4B). Enriched Golgi proteins include components of the Golgi-localized complex (COG1, COG3, COG4, COG5, COG6, COG7), the golgin family of proteins which play roles in vesicle tethering and docking (GOLGA2, GOLGA3, GOLGA5, GOLGB1) (Fig. 5.4B). Interestingly, a number of Sec proteins, which are help regulate vesicle trafficking between the ER and golgi, were also upregulated, including SEC16A, SEC23B, SEC24C, SEC24D, and SEC31A (Fig. 5.4B). This result may suggest a strong ER-Golgi protein production axis in INS(EGFP)HIGH cells. We also noted the upregulation of RBP4, a known human β cell heterogeneity marker which was upregulated in type 2 diabetes and obese individuals (252) (Fig. 5.4B). In the INS(EGFP)LOW cells, we found that interaction networks are primarily between proteins found in extracellular matrices and secreted ligands (Fig. 5.4A). There was also an interaction axis with SRP14, RPS26, and EEF1A, which may suggest that select translation and protein folding machinery functions are being utilized in INS(EGFP)LOW cells (Fig. 5.4A). In the INS(EGFP)HIGH cells, strong multiple networks were found in the Golgi and ER (Fig. 5.4B). We also found a very strong interaction axis of ER proteins, Sec proteins, and Golgi proteins, suggesting a pipeline of protein folding followed by protein export to the Golgi (Fig. 5.4B). Other interaction networks in INS(EGFP)HIGH cells include several interactions between mitochondria proteins and peroxisome proteins, which may hint at enhanced mitochondrial metabolism followed by breakdown of metabolites in the peroxisome (Fig. 5.4B). Overall, these 131 data suggested that proteins enriched in INS(EGFP)LOW cells were primarily in secreted and may play a role in formation of extracellular matrices, while INS(EGFP)HIGH cells had more to do with protein folding, trafficking, and metabolism. 5.2.5 Protein synthesis of the INS(EGFP)HIGH and INS(EGFP)LOW states Studies have shown that certain β cell subpopulations had larger size due to enlarged Golgi and ER (158). Therefore, we next investigated whether the enrichment of ER and Golgi proteins in INS(EGFP)HIGH cells had an effect on cell size. Interestingly, cells with higher EGFP expression were physically larger than those with lower EGFP expression (Fig. 5.5A). Next, to directly test our hypothesis that an enriched ER-Golgi axis might also be indicative of increased protein translation capacity, we performed O-propargyl-puromycin (OPP) assays on dispersed day 35 SCβ cells (Fig. 5.6A-C). Indeed, the INS(EGFP)HIGH cell state had significantly more translational activity compared to the INS(EGFP)LOW cell state across multiple culture conditions (Fig. 5.6B). The INS(EGFP)HIGH cell state had response to glucose, with higher glucose concentrations increasing translational capacity, while the INS(EGFP)LOW state had a milder response to glucose (Fig. 5.6B). Interestingly, we found that thapsigargin, an ER stress inducer, could only significantly inhibit translation without the addition of ITS, and only at basal (5 mM) glucose concentrations (Fig. 5.6B). This inhibition effect was stronger on the INS(EGFP)HIGH cell state. Insulin has been shown to have protective effects against thapsigargin, which may explain why thapsigargin had reduced effects on cells in higher glucose and ITS positive conditions (369). Overall, these results showed that the INS(EGFP)HIGH cell state has significantly more translational capacity than the INS(EGFP)LOW cell state. 132 5.2.6 Stability and resilience of the INS(EGFP)HIGH and INS(EGFP)LOW states Previously, we used live cell imaging to investigate dynamics on Ins2GFP knock-in mice, and found that endogenous Ins2 gene activity was dynamic, and that it was possible for cells to transition between high and low Ins2 activity states (306). We investigated whether similar dynamics exist at the endogenous INS gene loci in SCβ cells. We performed live cell imaging on dispersed INS-EGFP cells for up to 96 hours, and tracked EGFP fluorescence over time (Fig. 5.7A). We found that cells could indeed transition between the INS(EGFP)HIGH and INS(EGFP)LOW expression states; however, there were very few cells transitioning from the INS(EGFP)HIGH to the INS(EGFP)LOW state, with the majority transitioning from INS(EGFP)LOW to INS(EGFP)HIGH (Fig. 5.7A-C). These transitions could be influenced by glucose and ITS, with higher glucose concentrations and the addition of ITS increasing transitions rates from INS(EGFP)LOW to INS(EGFP)HIGH (Fig. 5.7B, C). To further investigate the stability of the two cell states, we also FACS purified and reaggregated cells from each cell state into spheroids (Fig. 5.8A)). Interestingly, we found that INS(EGFP)HIGH cells formed larger reaggregates than INS(EGFP)LOW cells (Fig. 5.8A). This may be due to the INS(EGFP)HIGH often being larger cells (Fig. 5.5A). We found that after four days of reaggregation, plus an additional 2 days of rest, a large number of the cells from the INS(EGFP)HIGH reaggregates transitioned down to INS(EGFP)LOW (Fig. 5.8B)). In contrast, few cells from the INS(EGFP)LOW state transitioned to INS(EGFP)HIGH (Fig. 5.8B). These results showed that dynamics and cell state transitions can occur in INS-EGFP SCβ cells, but in a manner that is dissimilar to what we previously observed in primary mice β cells. Due to the increased ER stress factors associated with the INS(EGFP)HIGH state, we also wondered whether there were differences in cell resilience between the two cell states. Therefore, 133 we performed an imaging-based survival screen with 48 different conditions, and tracked cell death (Fig. 5.9A). These conditions included glucose concentrations (5, 10, 20 mM), various concentrations of a cytokine cocktail (TNF-α, IL-1β, IFN-γ; 1x, 2.5x, 5x), thapsigargin concentrations (0.1, 1, 10 uM), and with and without ITS. We found that across all conditions examined, the INS(EGFP)HIGH state had considerably higher cell death than the INS(EGFP)LOW state (Fig. 5.9A). These results showed that while the INS(EGFP)HIGH state had higher protein production, they were also considerably more vulnerable to stress from various sources. 5.2.7 INS gene expression states in human donor β cells SCβ cells are generally viewed as being less mature and possess poorer glucose stimulated insulin secretion compared to primary human β cells. However, the implication that high EGFP expressing SCβ cells increased factors for β cell maturity made us wonder if they could be comparable to human donor islets. To investigate INS gene activity in human donor β cells, we acquired a construct with GFP under control of the rat insulin promoter (RIP). We then transduced the construct into dispersed human donor islets using an adenovirus (Fig. 5.10A). We then FACS purified the cells based on GFP fluorescence (Fig. 5.10B). We found that there was a bimodal distribution in GFP positive cells, though not as prominent as was found in mouse primary β cells and SCβ cells (Fig. 5.10B). We also noticed, similar to our findings in SCβ cells, that cells with higher INS gene activity were also larger than other cells (Fig. 5.10C). This data confirmed the bimodality of INS gene activity in human insulin producing cells. 134 5.3 Discussion This study characterized INS gene activity in stem cell-derived insulin producing cells. Using FACS and 3D live cell imaging, we found that INS gene activity was bimodal at the endogenous gene loci, with cells possessing either higher or lower INS gene activity. Our study provided a comprehensive examination of the INS(EGFP)HIGH and INS(EGFP)LOW activity states at the transcriptomic, proteomic, and functional level. Early studies on β cell heterogeneity focused on morphological differences, and it was shown that certain cells which produce more insulin also tend to be larger (164,166). It has also been shown that these cells have enlarged ER and Golgi apparatuses (166). Our proteomics data revealed that the INS(EGFP)HIGH expression state was enriched with ER protein folding factors, vesicle trafficking factors, and Golgi organization factors, suggesting an upregulation in protein synthesis and folding capacity. Cells in the INS(EGFP)HIGH activity state are also larger than INS(EGFP)LOW cells. It is possible that our development of SCβ cells have progressed to the point that we’ve recapitulated a subpopulation of β cells similar to those identified in early morphological heterogeneity studies. The OPP protein synthesis assay revealed that the INS(EGFP)HIGH activity state has considerably higher translational capacity than the INS(EGFP)LOW activity state. Previously, we’ve also performed an OPP assay on dispersed primary mouse β cells from Ins2GFP knock in mice (Figure 4.5). In that study, we found that while there was a significant upregulation in translational capacity in β cells with high Ins2 gene activity, the difference was not comparable to the near 2-fold difference we observed in SCβ cells (Chapter 4). Many studies have suggested that cells with lower levels of insulin production or reduced maturity factors may serve different roles in the islet, such as coordination or management of stress (175,176,273,306). The reduced 135 protein production in the INS(EGFP)LOW expression state may suggest that these cells currently do not yet possess some of the functions that other low insulin producing cell populations have. The relationship between stress and insulin production has been extensively studied, with one previous study showing how reducing insulin production directly reduces ER stress markers (111,215). Our current study supports this hypothesis, with the INS(EGFP)HIGH cell state possessing a high number of ER protein folding markers and being considerably more vulnerable to stress of all forms. Interestingly, we did not see enrichment in the INS(EGFP)HIGH cell state for the three main UPR pathways (ATF6 ,EIF2AK3, ERN1), though ERN1 was not detected in this study (Fig. 5.11). This may suggest that while the INS(EGFP)HIGH cells had a large number of markers that are traditionally associated with ER stress, the majority of cells have yet to be pushed into the UPR pathway for stress management. Another study used pseudotime analyses on human β cell scRNAseq data and suggested that β cells may cycle in and out of high and low INS expression states as a form of stress management (175). Our study elucidated this hypothesis, and showed that it was possible for mouse primary β cells to transition between high and low Ins2 activity states (306). Our transcriptomic studies also showed increased ER stress markers in primary mouse β cells with lower Ins2 gene activity (306). The stability of the INS(EGFP)HIGH and INS(EGFP)LOW activity states were investigated in the current study, and it was found that in dispersed cells, while it was more possible to induce transitions from INS(EGFP)LOW to INS(EGFP)HIGH with the addition of glucose, we did not find many cells transitioning from INS(EGFP)HIGH to INS(EGFP)LOW. This may suggest that the SCβ cells may not be able utilize cell state transitions to manage stress from a result of high insulin production. However, we did find that there were cells transitioning from INS(EGFP)HIGH to INS(EGFP)LOW in FACS purified and reaggregated SCβ cell spheroids. This 136 may indicate that utilization of cell state transitions to manage stress is still an option, but require more help from other SCβ cells. Overall, our study provides a comprehensive comparison of INS gene expression states at the transcriptomic, proteomic, and functional level. Our FACS and 3D imaging experiments identify the INS(EGFP)HIGH and INS(EGFP)LOW activity states in both dispersed and whole SCβ cell spheroids. Mass spec proteomics reveal strong differences in protein production machinery between the two cell states, confirmed by a roughly 2-fold difference in translational capacity with our OPP translational assay. Our survival screen further reveals differences in resilience between the two cell states, and shows how dynamics in INS gene activity exist in SCβ cells. Our study shows how the field of SCβ cell development has progressed and recapitulated certain aspects of primary β cells, but also highlight some of the weaknesses and hurdles these cells still need to overcome. AB2A eGFP Non-CodingINS Exon 3Inserted sequenceINSB SC-β cellsCountsSSC250K200K100K50K0150K02004006000 103 104 0 103 104 0 103 104H1 WT INS-EGFP Merged60% 19% 21%EGFP EGFP EGFPCHoechst EGFP merged INS(EGFP)HIGHINS(EGFP)LOWFigure 5.1. Heterogeneity of INS gene activity in stem cell derived insulin producing cells.(A) Schematic of the INS-EGFP stem cell line. (B) Identification of a bimodal distribution in INS gene activity byFACS. (C) Example images of 3D live cell imaging of intact SCβ cell spheroids. Scale bar is 30 um. (D) Densityplot of cells from intact INS-EGFP spheroids. Each distribution represents a spheroid. (n=10 spheroids). (E)Nearest neighbor analysis of cells from Ins2GFP knock-in mice (n=40 islets). One-way ANOVA. (F) Distance tocore of cells from Ins2GFP knock-in mice. Student’s T-test. Density: probability density function, represents theprobability a data is located within the defined range (i.e. ratio of the total). * p < 0.05INS(EGFP)HIGH - INS(EGFP)HIGHINS(EGFP)LOW - INS(EGFP)LOWINS(EGFP)HIGH - INS(EGFP)LOWD01020304050Average distance tonearest neighbors (μm)*050100150Average distance of individualbeta cell to core (μm)*E FINS(EGFP)HIGHINS(EGFP)LOWINS(EGFP)HIGHINS(EGFP)LOW137ANegative correlationPositive correlationINS(EGFP)HIGH INS(EGFP)LOWDB 20-2-4UMAP 2UMAP 1-4 0 443210EGFP6420mRNA ExpressionINSCFEINSTTRHSPA5HSP90B1CHGAG6PC2PCDH7EGFPEGFPGLIS3CACNA1BGRIA2GRIA1NCAM1INS(EGFP)HIGHINS(EGFP)LOW139 Figure 5.2. Single cell RNA sequencing analysis of the two INS gene activity states. (A) Expression of EGFP and INS. (B) UMAP of SCβ cells split into INS(EGFP)HIGH and INS(EGFP)LOW populations. (C) Differential gene analysis of INS(EGFP)HIGH and INS(EGFP)LOW activity states. (D) Gene set enrichment analyses of enriched pathways in INS(EGFP)HIGH cells. (E) Hierarchical clustering of genes positively correlated with EGFP. (F) Hierarchical clustering of genes negatively correlated with EGFP. NDAEGFPBC D EGFPE mRNA processingmRNA splicing, via spliceosomeDNA conformation changehistone modificationdouble-strand break repairInflammatory responseregulation of gene silencingG protein-coupled receptor signallingregulation of blood circulationpotassium ion transportresponse to endoplasmic reticulum stressER to Golgi vesicle-mediated transportorganic acid catabolic processvesicle coatingGolgi organizationvesicle budding from the membraneglycosylationERAD pathwaycellular respiration6 84 6 842-log10(FDR)10-1-2NES 0.20.30.40.50.62Gene ratio020406080100InsulinINS(EGFP)HIGHINS(EGFP)LOWNegative EGFPPC1: 28% variancePC2: 14% varianceHigh GFPLow GFPNegative GFP**fatty acid catabolic process-log10(FDR)Log2(EGFP positive/EGFP negative) Log2(INS(EGFP)HIGH/INS(EGFP)LOW)050100150EGFP2140141 Figure 5.3. Proteomic analysis of FACS purified INS-EGFP SCβ cells. (A) Principle component analysis of FACS purified INS-EGFP SCβ cells (n = 6 differentiations). (B) Insulin and EGFP protein expression of the three FACS purified cell populations (n = 6 differentiations). (C) Volcano plot of differentially expressed proteins comparing FACS purified EGFP positive and EGFP negative cells (n = 6 differentiations). (D) Volcano plot of differentially expressed proteins comparing FACS purified INS(EGFP)HIGH and INS(EGFP)LOW cells (n = 6 differentiations). (E) Gene set enrichment analysis of pathways enriched in INS(EGFP)HIGH and INS(EGFP)LOW cells. ABGPCRNode color: Log2 (INS(EGFP)HIGH/INS(EGFP)LOW)Node border: -Log10 (FDR)-2.3 -1.2 0.0 1.2 2.31.0 2.8 4.5 6.3 8.0142glycolysistyrosine receptorproteosomelysosomeendosomemitochondriaribosomenucleushistonesvesicleGolgiendoplasmic reticulumperoxisomeGPCRglycolysistyrosine receptorproteosomelysosomeendosomemitochondriaribosomenucleushistonesvesicleGolgiendoplasmic reticulumperoxisome142143 Figure 5.4. Integration of STRING networks and protein expression data of the two INS gene activity states in Cytoscape. (A) Integration of STRING networks and protein expression data of differentially abundant proteins (FDR lower than 0.05) in INS(EGFP)LOW cells in Cytoscape. Deeper color of the nodes represents the fold change (INS(EGFP)LOW / INS(EGFP)HIGH). Thickness of the border around the node represents FDR. (B) Integration of STRING networks and protein expression data of differentially abundant proteins (FDR lower than 0.05) in INS(EGFP)HIGH cells in Cytoscape. Deeper color of the nodes represents the fold change (INS(EGFP)HIGH / INS(EGFP)LOW). Thickness of the line around the node represents FDR. 250K150K100K50K050K200K100K 150K 200K 250K0SSCFSCINS(EGFP)HIGHINS(EGFP)LOW010080402060Count0100 80 40 2060CountINS(EGFP)HIGHINS(EGFP)LOWFigure 5.5. FACS plots of purified populations of INS(EGFP)HIGH and INS(EGFP)LOW cells. (A) FSC and SSC FACS plots, as well as FSC and SSC histograms of sorted cells.144INS(EGFP)HIGHINS(EGFP)LOWC5 10 20 5 10 201 11*****BGlucose (mM)Thapsigargin (µM)5 10 20 5 10 20******A NDT2DEGFPNuclearMask OPP MergeITS supplement5 10 20 5 10 20Glucose (mM)Thapsigargin (µM)5 10 20 5 10 201 111 111 11ITS supplement + + + + + ++ + + + + +Figure 5.6. OPP protein synthesis assay of INS(EGFP)HIGH and INS(EGFP)LOW cells.(A) Representative images of OPP protein synthesis assay on dispersed INS-EGFP SCβ cells. The blue wavelengthis the Nuclearmask cell nuclei stain, the green wavelength is EGFP, and the red wavelength is the OPP marker fornascent peptide detection. ITS supplement includes insulin at 0.17 μM, transferrin at 6 nM, selenium at 3 nM, andethanolamine at 3 μM. Scale bar is 40 μm. (B) Comparison of translational capacity of INS(EGFP)HIGH andINS(EGFP)LOW activity states across various conditions. Two-way ANOVA. (C) EGFP fluorescence inINS(EGFP)HIGH and INS(EGFP)LOW activity states across various conditions.050100Protein translation(OPP fluorescence)0246810EGFP fluorescence145AA100INS(EGFP)LOW(5 mM glucose) INS(EGFP)LOW(20 mM glucose)INS(EGFP)HIGH(5 mM glucose) INS(EGFP )HIGH(20 mM glucose)INS(EGFP)LOW to INS(EGFP)HIGHINS(EGFP)HIGH to INS(EGFP)LOWITS+ITS-96 hrsC48 hrs20105Glucose (mM)20105Glucose (mM)Relative fluorescence intensityBFigure 5.7. Investigating dynamics and stability in dispersed INS(EGFP)HIGH and INS(EGFP)LOW cells .(A) Distribution of EGFP fluorescence across all cells at the first timepoint during imaging. (B) Cell traces ofEGFP fluorescence, split into INS(EGFP)HIGH and INS(EGFP)LOW at 2 hours and tracked for 96 hours. Each tracerepresents EGFP over time of one cell. (C) Quantification of cell state transitions with and without ITS, in variousglucose media concentrations at various timepoints.INS(EGFP)LOW to INS(EGFP)HIGHINS(EGFP)HIGH to INS(EGFP)LOW72 hrs146High GFP reaggregates Low GFP reaggregatesAB103 104EGFPCounts02003001000-103103105104RFP105102101103 104EGFP105102101103 104EGFP105102101INS(EGFP)HIGHINS(EGFP)LOWFigure 5.8. Reaggregation of FACS purified SCβ cells.(A) Representative images of reaggregates made from FACS purified INS(EGFP)HIGH and INS(EGFP)LOW cells.Note the considerably larger size of the INS(EGFP)HIGH reaggregates. (B) FACS plots of EGFP fluorescence inINS(EGFP)HIGH and INS(EGFP)LOW reaggregates.147Figure 5.9. Live cell imaging experiments investigating differential cell death in INS(EGFP)HIGH andINS(EGFP)LOW cells .(A) Live cell imaging based survival screen. Results shown are after 48 hours of treatment with 48 differentconditions, including various cytokine, glucose, and thapsigargin concentrations, and with and without ITS.GlucoseThapsigargin (uM)Cytokine cocktail5 5 5 5 10 10 1010 20 202020 5 5 5 5 10 10 1010 20 202020 50.1 1 10- 0.1 1 10- 0.1 1 10- - - - - - - - - - - - - -- 1x 2.5x 5x- 1x 2.5x 5x - 1x 2.5x 5x- - - - - - - - - - - - -ITS+ITS-0.00.10.20.30.40.5% PI positive cells0.00.10.20.30.40.5% PI positive cellsGlucoseThapsigargin (uM)Cytokine cocktail5 5 5 5 10 10 1010 20 202020 5 5 5 5 10 10 1010 20 202020 50.1 1 10- 0.1 1 10- 0.1 1 10- - - - - - - - - - - - - -- 1x 2.5x 5x- 1x 2.5x 5x - 1x 2.5x 5x- - - - - - - - - - - - -A148CB100 101 102 103 104 105010080402060100 101 102 103 104 105 100 101 102 103 104 10510010110210410510390% 4.5% 5.5%GFP710 emissionCount010080402060Count02040100 80 60Count0100K80K40K20K60K100K80K40K0 20K 60KFSCSSCAFigure 5.10. FACS purification of INS gene expression states in human primary islets using a RIP-GFP adenovirus.(A) Representative images of cells transduced with an RIP-GFP adenovirus. (B) FACS plots of cellssorted into high and low INS activity states. (C) Comparison of cell size high and low INS activity states.149AT2DA02004006008001000EIF2AK30500100015002000ATF6INS(EGFP)HIGHINS(EGFP)LOWFigure 5.11. Protein abundance of major UPR markers in INS(EGFP)HIGH and INS(EGFP)LOW cells. (A) Bar plots of protein abundance. Error bars are SEM.150151 Chapter 6: A prioritized medium-throughput screen on human stem cell-derived insulin secreting beta cells identifies FGF5, FGF8F, FGF6, FGF4, and FGF19 as protective factors 6.1 Introduction Type 1 diabetes is the result of autoimmune destruction of pancreatic β cells, which are responsible for maintaining glucose homeostasis via production of the hormone insulin (15,28,232). To address the loss of β cells, cell replacement therapies have been proposed. Stem cell-derived β cells (SCβ cells) have emerged as a promising source of cells due to their potential to replicate indefinitely (195). However, several issues need to be addressed before SCβ cells are ready for broad clinical use. For example, current SCβ cells do not exactly match the insulin production and secretion patterns of primary human β cells (195,197,351,352). The resilience of SCβ cells during the processes of differentiation, transplantation, engraftment and function in autoimmune and alloimmune environments also remains a challenge (370). The production of insulin itself is a stress on mature  cells (215) and presumably also plays a role in SCβ cell fragility. These stresses, if unmanaged, could result in the death and dysfunction of SCβ cells (303,371). While studies have been done to understand the effects of various sources of stress in SCβ cells, most studies were done in single time points or single doses, which lack temporal information and do not provide info on dose effects (372-374). Therefore, it is important to comprehensively examine how SCβ cells respond to stress in a time and dose dependent manner, and to find ways to alleviate these stresses to prevent excessive SCβ cell loss and to reduce the number of cells needed per transplant. 152 In the present study, we generated a prioritized list of ligand-receptor pairs from bulk RNAseq, single cell RNAseq (scRNAseq), and proteomics datasets. We utilized this prioritized ligand list to screen for regulators of SCβ cell survival and insulin production in the context of cytokine-induced cell death, and revealed fibroblast growth factors (FGFs) as novel factors which can used to improve SCβ cell survival and function. 6.2 Results 6.2.1 Live imaging of SCβ cell response to various stressors We have previously used long-term, live cell, multi-parameter, robotic imaging as a tool for tracking cell death over time (375,376). To find the optimal conditions to conduct our screen, and to understand how SCβ cells respond to stress, we performed an initial stress assay in which we exposed SCβ cells to various culture conditions. These conditions include 1) three doses of glucose (5, 10, 20 mM) to simulate hyperglycemia; 2) three doses of thapsigargin (Tg) to induce ER stress (0.1, 1, 10 M); and 3) three doses of a cytokine cocktail comprising of tumour necrosis factor α (TNF-α), interleukin 1 β (IL-1β), and interferon γ (IFN-γ) at (TNF-α 1.6 nM, IL-1β 0.7 nM, IFN-γ 0.7 nM at 1x, 2.5x, 5x, 10x concentrations) to mimic immune attack; and 4) with and without the commercial insulin-transferrin-selenium (ITS) supplement. ITS is a key reagent used for SCβ cell culture and contains high concentrations of insulin, which is known to have protective effects on human  cells (243). We differentiated and cultured INS-EGFP stem cell-derived cells to day 35, dispersed the spheroids into single cells, treated them with a total of 48 conditions, stained them with Hoechst cell nuclei marker and propidium iodide (PI) cell death marker, and performed live cell imaging (Fig. 6.1A). We also tracked EGFP as a readout for INS gene activity over 96 hours of imaging (Fig. 6.1B). For Tg treatments, we found that Tg induced 153 cell death in a dose-dependent manner, and that cell death was mildly reduced in ITS positive conditions (Fig. 6.1C, D). For the cytokine cocktail, we also found that cell death was dose dependent, and was significantly higher than the Tg treatments (Fig. 6.1E, F). Similarly, cell death was reduced with the addition of ITS, but not to a high extent in higher concentrations of cytokine cocktail (Fig. 6.1E, F). We also noted that cell death seemed to have two major temporal phases, with one occurring shortly after start of experiment, and another occurring roughly 48 hours after treatment. We also investigated INS gene activity in response to various stressors. We tracked and recorded EGFP fluorescence as previously described (306). We found that in most conditions, the application of either Tg or cytokines reduced INS gene activity (Fig. 6.2A-D). Instead, we found that INS gene activity was primarily controlled by glucose concentrations, with 20 mM glucose concentrations increasing EGFP fluorescence at twice the rate of 5 mM glucose (Fig. 6.2E). Together our data show that SCβ cell death can be induced dose-dependently by stressors, and that SCβ cell INS gene activity could be manipulated by high glucose concentrations. 6.2.2 A high-throughput screen of 148 ligands for mediators of cell survival We chose a condition that induced reliable but not excessive cell death (10 mM glucose, ITS negative, and TNF-α 1.6 nM, IL-1β 0.7 nM, IFN-γ 0.7 nM) for the screen of prioritized ligands. As ITS is both a standard reagent in late stage SCβ cell culture and has been shown to be protective, we based our choice on the ‘rescue’ effects of ITS by comparing ITS positive conditions against their ITS negative counterparts, as well as balancing the amount of insulin that the cells might be producing to prevent instability in the culture and compounding protective effects. Our rationale behind this choice was 1) the cytokine conditions induced a consistently 154 high level of cell death; 2) cytokine 1x conditions were the only cytokine conditions which displayed a clear response to the addition of ITS, suggesting that this condition is ‘rescuable’; 3) out of the three glucose conditions, though the 20 mM glucose has the highest "rescue" effect, 20 mM glucose may be a bit too stimulatory, as shown with our EGFP fluorescence data; 4) 5 mM glucose with 1x cytokine had lower rescue effects than 10 mM (Fig. 6.1A-E, Fig. 6.2A-E). Overall, we reasoned that Cyto1x, 10 mM glucose without ITS was the best choice, being more stable than 5 mM glucose and less stimulatory than 20 mM glucose. Similar to our stress condition pre-screen, we conducted 96-hour live cell imaging experiments on day 35 dispersed SCβ cells; we used EGFP as a readout for INS gene activity and PI as a cell death marker. Importantly, these experiments were repeated 5 independent times, providing robust results. After 72 hours, a number of fibroblast growth factors (FGF5, FGF8F, FGF6, FGF4, FGF19) showed significant protective effects (Fig. 6.3A). On the other end of the spectrum, we found that various immune factors, such as IFNW1, IFNA14, IFNA16, IFNA17, and IL28B increased cell death compared to our cytokine control (Fig. 6.3A). After 96 hours of imaging, we observed continued protective effects from various FGFs, with FGF5, FGF19, and FGF8F being the standouts and having a final cell death percentage comparable to our untreated control (Fig. 7B). In regards to ligands which increased cell death compared to our cytokine control, we found that a large number of ligands with high cell death (over 80%) at 96 hours formed a cluster with relatively little difference between each other (Fig. 6.3B). This data shows that members of the FGF family of ligands had a strong protective effect against immune induced cell death, while the addition of interferons may exacerbate the cytotoxic effects of the cytokine cocktail. 155 We next investigated INS gene activity. We only included data which showed the cells were alive throughout the study. The peak of EGFP fluorescence was between the 48 and 72 hours (Fig. 6.2A, B), so we focused on these two timepoints (Fig. 6.4A, B) As expected, our untreated control and untreated IBMX positive control had the highest levels of EGFP fluorescence (Fig. 6.4A, B). Compared to the cytokine cocktail control, the most significant upregulators of INS gene activity were IFNW1 and WNT16, while the most significant downregulators are IL6, OSM, and VEGFD. However, it should be noted that IFNW1 had increased cell death compared to our cytokine control, and the upregulation of EGFP may be a result of positive selection. Despite showing strong protective effects against the cytokine cocktail, the members of the FGF family did not reverse the detrimental effects on INS gene activity, showing EGFP fluorescence similar to the cytokine control (Fig. 6.4A, B). Overall, these data suggested that members of the FGF family protect SCβ cells against cytokine-induced apoptosis independently of effects on insulin production. Finally, we reanalyzed our cell death analysis in the context of the INS(EGFP)HIGH and INS(EGFP)LOW activity states that were described in Chapter 5. We found that across all conditions, INS(EGFP)HIGH cells were more fragile compared to INS(EGFP)LOW cells (Fig. 6.5A). Interestingly, we found that when comparing FGF5, FGF8F, and FGF19 to our untreated controls, that while INS(EGFP)HIGH cell death was comparable, there was a strong difference in INS(EGFP)LOW cell death (Fig. 6.5A). This may suggest that the FGFs are preferentially protective towards cells with lower levels of insulin production. 156 6.3 Discussion In this study, we aimed to systematically map intercellular signaling in human islets and SC-islets, build prioritized lists of ligands and receptors in SC-islets, and identify factors which mediate SCβ cell survival. Utilizing this list, we identified multiple members of the FGF family which exert strong protective effects against immune induced SCβ cell death. The effects of stress on primary β cells is well documented (140,142,145,375), but less was known about the effects of ER stress and cytokine-induced stress on stem cell-derived insulin producing cells (372-374,377). Here, we provide a comprehensive study with multiple doses of stress inducing conditions on SCβ cells with temporal information. We showed that SCβ cell death in response to Tg and cytokine cocktails are dose-dependent. Prior studies have shown that treatment with cytokines on INS-1E and human islet cells induces differential gene expression after 24 hours and 48 hours of treatment respectively (378). Interestingly, we found that at the 24-hour time-point, a major ramp up in cell death is observed which is reduced after 48 hours, which may coincide with the differential gene expression observed in the aforementioned study (378). INS gene activity was reduced by stresses and influenced by glucose levels. We have previously shown how endogenous Ins2 gene activity can be influenced by glucose and serum levels (306). While the GSIS capacity of SCβ cells is not yet comparable with those of primary cells, it seems that activity at the endogenous INS gene loci in SCβ cells can respond well to glucose concentrations. Using our prioritized list of ligands, we identified several members of the FGF family of ligands as being protective against immune induced apoptosis. Effects of FGFs on β cells have been documented. FGF1 has been shown to increase proliferation and stimulate insulin secretion in INS-1E cells and isolated rat pancreatic islets (379). Likewise, FGF21 has been shown to 157 improve INS-1E and rat islet function and survival via signalling of the extracellular signal–regulated kinase 1/2(ERK 1/2) and Akt pathways (380). While not directly tested on insulin producing cells, a study identified a positive relationship between FGF19 serum levels and β cell function in humans (381). Our study is the first to show that some of the effects in FGFs can extend to SCβ cells, and we also identify novel members of the FGF which may modulate SCβ cell survival. Interestingly, of our four FGF8 isoforms (FGF8A, FGF8B, FGF8E, FGF8F) only FGF8E and FGF8F had protective effects. Due to FGF8E and FGF8F isoforms having only recently appeared in placental mammals, it has been suggested that they may possess distinct functions compared to FGF8A and FGF8B (382). The primary difference between the earlier and later isofroms is the 1C exon, a region where mutations have been known to result in idiopathic hypogonadotropic hypogonadism, a disease associated with deficient gonadotropin releasing hormone (GnRH) (383). More studies are needed to investigate the role of these FGF8 isoforms, possibly with in silico studies using AlphaFold to identify key binding regions in the 1C exon (384). We found that FGFs are preferentially protective towards cells in the INS(EGFP)LOW state. Studies have shown that  cells which are exposed to stress sometimes de-differentiate, reducing expression of major factors that include known  cell maturity and function markers (312,385). FGFs are used to induce proliferation in early stages of SC cell differentiation, and it has been previously suggested that dedifferentiated cells are more proliferative (215,224). These results support the idea that INS(EGFP)LOW cells are more resilient, and may have potential to be used for transplantation followed by in vivo maturation as opposed to using the more fragile mature cells. 158 In summary, we integrated multiple online resources and databases and investigated the cell-cell interactions in primary human islets and SC islets. We identified and ranked the top interactions in human primary islets and compared these interactions to those in SC islets. We then generated a prioritized list of ligands based on the discrepancies between human islets and SC islets, and conducted a targeted imaging-based screen to identify factors that can protect SC cells from stresses associated with type 1 diabetes. Through replication, we identified multiple members of the FGF ligand family that exhibited reproducible protection in the context of cytokine-induced apoptosis. These results reveal possible pathways to target in SCβ cells to mediate the stress of immune induced death in cell replacement therapy. This work is an important step towards understanding the differences between human β cells and stem cell-derived insulin producing cells, and towards a cell therapy for diabetes. A10 uM Tg1 uM Tg0.1 uM Tg0.02% DMSO10x CC5x CC2.5x CC1x CCControl105 20ITS- ITS+105 20Glucose (mM)%Cell death (GFP+ cells)-log10(adj. p-value)01234CD F105 20ITS- ITS+105 2010 mM glucoseE0 hour96 hourB96 hours after treatmentHoechst EGFP PI10 uM ThapsigarginDMSO Control 2.5x CytokinesCytokine cocktail (CC):TNF-α 1.4 nM, IL-1β: 0.6 nM, IFN-γ: 0.6 nMCC 1xCC 2.5xCC 5xCC 10xUntreated controlCC 1xCC 2.5xCC 5xCC 10xUntreated controlITS+ITS-Tg 0.1 uMTg 1 uMTg 10 uMTg 0.1 uMTg 1 uMDMSO 0.02%Tg 10 uMDMSO 0.02%ITS+ITS-Cytokine cocktail (CC):TNF-α 1.4 nMIL-1β: 0.6 nMIFN-γ: 0.6 nM160 Figure 6.1. Stress assay on SCβ cells using live cell imaging. (A) Schematic depicting the INS-EGFP stem cell line. (B) Example images of the assay at 0 hours and 96 hours of imaging. Blue wavelength is Hoechst cell nuclei dye, red is the cell death marker propidium iodide, green is EGFP. Scale bar is 40 um. (C) Survival curves depicting dose dependent cell death induced by various concentrations of thapsigargin at 10 mM glucose. Error bars are SEM. (D) Dot plot of EGFP positive cell death thapsigargin conditions after 96 hours of imaging. Size of dots denotes –log10 (adj. p-value) compared to control. Color of the dots represent percentage of PI positive cells. (E) Survival curves depicting dose dependent cell death induced by various concentrations of cytokine cocktail at 10 mM glucose. Error bars are SEM. (F) Dot plot of EGFP positive cell death in cytokine cocktail conditions after 96 hours of imaging. Size of dots denotes –log10 (adj. p-value) compared to control. Color of the dots represent percentage of PI positive cells. Two-way ANOVA. CA0.02% DMSO0.1 μM Tg1.0 μM Tg10 μM Tguntreated controlCC 1xCC 2.5xCC 5x5 10 20 5 10 20ITS- ITS+Glucose (mM)10-1-225 mM glucoseBTg 0.1 uMTg 1 uMTg 10 uMTg 0.1 uMTg 1 uMDMSO 0.02%Tg 10 uMDMSO 0.02%ITS+ITS-Cytokine cocktail (CC):TNF-α 1.4 nM, IL-1β: 0.6 nM, IFN-γ: 0.6 nMCC 1xCC 2.5xCC 5xCC 10xUntreated controlCC 1xCC 2.5xCC 5xCC 10xUntreated controlITS+ITS-Cytokine cocktail (CC):TNF-α 1.4 nM, IL-1β: 0.6 nM, IFN-γ: 0.6 nMc20 mM glucoseFigure 6.2. INS gene activity in the context of various stress conditions.(A) Line graphs of EGFP fluorescence over time in 5 mM glucose conditions in various doses of thapsigargin andcytokine cocktail . (B) Line graphs of EGFP fluorescence over time in 20 mM glucose conditions in various dosesof thapsigargin and cytokine cocktail. (C) Heatmap of INS gene activity (AUC) over 96 hours of imaging across allconditions measured.B161FGF5FGF8FUntreated controlFGF19RANTESFGF21FGF22PTHBLCFGF23NODALSSTIL29ACTIVINCBMP5IFNA7WNT8AGDF3TNFAIFNA5GDF8TRANSFERRINWNT1LIFIL17BMCP3VEGFDPRLCCL14IFN16IFN17IFN14IFNW1Pro-apoptotic cytokine controlFGF5FGF8FUntreated controlFGF6RANTESFGF4FGF19FGF8EMIF FGF23UCN3FGF8AIL29BMP5MIGIFN16IFNW1IL11GDF8TRANSFERRINTGFB1IFNA8IFNB1MCP3VEGFDPRLOSMIFN14IFNA5IFNA17IFNA1072 hours after treatment96 hours after treatmentPro-apoptotic cytokine controlBA0 25 50 75 100Time (hours)% cell death (EGFP+)0755025100Untreated controlCytokine controlFGF19FGF5FGF8FFigure 6.3. Screening for effectors of SCβ cell survival against cytokine induced cell death with a prioritizedlist of 148 ligands.(A) Total cell death against EGFP positive cell death after 72 hours of treatment. (B) Total cell death against EGFPpositive cell death after 96 hours of treatment. Size of the dots denotes –log10 (adj. p-value) compared with pro-apoptotic cytokine control. Error bars are SEM. Base condition for all treatments except untreated controls were 10mM glucose, with 1x cytokine cocktail, without ITS. Untreated controls were 10 mM glucose, without ITS, withoutcytokine cocktail. n=5 differentiations. One-way ANOVA.% EGFP+ cell death% total cell death% total cell death-log10 (adj. p-value)0123-log10 (adj. p-value)0123FGF5FGF8FFGF19IFNB1IFNW1IFN14FGF6FGF4IFNA16GDF8IL28BINSVEGFDRANTESBLCACTHWNT16IFNA5IFNA10IFNA17MCP3GHFGF23MIFOSMFGF8EFGF8AUCN3IL10DHHFIL1DFGF19IFNB1IFNW1IFN14IFNA16GDF8IL28BBMP10VEGFDRANTESVIPR2ACTHIFNA5IL6IFNA8MCP2GHFGF23MIFOSMFGF8EFGF8A IL36B2LIFWNT16FGF6FGF4FGF5FGF8FWNT8AGDF3IFNA21SSTND T2D% EGFP+ cell deathPro-apoptotic cytokine cocktail48 hours after treatmentNo apoptotic cytokine cocktail, IBMXNo apoptotic cytokine cocktail, untreatedNo apoptotic cytokine cocktail, IBMXNo apoptotic cytokine cocktail, untreatedPro-apoptotic cytokine cocktail72 hours after treatmentRelative EGFP fluorescenceRelative EGFP fluorescenceABIL10IL6EPOFigure 6.4. Analysis of INS gene activity in the context of cytokine cocktail and ligands.(A) Average EGFP fluorescence against GFP positive cell death after 48 hours of treatment. (B) Average EGFPfluorescence against GFP positive cell death after 72 hours of treatment. Error bars are SEM. Deceased cells werefiltered using the red wavelength. n=5 differentiations. Size of dots denote adjusted p-value when comparingtreatments with pro-apoptotic cytokine control. One-way ANOVA.-log10 (adj. p-value)0123-log10 (adj. p-value)0123Figure 6.5. Analysis of cell death assay in the context of INS(EGFP)HIGH and INS(EGFP)LOW states.(A) Cell death at 96 hours in the context of INS(EGFP)HIGH and INS(EGFP)LOW cellular expression states. Dottedline represents x=y. Error bars are SEM. n=5 differentiations. Size of dots denotes adjusted p-value forINS(EGFP)HIGH cell death when comparing treatments to pro-apoptotic cytokine control. One-way ANOVA.Untreatedcontrol 0.00.40.81.2FGF8FFGF5 FGF19Pro-apoptotic cytokine control-log10 (adj. p-value)% INS(EGFP)HIGH cell death% INS(EGFP)LOWcell deathA164165 Chapter 7: Conclusion 7.1 Summary and overall discussion The overall aim of this thesis was to identify and characterize heterogeneity in insulin gene activity, as well as investigate the dynamics and mechanisms behind insulin gene activity state transitions. I utilized an Ins2GFP knock in mouse model which reported gene activity at the endogenous gene loci via GFP expression and showed that Ins2 gene activity had a biomodal distribution (Ins2(GFP)HIGH and Ins2(GFP)LOW). I further characterized the high and low Ins2 gene activity states using qPCR, scRNAseq, and FACS purified proteomics data. I found that the Ins2(GFP)HIGH expression state had a more mature β cell profile and were also more vulnerable to stress. Using live cell imaging, I found that it was possible for the cells to transition between the Ins2(GFP)HIGH and Ins2(GFP)LOW activity states, and that Ins2 cell state transitions could be modulated by cAMP dynamics. I then investigated INS gene activity in the context of stem cell-derived insulin producing cells with an INS-EGFP knock-in add on stem cell line. Similar to the findings in mice primary cells, I found that EGFP expression had a bimodal distribution (INS(EGFP)HIGH and INS(EGFP)LOW). Once again, I characterized the two activity states at the transcriptome and proteome levels with scRNAseq and FACS purified proteomics, respectively. I found that the INS(EGFP)HIGH state was associated with β cell maturity and protein folding and was also significantly more vulnerable to various stress conditions. I next investigated signaling pathways which may help modulate SCβ cell survival in the context of immune-induced stress using a prioritized list of ligands generated from multiple publicly available bulk RNAseq, scRNAseq, and proteomic datasets. Using live cell imaging, I found that members of the FGF family, in particular FGF5, FGF19, FGF8F, and FGF4 had strong protective effects against cytokine induced apoptosis. This study identified that insulin gene activity is bimodal, and is the 166 first to show cell state transitions occur in real time in both primary and stem cell-derived insulin producing cells. This study yielded insight into cell states at the transcriptome and proteome level, and identifies key signaling pathways which may be targeted in cell replacement therapy and cell regeneration therapy in the context of diabetes. Heterogeneity of β cells is a well-studied phenomenon, with many of these studies being centered around the β cell’s ability to produce and secrete insulin (159,181,273,304). Using single nuclei ATAC-seq, Chiou et al found a high insulin β cell subcluster which had enriched factors related to β cell maturity, and a low insulin β cell subcluster which had factors related to ER stress (268). Similarly, Xin et al also found that primary human islet  cells can be split into high INS and low INS subpopulations using scRNAseq (175). Farack et al found using mFISH that Ins2 mRNA in mice occurs across a wide spectrum, with some “extreme” β cells possessing twice the amount of Ins2 mRNA as compared to the average (238). However, ATAC-seq cannot directly infer gene activity, and insulin mRNA content is further regulated by mRNA degradation and stability and thus cannot be used to directly measure insulin production. This study is the first to utilize the endogenous insulin gene and report on insulin gene activity, and reveal a bimodal distribution in gene activity in both primary and stem cell-derived insulin producing cells. Many studies have identified various β cells subpopulations in islets, but it is unclear whether this heterogeneity is reflective of stable subpopulations or transient cell states. Xin et al utilized pseudotime analyses on human scRNAseq data to suggest that cells could transition between high INS, low UPR and low INS, high UPR expression states (175). Szabat et al showed using live cell imaging and a Pdx1/INS dual reporter lentivirus that MIN6 and human cells could transition from less mature to more mature states (172,239). Similarly, I utilized RNA velocity 167 on our scRNAseq datasets from Ins2GFP knock in mice to show that velocity vectors were directed from the Ins2(GFP)LOW state to the more mature Ins2(GFP)HIGH state. I also showed using live cell imaging cells transitioning between the high and low insulin gene activity states in both Ins2GFP knock in mouse primary β cells and INS-EGFP knock-in add-on SCβ cells. However, it should be noted that the INS-EGFP SCβ cells did not have as many high to low transitions as in the Ins2GFP cells, indicating that some of the dynamics in primary cells have yet to be fully recapitulated in SCβ cells. These studies confirm that at least part of the heterogeneity in insulin production that was reported can be accounted for by dynamics in insulin transcriptional activity. The relationship between stress and insulin production is an important field of study (141,142). ER stress as result of insulin production is believed to play a key role in β cell dysfunction and death in the context of diabetes (111,140,149,386). It has been shown that reduction of insulin production reduces UPR markers and may induce proliferation (215). Xin et al’s pseudotime analysis suggests that the transitions from a high INS, low UPR state to a low INS, high UPR state may be a form of rest for β cells (175). Supporting this hypothesis, the live cell imaging analyses reveal that primary β cells from Ins2GFP knock-in mice can indeed transition from the Ins2(GFP)HIGH to the Ins2(GFP)LOW state. I also found that the Ins2(GFP)LOW state had enrichment for ER stress markers at the mRNA and protein level, similar to the low INS, low UPR subpopulation in the Xin study. As mentioned, the analyses on dispersed INS-EGFP SCβ cells showed a relative lack of high to low transitions, though there were a fair percentage of cells transitioning from INS(EGFP)LOW to INS(EGFP)HIGH. The scRNAseq and FACS purified proteomics studies on the two INS-EGFP states also reveal that the INS(EGFP)HIGH state has enrichment for ER protein folding markers, in contrast to the mice data. 168 The lack of INS(EGFP)HIGH to INS(EGFP)LOW transitions may deprive the cells of much needed rest, resulting in the SCβ cells to be not as capable of resolving stress as mature primary β cells. cAMP is an important part of β cell signaling (133,134,347). The incretin GLP-1, a cAMP inducer, has been shown to impact β cells via PKA-dependent and PKA-independent mechanisms, effecting β cell function and survival (98,136,348). Both the human INS promoter and mouse Ins2 promoter sequences have multiple CREBs (CRE binding sites), hinting at a role in insulin promoter activity for cAMP (100,103). This is verified by experiments on INS-1 cell lines, in which insulin mRNA was increased in response to GLP-1 in a PKA-independent manner (387). Other than PKA, molecules such as calcineurin have been shown to be influenced by cAMP (388). Calcineurin has also been shown to regulate β cell survival (338). The calcineurin inhibitor tacrolimus has been shown to reduce insulin mRNA content in HIT and MIN6 cells (389). In this study, I provide evidence on how positive regulators of cAMP, such as GLP-1 and IBMX, can induce transitions from the Ins2(GFP)LOW state to the Ins2(GFP)HIGH state, while calcineurin inhibitors, such as okadaic acid and cyclosporine A induce transitions from the Ins2(GFP)HIGH state to Ins2(GFP)LOW. IBMX also had a strong effect on INS-EGFP SCβ cells, greatly increasing EGFP fluorescence compared to untreated controls. These results suggest that insulin gene activity state transitions and dynamics are at least partially governed by cAMP dynamics. In the process of identifying mature β cells, as well as the progression towards developing mature SCβ cells for transplant, numerous markers for β cell maturity have been identified (79,82,101,172,390). Some of these studies have found correlation between β cell maturity and fragility. For instance, several studies have shown that induction of stress may reduce β cell maturity markers, supporting the hypothesis that β cells may transition to a less mature state to 169 alleviate stress (312,391). Human donor islets from diabetic patients and diabetic mouse models also have reduced maturity markers (307,392). The characterization of the high and low insulin gene activity states at the transcriptome and proteome levels in mice primary cells and SCβ cells showed that cells with higher insulin gene activity also have a more mature β cell profile. The live cell imaging experiments revealed that these more mature cells are also considerably more vulnerable to thapsigargin induced and cytokine induced stress. Ins2(GFP)HIGH cells treated with cytotoxic drugs, such as cyclohexamide and alpha amanitin, had increased GFP fluorescence, while GFP fluorescence in Ins2(GFP)LOW cells were unchanged. Furthermore, the prioritized ligand screen showed that while FGF5, FGF19, and FGF8F were strongly protective against cytokine induced cell death, they did not rescue INS gene activity in surviving SCβ cells. These results may suggest that more mature β cells are considerably more sensitive and fragile to insults. This is further supported by the fact that the FGFs were preferentially protective of INS(EGFP)LOW cells compared with our untreated control. More studies are needed to understand the tradeoff between β cell maturity and fragility. 7.2 Limitations The studies on insulin gene activity were primarily done using knock-in mouse or cell lines with a GFP or EGFP reporter under control of the endogenous insulin gene. However, due to the relative stability of GFP and EGFP, changes in fluorescence, especially decreases in fluorescence, may not be detected until much later after an event has occurred. These issues should be addressed in future studies which may utilize either real-time reporters such as the MS2-MCP system or the addition of a degron tag to prevent fluorophore buildup (393,394). 170 This study utilized mass spec-based proteomics analyses to characterize the high and low insulin activity states at the protein level. To analyze these various subpopulations, I utilized FACS purification. However, such experiments do not provide the resolution or flexibility afforded by single cell studies. As mass spec omics improve in efficiency, future studies should look to perform more heterogeneity studies using single cell proteomics (356). The characterization studies identified a fragility in the high insulin activity cells in both primary mouse β cells and SCβ cells. However, it is unknown whether this fragility is attributed to the stress of high insulin production, or possibly a result of the toxicity of GFP or EGFP. I did not find upregulation of CPP32, a marker that has been associated with GFP induced cell death (395). Furthermore in mouse primary cells markers for stress were enriched in the Ins2(GFP)LOW state, suggesting that GFP might not be a prominent contributor to this fragility. However, due to the widespread use of GFP and EGFP as markers in the diabetes field, especially in SCβ cell differentiation where it is commonly used as a marker for insulin, future studies should look to investigate the impact of utilizing these or similar fluorescent probes as markers in islet cells. The studies on SCβ cells, including both the prioritized screen and the investigation on SCβ cell insulin activity states, were primarily done on day 30-35 cells. However, recent investigations have shown that cells that were cultured up to day 50 may have improved GSIS, suggesting a further level of maturity and function (225). Mar et al also showed using an INS/GCG dual reporter cell line that by day 50, many of the more polyhormonal cells have become monohormonal, suggesting a more stable cell fate by this point (396). Future studies may look to repeat some of our experiments in the context of these older cells. 171 7.3 Future directions For this study on insulin gene activity states, I acquired both transcriptomic, proteomic, and functional data. However, all of these studies were conducted on separate SCβ cell differentiations or biological samples. It would be interesting to investigate the high and low insulin activity states in the context of techniques which can link gene expression data to functional assays in the same cell, such as with patch-seq techniques (184). Furthermore, while I investigated the two cell states in various contexts, I did not perform studies which investigate the core function of β cells, which is glucose stimulated insulin secretion. I am currently in the process of FACS purifying cells into the two cell states in both mice and SCβ cells and performing static GSIS assays on them. While this study has revealed the bimodality of insulin gene activity in human SCβ cells and mouse primary cells, I have not yet been able to investigate insulin gene activity in the context of human primary β cells. I am currently in the process of generating an adenoviral construct with mScarlet3 under control of the human insulin promoter. Using this construct, I will be able to analyze INS gene activity in human donor islets, and perform downstream profiling studies such as proteomics and transcriptomics. However, utilizing an INS-mscarlet3 construct transgene system to report for insulin gene activity also comes with the variability of copy-number insertions. While we intend to control for this by measuring and normalizing the amount of viral proteins present in our results, we cannot fully exclude this caveat from our studies. Future experiments should look to incorporate a constitutive reporter which can report for the amount of viral particles introduced. It has also been found that β cells may still exist in people who were diagnosed with type 1 diabetes for decades (27). Our studies have found that β cells with lower insulin gene activity are 172 more resilient than those with higher insulin gene activity, and it may be these cells which have been identified in type 1 diabetes patients. If these cells can be properly identified in patients, it may be possible to revitalize these cells or enhance their insulin producing abilities through application of small molecules that were described in Chapter 4. The prioritized screen identified several members of the FGF family as having strong protective effects against cytokine induced cell death. Prior investigations on FGFs in human β cells have been focused on FGF1 and FGF21 (379,380). The screen did indeed identify FGF21 as a promising protective factor in SCβ cells, but it is unknown if the other FGF candidates (FGF8F, FGF19, FGF5, FGF4, FGF22) have similar effects in human primary β cells. It would be interesting to perform a screen with our list of prioritized ligands on cells from human donor islets. The process of transplantation and maintenance of SCβ cells in patients with diabetes is costly, and survival after several months post-transplant has been poor (198). The identification of FGF factors as protectors against cytokine induced stress in SCβ cells opens new options for alleviating cell death beyond immunosuppressants, which are known to have strong side effects (397). Future studies should look to harness these findings to assist with cell survival in SCβ cells during differentiation, transplantation, and maintenance down the road. Throughout my doctoral study, I investigated dynamics and cell states in the context of insulin gene activity. 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Thesis/Dissertation
eng
Cell and Developmental Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Johnson, James D.
Dynamic insulin gene activity states in insulin producing cells
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