 Alexandra Cuaycal, PhD
University of Florida | Integrating IIDP Human Islet Data to Investigate Beta Cell Dysfunction in Diabetes
0000-0002-9060-6326
2025-2026 | Diabetes is a chronic, multifactorial metabolic disease. Both type 1 diabetes (T1D) and type 2 diabetes (T2D) are characterized by β-cell dysfunction and loss. While hundreds of genetic risk loci have been identified for T1D and T2D, their involvement in β-cell physiology is less understood. β-cell failure is attributed to lipo- and glucotoxicity in the case of T2D, whereas the metabolic deficit in T1D is less understood but attributed to β-cell loss. Nonetheless, recent studies have shown that residual β-cell mass persist, even in individuals with long-standing T1D. Similarly, recent findings demonstrate that the β-cell mass in prediabetic and early T2D is comparable to donors with no diabetes, despite declining β-cell function. Consequently, understanding the dynamics of β-cell failure and mass is crucial to advance therapies that focus on residual β-cells to prevent, delay onset or reverse disease. Our long-term goal is to elucidate the relationship between β-cell dysfunction and remining β-cell mass during T1D and T2D pathogenesis. The overall objectives of this application are to (i) characterize β-cell dysfunction in islets from T1D and T2D donors through integration of IIDP and HPAP's functional datasets and (ii) correlate first phase insulin response (FPIR), islet endocrine cell composition and diabetes risk score in T1D and T2D. Our research plan exploits the availability of functional data resources generated through organ procurement efforts. Our central hypothesis is that islets from T1D and T2D donors fail to respond to high glucose despite a residual β-cell mass. The rationale for this project is that the integration of functional, β-cell composition and diabetes risk data advances our knowledge of the relationship between β-cell dysfunction and mass, two major declining factors commonly affecting individuals genetically predisposed to progress to T1D or T2D. The central hypothesis will be tested by first characterizing β-cell dysfunction through an in-depth bioinformatics approach that includes the evaluation of FPIR by considering donor-donor basal insulin release variability. We will normalize perifusion data to the basal, low glucose (LG) stimulation and calculate area under the curve (AUC)/min. Hence, providing an unbiased measure of β-cell response to high glucose (HG) or KCl. Secondly, we will also fit a complex mixed-effects linear model (LMM) with a restricted maximum likelihood (REML) method to model the response to HG with islet endocrine cell composition (as β-cell fraction and α- and/or β-cell ratio), donor's diabetes risk (T1D GRS2 and T2D risk) and phenotypical parameters (sex, age, BMI, others). The research proposed is innovative because it will focus on the interplay between functional, islet endocrine cell composition, and donor's diabetes risk plus other parameters using a sophisticated bioinformatics pipeline to allow for a deep understanding of the relationship between β-cell function and β-cell mass. The proposed research is significant because it will advance our understanding of β-cell failure in at-risk individuals and provide insights into parameters important to design therapies aimed at preserving/restoring remaining β-cells in diabetes. |
 Fan Feng, PhD
Vanderbilt University Medical Center | Integrating Islet Cell Spatial Organization with Donor Characteristics and Function
0000-0002-5990-312X
2025-2026 | Pancreatic islet function is influenced by interactions among islet cells and paracrine signaling. Disruptions in cell-cell communication within islets can lead to β cell dysfunction, resulting in dysregulation of glucose homeostasis. While previous studies have highlighted differences in the spatial organization of human islets compared to those in mice, there remains a lack of comprehensive, large-scale analyses of the distribution of endocrine cells and their functional implications. This study aims to quantitatively define islet endocrine cell organization and its association with donor characteristics, islet function, and disease states. We will utilize an extensive dataset from the Integrated Islet Distribution Program (IIDP) Research Data Repository (RDR) and donor-matched histological images focusing on three primary endocrine cell types: α, β, and δ cells, available through Pancreatlas. We propose that characteristic patterns of islet endocrine cell organization can be captured by graph-based computational approaches. Additionally, we hypothesize that these organizational patterns correlate with disease states, donor characteristics (e.g., age, sex, ethnicity, and genetic risk scores), and islet physiology. To test the hypotheses, we will apply spatial analysis approaches, including statistical models and graph-based machine learning approaches, to extract quantitative features of >25,000 islets from ∼1,000 immunohistochemistry (IHC) images, corresponding to ∼500 organ donors. These features will be integrated with multimodal IIDP datasets, including genotype, demographics, disease states, and functional perifusion assays, to investigate their association with islet physiology. In Aim 1, we will process the images and construct a cell-neighbor graph for each islet, in which nodes represent cells, and edges capture spatial proximity and potential paracrine interactions between cells. We will compute graph features, including node degree distribution, centrality measures, and machine learning-based graph embeddings, to identify characteristic patterns of islet endocrine cell organization. In Aim 2, we will integrate these structural features with multimodal IIDP datasets, including genotype, donor demographics, disease states, and islet physiology. We plan to apply multivariate statistical models and machine learning approaches to examine how islet spatial organization correlates with donor characteristics and disease states. We will also link cell organization to islet physiology to assess whether specific organizational patterns predict altered insulin and glucagon secretion dynamics. The proposed studies will offer new insights into how spatial arrangements of endocrine cells relate to islet function and diabetes pathophysiology. By establishing a robust computational framework for islet spatial analysis, we aim to advance our understanding of human islet cell organization and its role in diabetes. The results of this study will have a direct impact on the islet biology field and provide insights for future therapeutic strategies aimed at preserving β cell function, as well as β cell replacement and regeneration. |
 Liu Wang, PhD
Mayo Clinic Arizona | Single Cell Multiomic Analysis Reveals Mechanisms of Beta Cell Dysfunction in Diabetes
0000-0001-8252-0170
2025-2026 | Type 2 diabetes (T2D) is a complex metabolic disorder that affects over 38.4 million Americans, with incidence expected to rise globally. The dysfunction of beta cells in pancreatic islets is a driving force of T2D pathogenesis. Genome-wide association studies (GWAS) have revealed that numerous T2D-associated SNPs (genetic variants) lie far from gene promoters, suggesting a crucial role for distal cis-elements in regulating gene expression. Mechanistically, SNPs located in enhancers can modulate target gene expression via variant-specific enhancer activity changes. Yet one challenge in studying the function of the diabetic variants is to identify the specific tissue/cell type that mediate the genetic risk. My preliminary studies using multiomic approach found that beta cell-specific primed enhancers, marked by specific histone modifications, are particularly vulnerable to metabolic stress. Therefore, I hypothesize that functional SNPs residing in cell type-specific primed enhancers mediate the binding of upstream transcription factors (TFs), which in turn result in changes in target gene expression and stress responses in human beta cells. The aim of this study is to determine the role of primed enhancer SNPs in modulating gene expression and diabetes risk. We have received human islets from the Integrated Islet Distribution Program (IIDP) and conducted Paired-Tag experiments along with bulk RNA-seq experiments to analyze gene regulation and enhancer activity. To test hypothesize, I will integrate donor characteristics, functional islet phenotyping, and genotyping data from IIDP's Research Data Repository with Paired-Tag data to systematically identify and functionally characterize primed enhancer SNPs in human pancreatic islets. We have integrated islet specific histone modification ChIP-seq data (H3K4me1 and H3K27ac) from in-house and public database to identify primed enhancer regions in pancreatic islets. Preliminary analysis showed that these enhancer regions contain GWAS SNPs linked to diabetes risk. First, I will investigate how donor-specific factors such as sex, BMI, and HbA1c levels influence enhancer-gene regulatory interactions. Next, I will integrate IIDP human islet genotyping data to identify donor SNPs within primed enhancers linked to T2D risk. I will quantify SNPs-containing enhancer activity to their target genes. Lastly, I will identify putative transcription factor binding at these SNP loci and examine whether SNPs disrupt enhancer function. Results from this aim will provide insight into how SNPs within primed enhancers contribute to diabetes susceptibility by regulating islet cell gene expression. Our study aims to uncover the regulatory roles of primed enhancer SNPs in gene expression and treatment response in diabetes. Findings will enhance our understanding of how genetic variants in primer enhancers contribute to beta cell specific expression dynamics and T2D risk. This project will characterize the role of a T2D-related variant in mediating differential anti-diabetic drug responses. Taken together, this project will pave the way for identifying new genetic biomarkers that could inform precision medicine strategies for diabetes management. |
 Lu Zhang, BS, PhD
Stanford University | Identification of Regulators of Insulin Secretion through Data Integration in Human Islets
0009-0003-6878-8834
2025-2026 | Dysregulation of glucose homeostasis is a feature of both Type 1 (T1D) and Type 2 diabetes (T2D) leading to chronic hyperglycemia which is a cause of both micro-and macro-vascular complications. Beta cells found in the pancreatic islets secrete insulin in response to elevated glucose after a meal and impaired secretion of insulin from these cells is a contributor to all forms of diabetes. In recent years, significant advancements have been made in our understanding of the genetic landscape for diabetes through the deployment of next-generation genotyping and sequencing approaches. Genome-Wide Association Studies (GWAS) have been widely applied, now revealing over a thousand disease-modifying genetic loci. Translation to improved understanding into the underlying molecular pathophysiology has been slow as most signals map to non-coding regions of the genome with presumed roles in gene regulation. Since this regulation is highly context specific related glycemic or anthropometric traits can provide crucial evidence informing on underlying cell types involved. Attempts to characterize the determinants of islet and beta cell function have been limited to the use of glycemic traits measured in blood (e.g. serum fasting glucose, insulin levels, and HbA1c) as surrogate measures for the underlying cellular processes. These measurements are poor proxies for insulin secretion due to confounding by insulin uptake in peripheral tissues and hepatic insulin clearance. Intravenous Glucose Tolerance Test (IVGTT) methods have attempted to measure secretion more directly through plasma C-peptide, yet this is similarly confounded by renal and hepatic clearance. Historical in vitro studies in human primary islets have been limited to a handful of genetic targets due to limited numbers of donors with paired donor functional readouts and genotyping. My project seeks to overcome these challenges by combining multimodal genetic, (epi)genomic, and cellular readouts of in vitro islet-cell function. My main objective is to identify genes, proteins and pathways which regulate insulin secretion in human islets which hold potential for therapeutic development, and precision medicine approaches. The Integrated Islet Distribution Program (IIDP) has assembled a collection of human ~250 donor islets with both deep cellular phenotyping and comprehensive array genotyping available through the Resource Data Repository (RDR). Utilizing a novel "GWAS-in-a-dish" framework, I aim to leverage this resource to systematically characterize genetic variants associated with islet-cell dysfunction genome-wide. I will capitalize on this recently established analytical framework, which has been optimized for small cohorts with rich multimodal data, to perform the first meta-analysis of human islet function across comparable human islet datasets, increasing statistical power to robustly identify genetic associations. Furthermore, with access to a state-of-the-art co-localization pipeline, I will establish which non-coding variants influence whole-islet gene expression (RNA-seq), chromatin accessibility (ATAC-seq), and protein abundance (Mass-Spec) thereby providing insight into the underlying molecular pathways. Collectively, my study will identify proteins and pathways in the islet which modulate human secretory function. My study will deliver novel insights into the molecular mechanisms governing diabetes pathogenesis, with potential to reveal opportunities for diabetes precision medicine. |