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Wu X, Yang X, Dai Y, Zhao Z, Zhu J, Guo H, Yang R. Single-cell sequencing to multi-omics: technologies and applications. Biomark Res 2024; 12:110. [PMID: 39334490 PMCID: PMC11438019 DOI: 10.1186/s40364-024-00643-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 08/17/2024] [Indexed: 09/30/2024] Open
Abstract
Cells, as the fundamental units of life, contain multidimensional spatiotemporal information. Single-cell RNA sequencing (scRNA-seq) is revolutionizing biomedical science by analyzing cellular state and intercellular heterogeneity. Undoubtedly, single-cell transcriptomics has emerged as one of the most vibrant research fields today. With the optimization and innovation of single-cell sequencing technologies, the intricate multidimensional details concealed within cells are gradually unveiled. The combination of scRNA-seq and other multi-omics is at the forefront of the single-cell field. This involves simultaneously measuring various omics data within individual cells, expanding our understanding across a broader spectrum of dimensions. Single-cell multi-omics precisely captures the multidimensional aspects of single-cell transcriptomes, immune repertoire, spatial information, temporal information, epitopes, and other omics in diverse spatiotemporal contexts. In addition to depicting the cell atlas of normal or diseased tissues, it also provides a cornerstone for studying cell differentiation and development patterns, disease heterogeneity, drug resistance mechanisms, and treatment strategies. Herein, we review traditional single-cell sequencing technologies and outline the latest advancements in single-cell multi-omics. We summarize the current status and challenges of applying single-cell multi-omics technologies to biological research and clinical applications. Finally, we discuss the limitations and challenges of single-cell multi-omics and potential strategies to address them.
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Affiliation(s)
- Xiangyu Wu
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Xin Yang
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Yunhan Dai
- Medical School, Nanjing University, Nanjing, China
| | - Zihan Zhao
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China
| | - Junmeng Zhu
- Department of Oncology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China
| | - Hongqian Guo
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
| | - Rong Yang
- Department of Urology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, 321 Zhongshan Road, Nanjing, 210008, Jiangsu, China.
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2
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Guan H, Zhao S, Li J, Wang Y, Niu P, Zhang Y, Zhang Y, Fang X, Miao R, Tian J. Exploring the design of clinical research studies on the efficacy mechanisms in type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2024; 15:1363877. [PMID: 39371930 PMCID: PMC11449758 DOI: 10.3389/fendo.2024.1363877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/08/2024] [Accepted: 08/23/2024] [Indexed: 10/08/2024] Open
Abstract
This review examines the complexities of Type 2 Diabetes Mellitus (T2DM), focusing on the critical role of integrating omics technologies with traditional experimental methods. It underscores the advancements in understanding the genetic diversity of T2DM and emphasizes the evolution towards personalized treatment modalities. The paper analyzes a variety of omics approaches, including genomics, methylation, transcriptomics, proteomics, metabolomics, and intestinal microbiomics, delineating their substantial contributions to deciphering the multifaceted mechanisms underlying T2DM. Furthermore, the review highlights the indispensable role of non-omics experimental techniques in comprehending and managing T2DM, advocating for their integration in the development of tailored medicine and precision treatment strategies. By identifying existing research gaps and suggesting future research trajectories, the review underscores the necessity for a comprehensive, multidisciplinary approach. This approach synergistically combines clinical insights with cutting-edge biotechnologies, aiming to refine the management and therapeutic interventions of T2DM, and ultimately enhancing patient outcomes. This synthesis of knowledge and methodologies paves the way for innovative advancements in T2DM research, fostering a deeper understanding and more effective treatment of this complex condition.
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Affiliation(s)
- Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Shuang Zhao
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Jiarui Li
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Ying Wang
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, China
| | - Ping Niu
- Department of Encephalopathy, The Affiliated Hospital of Changchun university of Chinese Medicine, Jilin, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Xinyi Fang
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate College, Beijing University of Chinese Medicine, Beijing, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- Graduate College, Beijing University of Chinese Medicine, Beijing, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang’anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
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3
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Rutter GA, Gresch A, Delgadillo Silva L, Benninger RKP. Exploring pancreatic beta-cell subgroups and their connectivity. Nat Metab 2024:10.1038/s42255-024-01097-6. [PMID: 39117960 DOI: 10.1038/s42255-024-01097-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 07/05/2024] [Indexed: 08/10/2024]
Abstract
Functional pancreatic islet beta cells are essential to ensure glucose homeostasis across species from zebrafish to humans. These cells show significant heterogeneity, and emerging studies have revealed that connectivity across a hierarchical network is required for normal insulin release. Here, we discuss current thinking and areas of debate around intra-islet connectivity, cellular hierarchies and potential "controlling" beta-cell populations. We focus on methodologies, including comparisons of different cell preparations as well as in vitro and in vivo approaches to imaging and controlling the activity of human and rodent islet preparations. We also discuss the analytical approaches that can be applied to live-cell data to identify and study critical subgroups of cells with a disproportionate role in control Ca2+ dynamics and thus insulin secretion (such as "first responders", "leaders" and "hubs", as defined by Ca2+ responses to glucose stimulation). Possible mechanisms by which this hierarchy is achieved, its physiological relevance and how its loss may contribute to islet failure in diabetes mellitus are also considered. A glossary of terms and links to computational resources are provided.
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Affiliation(s)
- Guy A Rutter
- CHUM Research Center and Faculty of Medicine, University of Montréal, Montréal, QC, Canada.
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
| | - Anne Gresch
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Luis Delgadillo Silva
- CHUM Research Center and Faculty of Medicine, University of Montréal, Montréal, QC, Canada
| | - Richard K P Benninger
- Department of Bioengineering, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
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4
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Mummey HM, Elison W, Korgaonkar K, Elgamal RM, Kudtarkar P, Griffin E, Benaglio P, Miller M, Jha A, Fox JEM, McCarthy MI, Preissl S, Gloyn AL, MacDonald PE, Gaulton KJ. Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.03.606460. [PMID: 39149326 PMCID: PMC11326183 DOI: 10.1101/2024.08.03.606460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.
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Affiliation(s)
- Hannah M Mummey
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
| | - Weston Elison
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Katha Korgaonkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Ruth M Elgamal
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Emily Griffin
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Paola Benaglio
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
| | - Alokkumar Jha
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Mark I McCarthy
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK*
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Patrick E MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
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5
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Ewald JD, Lu Y, Ellis CE, Worton J, Kolic J, Sasaki S, Zhang D, dos Santos T, Spigelman AF, Bautista A, Dai XQ, Lyon JG, Smith NP, Wong JM, Rajesh V, Sun H, Sharp SA, Rogalski JC, Moravcova R, Cen HH, Manning Fox JE, Atlas E, Bruin JE, Mulvihill EE, Verchere CB, Foster LJ, Gloyn AL, Johnson JD, Pepper AR, Lynn FC, Xia J, MacDonald PE. HumanIslets: An integrated platform for human islet data access and analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.19.599613. [PMID: 38948734 PMCID: PMC11212983 DOI: 10.1101/2024.06.19.599613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Comprehensive molecular and cellular phenotyping of human islets can enable deep mechanistic insights for diabetes research. We established the Human Islet Data Analysis and Sharing (HI-DAS) consortium to advance goals in accessibility, usability, and integration of data from human islets isolated from donors with and without diabetes at the Alberta Diabetes Institute (ADI) IsletCore. Here we introduce HumanIslets.com, an open resource for the research community. This platform, which presently includes data on 547 human islet donors, allows users to access linked datasets describing molecular profiles, islet function and donor phenotypes, and to perform various statistical and functional analyses at the donor, islet and single-cell levels. As an example of the analytic capacity of this resource we show a dissociation between cell culture effects on transcript and protein expression, and an approach to correct for exocrine contamination found in hand-picked islets. Finally, we provide an example workflow and visualization that highlights links between type 2 diabetes status, SERCA3b Ca2+-ATPase levels at the transcript and protein level, insulin secretion and islet cell phenotypes. HumanIslets.com provides a growing and adaptable set of resources and tools to support the metabolism and diabetes research community.
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Affiliation(s)
- Jessica D. Ewald
- Institute of Parasitology, McGill University, Montreal, QC
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yao Lu
- Institute of Parasitology, McGill University, Montreal, QC
| | - Cara E. Ellis
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Jessica Worton
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Jelena Kolic
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Shugo Sasaki
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Dahai Zhang
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Theodore dos Santos
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Aliya F. Spigelman
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Austin Bautista
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
| | - Xiao-Qing Dai
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - James G. Lyon
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
| | - Nancy P. Smith
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | - Jordan M. Wong
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Varsha Rajesh
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Han Sun
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Seth A. Sharp
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - Jason C. Rogalski
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Renata Moravcova
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Haoning H Cen
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Jocelyn E. Manning Fox
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
| | | | - Ella Atlas
- Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON
| | - Jennifer E. Bruin
- Department of Biology & Institute of Biochemistry, Carleton University, Ottawa, ON
| | - Erin E. Mulvihill
- Department of Biochemistry, Microbiology and Immunology, University of Ottawa, ON
- University of Ottawa Heart Institute, Ottawa, ON
| | - C. Bruce Verchere
- Department of Surgery, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, BC
- Department of Pathology and Laboratory Medicine, BC Children’s Hospital Research Institute and University of British Columbia, Vancouver, BC
- Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver, BC
| | - Leonard J. Foster
- Department of Biochemistry & Molecular Biology, Michael Smith Laboratories, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Anna L. Gloyn
- Department of Pediatrics, Division of Endocrinology, Stanford School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA
| | - James D. Johnson
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, Vancouver, BC
| | - Andrew R. Pepper
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Surgery, University of Alberta, Edmonton, AB
| | - Francis C. Lynn
- Diabetes Research Group, BC Children’s Hospital Research Institute, Vancouver, BC, Canada
- Department of Surgery, School of Biomedical Engineering, University of British Columbia, Vancouver, BC
| | - Jianguo Xia
- Institute of Parasitology, McGill University, Montreal, QC
| | - Patrick E. MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, AB
- Department of Pharmacology, University of Alberta, Edmonton, AB
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6
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Schwartz SS, Herman ME. Gluco-regulation & type 2 diabetes: entrenched misconceptions updated to new governing principles for gold standard management. Front Endocrinol (Lausanne) 2024; 15:1394805. [PMID: 38933821 PMCID: PMC11199379 DOI: 10.3389/fendo.2024.1394805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Accepted: 05/16/2024] [Indexed: 06/28/2024] Open
Abstract
Our understanding of type 2 diabetes (T2D) has evolved dramatically. Advances have upended entrenched dogmas pertaining to the onset and progression of T2D, beliefs that have prevailed from the early era of diabetes research-and continue to populate our medical textbooks and continuing medical education materials. This review article highlights key insights that lend new governing principles for gold standard management of T2D. From the historical context upon which old beliefs arose to new findings, this article outlines evidence and perspectives on beta cell function, the underlying defects in glucoregulation, the remediable nature of T2D, and, the rationale supporting the shift to complication-centric prescribing. Practical approaches translate this rectified understanding of T2D into strategies that fill gaps in current management practices of prediabetes through late type 2 diabetes.
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Affiliation(s)
- Stanley S. Schwartz
- Main Line Health, Wynnewood, PA, and University of Pennsylvania, Philadelphia, PA, United States
| | - Mary E. Herman
- Social Alchemy: Building Physician Competency Across the Globe, Sacatepéquez, Guatemala
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7
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Yu V, Yong F, Marta A, Khadayate S, Osakwe A, Bhattacharya S, Varghese SS, Chabosseau P, Tabibi SM, Chen K, Georgiadou E, Parveen N, Suleiman M, Stamoulis Z, Marselli L, De Luca C, Tesi M, Ostinelli G, Delgadillo-Silva L, Wu X, Hatanaka Y, Montoya A, Elliott J, Patel B, Demchenko N, Whilding C, Hajkova P, Shliaha P, Kramer H, Ali Y, Marchetti P, Sladek R, Dhawan S, Withers DJ, Rutter GA, Millership SJ. Differential CpG methylation at Nnat in the early establishment of beta cell heterogeneity. Diabetologia 2024; 67:1079-1094. [PMID: 38512414 PMCID: PMC11058053 DOI: 10.1007/s00125-024-06123-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 01/09/2024] [Indexed: 03/23/2024]
Abstract
AIMS/HYPOTHESIS Beta cells within the pancreatic islet represent a heterogenous population wherein individual sub-groups of cells make distinct contributions to the overall control of insulin secretion. These include a subpopulation of highly connected 'hub' cells, important for the propagation of intercellular Ca2+ waves. Functional subpopulations have also been demonstrated in human beta cells, with an altered subtype distribution apparent in type 2 diabetes. At present, the molecular mechanisms through which beta cell hierarchy is established are poorly understood. Changes at the level of the epigenome provide one such possibility, which we explore here by focusing on the imprinted gene Nnat (encoding neuronatin [NNAT]), which is required for normal insulin synthesis and secretion. METHODS Single-cell RNA-seq datasets were examined using Seurat 4.0 and ClusterProfiler running under R. Transgenic mice expressing enhanced GFP under the control of the Nnat enhancer/promoter regions were generated for FACS of beta cells and downstream analysis of CpG methylation by bisulphite sequencing and RNA-seq, respectively. Animals deleted for the de novo methyltransferase DNA methyltransferase 3 alpha (DNMT3A) from the pancreatic progenitor stage were used to explore control of promoter methylation. Proteomics was performed using affinity purification mass spectrometry and Ca2+ dynamics explored by rapid confocal imaging of Cal-520 AM and Cal-590 AM. Insulin secretion was measured using homogeneous time-resolved fluorescence imaging. RESULTS Nnat mRNA was differentially expressed in a discrete beta cell population in a developmental stage- and DNA methylation (DNMT3A)-dependent manner. Thus, pseudo-time analysis of embryonic datasets demonstrated the early establishment of Nnat-positive and -negative subpopulations during embryogenesis. NNAT expression is also restricted to a subset of beta cells across the human islet that is maintained throughout adult life. NNAT+ beta cells also displayed a discrete transcriptome at adult stages, representing a subpopulation specialised for insulin production, and were diminished in db/db mice. 'Hub' cells were less abundant in the NNAT+ population, consistent with epigenetic control of this functional specialisation. CONCLUSIONS/INTERPRETATION These findings demonstrate that differential DNA methylation at Nnat represents a novel means through which beta cell heterogeneity is established during development. We therefore hypothesise that changes in methylation at this locus may contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes. DATA AVAILABILITY The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD048465.
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Affiliation(s)
- Vanessa Yu
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Fiona Yong
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore
| | - Angellica Marta
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | | | - Adrien Osakwe
- Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada
| | - Supriyo Bhattacharya
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Sneha S Varghese
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA, USA
| | - Pauline Chabosseau
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Sayed M Tabibi
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Keran Chen
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Biomedical Research Centre, School of Biological Sciences, University of East Anglia, Norwich, UK
| | - Eleni Georgiadou
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
| | - Nazia Parveen
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA, USA
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Zoe Stamoulis
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK
- Medical Sciences Division, University of Oxford, Oxford, UK
| | - Lorella Marselli
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Carmela De Luca
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Marta Tesi
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Giada Ostinelli
- CHUM Research Center and Faculty of Medicine, University of Montréal, Montréal, QC, Canada
| | - Luis Delgadillo-Silva
- CHUM Research Center and Faculty of Medicine, University of Montréal, Montréal, QC, Canada
| | - Xiwei Wu
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, Duarte, CA, USA
| | - Yuki Hatanaka
- MRC Laboratory of Medical Sciences, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | | | | | | | - Nikita Demchenko
- MRC Laboratory of Medical Sciences, London, UK
- Imaging Resource Facility, Research Operations, St George's, University of London, London, UK
| | | | - Petra Hajkova
- MRC Laboratory of Medical Sciences, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | | | | | - Yusuf Ali
- Nutrition, Metabolism and Health Programme & Centre for Microbiome Medicine, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, Republic of Singapore
- Singapore Eye Research Institute (SERI), Singapore General Hospital, Singapore, Republic of Singapore
- Clinical Research Unit, Khoo Teck Puat Hospital, National Healthcare Group, Singapore, Republic of Singapore
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa, Italy
| | - Robert Sladek
- Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada
- Departments of Medicine and Human Genetics, McGill University, Montréal, QC, Canada
| | - Sangeeta Dhawan
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, Duarte, CA, USA
| | - Dominic J Withers
- MRC Laboratory of Medical Sciences, London, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, London, UK
| | - Guy A Rutter
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.
- CHUM Research Center and Faculty of Medicine, University of Montréal, Montréal, QC, Canada.
| | - Steven J Millership
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, London, UK.
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8
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Deshmukh A, Chang K, Cuala J, Vanslembrouck B, Georgia S, Loconte V, White KL. Subcellular Feature-Based Classification of α and β Cells Using Soft X-ray Tomography. Cells 2024; 13:869. [PMID: 38786091 PMCID: PMC11119489 DOI: 10.3390/cells13100869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 05/25/2024] Open
Abstract
The dysfunction of α and β cells in pancreatic islets can lead to diabetes. Many questions remain on the subcellular organization of islet cells during the progression of disease. Existing three-dimensional cellular mapping approaches face challenges such as time-intensive sample sectioning and subjective cellular identification. To address these challenges, we have developed a subcellular feature-based classification approach, which allows us to identify α and β cells and quantify their subcellular structural characteristics using soft X-ray tomography (SXT). We observed significant differences in whole-cell morphological and organelle statistics between the two cell types. Additionally, we characterize subtle biophysical differences between individual insulin and glucagon vesicles by analyzing vesicle size and molecular density distributions, which were not previously possible using other methods. These sub-vesicular parameters enable us to predict cell types systematically using supervised machine learning. We also visualize distinct vesicle and cell subtypes using Uniform Manifold Approximation and Projection (UMAP) embeddings, which provides us with an innovative approach to explore structural heterogeneity in islet cells. This methodology presents an innovative approach for tracking biologically meaningful heterogeneity in cells that can be applied to any cellular system.
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Affiliation(s)
- Aneesh Deshmukh
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
| | - Kevin Chang
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
| | - Janielle Cuala
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
- Medical Biophysics Program, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Bieke Vanslembrouck
- Department of Anatomy, School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Senta Georgia
- Department of Stem Cell Biology and Regenerative Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Valentina Loconte
- Department of Anatomy, School of Medicine, University of California San Francisco, San Francisco, CA 94143, USA
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Kate L. White
- Department of Chemistry, Bridge Institute, Michelson Center for Convergent Bioscience, University of Southern California, Los Angeles, CA 90089, USA; (A.D.); (K.C.)
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
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9
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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10
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Leenders F, de Koning EJP, Carlotti F. Pancreatic β-Cell Identity Change through the Lens of Single-Cell Omics Research. Int J Mol Sci 2024; 25:4720. [PMID: 38731945 PMCID: PMC11083883 DOI: 10.3390/ijms25094720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 04/12/2024] [Accepted: 04/17/2024] [Indexed: 05/13/2024] Open
Abstract
The main hallmark in the development of both type 1 and type 2 diabetes is a decline in functional β-cell mass. This decline is predominantly attributed to β-cell death, although recent findings suggest that the loss of β-cell identity may also contribute to β-cell dysfunction. This phenomenon is characterized by a reduced expression of key markers associated with β-cell identity. This review delves into the insights gained from single-cell omics research specifically focused on β-cell identity. It highlights how single-cell omics based studies have uncovered an unexpected level of heterogeneity among β-cells and have facilitated the identification of distinct β-cell subpopulations through the discovery of cell surface markers, transcriptional regulators, the upregulation of stress-related genes, and alterations in chromatin activity. Furthermore, specific subsets of β-cells have been identified in diabetes, such as displaying an immature, dedifferentiated gene signature, expressing significantly lower insulin mRNA levels, and expressing increased β-cell precursor markers. Additionally, single-cell omics has increased insight into the detrimental effects of diabetes-associated conditions, including endoplasmic reticulum stress, oxidative stress, and inflammation, on β-cell identity. Lastly, this review outlines the factors that may influence the identification of β-cell subpopulations when designing and performing a single-cell omics experiment.
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Affiliation(s)
| | | | - Françoise Carlotti
- Department of Internal Medicine, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands; (F.L.); (E.J.P.d.K.)
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11
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Fang X, Zhang Y, Miao R, Zhang Y, Yin R, Guan H, Huang X, Tian J. Single-cell sequencing: A promising approach for uncovering the characteristic of pancreatic islet cells in type 2 diabetes. Biomed Pharmacother 2024; 173:116292. [PMID: 38394848 DOI: 10.1016/j.biopha.2024.116292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 02/03/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024] Open
Abstract
Single-cell sequencing is a novel and rapidly advancing high-throughput technique that can be used to investigating genomics, transcriptomics, and epigenetics at a single-cell level. Currently, single-cell sequencing can not only be used to draw the pancreatic islet cells map and uncover the characteristics of cellular heterogeneity in type 2 diabetes, but can also be used to label and purify functional beta cells in pancreatic stem cells, improving stem cells and islet organoids therapies. In addition, this technology helps to analyze islet cell dedifferentiation and can be applied to the treatment of type 2 diabetes. In this review, we summarize the development and process of single-cell sequencing, describe the potential applications of single-cell sequencing in the field of type 2 diabetes, and discuss the prospects and limitations of single-cell sequencing to provide a new direction for exploring the pathogenesis of type 2 diabetes and finding therapeutic targets.
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Affiliation(s)
- Xinyi Fang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yanjiao Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Runyu Miao
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China; Graduate College, Beijing University of Chinese Medicine, Beijing 100029, China
| | - Yuxin Zhang
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Ruiyang Yin
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China
| | - Huifang Guan
- College of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Jilin 130117, China
| | - Xinyue Huang
- First Clinical Medical College, Changzhi Medical College, Shanxi 046013, China
| | - Jiaxing Tian
- Institute of Metabolic Diseases, Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing 100053, China.
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12
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Shilo S, Segal E. Endocrinology in the multi-omics era. Nat Rev Endocrinol 2024; 20:73-74. [PMID: 38052868 DOI: 10.1038/s41574-023-00931-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Affiliation(s)
- Smadar Shilo
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
- The Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
| | - Eran Segal
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel.
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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13
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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14
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Camunas-Soler J. Integrating single-cell transcriptomics with cellular phenotypes: cell morphology, Ca 2+ imaging and electrophysiology. Biophys Rev 2024; 16:89-107. [PMID: 38495444 PMCID: PMC10937895 DOI: 10.1007/s12551-023-01174-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 11/29/2023] [Indexed: 03/19/2024] Open
Abstract
I review recent technological advancements in coupling single-cell transcriptomics with cellular phenotypes including morphology, calcium signaling, and electrophysiology. Single-cell RNA sequencing (scRNAseq) has revolutionized cell type classifications by capturing the transcriptional diversity of cells. A new wave of methods to integrate scRNAseq and biophysical measurements is facilitating the linkage of transcriptomic data to cellular function, which provides physiological insight into cellular states. I briefly discuss critical factors of these phenotypical characterizations such as timescales, information content, and analytical tools. Dedicated sections focus on the integration with cell morphology, calcium imaging, and electrophysiology (patch-seq), emphasizing their complementary roles. I discuss their application in elucidating cellular states, refining cell type classifications, and uncovering functional differences in cell subtypes. To illustrate the practical applications and benefits of these methods, I highlight their use in tissues with excitable cell-types such as the brain, pancreatic islets, and the retina. The potential of combining functional phenotyping with spatial transcriptomics for a detailed mapping of cell phenotypes in situ is explored. Finally, I discuss open questions and future perspectives, emphasizing the need for a shift towards broader accessibility through increased throughput.
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Affiliation(s)
- Joan Camunas-Soler
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, University of Gothenburg, 405 30 Gothenburg, Sweden
- Wallenberg Centre for Molecular and Translational Medicine, Sahlgrenska Academy, University of Gothenburg, 405 30 Gothenburg, Sweden
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15
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Wang J, Yan D, Cui H, Zhang R, Ma X, Chen L, Hu C, Wu J. Identification of eight genomic protective alleles for mitochondrial diabetes by Kinship-graph convolutional network. J Diabetes Investig 2024; 15:52-62. [PMID: 38157301 PMCID: PMC10759726 DOI: 10.1111/jdi.14125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/16/2023] [Accepted: 11/21/2023] [Indexed: 01/03/2024] Open
Abstract
AIMS Nearly 85% of maternally inherited diabetes and deafness (MIDD) are caused by the m.3243A>G mutation in the mitochondrial DNA. However, the clinical phenotypes of MIDD may also be influenced by the nuclear genome, this study aimed to investigate nuclear genome variants that influence clinical phenotypes associated with m.3243A>G mutation in MIDD based on whole-genome sequencing of the patients belonging to pedigrees. MATERIALS AND METHODS We analyzed a whole-genome sequencing (WGS) dataset from blood samples of 38 MIDD patients with the m.3243A > G mutation belonging to 10 pedigrees, by developing a Kinship-graph convolutional network approach, called Ki-GCN, integrated with the conventional genome-wide association study (GWAS) methods. RESULTS We identified eight protective alleles in the nuclear genome that have protective effects against the onset of MIDD, related deafness, and also type 2 diabetes. Based on these eight protective alleles, we constructed an effective logistic regression model to predict the early or late onset of MIDD patients. CONCLUSIONS There are protective alleles in the nuclear genome that are associated with the onset-age of MIDD patients and might also provide protective effects on the deafness derived from MIDD patients.
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Affiliation(s)
- Jiahao Wang
- CAS Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghaiChina
- College of Life SciencesUniversity of Chinese Academy of SciencesBeijingChina
| | - Dandan Yan
- Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Centre for Diabetes, Shanghai Diabetes InstituteShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Haoyue Cui
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
| | - Rong Zhang
- Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Centre for Diabetes, Shanghai Diabetes InstituteShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Xiaojing Ma
- Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Centre for Diabetes, Shanghai Diabetes InstituteShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
| | - Luonan Chen
- CAS Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghaiChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiangChina
| | - Cheng Hu
- Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Centre for Diabetes, Shanghai Diabetes InstituteShanghai Jiao Tong University Affiliated Sixth People's HospitalShanghaiChina
- Institute for Metabolic DiseaseFengxian Central Hospital Affiliated to Southern Medical UniversityShanghaiChina
| | - Jiarui Wu
- CAS Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell ScienceChinese Academy of SciencesShanghaiChina
- School of Life Science and TechnologyShanghaiTech UniversityShanghaiChina
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhouZhejiangChina
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16
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Yu V, Yong F, Marta A, Khadayate S, Osakwe A, Bhattacharya S, Varghese SS, Chabosseau P, Tabibi SM, Chen K, Georgiadou E, Parveen N, Suleiman M, Stamoulis Z, Marselli L, De Luca C, Tesi M, Ostinelli G, Delgadillo-Silva L, Wu X, Hatanaka Y, Montoya A, Elliott J, Patel B, Demchenko N, Whilding C, Hajkova P, Shliaha P, Kramer H, Ali Y, Marchetti P, Sladek R, Dhawan S, Withers DJ, Rutter GA, Millership SJ. Differential CpG methylation at Nnat in the early establishment of beta cell heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.04.527050. [PMID: 38076935 PMCID: PMC10705251 DOI: 10.1101/2023.02.04.527050] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
Aims/hypothesis Beta cells within the pancreatic islet represent a heterogenous population wherein individual sub-groups of cells make distinct contributions to the overall control of insulin secretion. These include a subpopulation of highly-connected 'hub' cells, important for the propagation of intercellular Ca2+ waves. Functional subpopulations have also been demonstrated in human beta cells, with an altered subtype distribution apparent in type 2 diabetes. At present, the molecular mechanisms through which beta cell hierarchy is established are poorly understood. Changes at the level of the epigenome provide one such possibility which we explore here by focussing on the imprinted gene neuronatin (Nnat), which is required for normal insulin synthesis and secretion. Methods Single cell RNA-seq datasets were examined using Seurat 4.0 and ClusterProfiler running under R. Transgenic mice expressing eGFP under the control of the Nnat enhancer/promoter regions were generated for fluorescence-activated cell (FAC) sorting of beta cells and downstream analysis of CpG methylation by bisulphite and RNA sequencing, respectively. Animals deleted for the de novo methyltransferase, DNMT3A from the pancreatic progenitor stage were used to explore control of promoter methylation. Proteomics was performed using affinity purification mass spectrometry and Ca2+ dynamics explored by rapid confocal imaging of Cal-520 and Cal-590. Insulin secretion was measured using Homogeneous Time Resolved Fluorescence Imaging. Results Nnat mRNA was differentially expressed in a discrete beta cell population in a developmental stage- and DNA methylation (DNMT3A)-dependent manner. Thus, pseudo-time analysis of embryonic data sets demonstrated the early establishment of Nnat-positive and negative subpopulations during embryogenesis. NNAT expression is also restricted to a subset of beta cells across the human islet that is maintained throughout adult life. NNAT+ beta cells also displayed a discrete transcriptome at adult stages, representing a sub-population specialised for insulin production, reminiscent of recently-described "βHI" cells and were diminished in db/db mice. 'Hub' cells were less abundant in the NNAT+ population, consistent with epigenetic control of this functional specialization. Conclusions/interpretation These findings demonstrate that differential DNA methylation at Nnat represents a novel means through which beta cell heterogeneity is established during development. We therefore hypothesise that changes in methylation at this locus may thus contribute to a loss of beta cell hierarchy and connectivity, potentially contributing to defective insulin secretion in some forms of diabetes.
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Affiliation(s)
- Vanessa Yu
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Fiona Yong
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, 637553, Singapore
| | - Angellica Marta
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Sanjay Khadayate
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Adrien Osakwe
- Departments of Medicine, Human Genetics and Quantitative Life Sciences, McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
| | - Supriyo Bhattacharya
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Sneha S. Varghese
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Pauline Chabosseau
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Sayed M. Tabibi
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Keran Chen
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Eleni Georgiadou
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Nazia Parveen
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Mara Suleiman
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Zoe Stamoulis
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Lorella Marselli
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Carmela De Luca
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Marta Tesi
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Giada Ostinelli
- CHUM Research Center and Faculty of Medicine, University of Montréal, 900 Rue St Denis, Montréal, H2X OA9, QC, Canada
| | - Luis Delgadillo-Silva
- CHUM Research Center and Faculty of Medicine, University of Montréal, 900 Rue St Denis, Montréal, H2X OA9, QC, Canada
| | - Xiwei Wu
- Department of Computational and Quantitative Medicine, Beckman Research Institute, City of Hope, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Yuki Hatanaka
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Alex Montoya
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - James Elliott
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Bhavik Patel
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Nikita Demchenko
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Chad Whilding
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Petra Hajkova
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Pavel Shliaha
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Holger Kramer
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
| | - Yusuf Ali
- Nutrition, Metabolism and Health Programme & Centre for Microbiome Medicine, Lee Kong Chian School of Medicine, Nanyang Technological University Singapore, Singapore, 308232
- Singapore Eye Research Institute (SERI), Singapore General Hospital, Singapore, 168751
- Clinical Research Unit, Khoo Teck Puat Hospital, National Healthcare Group, Singapore, 768828
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, and AOUP Cisanello University Hospital, University of Pisa, Pisa 56126, Italy
| | - Robert Sladek
- Departments of Medicine, Human Genetics and Quantitative Life Sciences, McGill University and Genome Quebec Innovation Centre, Montreal, QC, Canada
| | - Sangeeta Dhawan
- Department of Translational Research and Cellular Therapeutics, Arthur Riggs Diabetes and Metabolism Research Institute, City of Hope, 1500 E Duarte Road, Duarte, CA 91010, USA
| | - Dominic J. Withers
- MRC Laboratory of Medical Sciences, Du Cane Road, London, W12 0NN, UK
- Institute of Clinical Sciences, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Guy A. Rutter
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
- Lee Kong Chian School of Medicine, Nanyang Technological University, 637553, Singapore
- CHUM Research Center and Faculty of Medicine, University of Montréal, 900 Rue St Denis, Montréal, H2X OA9, QC, Canada
| | - Steven J. Millership
- Department of Metabolism, Digestion and Reproduction, Faculty of Medicine, Imperial College London, Du Cane Road, London, W12 0NN, UK
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Elgamal RM, Kudtarkar P, Melton RL, Mummey HM, Benaglio P, Okino ML, Gaulton KJ. An Integrated Map of Cell Type-Specific Gene Expression in Pancreatic Islets. Diabetes 2023; 72:1719-1728. [PMID: 37582230 PMCID: PMC10588282 DOI: 10.2337/db23-0130] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Accepted: 08/03/2023] [Indexed: 08/17/2023]
Abstract
Pancreatic islets consist of multiple cell types that produce hormones required for glucose homeostasis, and islet dysfunction is a major factor in type 1 and type 2 diabetes. Numerous studies have assessed transcription across individual cell types using single-cell assays; however, there is no canonical reference of gene expression in islet cell types that is also easily accessible for researchers to query and use in bioinformatics pipelines. Here we present an integrated map of islet cell type-specific gene expression from 192,203 cells from single-cell RNA sequencing of 65 donors without diabetes, donors who were type 1 diabetes autoantibody positive, donors with type 1 diabetes, and donors with type 2 diabetes from the Human Pancreas Analysis Program. We identified 10 distinct cell types, annotated subpopulations of several cell types, and defined cell type-specific marker genes. We tested differential expression within each cell type across disease states and identified 1,701 genes with significant changes in expression, with most changes observed in β-cells from donors with type 1 diabetes. To facilitate user interaction, we provide several single-cell visualization and reference mapping tools, as well as the open-access analytical pipelines used to create this reference. The results will serve as a valuable resource to investigators studying islet biology. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Ruth M. Elgamal
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Rebecca L. Melton
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA
| | - Hannah M. Mummey
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA
| | - Paola Benaglio
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
| | - Mei-Lin Okino
- Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California, San Diego, La Jolla, CA
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