Science, Faculty of
DSpace
UBCV
Kamal, Sepehr
Master of Science - MSc
University of British Columbia
Diabetes is caused by the dysfunction and/or destruction of insulin-producing pancreatic β-cells, and curative therapy will require their repair, regeneration, or replacement. Beyond insulin, islets have been reported to contain transcripts for hundreds of other ligands and receptors. Some of these intercellular signals may have therapeutic benefit in diabetes, or in efforts to generate insulin-producing cells from stem cells. Over 90% of the small molecules and proteins used to generate stem cell-derived β-like cells (SCβ-cells) are activators or inhibitors of receptors, and modulation of signaling pathways can alter SCβ-cell differentiation. In this study, we systematically mapped intercellular signaling in human islets and stem cell-derived cells, and built prioritized lists of protein ligands and receptors to guide future screening studies. First, we created a custom ligand-receptor database of 422 protein ligands, 349 receptors, and 1552 interactions. We then integrated multiple datasets to rank ligands and receptors in stem cell-derived cells based on gene expression, protein abundance, and differential expression and abundance compared to human islets. We combined the ligand and receptor ranks to rank each interacting ligand-receptor pair. Finally, we presented three prioritized lists to guide future screens: scarce ligands with abundant receptors in stem cell-derived cells, scarce receptors with abundant ligands in stem cell-derived cells, and abundant receptors with abundant ligands in stem cell-derived cells. Collectively, we expect these data will enable future studies to define the roles of ligands and receptors in diabetes and diabetes therapies.
PRIORITIZATION OF LIGANDS AND RECEPTORS IN HUMAN ISLETS AND STEM CELL-DERIVED CELLS by Sepehr Kamal B.Sc., The University of British Columbia, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Genome Science and Technology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2023 © Sepehr Kamal, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: PRIORITIZATION OF LIGANDS AND RECEPTORS IN HUMAN ISLETS AND STEM CELL-DERIVED CELLS submitted by Sepehr Kamal in partial fulfilment of the requirements for the degree of Master of Science in Genome Science and Technology Examining Committee: Dr. James D. Johnson, Professor, Cellular & Physiological Sciences, UBC Supervisor Dr. Francis C. Lynn, Associate Professor, Surgery, UBC Supervisory Committee Member Dr. Wyeth Wasserman, Professor, Medical Genetics, UBC Supervisory Committee Member iii Abstract Diabetes is caused by the dysfunction and/or destruction of insulin-producing pancreatic β-cells, and curative therapy will require their repair, regeneration, or replacement. Beyond insulin, islets have been reported to contain transcripts for hundreds of other ligands and receptors. Some of these intercellular signals may have therapeutic benefit in diabetes, or in efforts to generate insulin-producing cells from stem cells. Over 90% of the small molecules and proteins used to generate stem cell-derived β-like cells (SCβ-cells) are activators or inhibitors of receptors, and modulation of signaling pathways can alter SCβ-cell differentiation. In this study, we systematically mapped intercellular signaling in human islets and stem cell-derived cells, and built prioritized lists of protein ligands and receptors to guide future screening studies. First, we created a custom ligand-receptor database of 422 protein ligands, 349 receptors, and 1552 interactions. We then integrated multiple datasets to rank ligands and receptors in stem cell-derived cells based on gene expression, protein abundance, and differential expression and abundance compared to human islets. We combined the ligand and receptor ranks to rank each interacting ligand-receptor pair. Finally, we presented three prioritized lists to guide future screens: scarce ligands with abundant receptors in stem cell-derived cells, scarce receptors with abundant ligands in stem cell-derived cells, and abundant receptors with abundant ligands in stem cell-derived cells. Collectively, we expect these data will enable future studies to define the roles of ligands and receptors in diabetes and diabetes therapies. iv Lay Summary Diabetes occurs when insulin-producing cells in the pancreas, known as β-cells, are destroyed or stop functioning properly. A cure for diabetes will require these cells to be repaired or replaced. Beyond insulin, β-cells likely make hundreds of other proteins that can send signals between cells. In this study, we identified the top proteins by which β-cells communicate with other cells. We also identified the top proteins by which β-cells created in the lab from stem cells communicate with other cells. Some of proteins we identified may have therapeutic benefit in diabetes, or in efforts to generate insulin-producing cells from stem cells. v Preface Unless otherwise specified, I designed, performed, and analyzed all bioinformatics experiments presented in this thesis under the supervision of Dr. James Johnson. Dr. Wyeth Wasserman provided guidance for bioinformatics analyses. Several individuals provided processed data that I analyzed in this thesis. George Brownrigg provided the Lund 2014 RNA-seq data. Dr. Jelena Kolic and Grace Sun provided the proteomics data generated in collaboration with Dr. Leonard Foster’s lab at the University of British Columbia. Meltem Omur provided the scRNA-seq datasets generated by Samantha Yoon and Samantha Mar in Dr. Francis Lynn’s lab at the BC Children's Hospital Research Institute. Dr. Howard Cen provided the MacDonald-Gloyn 2023 human islet RNA-seq data, which was generated by Dr. Anna Gloyn at Stanford University and Dr. Patrick MacDonald at the University of Alberta. I obtained the rest of the data from publicly available sources. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ............................................................................................................................... iv Preface .............................................................................................................................................v Table of Contents ......................................................................................................................... vi List of Figures ............................................................................................................................... ix List of Symbols ...............................................................................................................................x List of Abbreviations ................................................................................................................... xi Acknowledgements .................................................................................................................... xiii Dedication ................................................................................................................................... xiv Chapter 1: Introduction ................................................................................................................1 1.1 Global burden of diabetes ............................................................................................... 1 1.2 Type 1 diabetes ............................................................................................................... 1 1.3 Pancreatic islets ............................................................................................................... 2 1.4 Insulin-based therapies for type 1 diabetes ..................................................................... 3 1.5 Cell therapies for type 1 diabetes .................................................................................... 4 1.5.1 β-cell replacement via islet transplantation ................................................................ 4 1.5.2 Stem cell-derived β-like cells...................................................................................... 5 1.6 Cell-cell communication ................................................................................................. 7 1.6.1 Overview of cell signaling .......................................................................................... 7 1.6.2 Ligands and receptors ................................................................................................. 7 1.6.3 The secretory pathway ................................................................................................ 8 1.6.4 Signal transduction and cellular responses ................................................................. 9 vii 1.6.5 Paracrine, autocrine, endocrine, and juxtacrine signaling .......................................... 9 1.7 Ligands and receptors in the human genome ................................................................ 12 1.8 Analysis of cell signaling at single-cell resolution ....................................................... 14 1.9 Intercellular signaling in human islets .......................................................................... 15 1.10 Intercellular signaling in SC-islets ................................................................................ 18 1.11 Thesis investigation ...................................................................................................... 18 Chapter 2: Materials and methods .............................................................................................20 2.1 Data analysis and statistics............................................................................................ 20 2.2 Custom ligand and receptor lists and interaction pairs ................................................. 22 2.3 Bulk gene RNA expression ........................................................................................... 22 2.4 Proteomics..................................................................................................................... 23 2.5 Correlation between gene expression & protein abundance ......................................... 24 2.6 Single-cell gene expression........................................................................................... 24 2.7 Intra-islet signaling analysis with CellChat .................................................................. 25 2.8 Ligand and receptor ranking in SC-islets...................................................................... 25 2.9 Interaction ranking and prioritization in SC-islets ........................................................ 26 Chapter 3: Results........................................................................................................................28 3.1 Creation of a custom ligand-receptor resource ............................................................. 28 3.2 Top ligands and receptors in human islets based on bulk RNA-seq............................. 33 3.3 Correlation of gene expression and protein abundance in human islets ....................... 35 3.4 Differential protein abundance between human islets and SC-islets ............................ 37 3.5 Differential gene expression between SCβ-cells and human β-cells ............................ 38 3.6 Top intra-islet signaling pathways in SC-islets and human islets................................. 41 viii 3.7 Ligand and receptor prioritization in SC-islets ............................................................. 44 Chapter 4: Discussion ..................................................................................................................50 4.1 Limitations .................................................................................................................... 53 4.2 Future Directions .......................................................................................................... 54 4.3 Overall conclusion ........................................................................................................ 56 Bibliography .................................................................................................................................57 ix List of Figures Figure 1.1: Cellular composition of the pancreas. .......................................................................... 3 Figure 1.2: Continuous glucose monitoring before and after islet transplant. ................................ 5 Figure 1.3: Four forms of intercellular signaling. ......................................................................... 11 Figure 1.4: OmniPath intercellular communication roles and contributing resources. ................ 13 Figure 1.5: Approach to analyze cell-cell communication at single-cell resolution. .................... 15 Figure 1.6: Overview of intra-islet signaling and relation to glucose concentration. ................... 16 Figure 1.7: Islets express many ligands and receptor genes. ........................................................ 18 Figure 2.1: Overview of study. ..................................................................................................... 21 Figure 3.1: Semi-manual curation of 422 ligands and 349 receptors. .......................................... 31 Figure 3.2: Custom ligand-receptor interaction resource with 1552 interactions. ........................ 33 Figure 3.3: Top ligands and receptors in human islets based on bulk RNA-seq. ......................... 35 Figure 3.4: Correlation between protein abundance and gene expression in human islets. ......... 37 Figure 3.5: Differential protein abundance between SC-islets and human islets. ........................ 38 Figure 3.6: Differential gene expression between SCβ-cells and human β-cells. ........................ 41 Figure 3.7: Intra-islet signaling pathways in SC-islets and human islets. .................................... 44 Figure 3.8: Ligand and receptor ranks in SC-islets based on 15 parameters. ............................... 47 Figure 3.9: Prioritized ligand-receptor interactions in SC-islets based on 3 criteria. ................... 49 x List of Symbols α Alpha β Beta δ Delta xi List of Abbreviations cAMP Cyclic adenosine monophosphate DAG Diglyceride DLRP Database of Ligand-Receptor Partners ECM Extracellular matrix ERK Extracellular signal-regulated kinases FACS Fluorescence-activated cell sorting GABA gamma-aminobutyric acid GCGR Glucagon Receptor GEO Gene expression omnibus GLP-1 Glucagon-like peptide-1 GTEx Genotype-tissue expression project HGNC HUGO Gene Nomenclature Committee HPMR Human Plasma Membrane Receptome IP3 Inositol trisphosphate IUPHAR International Union of Basic and Clinical Pharmacology MAPK Mitogen-activated protein kinase mRNA Messenger ribonucleic acid PPY Pancreatic Polypeptide PPYR1 Pancreatic Polypeptide Receptor 1 RNA Ribonucleic acid SC-islets Stem cell-derived islet-like clusters xii scRNA-seq Single-cell RNA sequencing SCβ-cells Stem cell-derived β-like cells STRING Search Tool for the Retrieval of Interacting Genes/Proteins TPM Transcripts per million UCN3 Urocortin 3 xiii Acknowledgements First and foremost, I would like to thank my supervisor, Dr. James Johnson, for his invaluable guidance and mentorship over many years. I am particularly grateful for his patience and understanding during my prolonged absence from my studies to focus on my health. He has been an inspiring role model for me in terms of leadership. I would also like to thank my committee members, Dr. Wyeth Wasserman and Dr. Francis Lynn, for sharing their expert advice and unique perspectives. I am grateful for their support throughout my studies. I am also deeply grateful to everyone in my lab and collaborating labs for their support, encouragement, and assistance throughout my graduate studies. This project was very collaborative, and I was fortunate to receive help from many talented individuals. A special thank you to Jamie Chu for many helpful discussions. Lastly, I thank my family, friends, and partner for their unwavering support and encouragement throughout my studies. Their love, encouragement, and understanding have made this achievement possible. xiv Dedication To Mom, Dad, and Kas 1 Chapter 1: Introduction 1.1 Global burden of diabetes Diabetes is a chronic metabolic disorder defined by elevated blood glucose levels [1, 2]. It affects 537 million people globally and is a leading cause of death and disability, with 6.7 million adults aged 20–79 estimated to have died in 2021 due to diabetes or associated complications [3]. The main long-term complications are cardiovascular disease, neuropathy, retinopathy, and nephropathy [4-6]. In addition to its health impacts, diabetes significantly impacts the economy and healthcare resources. Total diabetes-related global health expenditures for adults were $966 billion in 2021, a 316% increase compared to 15 years ago [3]. When also including the indirect costs from loss of productivity or earnings, the total global economic burden of diabetes is predicted to reach $2.1 trillion in 2030 [7]. 1.2 Type 1 diabetes Type 1 diabetes is one of the two main types of diabetes and accounts for between 5-10% of all diabetes cases [1]. The most common onset age is ten; however, onset can be at any age [8]. Type 1 diabetes is characterized by the autoimmune destruction of insulin-producing pancreatic β-cells. Without insulin, tissues such as skeletal muscle cannot uptake glucose from the blood, resulting in high blood glucose levels. It is believed type 1 diabetes is initiated by the presentation of β-cell peptides to autoreactive CD4+ T lymphocytes by antigen-presenting cells [4]. The most common autoantigens in type 1 diabetes are insulin (INS), glutamic acid decarboxylase (GAD65), insulinoma-associated protein 2 (IA-2), and zinc transporter 8 (ZnT8) [9, 10]. The exact mechanism precipitating type 1 diabetes is not fully understood, but a combination of genetic and environmental factors appears to be involved [11, 12]. The identical 2 twin concordance rate is 30–70% [13], and a child with a parent with type 1 diabetes has a 1-9% chance of developing the condition [14]. Proposed environmental triggers include viral infections early in life, decreased gut-microbiome diversity, and vitamin D deficiency; however, more research is needed to determine the connection between each trigger and type 1 diabetes [15]. 1.3 Pancreatic islets Pancreatic islets are clusters of endocrine cells responsible for glucose homeostasis. An adult human pancreas has approximately 3.6-14.8 million islets, each containing 1000-3000 cells [16, 17]. Even though pancreatic islets make up only 1-2% of the pancreas by volume, they receive 15% of its blood flow [18]. Adult human islets are approximately 50% β-cells, 40% α-cells, and 10% δ-cells (Figure 1.1) [19, 20]. Each of these cells produces and secretes one major hormone that has become integral to their identity: β-cells secrete insulin, α-cells secrete glucagon, δ-cells secrete somatostatin [17]. Insulin is the primary hormone that lowers blood glucose, while glucagon is counter-regulatory to insulin and acts to raise blood glucose. In addition to these cell types, islets contain trace numbers of immune, endothelial, and neural cells [21]. 3 Figure 1.1: Cellular composition of the pancreas. Pancreatic islets are small clusters of endocrine cells in the pancreas surrounded by exocrine tissue. The main cell types within islets are insulin-secreting β-cells, glucagon-secreting α-cells, somatostatin-secreting δ-cells, and pancreatic polypeptide-secreting PP-cells. Figure reprinted with permission from Ellis et al. 2017 [22]. 1.4 Insulin-based therapies for type 1 diabetes Insulin injections are the mainstay of treatment for type 1 diabetes [23]. Before the discovery of insulin by Canadian scientists Frederick Banting and Charles Best in 1922, children who developed type 1 diabetes typically only lived for one to two years after diagnosis [24]. The commercial availability of recombinant human insulin in 1982 improved the quality of insulin available to patients and reduced its supply and price volatility [25]. Type 1 diabetes is now a manageable condition requiring blood glucose measurements and insulin injections multiple times daily [26, 27]. It is not an overstatement to say insulin injections have saved millions of lives. 4 Researchers have developed newer products to improve insulin therapies. These products include continuous glucose monitors, long-lasting insulins, and insulin pumps [28, 29]. Notably, the first hybrid closed-loop automated insulin delivery device received FDA approval in 2016 for patients aged 14 and up [30]. These devices combine a continuous glucose monitor, user-inputted details about upcoming meals, and an insulin pump. They determine insulin needs throughout the day and deliver doses automatically. Insulin therapies, however, fall short of a cure. Even with intensive treatment, blood glucose levels can remain highly variable, meaning insulin therapy alone cannot entirely eliminate the risk of long-term complications [31, 32]. Insulin therapy also impairs patients’ quality of life, and insulin over–administration can lead to life-threatening acute hypoglycemia [31, 32]. 1.5 Cell therapies for type 1 diabetes 1.5.1 β-cell replacement via islet transplantation Given that only 1.3 grams of β-cells regulate blood glucose for the whole human body, β-cell replacement therapy presents an attractive alternative to insulin therapy for type 1 diabetes [16, 33]. This idea drove research during the 1990s at the University of Alberta, leading to the development of the Edmonton Protocol [34]. The Edmonton Protocol is a method for isolating islets from a cadaveric organ donor and transplanting them into a patient with type 1 diabetes. The protocol also includes immunosuppression therapy for the transplant recipient. Over 1500 patients have received islet transplants since 2000 [35]. Remarkably, islet transplantation yields improved glycemic control (Figure 1.2) and decreased long-term diabetes complications compared to intensive insulin-based therapy [36-38]. Approximately 50-70% of transplant 5 recipients are insulin-independent five years post-transplant [35, 39]. Overall, cadaveric islet transplants have established precedence for cell therapy for type 1 diabetes. Cadaveric islet transplants do, however, have significant limitations that prevent their widespread use. For example, the supply of cadaveric organ donors is very limited compared to the number of people with type 1 diabetes, and transplant recipients require lifelong immunosuppressive therapy to prevent transplant rejection [35, 40]. These limitations have motivated research into alternative sources of β-cells for transplantation. Figure 1.2: Continuous glucose monitoring before and after islet transplant. Continuous blood glucose monitoring of an individual with type 1 diabetes before and 9 months after islet transplantation. Each line color represents a different day. The gray shaded box represents the normal blood glucose range for people without diabetes (54-140 mg/dL or 3.0-7.8 mM). Figure adapted with permission from Latres et al. 2019 [38]. 1.5.2 Stem cell-derived β-like cells Stem cells have the potential to serve as an unlimited source of pancreatic β-cells for cell replacement therapy because of their ability to self-renew and differentiate into more specialized cell types [41]. However, generating a β-cell from a stem cell has proven challenging, and the in vitro development of a true β-cell from an embryonic stem cell remains elusive [42, 43]. In 2007, ViaCyte showed that stage 4 (S4) pancreatic endoderm cells derived from human embryonic 6 stem cells differentiate into glucose-responsive endocrine cells after implantation into mice [44]. In 2014, they launched a combined phase 1 and 2 clinical trial to evaluate the safety and efficacy of a subcutaneous implant device containing these S4 cells (clinicaltrials.gov: NCT02239354), however the trial was terminated due to insufficient functional product engraftment. In 2017, ViaCyte launched another clinical trial to test an alternative non-immunoprotective device for cell delivery tailored for patients at high risk of severe hypoglycemic episodes (clinicaltrials.gov: NCT03163511), and the trial is still underway [45, 46]. Long-term cell survival and efficacy are yet to be shown for S4 cells in the ViaCyte trials [47]. There are also challenges related to variable in vivo differentiation and inflammatory responses against macroencapsulation devices [45]. It remains unclear whether true β-cells are required to reverse diabetes in humans or whether these insulin–producing “β-like” cells are sufficient [48]. In 2014, two research groups independently published the first evidence of glucose–responsive insulin production in stem cell-derived β-like cells (SCβ-cells) developed using a seven-stage differentiation protocol [49, 50]. One of these groups, which included researchers from the University of British Columbia, showed that stage 7 (S7) SCβ-cells transplanted into streptozotocin-treated mice reversed diabetes more rapidly and with fewer cells than S4 cells. Several recent studies have demonstrated further advances toward generating mature glucose-responsive β-cells from stem cells using techniques such as purifying and reaggregating immature β-like cells [51, 52]. Critically, no studies have yet achieved glucose-stimulated insulin secretion that matches human islets [51, 53-57]. 7 1.6 Cell-cell communication 1.6.1 Overview of cell signaling Cells do not live in isolation. Signaling is the foundation by which cells communicate with each other, communicate with themselves, and respond to their environment [58-60]. Signals can be mechanical, such as temperature and voltage, or chemical, such as small molecules and proteins. In multicellular organisms, cells typically communicate with one another using chemical signals such as growth factors, hormones, neurotransmitters, and extracellular matrix proteins. The ability of cells to receive signals and respond to their environment is the basis of development, homeostasis, and immune systems, and dysregulation of cell signaling can lead to diseases such as cancer, diabetes, and neurodegenerative disorders [61-64]. 1.6.2 Ligands and receptors Ligands are signaling molecules produced by cells to send signals [65]. They bind to receptor proteins on a target cell and induce a conformational change in the receptors. Ligands are either secreted into the extracellular space or are membrane-bound. Most secreted ligands bind to receptors on the cell surface of a target cell, however, some small hydrophobic ligands, such as the steroid hormone estradiol, pass through the plasma membrane and bind to intracellular receptors in the target cell. The binding of a ligand to its receptor is highly specific and involves precise molecular interactions between the ligand and receptor. Some ligands can bind to multiple receptors, and some receptors can bind to multiple ligands. There are several classes of cell-surface receptors [65]. Ligand-gated ion channels are ion channels that open or close in response to a specific ligand. They comprise an extracellular domain that binds to a ligand and a transmembrane domain with an ion channel. G protein- 8 coupled receptors are the largest and most diverse family of receptors and are the targets of 34% of all FDA-approved drugs [66]. They have seven transmembrane domains and activate G proteins on the intracellular side of the membrane [67]. Enzyme-linked receptors have an intracellular enzymatic domain which is activated upon ligand binding. Receptor tyrosine kinases are a subtype of enzyme-linked receptors composed of an intracellular tyrosine kinase. Examples of receptor tyrosine kinases include the fibroblast growth factor receptor (FGFR) and vascular endothelial growth factor receptor (VEGFR) families [68]. 1.6.3 The secretory pathway Secreted proteins, such as secreted ligands, contain an N-terminus signal sequence that is recognized by a signal recognition particle during translation [69]. The signal recognition particle guides the preprotein to the endoplasmic reticulum. The preprotein is then translocated into the endoplasmic reticulum lumen, where it undergoes modifications such as glycosylation, protein folding, and disulfide bond formation. The protein is then transported to the Golgi body, where it is further processed and packaged into vesicles for secretion [70]. Additional protein modifications can occur in secretory vesicles; for example, proinsulin is cleaved by proteases in secretory vesicles to form insulin [71]. Secretory vesicles then fuse with the plasma membrane via exocytosis and release the mature proteins into the extracellular space [72]. In constitutive secretion, secretory vesicles are continually released from the cell. In regulated secretion, secretory vesicles are stored in the cell and then released in response to a specific stimulus. 9 1.6.4 Signal transduction and cellular responses When a cell receives a signal via a receptor, it triggers a chain reaction of events inside the cell that leads to an intracellular response [73]. The chain reaction begins with activation of a protein or molecule on the intracellular side of the plasma membrane, for example, the G protein associated with G protein-coupled receptors. Examples of intracellular signaling pathways include the MAPK/ERK, cAMP-dependent, and IP3/DAG pathways [73]. These pathways involve molecular events, typically protein phosphorylation, and can involve secondary messengers such as cAMP, Ca2+, and IP3. Each step of the pathway amplifies the receptor’s initial signal. These pathways terminate by producing a cellular response, including transcriptional and post-transcriptional gene regulation [74]. Activation or inactivation of a transcription factor by a signaling pathway can lead to changes in gene regulation for multiple genes simultaneously, providing another layer of signal amplification that can lead to a robust cellular response from an initial external signal [75]. 1.6.5 Paracrine, autocrine, endocrine, and juxtacrine signaling There are four forms of chemical cell signaling: paracrine, autocrine, endocrine, and juxtacrine (Figure 1.3) [76]. In paracrine signaling, a cell secretes ligands to act on nearby target cells. This type of signaling plays a vital role in coordinating cellular responses within tissues and is particularly important during development. Paracrine signaling includes neurotransmitter release across synapses, cytokine release by immune cells, and growth factor release during tissue repair. Autocrine signaling is when a cell releases a ligand that acts on itself via receptors on its cell membrane. In endocrine signaling, endocrine glands secrete hormones into the bloodstream that travel long distances through the body to reach their target cells. This type of 10 signaling is essential for regulating physiological processes requiring coordination between organs. Examples of hormones include insulin, secreted by the pancreas, and thyroid-stimulating hormone, secreted by the pituitary gland. Juxtacrine signaling involves direct cell-cell contact or cell-extracellular matrix (ECM) contact and is important for maintaining tissue structure [58, 59]. In juxtacrine signaling, a membrane-bound or ECM-bound ligand interacts with a receptor on the surface of a neighboring cell. Examples of juxtacrine signaling are Notch signaling during cell differentiation and ECM-integrin interactions during cell adhesion. Overlaps can also occur between these four types of signaling. Many ligands can act simultaneously via paracrine and autocrine signaling, and some ligands can act both via paracrine and endocrine signaling. A functional analysis of 730 proteins predicted to be secreted into human blood identified cytokines, interleukins, interferons, and chemokines (altogether 154 proteins), complement and coagulation factors (68 proteins), hormones (75 proteins), growth factors (33 proteins), and enzymes (83 proteins) [77, 78]. Another study found that 1463 proteins were detected in human blood plasma across 50,000 individuals [79]. Therefore, many traditional paracrine ligands may also act over long distances throughout the body. For example, while epinephrine acts as a neurotransmitter when released by neurons, it acts as a hormone when released by the adrenal glands [80]. Overall, these four forms of signaling are the basis of intercellular signaling. 11 Figure 1.3: Four forms of intercellular signaling. Illustration of paracrine, autocrine, endocrine, and juxtacrine signaling. Figure adapted with permission from Armingol 2019 [76]. 12 1.7 Ligands and receptors in the human genome There have been significant efforts over the last two decades to identify all the protein ligands, receptors, and interacting ligand-receptor pairs in the human genome. Early efforts included the Database of Ligand-Receptor Partners (DLRP) [81], IUPHAR [82], and Human Plasma Membrane Receptome (HPMR) [83]. These studies together comprised a non-redundant set of 1,179 interacting pairs. While a useful starting point, these lists quickly became outdated, were missing many well-established ligand-receptor pairs, and contained entries that were not true ligands or receptors [84]. In 2015, Ramilokswki et al. improved on these lists by incorporating proteins predicted to be either secreted or localized to the plasma membrane, identifying protein-protein interactions using STRING [85], and manually curating primary literature citations to support each interaction [84]. Ramilokswki et al. (2015) contains 642 ligands, 589 receptors, and 1,894 literature-supported interactions [84]. Numerous recent resources have built on Ramilokswki et al. (2015) and each other to create improved ligand-receptor resources [86-89]. In 2020, the same group that published Ramilokswki et al. (2015) published connectomeDB2020 as a follow-up [90]. ConnectomeDB2020 consists of 1751 pairs from their 2015 resource, 121 pairs from CellphoneDB v2 [86], 50 pairs from RNA-Magnet [89], 22 pairs from SingleCellSignalR [87], 9 pairs from ICELLNET [88], and 340 new manually curated pairs. ConnectomeDB2020 also excluded 143 pairs from Ramilokswki et al. (2015) whose PubMed IDs were determined to be incorrect and did not support the interaction [90]. Overall, connectomeDB2020 is the most up-to-date manually curated ligand-receptor resource. OmniPath is a meta-resource published in 2021 to integrate scattered intercellular communication resources and link them to intracellular signaling pathways [91]. OmniPath 13 combined 27 intercellular signaling resources (Figure 1.4B), 48 molecular interaction network resources, protein complex information, and functional annotations. Omnipath defined eight major categories of intercellular communication (Figure 1.4A), annotated ten locational categories, and included 14,348 unique ligand-receptor interactions. OmniPath’s inclusion of protein complexes is an important aspect that many other resources lack, as proteins involved in intercellular communication often function as part of a complex [86]. Figure 1.4: OmniPath intercellular communication roles and contributing resources. (A) Eight major functional categories of intercellular communication defined by OmniPath. (B) Ligand-receptor interaction resources included in OmniPath. Figure adapted with permission from Turei 2021 [91]. 14 1.8 Analysis of cell signaling at single-cell resolution The combination of new intercellular signaling resources and the recent widespread adoption of single-cell RNA-sequencing (scRNA-seq) has led to the development of new computational tools to analyze cell-cell communication at single-cell resolution [92]. These tools combine a ligand-receptor matrix and cell-gene matrix to estimate the likelihood of crosstalk between source and target cell clusters (Figure 1.5). These tools allow researchers to gain insights into how different cell types within tissues may communicate with one another and how this communication is modified under specific conditions or by diseases. Examples of these tools include CellChat [93], NicheNet [94], Differential NicheNet [95], NATMI [90], Scriabin [96], RSoptSC [97], and InterCellar [98]. These tools are designed to analyze scRNA-seq data and are developed in R or Python. The choice of single-cell intercellular communication analysis tool is an important consideration for researchers looking to analyze their data [99]. Each analysis tool uses different scoring systems, and there is no gold standard to benchmark these tools [76, 100]. A 2022 study compared the results from seven different analysis tools and found that the choice of tool strongly influenced the resulting intercellular interaction predictions [99]. As part of this study, they developed LIANA, a tool that establishes a common interface through which researchers can run their data through seven analysis tools simultaneously and compare the results. 15 Figure 1.5: Approach to analyze cell-cell communication at single-cell resolution. Knowledge of interacting ligand-receptor pairs is combined with a cell-gene matrix generated from scRNA-seq to infer cell-cell communication. Figure adapted with permission from Shao et al. 2020 [92]. 1.9 Intercellular signaling in human islets Intercellular signaling within islets is critical in pancreas organogenesis and mature islet function, and dysregulation of these signals contributes to diabetes pathophysiology [101-105]. In adult islets, β-cells secrete GABA and serotonin to inhibit α-cells and secrete UCN3 to stimulate δ-cells (Figure 1.6A). α-cells secrete glucagon and GLP-1 to stimulate β-cells [106], and δ-cells secrete somatostatin to inhibit both α-cells and β-cells. Some of these signals depend on concurrent glucose stimulation of β-cells, and collectively these signals regulate β-cell insulin secretion (Figure 1.6B) [103]. Some ligands also have multiple functions. For example, in addition to acting as a hormone, insulin acts via autocrine signaling on β-cells. There is evidence that autocrine insulin signaling regulates β-cell proliferation, apoptosis, and insulin secretion [107-109]. In addition to these well-studied protein ligands, it has been reported that islets contain transcripts for hundreds of other ligands and receptors (Figure 1.7) [110]. The roles for many of these ligands and receptors in islets are poorly understood. Several signaling pathways, including 16 Notch, WNT, and EGFR, are known to regulate pancreatic endocrine cell differentiation [111-116]. Many non-canonical endocrine and non-endocrine islet cells also make contributions to paracrine signaling within islets [104, 117]. Ghrelin secreted by epsilon cells may stimulate δ-cells to secrete somatostatin [118], PPY secreted by PP-cells may act on α-cells via the PPY receptor (PPYR1) [119], and pericytes may sustain β-cell maturity [120]. Signals coming from beyond the pancreas are also crucial to islet function. For example, FGF21 from the liver has been shown to be involved in glucagon receptor (GCGR) antagonism-induced β cell regeneration [121]. Overall, there is much more to learn about intercellular communication in developing and mature islets. Figure 1.6: Overview of intra-islet signaling and relation to glucose concentration. (A) Overview of main paracrine signals exchanged between β-cells, α-cells, and δ-cells. (B) Influence of glucose concentration on the secretion of paracrine signals by islet cells. Figure adapted with permission from Huising 2020 [103]. 17 18 Figure 1.7: Islets express many ligands and receptor genes. Expression of islet ligands and their associated receptors in human, rat, and mouse islets. Figure adapted with permission from Yang et a. 2011 [110]. 1.10 Intercellular signaling in SC-islets Stem cell differentiation protocols are designed based on our knowledge of the signaling pathways involved in pancreas development. In fact, over 90% of the small molecules and proteins used to generate SCβ-cells are activators or inhibitors of receptors involved in embryonic signaling pathways such as WNT, FGF, Notch, Nodal, and retinoic acid [49, 50, 56, 122, 123]. Modulating some of these signaling pathways in stage 6 or 7 has been shown to alter SCβ-cell differentiation or maturation [57, 124, 125]. It is still unclear, however, how signaling within stem cell-derived islet-like clusters (SC-islets) compares to human islets. As the cellular composition of SC-islets is very different from human islets, it is likely that paracrine signals within SC-islets significantly differ from human islets [55]. Comparative expression analysis between human islets and SC-islets may help identify ligands and receptors that can be targeted to improve stem cell differentiation protocols [103, 126]. 1.11 Thesis investigation There are no published comprehensive analyses of intra-islet signaling using the latest resources and technologies. There are also no published comprehensive analyses of how intra-islet signaling is modified in diabetes or how signaling within SC-islets compares to human islets. We have previously shown that high-content screening is an effective approach to identify the roles of protein ligands and receptors in islets [127]. For this study, we predicted that an 19 unbiased in silico prioritization of the ligands and receptors in SC-islets and human islets could guide future studies. We first aimed to curate a custom set of protein ligands and receptors that could be used to build prioritized lists. We obtained ligand-receptor data from OmniPath and semi-manually filtered the data to select only ligands and receptors appropriate for our study. We then aimed to systematically map intercellular signaling in human islets and SC-islets. We analyzed multiple scRNA-seq, bulk RNA-seq, and proteomics datasets to identify ligands and receptors of interest in SC-islets and human islets based on multiple criteria. We identified ligands and receptors whose gene expression and protein abundance were most dissimilar between SC-islets and human islets. We also determined the correlation of gene expression to protein abundance, and identified the top intra-islet signaling pathways in SC-islets and human islets at single-cell resolution. We then aimed to rank ligands and receptors based on expression in SC-islets and differential expression compared to human islets. We ranked each ligand and receptor based on a total of 15 parameters. We ranked each interacting ligand-receptor pair by combining the ligand and receptor ranks. Finally, we aimed to build three prioritized lists: scarce ligands with abundant receptors in stem cell-derived cells, scarce receptors with abundant ligands in stem cell-derived cells, and abundant receptors with abundant ligands in stem cell-derived cells. Collectively, we expect the data from this study will enable future studies to define the roles of ligands and receptors in diabetes and diabetes therapies. 20 Chapter 2: Materials and methods 2.1 Data analysis and statistics All data analysis was performed with R version 4.1.2 in R Studio version 2021.09.2 on a local computer [128, 129]. Figure 2.1 provides an overview of this study's methods, data inputs, and data outputs. For all statistical analyses, differences were considered significant if the corrected p-value was less than 0.05. 21 Figure 2.1: Overview of study. Flowchart overview of methods, data inputs, and data outputs in this study. 22 2.2 Custom ligand and receptor lists and interaction pairs Ligand, receptor, and interaction data were downloaded from the OmniPath web service using OmniPathR version 3.3.20 (https://github.com/saezlab/OmnipathR) [91, 130, 131]. Ligand and receptor lists were obtained using the import_omnipath_intercell function. Protein complexes and non-secreted ligands were excluded. Ligand-receptor interactions were obtained using the import_omnipath_interactions function. Duplicate genes corresponding to the same HGNC symbol were merged. Ligands, receptors, and interactions without literature support were excluded. Ligand and receptor lists were filtered in two steps. The OmniPath consensus score, which is the number of resources in the OmniPath database supporting the classification of an entity into a category, was used to filter out ligands and receptors that were only present in a few resources. The consensus score thresholds were chosen based on the distribution of consensus scores and the point at which the majority of the genes seemed to be true ligands and receptors based on our knowledge. Next, the remaining ligands and receptors were manually reviewed to determine whether they were appropriate for inclusion. Manual review was done by examining the OmniPath category, Entrez gene summary, OmniPath interaction partners, and gene ontology annotations for each gene. As required, literature references were also reviewed. Ligand and receptor pathway annotations were obtained from CellChat via OmniPath. 2.3 Bulk gene RNA expression Three bulk RNA-seq datasets were used in this study. I was provided processed data outputs from Kallisto for the Lund 2014 human islet dataset (n=89), which contained islet donors of varying diabetes status (GEO: GSE50244) [132]. Transcripts per million (TPM) was used to 23 quantity gene expression to allow for accurate between-gene expression comparisons [133]. Gene level TPM values were calculated by taking the sum of all transcripts corresponding to the same Ensembl gene. Some HGNC symbols corresponded to multiple Ensembl genes. The highest TPM value corresponding to the HGNC symbol was used in these cases. I was provided TPM values for the MacDonald-Gloyn 2023 human islet dataset, which included 82 non-diabetic and 8 type 2 diabetic donors (n=90, data unpublished). For both these datasets, the mean TPM across all donors was used. To determine islet gene expression specificity, islet gene expression was compared to the expression across 54 human tissues from the Genotype-Tissue Expression (GTEx) Project [134, 135]. Median gene-level TPM by tissue from GTEx Analysis V8 was downloaded via the GTEx portal. The highest TPM value corresponding to each HGNC symbol was used. 2.4 Proteomics I was provided unpublished, in-house protein abundance data that had been generated using liquid chromatography with tandem mass spectrometry. This data included human islet donors (n=133) and SC-islets (n=18). 90 human islet donors were donor-matched to the MacDonald-Gloyn 2023 bulk RNA-seq dataset. The SC-islets had been generated from an INSGFP/WT reporter human embryonic stem cell line using a stage 7 protocol and were a combination of S7 unsorted SC-islets (n=12) and S7 FACS-sorted SC-islets (n=6). The mean protein abundance was used for each group. The abundances were normalized to protein amino acid lengths obtained from UniProt to allow for accurate between-protein abundance comparisons. For proteins with multiple amino acid lengths, the mean length was used. 24 I was provided the data output from a differential protein abundance analysis between the non-diabetic human islets (n=118) and pooled SC-islets (n=18). The data from the S7 unsorted SC-islets and S7 sorted SC-islets were pooled into a single SC-islet group. A protein was considered significantly differentially abundant if the absolute log2-fold change was >1 and the adjusted p-value was <0.05. 2.5 Correlation between gene expression & protein abundance Only the 90 human islet donors with matching proteomics and transcriptomics data were used to determine the correlation between gene expression and protein abundance. The proteomics was compared to the MacDonald-Gloyn 2023 and Lund 2014 RNA-seq datasets. The linear correlation between protein and RNA was calculated using Pearson’s correlation coefficient. 2.6 Single-cell gene expression I was provided a human islet scRNA-seq dataset generated from three cadaveric donors (n=68,650 cells, GEO: GSE196715) [136]. I was also provided scRNA-seq datasets generated from S7 unsorted SC-islets (n=6875 cells, data unpublished) and S7 FACS-sorted SC-islets (n=5355 cells, data unpublished). The SC-islets had been generated from an INSGFP/WT reporter human embryonic stem cells using a stage 7 protocol. These three datasets were all provided as Seurat objects with completed clustering and quality control. I was provided the differential gene expression output from an integration analysis between the human β-cells in the human islet dataset, and SCβ-cells in the S7 sorted SC-islets dataset. 25 Seurat version 4.1.0 was used to analyze scRNA-seq data (https://github.com/satijalab/seurat) [137, 138]. Differentially expressed genes with adjusted p-values equal to zero were assigned adjusted p-values equivalent to the lowest non-zero values. A gene was considered as significantly differentially expressed if the absolute log2-fold change was >1 and the adjusted p-value was <0.05. 2.7 Intra-islet signaling analysis with CellChat Intra-islet signaling in human islets and S7 sorted SC-islets were analyzed using CellChat version 1.1.3 (https://github.com/sqjin/CellChat) [93]. The RNA assays from Seurat were used as inputs for CellChat. The “secreted signaling” subset of the default CellChat interaction database was used. Gene expression was projected onto the protein-protein interaction network to compensate for the potential dropout effects of signaling genes. 2.8 Ligand and receptor ranking in SC-islets Three additional publicly available datasets were obtained and used in rankings. Bulk RNA-seq of S6 SC-islets (n=1) and human islets (n=1) from Melton’s group was obtained from GEO (GEO: GSE139816) [139]. A gene was considered differentially expressed in the Melton bulk RNA-seq dataset if the absolute log2-fold change was >1. Expression of S6 SCβ-cells and differential expression of S6 SCβ-cells compared to human β-cells from Melton’s group was obtained from Veres et al., 2019 [53]. A gene was considered differentially expressed in the Melton scRNA-seq dataset if the absolute log2-fold change was >1. Differential expression of S7 SCβ-cells compared to human β-cells from Otonkoski’s group was obtained from Balboa et al., 26 2022 [54]. A gene was considered differentially expressed in the Otonkoski scRNA-seq dataset if the absolute log2-fold change was >0.5 and the adjusted p-value was <0.05. Ligands and receptors were ranked across a total of 15 parameters in SC-islets. 5 parameters were measures of gene expression or protein abundance (expression), 5 parameters were measures of higher gene expression or protein abundance compared to human islets (upregulation), and 5 parameters were measures of lower gene expression or protein abundance compared to human islets (downregulation). To increase the number of hits for ranking, the absolute log2-fold change threshold to be considered upregulated or downregulated was decreased to >0 for the proteomics and >0.5 for the Lynn scRNA-seq. Ligands and receptors were ranked on a percent scale for each parameter, with the top rank assigned a value of one and the bottom rank assigned a value of zero. Top abundant ligands and receptors were defined by high expression and upregulation ranks. Top scarce ligands and receptors were defined by low expression ranks and high downregulation ranks. The overall abundant rank and scarce rank for each ligand and receptor was calculated as the mean rank across the 10 relevant parameters. Overall, the rankings were 50% based on expression in SC-islets and 50% based on differential expression compared to human islets. 2.9 Interaction ranking and prioritization in SC-islets Ligand and receptor ranks were combined to create three prioritized lists of interactions in SC-islets: abundant receptors with abundant ligands, scarce receptors with abundant ligands, and scarce ligands with abundant receptors. Interactions were ranked by calculating the product of the ligand and receptor ranks for each interacting ligand-receptor pair, as done previously 27 [86]. For receptors and ligands with multiple interactions, their highest ranked interaction partner was used for their final rank in the prioritized lists. 28 Chapter 3: Results 3.1 Creation of a custom ligand-receptor resource Published ligand-receptor databases contain many entries that are likely not true ligands or receptors due to a combination of incorrect functional or locational annotations [140-142] and loose definitions of the terms “ligand” and “receptor”. For this study, we focused on the subset of cell-cell signaling involving secreted protein ligands (such as hormones, cytokines, and growth factors) and the plasma membrane bound receptors for these types of ligands. Therefore, we first created a custom ligand and receptor resource that would be appropriate for this study. We included only ligands that were literature-supported (at least one literature reference) secreted proteins known to bind to receptors on the plasma membrane. We included only receptors that were literature-supported proteins on the plasma membrane that are known receptors for secreted protein ligands. We excluded intracellular receptors, ECM proteins, major histocompatibility complex proteins, non-protein ligands, complement proteins, protein complexes, and extracellular enzymes. We obtained an initial list of 927 protein ligands and 4285 receptors from OmniPath. While the number of receptors was high, we found that many were not true receptors. Many of these receptors were annotated as secreted or had a primary function as a ligand. The OmniPath consensus score measures the number of resources that include each ligand or receptor. We used the OmniPath consensus score as a measure of literature support to select only ligands and receptors whose roles are well established. We included only ligands with a consensus score above four and only receptors with a consensus score above seven (Figure 3.1A). Notably, this excluded 2524 receptors with a consensus score equal to one. We then manually reviewed the remaining 490 ligands and 587 receptors, and excluded an additional 68 ligands and 238 29 receptors (Figure 3.1B). Examples of ligands that we manually excluded were the ECM protein LAMA1, the complement protein C4B, and the membrane-bound ligand CD40LG. Examples of receptors that we manually excluded were the ECM protein ITGB2 and the non-protein ligand-receptor ADORA1. After manually filtering, we had a final list of 422 protein ligands and 349 receptors. The top ligands based on OmniPath consensus score were BMP2, IL10, and INHBA, and the top receptors were KDR, EGFR, and TGFBR1. The signaling pathways with the highest ligand count in our final list were CCL, WNT, and FGF, and the signaling pathways with the highest receptor count were ncWNT, CCL, and EPHA. (Figure 3.1C). Of the top represented pathways, the IL4, NT, and IL2 pathways had the highest mean consensus scores for the ligands, and the IL4, CXCL, and NOTCH pathways had the highest mean consensus scores for the receptors. These data indicate these pathways have robust literature support for their functions in intercellular signaling. We next identified the interacting pairs between our curated ligand and receptor lists. We obtained an initial list of 40,014 ligand-receptor interaction pairs with literature support from OmniPath. After filtering to include only the selected 422 ligands and 349 receptors, we were left with 1552 interaction pairs (Figure 3.2A,D). 26 ligands and 27 receptors had no interaction pairs. The mean number of interactions was 3.68 per ligand and 4.45 per receptor. The ligands with the most interactions were POMC and WNT5A, with 14 interactions each. Interestingly, the receptors with the most interactions had much higher interaction counts than the ligands (Figure 3.2B). The receptors with the most interactions were MTNR1A, ACVR2B, and ACVR2A, with 44, 27, and 26 interactions, respectively. The number of resources which supported each interaction ranged from 1 to 32 (Figure 3.2C). The interactions with the most literature support 30 were TNF-TNFRSF1A, EGF-EGFR, TNF-TNFRSF1B, EPO-EPOR, and HGF-MET. Collectively, these data demonstrate we created a custom ligand-receptor resource that could be used to analyze cell-cell signaling in islets and create prioritized lists. 31 Figure 3.1: Semi-manual curation of 422 ligands and 349 receptors. (A) Distribution of OmniPath consensus scores for initial lists of 927 ligands and 4285 receptors obtained from OmniPath. Dashed vertical lines indicate the manually selected thresholds for inclusion. (B) Distribution of OmniPath consensus scores for manually excluded and included ligands and receptors. (C) Top 25 signaling pathways with the most ligands (left) and receptors (right) in the final lists. Dot size indicates the number of ligands or receptors corresponding to the pathway. The color indicates the mean consensus score across all ligands or receptors in the pathway. 32 33 Figure 3.2: Custom ligand-receptor interaction resource with 1552 interactions. (A) All 1552 interactions between the final filtered list of ligands (left) and receptors (right) are shown in alphabetical order. 26 out of 422 ligands and 27 out of 349 receptors had no interactions and are not shown. Each line represents a ligand-receptor pair. The line color indicates the number of resources supporting the interaction. The dot size indicates the number of interactions per ligand or receptor. Labels indicate the top 25 ligands and receptors with the most interactions, with at least 5 resources supporting their interactions. (B) Distribution of the number of interactions for each ligand and receptor. (C) Distribution of the number of resources in OmniPath that support each of the 1552 interactions. (D) Overview of the ligand-receptor interactions in our custom resource. 3.2 Top ligands and receptors in human islets based on bulk RNA-seq We analyzed the expression of ligands and receptors in human islets using two separate bulk RNA-seq datasets: MacDonald-Gloyn 2023 (n=90) and Lund 2014 (n=89). Interestingly, the expression of ligands (R = 0.82, p-value < 2.2e-16) and receptors (R = 0.85, p-value < 2.2e-16) were strongly correlated between datasets even though the datasets were created by different research groups nine years apart (Figure 3.3A-B). We compared islet gene expression to the GTEx database of 54 human tissues to determine the islet specificity for each ligand and receptor. The top expressed and top islet-specific ligands in both datasets were the classic islet ligands GCG, INS, SST, IAPP, and PPY (Figure 3.3C). The top expressed receptors in MacDonald-Gloyn 2023 were DDR1, TNFRSF12A, and ADIPOR1, and the top expressed receptors in Lund 2014 were TNFRSF21, PDGFRB, and TNFRSF1A. While not in the top 25 expressed receptors, the top three islet-specific receptors in both datasets were GLP1R, RXFP3, and MTNR1B. Overall, these data identify the top ligands and receptors in human islets and demonstrate strong agreement between the Lund 2014 and MacDonald-Gloyn 2023 bulk RNA-seq datasets. 34 35 Figure 3.3: Top ligands and receptors in human islets based on bulk RNA-seq. (A-B) Correlation of mean gene expression for ligands (A) and receptors (B) between the Lund 2014 human islet dataset (n=89) and MacDonald-Gloyn 2023 human islet dataset (n = 90). (C) Top 25 expressed ligands and receptors in human islets based on mean expression in MacDonald-Gloyn 2023. Dot size indicates expression and color indicates islet specificity compared to the GTEx database of 54 human tissues. 3.3 Correlation of gene expression and protein abundance in human islets While transcriptomes are easier to quantify than proteomes, proteins are the primary actors in cells. There is also strong evidence that mRNA levels are poor predictors of protein abundance [143-146]. Therefore, we incorporated proteomics data to measure ligand and receptor proteins directly. We examined proteomics data from the same 90 human islet donors as the MacDonald-Gloyn 2023 RNA-seq dataset. There were 8194 proteins detected in the proteomics, including 41 ligands and 41 receptors. The most abundant ligands were MIF, INS, and GCG, and the most abundant receptors were TFR2, NCSTN, and LEPR. We determined the mRNA-protein correlation using both the Lund 2014 and donor-matched MacDonald-Gloyn 2023 datasets. Interestingly, the donor-matched RNA-seq dataset (Figure 3.4B) did not have a stronger protein-mRNA correlation than the non-donor-matched Lund 2014 dataset (Figure 3.4A). This finding is likely explained by the strong correlation between the RNA-seq datasets (Figure 3.3). Interestingly, the ligands (R = 0.55, p-value < 0.0002) had a significantly higher protein-mRNA correlation than the receptors (R = 0.02, p-value < 0.91). The correlation for the ligands was slightly higher than the genome-wide correlation of R = 0.49. These results indicate protein abundance did not correlate with mRNA abundance for islet cell receptors. The ligands with the lowest protein to RNA ratio were IL16, BMP1, and ANGPTL4, and the receptors with the lowest protein to RNA ratio were PLXNA1, EPHB4, and IL3RA. Overall, we show the correlation between protein abundance and gene 36 expression for ligands and receptors in human islets, and demonstrate the importance of including proteomics in this study. 37 Figure 3.4: Correlation between protein abundance and gene expression in human islets. (A-B) Correlation between proteomics (n=90) and two separate bulk RNA-seq datasets for all ligands (left) and receptors (right) detected in the proteomics. Protein abundances were normalized to the amino acid length of the proteins. (A) Lund 2014 bulk RNA-seq dataset (n=89). (B) MacDonald-Gloyn 2023 bulk RNA-seq dataset (n=90) with donor-matched samples to the proteomics. 3.4 Differential protein abundance between human islets and SC-islets We next compared ligand and receptor protein abundances between non-diabetic human islet donors and a mixed pool of S7 sorted and S7 unsorted INSGFP/WT SC-islets (Figure 3.5A). We found 4 ligands upregulated and 11 downregulated in SC-islets compared to human islets (Figure 3.5C left). The top upregulated ligands were MDK, BMP1, and IGF2, and the top downregulated ligands were INS, ANXA1, and PPY. There was 1 receptor upregulated and 4 receptors downregulated in SC-islets compared to human islets (Figure 3.5C right). The top upregulated receptor was ROR2 and the top downregulated receptors were NRP1, TFRC, and IFNGR1. While its fold change was just below the threshold for inclusion, IGF2R was also significantly upregulated in SC-islets. Interestingly, IGF2BP2, which binds to and regulates the translation of IGF2 mRNA [147, 148], was also one of the top upregulated proteins genome-wide (Figure 3.5B). The combination of IGF2, IGF2R, and IGF2BP2 upregulation makes this the top upregulated signaling pathway at the protein level in SC-islets. Overall, we identified the top differentially abundant ligand and receptor proteins between SC-islets and human islets. 38 Figure 3.5: Differential protein abundance between SC-islets and human islets. (A) Overview of differentiation protocol used to generate sorted and unsorted S7 SC-islets. (B-C) Differential protein abundance between non-diabetic human islets (n=118) and SC-islets (n=18). Proteins in red and blue are significantly differentially abundant (absolute log2FC > 1 and adjusted p-value < 0.05). (B) All 8194 proteins detected in proteomics. IGF2BP2 is highlighted as it regulates IGF2 expression. (C) Differentially abundant ligands (left) and receptors (right). 3.5 Differential gene expression between SCβ-cells and human β-cells We next compared SCβ-cells and human β-cells at single-cell resolution to determine how ligand and receptor expression compares between these cell types. We examined scRNA-seq of human islets, S7 unsorted SC-islets, and S7 sorted islets, and selected the SCβ-cell cluster from the S7 sorted dataset to compare to human β-cells (Figure 3.6A). The clusters contained 891 SCβ-cells and 29,956 human β-cells. As expected for this knock-in cell line, we found eGFP was the top upregulated gene in SCβ-cells (Figure 3.6B). We found two ligands upregulated and 39 four downregulated in SCβ-cells compared to human β-cells (Figure 3.6C). The upregulated ligands were DLK1 and PYY, and the downregulated ligands were IAPP, INS, SPP1, and NPY. Interestingly, IAPP and INS are canonical β-cells maturity markers [149], while NPY is a marker of immature β-cells [150]. One receptor was upregulated and one was downregulated in SCβ-cells compared to human β-cells. However, multiple additional receptors were just below the fold change threshold to be considered upregulated (Figure 3.6C). The top upregulated receptor was ACVR1C, and the top downregulated receptor was TNFRSF12A. INS, ACVR1C, and TNFRSF12A expression was evenly distributed among cells in the SCβ-cell and human β-cell clusters, however IAPP expression was limited to only a subset of the SCβ-cells (Figure 3.6D). This subset may correspond to a more mature subpopulation of SCβ-cells. Overall, these data identify the top differentially expressed ligands and receptors between SCβ-cells and human β-cells. 40 41 Figure 3.6: Differential gene expression between SCβ-cells and human β-cells. (A) UMAP projections for S7 unsorted SC-islet (n=6875 cells), S7 sorted SC-islet (n=5355 cells), and human islet (n=68,650 cells) scRNA-seq datasets. Points represent individual cells and color indicates cell type annotations. (B-C) Differential gene expression between SCβ-cells (n=891 cells) and human β-cells (n=29,956 cells) for all genes (B) and ligands and receptors (C). Genes in red and blue are significantly differentially expressed (absolute log2FC > 1 and adjusted p-value < 0.05). Genes just below the absolute fold change threshold but with significant p-values are also labeled. (D) Single-cell gene expression of the top two differentially expressed ligands (INS & IAPP) and receptors (TNFRSF12A & ACVR1C) between SCβ-cells and human β-cells. 3.6 Top intra-islet signaling pathways in SC-islets and human islets We used CellChat to expand our single-cell analysis to identify incoming and outgoing signaling pathways in human islets and S7 sorted SC-islets [93]. We identified 6557 total inferred interactions in human islets (Figure 3.7A), however we identified only 42 total inferred interactions in S7 sorted SC-islets (Figure 3.7B,E). This difference is likely due to the SC-islet dataset containing 10x fewer cells. Compared to other intercellular communication tools, such as CellPhoneDB, CellChat has been shown to infer fewer interactions but capture the strongest and likely most significant interactions [93]. CellChat has also been shown to be fairly robust to noisy data [99]. Therefore, we could still draw insights from the SC-islets analysis despite the lack of depth in the results. We found that α-cells were by far the strongest sources of signals in human islets, while α-cells, β-cells, and pericytes were the strongest receivers of signals (Figure 3.7A). Interestingly, pericytes contributed significantly to cell-cell signaling even though they were much less abundant than α-cells and β-cells (Figure 3.6A). In SC-islets, pancreatic progenitors were by far the largest overall contributors to cell-cell signaling (Figure 3.7B), even though they were also not the largest cell population (Figure 3.6A). Pancreatic progenitors were the strongest senders of 42 signals, and pancreatic progenitors, immature β-cells, and SCβ-cells were the strongest receivers of signals. The MK pathway was the strongest signaling pathway in both SC-islets and human islets (Figure 3.7C-D). In human islets, α-cells sent signals via the MK pathway to β-cells, pericytes, and α-cells themselves (Figure 3.7C). In SC-islets, pancreatic progenitors sent signals via the MK pathway mainly to themselves (Figure 3.7D). Human β-cells both sent and received signals via the WNT pathway, while SCβ-cells only sent WNT signals. Both human β-cells and SCβ-cells received strong signals via the IGF pathway. In human islets, the IGF signals originated from pericytes, while in SC-islets they originated from pancreatic progenitors. In human islets, duct cells and endothelial cells sent signals to β-cells and α-cells via the SPP1 and ANGPT pathways. These pathways were not detected in the SC-islets. Overall, these data identify the signaling pathways by which islet cells communicate with each other and highlight the contributions of non-endocrine cell types to intra-islet signaling. 43 44 Figure 3.7: Intra-islet signaling pathways in SC-islets and human islets. (A) Number of interactions (left) and strength of interactions (right) between all 11 cell types in human islets. (B) Number of interactions (left) and strength of interactions (right) between all 5 cell types in S7 sorted SC-islets. (C-D) Top signaling pathways and cell types contributing to intercellular communication in (C) human islets and (D) S7 sorted SC-islets. Bar plot on top indicates the total signaling strength of each cell group. Bar plot on right hand side indicates total contribution of each signaling pathway. (E) Total number of inferred interactions by CellChat in S7 sorted SC-islets and human islets. 3.7 Ligand and receptor prioritization in SC-islets We combined our datasets with publicly available datasets to identify ligands and receptors in SC-islets with consistent patterns across studies, experimental methods, and differentiation protocols. We ranked ligands and receptors across 15 parameters grouped into three categories with 5 parameters each. The three categories were expression in SC-islets, upregulation in SC-islets compared to human islets, and downregulation in SC-islets compared to human islets (Figure 3.8A-B). We defined abundant ligands and receptors by high expression in SC-islets and higher expression in SC-islets compared to human islets. The top abundant ligands were MDK, SEMA3A, and GCG (Figure 3.8C). MDK was highly expressed in all five parameters and was upregulated in SC-islets in four out of five parameters. Interestingly, some ligands showed a discrepancy in differential expression between datasets. AGT was upregulated in the proteomics but was downregulated in both the Melton scRNA-seq and bulk RNA-seq. SST was upregulated in the Lynn scRNA-seq but downregulated in the Melton and Otonkoski scRNA-seq. These discrepancies may represent biological differences between research groups in the SC-islet cells. The top abundant receptors were PLXNC1, PLXNA2, and IGF2R (Figure 3.8D). They were highly expressed in all five parameters and were upregulated in four out of five parameters. 45 ACVR1C was highly expressed and upregulated in all RNA-seq datasets but was not detected in proteomics. We defined scarce ligands and receptors by low expression in SC-islets and lower expression in SC-islets compared to human islets. The top scarce ligands were EDN3, NPY, and SPP1 (Figure 3.8E). These ligands had moderate expression in SC-islets and were strongly downregulated in multiple parameters compared to human islets. UCN3, a marker of β-cell maturity [151], was downregulated in SC-islets in three parameters. The top scarce receptors were TNFRSF12A, NGFR, and TNFRSF14 (Figure 3.8F). TNFRSF12A was also the second highest expressed receptor in human islets based on bulk RNA-seq (Figure 3.3C), and was the only receptor downregulated in the Lynn scRNA-seq (Figure 3.6C). Overall, these data thoroughly identify the top ligands and receptors in SC-islets and compare their expression to human islets. We next prioritized ligand-receptor interactions in SC-islets based on three combinations of the ligand and receptor ranks (Figure 3.9A-C). The top interactions in SC-islets between abundant receptors and abundant ligands were SEMA3A-PLXNA2, GDF11-ACVR1C, and IGF2-IGF2R (Figure 3.9A). The high rank of IGF2-IGF2R showed that the RNA-seq datasets were aligned with the proteomics, which had identified IGF2-IGF2R as the top upregulated interaction (Figure 3.5C). The top interactions in SC-islets with scarce receptors and abundant ligands were VEGFB-FLT1, GRN-TNFRSF1B, and NTS-NGFR (Figure 3.9B). The top interactions with scarce ligands and abundant receptors were TGFB1-EGFR, FGF21-FGFR1, and FGF21-FGFR4 (Figure 3.9C). In all three prioritized interaction lists, some ligands and receptors appeared multiple times with different interaction partners. For example, FGFR1 was present in four of the top 50 interactions between abundant receptors and abundant ligands, and 46 FGF21 was present in three of the top five interactions between scarce ligands and abundant receptors. To guide future screens, we simplified the three prioritized lists by selecting only the highest-ranked interaction partner for each ligand or receptor (Figure 3.9D-F). In summary, we prioritized the ligands, receptors, and interactions in islets by combining multiple datasets. These data improve our understanding of islet intercellular communication and enable researchers to focus future studies on ligands and receptors most likely to have a biological effect on SC-islets. 47 Figure 3.8: Ligand and receptor ranks in SC-islets based on 15 parameters. (A-F) Ligand and receptor ranks across 15 parameters split into 3 categories: expression, upregulation, and downregulation. Each column corresponds to a parameter. Research group, cell type, and method for each parameter are indicated above the columns. (A-B) All 422 ligands (A) and 349 receptors (B). (C-D) Top 25 abundant ligands (C) and receptors (D) in SC-islets. Abundant ligands and receptors are defined by high expression in SC-islets and higher expression in SC-islets compared to human islets. (E-F) Top 25 scarce ligands (E) and receptors (F) in SC-islets. Scarce ligands and receptors are defined by low expression in SC-islets and lower expression in SC-islets compared to human islets. 48 49 Figure 3.9: Prioritized ligand-receptor interactions in SC-islets based on 3 criteria. (A-C) Top 50 interactions in SC-islets between abundant receptors and abundant ligands (A), scarce receptors and abundant ligands (B), and scarce ligands and abundant receptors (C). (D-F) Top 50 abundant receptors with abundant ligands (D), scarce receptors with abundant ligands (E), and scarce ligands with abundant receptors (F). 50 Chapter 4: Discussion In this study, we aimed to systematically map intercellular signaling in human islets and SC-islets and build prioritized lists of ligands and receptors in SC-islets. We created a custom ligand-receptor resource suitable for these goals containing 422 protein ligands, 349 receptors, and 1552 interactions. We presented the first reported analysis of intercellular communication in islets to combine proteomics, bulk RNA-seq, scRNA-seq, and novel ligand-receptor resources. We also presented the first reported systematic comparison of intra-islet signaling between human islets and SC-islets at single-cell resolution. We used proteomics data to complement RNA-seq and directly measure protein abundances. We found that some ligands and receptors, such as AGT, were only identified as upregulated by proteomics. We also found that ligands had a moderate mRNA-to-protein correlation, and receptors had no correlation. We speculate this difference between ligands and receptors could be due to the longevity, low efficiency of capture, and low abundance of membrane proteins [152, 153]. Our finding also aligns with a 2016 study that found proteins possessing transmembrane domains were less likely to be detected in mass-spectrometry proteomics [154]. These findings demonstrate the importance of including proteomics in future research on intercellular signaling. Intercellular signals play critical roles in adult islet function, and their dysregulation contributes to diabetes pathophysiology [101-104]. Our results highlighted the contributions of α-cells, pericytes, duct cells, and endothelial cells to intra-islet signaling. While paracrine signals from α-cells are well studied [103, 105, 155], paracrine signals from these other islet cell types have only recently been identified [101, 156]. Pericytes have been shown to promote β-cell function and maturation by secreting BMP4 [120, 157-159]. Interestingly, we did not detect 51 BMP4 in our human islet scRNA-seq and found BMP signaling occurred in the opposite direction; β-cells sent signals to pericytes via BMPs. The data we presented provide direction for future studies to elucidate the roles of these non-endocrine cells in islet paracrine signaling. A recent study by Bosi et al. used CellPhoneDB and single-cell transcriptomics to identify 9707 interactions in human islets from non-diabetic donors [160]. We identified 6557 inferred interactions in human islets using a single-cell dataset containing 20x more cells. However, a direct comparison of the number of interactions between studies is unlikely to be meaningful due to inherent differences in the datasets and analysis tools between studies. Bosi et al. identified EGFR, FGFR1, and FGFR2 as top receptors involved in multiple interactions in human islets. Interestingly, while we did not analyze individual ligand-receptor pairs in human islets, we identified EGFR and FGFR1 as top abundant receptors with abundant ligands in SC-islets. These receptors have well-established roles in islet cell survival and development [161-163]. To create non-redundant prioritized ligand and receptor lists, we selected only the highest-ranked interaction for ligands and receptors with multiple interactions. In contrast, Bosi et al. used the number of interactions associated with each ligand or receptor as a key component of their ranking system. Our results may have differed if we had used a similar approach. We identified the MK pathway as the top pathway to intra-islet signaling in both human islets and SC-islets. We also found that MDK, the main MK pathway ligand, was the top upregulated ligand at the protein level in SC-islets compared to human islets. Interestingly, a recent study also identified MK as one of the top three contributing pathways in the developing fetal human pancreas [164]. MDK is known to play a role in embryogenesis and is upregulated in various cancers [165], however its role is islet biology is unknown. Future studies could examine the roles of MDK in human islets and SC-islets. 52 We presented three prioritized lists of interactions in SC-islets to guide future studies. We found FGF21 was in three of the top five interactions between scarce ligands and abundant receptors in SC-islets, indicating FGF21 could be a “missing” ligand. FGF21 has been shown to improve β-cell survival and function [166], protect against type 2 diabetes in mice [167], and improve islet engraftment following islet transplantation in mice [168]. Interestingly, a recent study showed that FGF21 from the liver is involved in β-cell regeneration [121]. Multiple studies have also shown that unknown soluble ligands from the host promote rapid in vivo maturation of transplanted SCβ-cells [49, 169, 170]. FGF21 could be one of these unknown ligands. We identified WNT5A-FZD3 as one of the top 20 interactions between abundant ligands and abundant receptors in SC-islets. Interestingly, a recent study identified WNT5A-FZD3 as a top interaction between mesenchymal-epithelial cells and stem cell-derived endocrine progenitors [171]. Their follow-up studies also showed this interaction promotes β-cell differentiation [171]. It is possible that other top interactions we identified between abundant ligands and abundant receptors also promote SCβ-cell differentiation. Their study provides an example of how the data we presented could be used to gain further insights into the roles of intercellular interactions in islets. One of the strongest upregulated interactions we identified in SC-islets compared to human islets was IGF2-IGF2R. IGF2 and IGF2R were both upregulated at the protein level and IGF2BP2, which promotes translation of IGF2 mRNA [147, 148], was one of the top upregulated proteins genome-wide. IGF1R and INSR are also receptors for IGF2 and were in the top 20 abundant receptors as well. IGF2R has been shown to act as a negative regulator of IGF2 in skeletal tissue [172], and IGF2 overexpression may induce β-cell death [173]. However, the roles for these proteins in islets are unclear [174]. IGF2, IGF2R, and IGF2BP2 upregulation has 53 not previously been reported in SC-islets, likely because the upregulation is at the protein level and may not be detectable by RNA-seq. 4.1 Limitations As an in silico systematic study, we could not assess the biological significance of our findings. For example, while we found that the MK pathway was the top pathway in both SC-islets and human islets, we could not assess the importance of this pathway to islet biology. Some ligands and receptors that we identified as abundant may not be biologically relevant. It is also possible that some predicted interactions do not occur at all. Future bottom-up studies are required to functionally validate our findings and determine the roles of these ligands and receptors. This informatics study was dependent on available data. The Melton SC-islet bulk RNA-seq dataset was an n=1 experiment, and the Lynn S7 SC-islet dataset contained only 6000 cells. Additional bulk RNA-seq with higher replicate counts and scRNA-seq with higher cell counts would help distinguish biological insights from experimental noise. scRNA-seq also has multiple limitations that are important to consider for this study. Gene dropout may occur in genes with low expression, noise may not be filtered appropriately, and cell clustering is an approximate process that may not carry biological meaning beyond transcriptional similarity [175, 176]. We used data from three differential expression analyses to identify differences between SCβ-cells and human β-cells. There is currently debate in the scRNA-seq community on how to perform differential expression analyses, and it has been shown that these analyses can identify hundreds of false positives [177, 178]. The results we presented should therefore be interpreted with caution. 54 Most of the data in this study were from RNA-seq experiments, but ligands and receptors function as proteins. As discussed, RNA abundance was not correlated with protein abundance for receptors and was only moderately correlated for ligands. Furthermore, proteomics detected only approximately 10% of the ligands and receptors, and proteins not detected in proteomics could not be assumed to have low abundance. Some highly abundant ligand and receptor proteins were likely not detected in the proteomics due to instrument detection limits, sample loss, peptide mis-cleavage, and missing peptide-spectrum matches [179, 180]. Our custom ligand-receptor resource included only well-established ligands and receptors. This approach likely filtered out biologically interesting ligands and receptors with less literature support. We measured literature support by the number of resources containing a ligand or receptor. This approach has limitations, as erroneous annotations are likely to be propagated across resources [142, 181]. Non-true ligands could be described in many resources, while biologically important ligands could be described in only one resource. For example, ISM1 [182] and LEAP2 [183] were recently identified as novel ligands involved in glucose metabolism. Neither of these ligands was present in our custom resource. Many ligands and receptors also function as part of multi-protein complexes [91], which our custom ligand-receptor resource did not consider. Overall, it is important to consider these limitations while interpreting the results presented in this study. 4.2 Future Directions We prioritized protein ligands and receptors that could be targeted in SC-islets to lead to a biological response. However, we could not predict the biological effect of targeting each ligand and receptor. Analysis tools such as NicheNet integrate knowledge of downstream 55 signaling pathways to predict the target genes of ligand-receptor interactions [94]. Future studies could use tools such as NicheNet to identify which prioritized ligands and receptors are most likely to lead to desired outcomes, such as increased expression of β-cell maturity markers. This study could be extended to incorporate data from novel experimental approaches. Combined scRNA-seq and spatial transcriptomics provide standard scRNA-seq data together with spatial information about the location of cells in tissues [184, 185]. Cells that are physically closer to each other are more likely to communicate with each other, as paracrine and juxtacrine signaling occurs over very short distances [186]. Therefore, this method would provide an additional layer of information for inferring intercellular communication. CellChat recently added support for spatial transcriptomics, enabling the inclusion of spatial information in the prediction of ligand-receptor interactions between cells. CellChat could be used to analyze a recently published dataset of combined scRNA-seq and spatial transcriptomics of the human fetal pancreas [187]. Analysis of fetal pancreas could also identify currently unknown intercellular signals that drive endocrine cell differentiation. As SCβ-cells resemble fetal, not adult β-cells [188], an improved understanding of intercellular communication in the developing pancreas could provide a blueprint for generating SCβ-cells in vitro. High-content screening is an effective approach to identify the roles of ligands and receptors in islets [126, 127]. To date, no unbiased screens of ligands and receptors have been published in SCβ-cells. We expect there are undiscovered pathways that can either be activated or inactivated in SCβ-cells to improve stem cell differentiation protocols. As exhaustive in vitro screening of all ligands and receptors may not be feasible, in silico prioritization can help focus efforts on candidate targets most likely to have a significant biological impact. The three prioritized lists presented in this study can guide future screens in stem cell-derived cells for 56 ligand treatment, receptor overexpression, and receptor knock-out. Based on previous studies, we expect these screens could discover ways to modify human embryonic stem cells or differentiation protocols to improve SCβ-cell survival [189], maturation [190, 191], or immune protection [192, 193]. 4.3 Overall conclusion Intercellular communication is critical to islet function, and its dysregulation is associated with diabetes. 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Thesis/Dissertation
eng
Genome Science and Technology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Johnson, James D.
Prioritization of ligands and receptors in human islets and stem cell-derived cells
Text