Biology subjects

Brissova, M.

Publications and source records attributed to Brissova, M..

14 recordsLinked to original sources

bioRxiv2026

The Human Pancreas Cell Atlas defines a healthy reference framework for disease contextualization and translational benchmarking

A central challenge in single-cell biology is distinguishing disease-associated remodeling from normal cellular heterogeneity. Addressing this challenge requires healthy reference frameworks that capture cellular diversity across individuals, technologies, and biological contexts. Here we present the Human Pancreas Cell Atlas (HPCA), a reference atlas of the healthy human pancreas integrating 815,126 single-cell and single-nucleus transcriptomes from 109 donors across 12 studies, diverse technologies, and demographics. Using benchmarked integration and community-driven annotations, HPCA defines 94 cell types and transcriptional states spanning endocrine, exocrine, immune, and stromal compartments. The atlas identifies rare endocrine populations, including a putative, spatially supported polyhormonal alpha-beta-delta state, and provides a unified framework for interpreting pancreatic cellular variation across diverse biological and demographic covariates. Projection of disease and model-system datasets onto HPCA contextualized endocrine and epithelial remodeling relative to healthy pancreatic states. Diabetes-associated endocrine cells remained embedded within the healthy endocrine state space while exhibiting disease-specific changes, as supported by spatial and eQTL concordance analyses. Integration with a pancreatic ductal adenocarcinoma atlas resolved injury-associated and malignant epithelial ecosystem regions across donors. Finally, the HPCA enables quantitative benchmarking of murine diabetes models and stem-cell-derived islets against human pancreatic reference states. Together, the HPCA establishes a healthy transcriptional coordinate system for interpreting disease-associated pathophysiology, experimental perturbation, and regenerative fidelity, illustrating how reference atlases can function as analytical frameworks rather than static cell catalogs.

bioinformatics↗

bioRxiv2026

PanKbase Integrated Single-Cell Map: A Comprehensive Atlas of Human Pancreatic Islets

Single-cell RNA sequencing (scRNA-seq) of human pancreatic islet tissue is a powerful tool for investigating type 1 diabetes (T1D). However, individual datasets are limited in size and fragmented across donors, laboratories, and experimental conditions. To address this, we constructed a comprehensive, integrated scRNA-seq atlas of isolated human pancreatic islets by collating publicly available data generated from tissue provided by resources including the Human Pancreas Analysis Program, the Integrated Islet Distribution Program, and Prodo Labs. Systematic quality controls were implemented to select high-quality samples, reads, and cells. During integration, we accounted for important variables such as age, sex, body mass index, origin study, treatments, islet distribution resources, and sequencing chemistry. Our single-cell atlas comprises 191 high-quality samples from 140 donors (59 female, 81 male) across five phenotypic groups: no diabetes (controls, n=69), autoantibody positivity without diabetes (n=12), pre-diabetes (n=11), T1D (n=12), and type 2 diabetes (T2D) (n=36). In total, the atlas contains 448,935 cells, capturing 13 distinct populations, including alpha cells (43.3%) and beta cells (26.8%), as well as groups such as immune cells (0.6%). Publicly available at www.pankbase.org, this atlas provides a platform for hypothesis-driven investigation of diabetes pathophysiology and, given rigorous quality control, is well-suited for downstream machine-learning applications. Article HighlightsO_LICurrent scRNA-seq datasets of pancreatic islet tissue are limited in size and scattered across donors, laboratories, and experimental conditions, underscoring the need for a consolidated resource. C_LIO_LIWe harmonized datasets from multiple sources to build a comprehensive single-cell map of isolated human pancreatic islets. C_LIO_LIOur atlas captures 448,935 cells from 191 high-quality samples across 140 donors and multiple phenotypic groups, identifying 13 distinct cell populations. C_LIO_LIAvailable at www.pankbase.org, the atlas provides a scalable, rigorously curated platform to support hypothesis-driven diabetes research and can enable a broad range of downstream computational applications. C_LI

genomics↗

bioRxiv2024

Mapping histological and functional maturation of human endocrine pancreas across early postnatal periods

Human endocrine cell differentiation and islet morphogenesis play critical roles in determining islet cell mass and function, but the events and timeline of these processes are incompletely defined. To better understand early human islet cell development and maturation, we collected 115 pediatric pancreata and mapped morphological and spatiotemporal changes from birth through the first ten years of life. Using quantitative analyses and a combination of complementary tissue imaging approaches, including confocal microscopy and whole-slide imaging, we developed an integrated model for endocrine cell formation and islet architecture, including endocrine cell type heterogeneity and abundance, endocrine cell proliferation, and islet vascularization and innervation. We also assessed insulin and glucagon secretory profiles in isolated islet preparations from pediatric donors aged 2 months to 10 years and found a temporal difference in the maturation of insulin secretion compared to glucagon secretion. This comprehensive summary of postnatal and pediatric pancreatic islet development provides a framework for future studies and integration of emerging genetic and genomic data related to islet biology and diabetes risk.

developmental biology↗

bioRxiv2024

Heterogeneous endocrine cell composition defines human islet functional phenotypes

Phenotyping and genotyping initiatives within the Integrated Islet Distribution Program (IIDP), the largest source of human islets for research in the U.S., provide standardized assessment of islet preparations distributed to researchers, enabling the integration of multiple data types. Data from islets of the first 299 organ donors without diabetes, analyzed using this pipeline, highlights substantial heterogeneity in islet cell composition associated with hormone secretory traits, sex, reported race and ethnicity, genetically predicted ancestry, and genetic risk for type 2 diabetes (T2D). While and {beta} cell composition influenced insulin and glucagon secretory traits, the abundance of {delta} cells showed the strongest association with insulin secretion and was also associated with the genetic risk score (GRS) for T2D. These findings have important implications for understanding mechanisms underlying diabetes heterogeneity and islet dysfunction and may provide insight into strategies for personalized medicine and {beta} cell replacement therapy.

physiology↗

bioRxiv2024

Building a literature knowledge base towards transparent biomedical AI

As artificial intelligence (AI) continues to advance and scale up in biomedical research, concerns about AIs trustworthiness and transparency have grown. There is a critical need to systematically bring accurate and relevant biomedical knowledge into AI applications for transparency and provenance. Knowledge graphs have emerged as a powerful tool that integrates heterogeneous knowledge by explicitly describing biomedical knowledge as entities and relationships between entities. However, PubMed, the largest and most comprehensive repository of biomedical knowledge, exists primarily as unstructured text and is under utilized for advanced machine learning tasks. To address the challenge, we developed LiteralGraph, a computational framework to extract biomedical terms and relationships from PubMed literature into a unified knowledge graph. Using this framework, we established the Genomic Literature Knowledge Base (GLKB), which consolidates 14,634,427 biomedical relationships between 3,276,336 biomedical terms from over 33 million PubMed abstracts and nine well-established biomedical repositories. The database is coupled with RESTful APIs and a user-friendly web interface that makes it accessible to researchers for various usages. We demonstrated the broad utility of GLKB towards transparent AI in three distinct application scenarios. In the LLM grounding scenario, we developed a Retrieval Augmented Generation (RAG) agent to reduce LLM hallucination in biomedical question answering. In the hypothesis generation scenario, we elucidated the potential functions of RFX6 in type 2 diabetes (T2D) using the vast evidence from PubMed articles. In the machine learning scenario, we utilized GLKB to provide semantic knowledge in predictive tasks and scientific fact-checking.

bioinformatics↗

bioRxiv2023

AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs, we developed AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX show the superior performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulated known islet pathobiology and showed differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

bioinformatics↗

bioRxiv2021

RFX6-mediated dysregulation defines human β cell dysfunction in early type 2 diabetes

A hallmark of type 2 diabetes (T2D), a major cause of world-wide morbidity and mortality, is dysfunction of insulin-producing pancreatic islet {beta} cells1-3. T2D genome-wide association studies (GWAS) have identified hundreds of signals, mostly in the non-coding genome and overlapping {beta} cell regulatory elements, but translating these into biological mechanisms has been challenging4-6. To identify early disease-driving events, we performed single cell spatial proteomics, sorted cell transcriptomics, and assessed islet physiology on pancreatic tissue from short-duration T2D and control donors. Here, through integrative analyses of these diverse modalities, we show that multiple gene regulatory modules are associated with early-stage T2D {beta} cell-intrinsic defects. One notable example is the transcription factor RFX6, which we show is a highly connected {beta} cell hub gene that is reduced in T2D and governs a gene regulatory network associated with insulin secretion defects and T2D GWAS variants. We validated the critical role of RFX6 in {beta} cells through direct perturbation in primary human islets followed by physiological and single nucleus multiome profiling, which showed reduced dynamic insulin secretion and large-scale changes in the {beta} cell transcriptome and chromatin accessibility landscape. Understanding the molecular mechanisms of complex, systemic diseases necessitates integration of signals from multiple molecules, cells, organs, and individuals and thus we anticipate this approach will be a useful template to identify and validate key regulatory networks and master hub genes for other diseases or traits with GWAS data.

cell biology↗

bioRxiv2021

Alpha cell dysfunction in early type 1 diabetes

Multiple islet autoantibodies (AAb) predict type 1 diabetes (T1D) and hyperglycemia within 10 years. By contrast, T1D develops in just [~]15% of single AAb+ (generally against glutamic acid decarboxylase, GADA+) individuals; hence the single GADA+ state may represent an early stage of T1D amenable to interventions. Here, we functionally, histologically, and molecularly phenotype human islets from non-diabetic, GADA+ and T1D donors. Similar to the few remaining beta cells in T1D islets, GADA+ donor islets demonstrated a preserved insulin secretory response. By contrast, alpha cell glucagon secretion was dysregulated in both T1D and GADA+ islets with impaired glucose suppression of glucagon secretion. Single cell RNA sequencing (scRNASeq) of GADA+ alpha cells revealed distinct abnormalities in glycolysis and oxidative phosphorylation pathways and a marked downregulation of PKIB, providing a molecular basis for the loss of glucose suppression and the increased effect of IBMX observed in GADA+ donor islets. The striking observation of a distinct early defect in alpha cell function that precedes beta cell loss in T1D suggests that not only overt disease, but also the progression to T1D itself, is bihormonal in nature.

physiology↗

bioRxiv2021

Combinatorial transcription factor profiles predict mature and functional human islet α and β cells

Islet-enriched transcription factors (TFs) exert broad control over cellular processes in pancreatic and {beta} cells and changes in their expression are associated with developmental state and diabetes. However, the implications of heterogeneity in TF expression across islet cell populations are not well understood. To define this TF heterogeneity and its consequences for cellular function, we profiled >40,000 cells from normal human islets by scRNA-seq and stratified and {beta} cells based on combinatorial TF expression. Subpopulations of islet cells co-expressing ARX/MAFB ( cells) and MAFA/MAFB ({beta} cells) exhibited greater expression of key genes related to glucose sensing and hormone secretion relative to subpopulations expressing only one or neither TF. Moreover, all subpopulations were identified in native pancreatic tissue from multiple donors. By Patch-seq, MAFA/MAFB co-expressing {beta} cells showed enhanced electrophysiological activity. Thus, these results indicate combinatorial TF expression in islet and {beta} cells predicts highly functional, mature subpopulations.

genomics↗

bioRxiv2020

SARS-CoV-2 Cell Entry Factors ACE2 and TMPRSS2 are Expressed in the Pancreas but Not in Islet Endocrine Cells

Summary/AbstractReports of new-onset diabetes and diabetic ketoacidosis in individuals with COVID-19 have led to the hypothesis that SARS-CoV-2, the virus that causes COVID-19, is directly cytotoxic to pancreatic islet {beta} cells. This would require binding and entry of SARS-CoV-2 into host {beta} cells via cell surface co-expression of ACE2 and TMPRSS2, the putative receptor and effector protease, respectively. To define ACE2 and TMPRSS2 expression in the human pancreas, we examined six transcriptional datasets from primary human islet cells and assessed protein expression by immunofluorescence in pancreata from donors with and without diabetes. ACE2 and TMPRSS2 transcripts were low or undetectable in pancreatic islet endocrine cells as determined by bulk or single cell RNA sequencing, and neither protein was detected in or {beta} cells from these donors. Instead, ACE2 protein was expressed in the islet and exocrine tissue microvasculature and also found in a subset of pancreatic ducts, whereas TMPRSS2 protein was restricted to ductal cells. The absence of significant ACE2 and TMPRSS2 co-expression in islet endocrine cells reduces the likelihood that SARS-CoV-2 directly infects pancreatic islet {beta} cells through these cell entry proteins.

cell biology↗

bioRxiv2020

Coordinated interactions between endothelial cells and macrophages in the islet microenvironment promote beta cell regeneration

Endogenous {beta} cell regeneration could alleviate diabetes, but proliferative stimuli within the islet microenvironment are incompletely understood. We previously found that {beta} cell recovery following hypervascularization-induced {beta} cell loss involves interactions with endothelial cells (ECs) and macrophages (M{Phi}s). Here we show that proliferative ECs modulate M{Phi} infiltration and phenotype during {beta} cell loss, and recruited M{Phi}s are essential for {beta} cell recovery. Furthermore, VEGFR2 inactivation in quiescent ECs accelerates islet vascular regression during {beta} cell recovery and leads to increased {beta} cell proliferation without changes in M{Phi} phenotype or number. Transcriptome analysis of {beta} cells, ECs, and M{Phi}s reveals that {beta} cell proliferation coincides with elevated expression of extracellular matrix remodeling molecules and growth factors likely driving activation of proliferative signaling pathways in {beta} cells. Collectively, these findings suggest a new {beta} cell regeneration paradigm whereby coordinated interactions between intra-islet M{Phi}s, ECs, and extracellular matrix mediate {beta} cell self-renewal.

cell biology↗

bioRxiv2020

Pancreatlas: a comprehensive and interactive image atlas of the human pancreas in health and disease

Human tissue phenotyping generates complex spatial information from numerous imaging modalities, yet images typically become static figures for publication and original data and metadata are rarely available. While comprehensive image maps exist for some organs, most resources have limited support for multiplexed imaging or have non-intuitive user interfaces. Therefore, we built a Pancreatlas resource that integrates several technologies into a novel interface, allowing users to access richly annotated web pages, drill down to individual images, and deeply explore data online. The current version of Pancreatlas contains over 800 unique images acquired by whole-slide scanning, confocal microscopy, and imaging mass cytometry, and is available at https://www.pancreatlas.org. To create this human pancreas-specific biological imaging resource, we developed a React-based web application and Python-based application programming interface, collectively called Flexible Framework for Integrating and Navigating Data (FFIND), which can be adapted beyond Pancreatlas to meet countless imaging or other structured data management needs.

cell biology↗

bioRxiv2019

Human pseudoislet system enables detection of differences in G-protein-coupled-receptor signaling pathways between α and β cells

G-protein-coupled-receptors (GPCRs) modulate insulin secretion from {beta} cells and glucagon secretion from cells. Here, we developed an integrated approach to study the function of primary human islet cells using genetically modified pseudoislets that resemble native islets across multiple parameters. We studied the Gi and Gq GPCR pathways by expressing the designer receptors exclusively activated by designer drugs (DREADDs) hM4Di or hM3Dq. Activation of Gi signaling reduced insulin and glucagon secretion, while activation of Gq signaling stimulated glucagon secretion but had both stimulatory and inhibitory effects on insulin secretion. Further, we developed a microperifusion system that allowed synchronous acquisition of GCaMP6f biosensor signal and hormone secretory profiles and showed that the dual effects for Gq signaling occur through changes in intracellular Ca2+. By combining pseudoislets with a microfluidic system, we co-registered intracellular signaling dynamics and hormone secretion and demonstrated differences in GPCR signaling pathways between human {beta} and cells.

cell biology↗

bioRxiv2019

Myt Transcription Factors prevent stress-response gene over-activation to enable postnatal pancreatic β cell proliferation and function

Although stress response maintains cell function and survival under adverse conditions, over-activation of late-stage stress-gene effectors causes dysfunction and death. Here we show that the Myelin Transcription Factors (Myt 1, 2, and 3 TFs) prevent this over-activation. Co-inactivating Myt TFs in mouse pancreatic progenitors compromised postnatal {beta}-cell function, proliferation, and survival, preceded by upregulation of late-stage stress-response genes Activating Transcription Factors (e.g., Atf4) and Heat Shock Proteins (Hsps). Myt1 binds the putative enhancers of Atf4 and Hsps, whose over-expression in mouse {beta} cells largely recapitulated the Myt mutant phenotypes. Moreover, Myt(MYT)-TF levels were upregulated in functional mouse and human {beta} cells by metabolic stress but downregulated in those of type 2 diabetic islets that display ATF4 and HSP over-activation. Lastly, human MYT knockdown caused stress-gene over-activation and death in Endo-{beta}H1 cells. These findings suggest that the Myt TFs restrict stress-response to physiologically tolerable levels in mice and human.

developmental biology↗