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Dang T, Yu J, Cao Z, Zhang B, Li S, Xin Y, Yang L, Lou R, Zhuang M, Shui W. Endogenous cell membrane interactome mapping for the GLP-1 receptor in different cell types. Nat Chem Biol 2024:10.1038/s41589-024-01714-1. [PMID: 39227725 DOI: 10.1038/s41589-024-01714-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Accepted: 07/29/2024] [Indexed: 09/05/2024]
Abstract
The GLP-1 receptor, one of the most successful drug targets for the treatment of type 2 diabetes and obesity, is known to engage multiple intracellular signaling proteins. However, it remains less explored how the receptor interacts with proteins on the cell membrane. Here, we present a ligand-based proximity labeling approach to interrogate the native cell membrane interactome for the GLP-1 receptor after agonist simulation. Our study identified several unreported putative cell membrane interactors for the endogenous receptor in either a pancreatic β cell line or a neuronal cell line. We further uncovered new regulators of GLP-1 receptor-mediated signaling and insulinotropic responses in β cells. Additionally, we obtained a time-resolved cell membrane interactome map for the receptor in β cells. Therefore, our study provides a new approach that is generalizable to map endogenous cell membrane interactomes for G-protein-coupled receptors to decipher the molecular basis of their cell-type-specific functional regulation.
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Affiliation(s)
- Ting Dang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jie Yu
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
- Lingang Laboratory, Shanghai, China
| | - Zhihe Cao
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bingjie Zhang
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Shanshan Li
- iHuman Institute, ShanghaiTech University, Shanghai, China
| | - Ye Xin
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Lingyun Yang
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Ronghui Lou
- iHuman Institute, ShanghaiTech University, Shanghai, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Min Zhuang
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
| | - Wenqing Shui
- iHuman Institute, ShanghaiTech University, Shanghai, China.
- School of Life Science and Technology, ShanghaiTech University, Shanghai, China.
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2
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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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3
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Xu H, Ye Y, Duan R, Gao Y, Hu Y, Gao L. Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306770. [PMID: 38711214 PMCID: PMC11234410 DOI: 10.1002/advs.202306770] [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: 09/17/2023] [Revised: 04/02/2024] [Indexed: 05/08/2024]
Abstract
Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.
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Affiliation(s)
- Han Xu
- School of Computer Science and TechnologyXidian UniversityXi'an710126China
| | - Yusen Ye
- School of Computer Science and TechnologyXidian UniversityXi'an710126China
| | - Ran Duan
- School of Electrical and Information EngineeringBeijing University of Civil Engineering and ArchitectureBeijing102616China
| | - Yong Gao
- Department of Computer ScienceThe University of British Columbia OkanaganKelownaBritish ColumbiaV1V 1V7Canada
| | - Yuxuan Hu
- School of Computer Science and TechnologyXidian UniversityXi'an710126China
| | - Lin Gao
- School of Computer Science and TechnologyXidian UniversityXi'an710126China
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4
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Wang J, Wen S, Chen M, Xie J, Lou X, Zhao H, Chen Y, Zhao M, Shi G. Regulation of endocrine cell alternative splicing revealed by single-cell RNA sequencing in type 2 diabetes pathogenesis. Commun Biol 2024; 7:778. [PMID: 38937540 PMCID: PMC11211498 DOI: 10.1038/s42003-024-06475-0] [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: 05/25/2023] [Accepted: 06/19/2024] [Indexed: 06/29/2024] Open
Abstract
The prevalent RNA alternative splicing (AS) contributes to molecular diversity, which has been demonstrated in cellular function regulation and disease pathogenesis. However, the contribution of AS in pancreatic islets during diabetes progression remains unclear. Here, we reanalyze the full-length single-cell RNA sequencing data from the deposited database to investigate AS regulation across human pancreatic endocrine cell types in non-diabetic (ND) and type 2 diabetic (T2D) individuals. Our analysis demonstrates the significant association between transcriptomic AS profiles and cell-type-specificity, which could be applied to distinguish the clustering of major endocrine cell types. Moreover, AS profiles are enabled to clearly define the mature subset of β-cells in healthy controls, which is completely lost in T2D. Further analysis reveals that RNA-binding proteins (RBPs), heterogeneous nuclear ribonucleoproteins (hnRNPs) and FXR1 family proteins are predicted to induce the functional impairment of β-cells through regulating AS profiles. Finally, trajectory analysis of endocrine cells suggests the β-cell identity shift through dedifferentiation and transdifferentiation of β-cells during the progression of T2D. Together, our study provides a mechanism for regulating β-cell functions and suggests the significant contribution of AS program during diabetes pathogenesis.
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Affiliation(s)
- Jin Wang
- Department of Endocrinology & Metabolism, Medical Center for Comprehensive Weight Control, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
| | - Shiyi Wen
- Department of Endocrinology & Metabolism, Medical Center for Comprehensive Weight Control, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Minqi Chen
- Key Laboratory of Stem Cells and Tissue Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Ministry of Education, Guangzhou, Guangdong, China
| | - Jiayi Xie
- Key Laboratory of Stem Cells and Tissue Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Ministry of Education, Guangzhou, Guangdong, China
| | - Xinhua Lou
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Haihan Zhao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Yanming Chen
- Department of Endocrinology & Metabolism, Medical Center for Comprehensive Weight Control, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
- Guangdong Provincial Key Laboratory of Diabetology & Guangzhou Municipal Key Laboratory of Mechanistic and Translational Obesity Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China
| | - Meng Zhao
- Key Laboratory of Stem Cells and Tissue Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Ministry of Education, Guangzhou, Guangdong, China.
| | - Guojun Shi
- Department of Endocrinology & Metabolism, Medical Center for Comprehensive Weight Control, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
- Guangdong Provincial Key Laboratory of Diabetology & Guangzhou Municipal Key Laboratory of Mechanistic and Translational Obesity Research, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, Guangdong, China.
- State Key Laboratory of Oncology in Southern China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China.
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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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Hua H, Wang Y, Wang X, Wang S, Zhou Y, Liu Y, Liang Z, Ren H, Lu S, Wu S, Jiang Y, Pu Y, Zheng X, Tang C, Shen Z, Li C, Du Y, Deng H. Remodeling ceramide homeostasis promotes functional maturation of human pluripotent stem cell-derived β cells. Cell Stem Cell 2024; 31:850-865.e10. [PMID: 38697109 DOI: 10.1016/j.stem.2024.04.010] [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: 09/10/2023] [Revised: 03/21/2024] [Accepted: 04/12/2024] [Indexed: 05/04/2024]
Abstract
Human pluripotent stem cell-derived β cells (hPSC-β cells) show the potential to restore euglycemia. However, the immature functionality of hPSC-β cells has limited their efficacy in application. Here, by deciphering the continuous maturation process of hPSC-β cells post transplantation via single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin sequencing (scATAC-seq), we show that functional maturation of hPSC-β cells is an orderly multistep process during which cells sequentially undergo metabolic adaption, removal of negative regulators of cell function, and establishment of a more specialized transcriptome and epigenome. Importantly, remodeling lipid metabolism, especially downregulating the metabolic activity of ceramides, the central hub of sphingolipid metabolism, is critical for β cell maturation. Limiting intracellular accumulation of ceramides in hPSC-β cells remarkably enhanced their function, as indicated by improvements in insulin processing and glucose-stimulated insulin secretion. In summary, our findings provide insights into the maturation of human pancreatic β cells and highlight the importance of ceramide homeostasis in function acquisition.
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Affiliation(s)
- Huijuan Hua
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yaqi Wang
- School of Life Sciences, Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China
| | | | - Shusen Wang
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Yunlu Zhou
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Yinan Liu
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhen Liang
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Huixia Ren
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Sufang Lu
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | | | - Yong Jiang
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | - Yue Pu
- Hangzhou Reprogenix Bioscience, Hangzhou, China
| | - Xiang Zheng
- Hangzhou Repugene Technology, Hangzhou, China
| | - Chao Tang
- Center for Quantitative Biology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhongyang Shen
- Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, China
| | - Cheng Li
- School of Life Sciences, Center for Bioinformatics, Center for Statistical Science, Peking University, Beijing, China.
| | - Yuanyuan Du
- Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
| | - Hongkui Deng
- MOE Engineering Research Center of Regenerative Medicine, School of Basic Medical Sciences, State Key Laboratory of Natural and Biomimetic Drugs, Peking University Health Science Center and the MOE Key Laboratory of Cell Proliferation and Differentiation, College of Life Sciences, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China; Changping Laboratory, Beijing, China.
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7
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Cai X, Zhang W, Zheng X, Xu Y, Li Y. scEM: A New Ensemble Framework for Predicting Cell Type Composition Based on scRNA-Seq Data. Interdiscip Sci 2024; 16:304-317. [PMID: 38368575 DOI: 10.1007/s12539-023-00601-y] [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: 06/12/2023] [Revised: 12/22/2023] [Accepted: 12/24/2023] [Indexed: 02/19/2024]
Abstract
With the advent of single-cell RNA sequencing (scRNA-seq) technology, many scRNA-seq data have become available, providing an unprecedented opportunity to explore cellular composition and heterogeneity. Recently, many computational algorithms for predicting cell type composition have been developed, and these methods are typically evaluated on different datasets and performance metrics using diverse techniques. Consequently, the lack of comprehensive and standardized comparative analysis makes it difficult to gain a clear understanding of the strengths and weaknesses of these methods. To address this gap, we reviewed 20 cutting-edge unsupervised cell type identification methods and evaluated these methods comprehensively using 24 real scRNA-seq datasets of varying scales. In addition, we proposed a new ensemble cell-type identification method, named scEM, which learns the consensus similarity matrix by applying the entropy weight method to the four representative methods are selected. The Louvain algorithm is adopted to obtain the final classification of individual cells based on the consensus matrix. Extensive evaluation and comparison with 11 other similarity-based methods under real scRNA-seq datasets demonstrate that the newly developed ensemble algorithm scEM is effective in predicting cellular type composition.
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Affiliation(s)
- Xianxian Cai
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Wei Zhang
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China.
| | - Xiaoying Zheng
- Operations research and planning department, Naval University of Engineering, Wuhan, 430033, China
| | - Yaxin Xu
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Yuanyuan Li
- School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan, China
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8
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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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9
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Chen H, Ryu J, Vinyard ME, Lerer A, Pinello L. SIMBA: single-cell embedding along with features. Nat Methods 2024; 21:1003-1013. [PMID: 37248389 PMCID: PMC11166568 DOI: 10.1038/s41592-023-01899-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library ( https://simba-bio.readthedocs.io ).
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Affiliation(s)
- Huidong Chen
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayoung Ryu
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Michael E Vinyard
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Adam Lerer
- Facebook AI Research, New York, NY, USA.
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
- Department of Pathology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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10
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Yang K, Zhang Y, Ding J, Li Z, Zhang H, Zou F. Autoimmune CD8+ T cells in type 1 diabetes: from single-cell RNA sequencing to T-cell receptor redirection. Front Endocrinol (Lausanne) 2024; 15:1377322. [PMID: 38800484 PMCID: PMC11116783 DOI: 10.3389/fendo.2024.1377322] [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: 01/27/2024] [Accepted: 04/18/2024] [Indexed: 05/29/2024] Open
Abstract
Type 1 diabetes (T1D) is an organ-specific autoimmune disease caused by pancreatic β cell destruction and mediated primarily by autoreactive CD8+ T cells. It has been shown that only a small number of stem cell-like β cell-specific CD8+ T cells are needed to convert normal mice into T1D mice; thus, it is likely that T1D can be cured or significantly improved by modulating or altering self-reactive CD8+ T cells. However, stem cell-type, effector and exhausted CD8+ T cells play intricate and important roles in T1D. The highly diverse T-cell receptors (TCRs) also make precise and stable targeted therapy more difficult. Therefore, this review will investigate the mechanisms of autoimmune CD8+ T cells and TCRs in T1D, as well as the related single-cell RNA sequencing (ScRNA-Seq), CRISPR/Cas9, chimeric antigen receptor T-cell (CAR-T) and T-cell receptor-gene engineered T cells (TCR-T), for a detailed and clear overview. This review highlights that targeting CD8+ T cells and their TCRs may be a potential strategy for predicting or treating T1D.
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Affiliation(s)
- Kangping Yang
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, China
| | - Yihan Zhang
- The Second Clinical Medicine School, Nanchang University, Nanchang, China
| | - Jiatong Ding
- The Second Clinical Medicine School, Nanchang University, Nanchang, China
| | - Zelin Li
- The First Clinical Medicine School, Nanchang University, Nanchang, China
| | - Hejin Zhang
- The Second Clinical Medicine School, Nanchang University, Nanchang, China
| | - Fang Zou
- Department of Endocrinology and Metabolism, The Second Affiliated Hospital of Nanchang University, Nanchang, China
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11
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Shilleh AH, Viloria K, Broichhagen J, Campbell JE, Hodson DJ. GLP1R and GIPR expression and signaling in pancreatic alpha cells, beta cells and delta cells. Peptides 2024; 175:171179. [PMID: 38360354 DOI: 10.1016/j.peptides.2024.171179] [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: 01/12/2024] [Revised: 02/09/2024] [Accepted: 02/13/2024] [Indexed: 02/17/2024]
Abstract
Glucagon-like peptide-1 receptor (GLP1R) and glucose-dependent insulinotropic polypeptide receptor (GIPR) are transmembrane receptors involved in insulin, glucagon and somatostatin secretion from the pancreatic islet. Therapeutic targeting of GLP1R and GIPR restores blood glucose levels in part by influencing beta cell, alpha cell and delta cell function. Despite the importance of the incretin-mimetics for diabetes therapy, our understanding of GLP1R and GIPR expression patterns and signaling within the islet remain incomplete. Here, we present the evidence for GLP1R and GIPR expression in the major islet cell types, before addressing signaling pathway(s) engaged, as well as their influence on cell survival and function. While GLP1R is largely a beta cell-specific marker within the islet, GIPR is expressed in alpha cells, beta cells, and (possibly) delta cells. GLP1R and GIPR engage Gs-coupled pathways in most settings, although the exact outcome on hormone release depends on paracrine communication and promiscuous signaling. Biased agonism away from beta-arrestin is an emerging concept for improving therapeutic efficacy, and is also relevant for GLP1R/GIPR dual agonism. Lastly, dual agonists exert multiple effects on islet function through GIPR > GLP1R imbalance, increased GLP1R surface expression and cAMP signaling, as well as beneficial alpha cell-beta cell-delta cell crosstalk.
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Affiliation(s)
- Ali H Shilleh
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Katrina Viloria
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | | | - Jonathan E Campbell
- Duke Molecular Physiology Institute, USA; Department of Medicine, Division of Endocrinology, Duke University, Durham, NC, USA; Department of Pharmacology and Cancer Biology, Duke University, Durham, NC, USA.
| | - David J Hodson
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
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12
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Xie G, Toledo MP, Hu X, Yong HJ, Sanchez PS, Liu C, Naji A, Irianto J, Wang YJ. NKX2-2 based nuclei sorting on frozen human archival pancreas enables the enrichment of islet endocrine populations for single-nucleus RNA sequencing. BMC Genomics 2024; 25:427. [PMID: 38689254 PMCID: PMC11059690 DOI: 10.1186/s12864-024-10335-w] [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: 01/03/2024] [Accepted: 04/22/2024] [Indexed: 05/02/2024] Open
Abstract
BACKGROUND Current approaches to profile the single-cell transcriptomics of human pancreatic endocrine cells almost exclusively rely on freshly isolated islets. However, human islets are limited in availability. Furthermore, the extensive processing steps during islet isolation and subsequent single cell dissolution might alter gene expressions. In this work, we report the development of a single-nucleus RNA sequencing (snRNA-seq) approach with targeted islet cell enrichment for endocrine-population focused transcriptomic profiling using frozen archival pancreatic tissues without islet isolation. RESULTS We cross-compared five nuclei isolation protocols and selected the citric acid method as the best strategy to isolate nuclei with high RNA integrity and low cytoplasmic contamination from frozen archival human pancreata. We innovated fluorescence-activated nuclei sorting based on the positive signal of NKX2-2 antibody to enrich nuclei of the endocrine population from the entire nuclei pool of the pancreas. Our sample preparation procedure generated high-quality single-nucleus gene-expression libraries while preserving the endocrine population diversity. In comparison with single-cell RNA sequencing (scRNA-seq) library generated with live cells from freshly isolated human islets, the snRNA-seq library displayed comparable endocrine cellular composition and cell type signature gene expression. However, between these two types of libraries, differential enrichments of transcripts belonging to different functional classes could be observed. CONCLUSIONS Our work fills a technological gap and helps to unleash frozen archival pancreatic tissues for molecular profiling targeting the endocrine population. This study opens doors to retrospective mappings of endocrine cell dynamics in pancreatic tissues of complex histopathology. We expect that our protocol is applicable to enrich nuclei for transcriptomics studies from various populations in different types of frozen archival tissues.
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Affiliation(s)
- Gengqiang Xie
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Maria Pilar Toledo
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Xue Hu
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Hyo Jeong Yong
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Pamela Sandoval Sanchez
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Chengyang Liu
- Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Ali Naji
- Department of Surgery, Hospital of the University of Pennsylvania, Philadelphia, PA, USA
| | - Jerome Irianto
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA
| | - Yue J Wang
- Department of Biomedical Sciences, College of Medicine, Florida State University, 1115 West Call Street, Tallahassee, FL, 32306, USA.
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13
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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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14
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Cuozzo F, Viloria K, Shilleh AH, Nasteska D, Frazer-Morris C, Tong J, Jiao Z, Boufersaoui A, Marzullo B, Rosoff DB, Smith HR, Bonner C, Kerr-Conte J, Pattou F, Nano R, Piemonti L, Johnson PRV, Spiers R, Roberts J, Lavery GG, Clark A, Ceresa CDL, Ray DW, Hodson L, Davies AP, Rutter GA, Oshima M, Scharfmann R, Merrins MJ, Akerman I, Tennant DA, Ludwig C, Hodson DJ. LDHB contributes to the regulation of lactate levels and basal insulin secretion in human pancreatic β cells. Cell Rep 2024; 43:114047. [PMID: 38607916 PMCID: PMC11164428 DOI: 10.1016/j.celrep.2024.114047] [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: 03/16/2023] [Revised: 02/19/2024] [Accepted: 03/19/2024] [Indexed: 04/14/2024] Open
Abstract
Using 13C6 glucose labeling coupled to gas chromatography-mass spectrometry and 2D 1H-13C heteronuclear single quantum coherence NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning β cell function. In both mouse and human islets, the contribution of glucose to the tricarboxylic acid (TCA) cycle is similar. Pyruvate fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While the conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is 6-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and β cell-specific LDHB expression. LDHB inhibition amplifies LDHA-dependent lactate generation in mouse and human β cells and increases basal insulin release. Lastly, cis-instrument Mendelian randomization shows that low LDHB expression levels correlate with elevated fasting insulin in humans. Thus, LDHB limits lactate generation in β cells to maintain appropriate insulin release.
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Affiliation(s)
- Federica Cuozzo
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Katrina Viloria
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Ali H Shilleh
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Daniela Nasteska
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Charlotte Frazer-Morris
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Jason Tong
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Zicong Jiao
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Geneplus-Beijing, Changping District, Beijing 102206, China
| | - Adam Boufersaoui
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Bryan Marzullo
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Daniel B Rosoff
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Oxford Kavli Centre for Nanoscience Discovery, University of Oxford, Oxford, UK
| | - Hannah R Smith
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Caroline Bonner
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000 Lille, France
| | - Julie Kerr-Conte
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000 Lille, France
| | - Francois Pattou
- University of Lille, Institut National de la Santé et de la Recherche Médicale (INSERM), Centre Hospitalier Universitaire de Lille (CHU Lille), Institute Pasteur Lille, U1190 -European Genomic Institute for Diabetes (EGID), F59000 Lille, France
| | - Rita Nano
- San Raffaele Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Lorenzo Piemonti
- San Raffaele Diabetes Research Institute, IRCCS Ospedale San Raffaele, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paul R V Johnson
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Rebecca Spiers
- Nuffield Department of Surgical Sciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Jennie Roberts
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Gareth G Lavery
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Centre for Systems Health and Integrated Metabolic Research (SHiMR), Department of Biosciences, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Anne Clark
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Carlo D L Ceresa
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - David W Ray
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK; Oxford Kavli Centre for Nanoscience Discovery, University of Oxford, Oxford, UK
| | - Leanne Hodson
- Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Amy P Davies
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Guy A Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK; CHUM Research Centre and Faculty of Medicine, University of Montreal, Montreal, QC, Canada; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Masaya Oshima
- Université Paris Cité, Institut Cochin, INSERM U1016, CNRS UMR 8104, 75014 Paris, France
| | - Raphaël Scharfmann
- Université Paris Cité, Institut Cochin, INSERM U1016, CNRS UMR 8104, 75014 Paris, France
| | - Matthew J Merrins
- Department of Medicine, Division of Endocrinology, Diabetes, and Metabolism, University of Wisconsin-Madison, Madison, WI 53705, USA; William S. Middleton Memorial Veterans Hospital, Madison, WI 53705, USA
| | - Ildem Akerman
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK
| | - Daniel A Tennant
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.
| | - Christian Ludwig
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK.
| | - David J Hodson
- Institute of Metabolism and Systems Research (IMSR) and Centre of Membrane Proteins and Receptors (COMPARE), University of Birmingham, Birmingham, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism (OCDEM), NIHR Oxford Biomedical Research Centre, Churchill Hospital, Radcliffe Department of Medicine, University of Oxford, Oxford, UK.
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15
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Hill TG, Hill DJ. The Importance of Intra-Islet Communication in the Function and Plasticity of the Islets of Langerhans during Health and Diabetes. Int J Mol Sci 2024; 25:4070. [PMID: 38612880 PMCID: PMC11012451 DOI: 10.3390/ijms25074070] [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: 02/27/2024] [Revised: 03/27/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
Islets of Langerhans are anatomically dispersed within the pancreas and exhibit regulatory coordination between islets in response to nutritional and inflammatory stimuli. However, within individual islets, there is also multi-faceted coordination of function between individual beta-cells, and between beta-cells and other endocrine and vascular cell types. This is mediated partly through circulatory feedback of the major secreted hormones, insulin and glucagon, but also by autocrine and paracrine actions within the islet by a range of other secreted products, including somatostatin, urocortin 3, serotonin, glucagon-like peptide-1, acetylcholine, and ghrelin. Their availability can be modulated within the islet by pericyte-mediated regulation of microvascular blood flow. Within the islet, both endocrine progenitor cells and the ability of endocrine cells to trans-differentiate between phenotypes can alter endocrine cell mass to adapt to changed metabolic circumstances, regulated by the within-islet trophic environment. Optimal islet function is precariously balanced due to the high metabolic rate required by beta-cells to synthesize and secrete insulin, and they are susceptible to oxidative and endoplasmic reticular stress in the face of high metabolic demand. Resulting changes in paracrine dynamics within the islets can contribute to the emergence of Types 1, 2 and gestational diabetes.
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Affiliation(s)
- Thomas G. Hill
- Oxford Centre for Diabetes, Endocrinology, and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK
| | - David J. Hill
- Lawson Health Research Institute, St. Joseph’s Health Care, London, ON N6A 4V2, Canada;
- Departments of Medicine, Physiology and Pharmacology, Western University, London, ON N6A 3K7, Canada
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16
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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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17
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Holmberg SR, Sakamoto Y, Kato A, Romero MF. The role of Na +-coupled bicarbonate transporters (NCBT) in health and disease. Pflugers Arch 2024; 476:479-503. [PMID: 38536494 PMCID: PMC11338471 DOI: 10.1007/s00424-024-02937-w] [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: 12/15/2023] [Revised: 02/26/2024] [Accepted: 02/27/2024] [Indexed: 04/11/2024]
Abstract
Cellular and organism survival depends upon the regulation of pH, which is regulated by highly specialized cell membrane transporters, the solute carriers (SLC) (For a comprehensive list of the solute carrier family members, see: https://www.bioparadigms.org/slc/ ). The SLC4 family of bicarbonate (HCO3-) transporters consists of ten members, sorted by their coupling to either sodium (NBCe1, NBCe2, NBCn1, NBCn2, NDCBE), chloride (AE1, AE2, AE3), or borate (BTR1). The ionic coupling of SLC4A9 (AE4) remains controversial. These SLC4 bicarbonate transporters may be controlled by cellular ionic gradients, cellular membrane voltage, and signaling molecules to maintain critical cellular and systemic pH (acid-base) balance. There are profound consequences when blood pH deviates even a small amount outside the normal range (7.35-7.45). Chiefly, Na+-coupled bicarbonate transporters (NCBT) control intracellular pH in nearly every living cell, maintaining the biological pH required for life. Additionally, NCBTs have important roles to regulate cell volume and maintain salt balance as well as absorption and secretion of acid-base equivalents. Due to their varied tissue expression, NCBTs have roles in pathophysiology, which become apparent in physiologic responses when their expression is reduced or genetically deleted. Variations in physiological pH are seen in a wide variety of conditions, from canonically acid-base related conditions to pathologies not necessarily associated with acid-base dysfunction such as cancer, glaucoma, or various neurological diseases. The membranous location of the SLC4 transporters as well as recent advances in discovering their structural biology makes them accessible and attractive as a druggable target in a disease context. The role of sodium-coupled bicarbonate transporters in such a large array of conditions illustrates the potential of treating a wide range of disease states by modifying function of these transporters, whether that be through inhibition or enhancement.
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Affiliation(s)
- Shannon R Holmberg
- Physiology & Biomedical Engineering, Mayo Clinic College of Medicine & Science, 200 1st Street SW, Rochester, MN 55905, USA
- Biochemistry & Molecular Biology, Mayo Clinic College of Medicine & Science, 200 1st Street SW, Rochester, MN, USA
| | - Yohei Sakamoto
- School of Life Science and Technology, Tokyo Institute of Technology, Midori-Ku, Yokohama, 226-8501, Japan
| | - Akira Kato
- School of Life Science and Technology, Tokyo Institute of Technology, Midori-Ku, Yokohama, 226-8501, Japan
| | - Michael F Romero
- Physiology & Biomedical Engineering, Mayo Clinic College of Medicine & Science, 200 1st Street SW, Rochester, MN 55905, USA.
- Nephrology & Hypertension, Mayo Clinic College of Medicine & Science, 200 1st Street SW, Rochester, MN, USA.
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18
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Ko KD, Sartorelli V. A deep learning adversarial autoencoder with dynamic batching displays high performance in denoising and ordering scRNA-seq data. iScience 2024; 27:109027. [PMID: 38361616 PMCID: PMC10867661 DOI: 10.1016/j.isci.2024.109027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2023] [Revised: 11/20/2023] [Accepted: 01/22/2024] [Indexed: 02/17/2024] Open
Abstract
By providing high-resolution of cell-to-cell variation in gene expression, single-cell RNA sequencing (scRNA-seq) offers insights into cell heterogeneity, differentiating dynamics, and disease mechanisms. However, challenges such as low capture rates and dropout events can introduce noise in data analysis. Here, we propose a deep neural generative framework, the dynamic batching adversarial autoencoder (DB-AAE), which excels at denoising scRNA-seq datasets. DB-AAE directly captures optimal features from input data and enhances feature preservation, including cell type-specific gene expression patterns. Comprehensive evaluation on simulated and real datasets demonstrates that DB-AAE outperforms other methods in denoising accuracy and biological signal preservation. It also improves the accuracy of other algorithms in establishing pseudo-time inference. This study highlights DB-AAE's effectiveness and potential as a valuable tool for enhancing the quality and reliability of downstream analyses in scRNA-seq research.
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Affiliation(s)
- Kyung Dae Ko
- Laboratory of Muscle Stem Cells & Gene Regulation, NIAMS, NIH, Bethesda, MD, USA
| | - Vittorio Sartorelli
- Laboratory of Muscle Stem Cells & Gene Regulation, NIAMS, NIH, Bethesda, MD, USA
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19
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Oropeza D, Herrera PL. Glucagon-producing α-cell transcriptional identity and reprogramming towards insulin production. Trends Cell Biol 2024; 34:180-197. [PMID: 37626005 DOI: 10.1016/j.tcb.2023.07.004] [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: 04/27/2023] [Revised: 07/07/2023] [Accepted: 07/11/2023] [Indexed: 08/27/2023]
Abstract
β-Cell replacement by in situ reprogramming of non-β-cells is a promising diabetes therapy. Following the observation that near-total β-cell ablation in adult mice triggers the reprogramming of pancreatic α-, δ-, and γ-cells into insulin (INS)-producing cells, recent studies are delving deep into the mechanisms controlling adult α-cell identity. Systematic analyses of the α-cell transcriptome and epigenome have started to pinpoint features that could be crucial for maintaining α-cell identity. Using different transgenic and chemical approaches, significant advances have been made in reprogramming α-cells in vivo into INS-secreting cells in mice. The recent reprogramming of human α-cells in vitro is an important step forward that must now be complemented with a comprehensive molecular dissection of the mechanisms controlling α-cell identity.
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Affiliation(s)
- Daniel Oropeza
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Pedro Luis Herrera
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, Geneva, Switzerland.
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20
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Xu Y, Zhang W, Zheng X, Cai X. Combining Global-Constrained Concept Factorization and a Regularized Gaussian Graphical Model for Clustering Single-Cell RNA-seq Data. Interdiscip Sci 2024; 16:1-15. [PMID: 37815679 DOI: 10.1007/s12539-023-00587-7] [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: 02/06/2023] [Revised: 09/14/2023] [Accepted: 09/17/2023] [Indexed: 10/11/2023]
Abstract
Single-cell RNA sequencing technology is one of the most cost-effective ways to uncover transcriptomic heterogeneity. With the rapid rise of this technology, enormous amounts of scRNA-seq data have been produced. Due to the high dimensionality, noise, sparsity and missing features of the available scRNA-seq data, accurately clustering the scRNA-seq data for downstream analysis is a significant challenge. Many computational methods have been designed to address this issue; nevertheless, the efficacy of the available methods is still inadequate. In addition, most similarity-based methods require a number of clusters as input, which is difficult to achieve in real applications. In this study, we developed a novel computational method for clustering scRNA-seq data by considering both global and local information, named GCFG. This method characterizes the global properties of data by applying concept factorization, and the regularized Gaussian graphical model is utilized to evaluate the local embedding relationship of data. To learn the cell-cell similarity matrix, we integrated the two components, and an iterative optimization algorithm was developed. The categorization of single cells is obtained by applying Louvain, a modularity-based community discovery algorithm, to the similarity matrix. The behavior of the GCFG approach is assessed on 14 real scRNA-seq datasets in terms of ACC and ARI, and comparison results with 17 other competitive methods suggest that GCFG is effective and robust.
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Affiliation(s)
- Yaxin Xu
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
| | - Wei Zhang
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China.
| | - Xiaoying Zheng
- Operations Research and Planning Department, Naval University of Engineering, Wuhan, 430033, China
| | - Xianxian Cai
- School of Sciences, East China Jiaotong University, Nanchang, 330013, China
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21
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Fang C, Dziedzic A, Zhang L, Oliva L, Verma A, Razak F, Papernot N, Wang B. Decentralised, collaborative, and privacy-preserving machine learning for multi-hospital data. EBioMedicine 2024; 101:105006. [PMID: 38377795 PMCID: PMC10884342 DOI: 10.1016/j.ebiom.2024.105006] [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: 10/15/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 02/22/2024] Open
Abstract
BACKGROUND Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healthcare to achieve better accuracy and generalizability. Sharing data across different healthcare institutions or jurisdictions is challenging because of complex and varying privacy and regulatory requirements. Hence, it is hard but crucial to allow multiple parties to collaboratively train an ML model leveraging the private datasets available at each party without the need for direct sharing of those datasets or compromising the privacy of the datasets through collaboration. METHODS In this paper, we address this challenge by proposing Decentralized, Collaborative, and Privacy-preserving ML for Multi-Hospital Data (DeCaPH). This framework offers the following key benefits: (1) it allows different parties to collaboratively train an ML model without transferring their private datasets (i.e., no data centralization); (2) it safeguards patients' privacy by limiting the potential privacy leakage arising from any contents shared across the parties during the training process; and (3) it facilitates the ML model training without relying on a centralized party/server. FINDINGS We demonstrate the generalizability and power of DeCaPH on three distinct tasks using real-world distributed medical datasets: patient mortality prediction using electronic health records, cell-type classification using single-cell human genomes, and pathology identification using chest radiology images. The ML models trained with DeCaPH framework have less than 3.2% drop in model performance comparing to those trained by the non-privacy-preserving collaborative framework. Meanwhile, the average vulnerability to privacy attacks of the models trained with DeCaPH decreased by up to 16%. In addition, models trained with our DeCaPH framework achieve better performance than those models trained solely with the private datasets from individual parties without collaboration and those trained with the previous privacy-preserving collaborative training framework under the same privacy guarantee by up to 70% and 18.2% respectively. INTERPRETATION We demonstrate that the ML models trained with DeCaPH framework have an improved utility-privacy trade-off, showing DeCaPH enables the models to have good performance while preserving the privacy of the training data points. In addition, the ML models trained with DeCaPH framework in general outperform those trained solely with the private datasets from individual parties, showing that DeCaPH enhances the model generalizability. FUNDING This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC, RGPIN-2020-06189 and DGECR-2020-00294), Canadian Institute for Advanced Research (CIFAR) AI Catalyst Grants, CIFAR AI Chair programs, Temerty Professor of AI Research and Education in Medicine, University of Toronto, Amazon, Apple, DARPA through the GARD project, Intel, Meta, the Ontario Early Researcher Award, and the Sloan Foundation. Resources used in preparing this research were provided, in part, by the Province of Ontario, the Government of Canada through CIFAR, and companies sponsoring the Vector Institute.
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Affiliation(s)
- Congyu Fang
- Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada
| | - Adam Dziedzic
- Vector Institute, Toronto, Canada; CISPA Helmholtz Center for Information Security, Germany; Department of Electrical and Computer Engineering, University of Toronto, Canada
| | - Lin Zhang
- Peter Munk Cardiac Centre, University Health Network, Canada; Simon Fraser University, Canada
| | - Laura Oliva
- Peter Munk Cardiac Centre, University Health Network, Canada
| | - Amol Verma
- St. Michael's Hospital, Unity Health Toronto, Canada; Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada
| | - Fahad Razak
- St. Michael's Hospital, Unity Health Toronto, Canada; Department of Medicine, University of Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Canada
| | - Nicolas Papernot
- Department of Computer Science, University of Toronto, Canada; Vector Institute, Toronto, Canada; Department of Electrical and Computer Engineering, University of Toronto, Canada.
| | - Bo Wang
- Department of Computer Science, University of Toronto, Canada; Peter Munk Cardiac Centre, University Health Network, Canada; Vector Institute, Toronto, Canada; Department of Laboratory Medicine and Pathobiology, Temerty Faculty of Medicine, University of Toronto, Canada.
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22
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Monnier L, Cournède PH. A novel batch-effect correction method for scRNA-seq data based on Adversarial Information Factorization. PLoS Comput Biol 2024; 20:e1011880. [PMID: 38386700 PMCID: PMC10914288 DOI: 10.1371/journal.pcbi.1011880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 03/05/2024] [Accepted: 01/30/2024] [Indexed: 02/24/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) technology produces an unprecedented resolution at the level of a unique cell, raising great hopes in medicine. Nevertheless, scRNA-seq data suffer from high variations due to the experimental conditions, called batch effects, preventing any aggregated downstream analysis. Adversarial Information Factorization provides a robust batch-effect correction method that does not rely on prior knowledge of the cell types nor a specific normalization strategy while being adapted to any downstream analysis task. It compares to and even outperforms state-of-the-art methods in several scenarios: low signal-to-noise ratio, batch-specific cell types with few cells, and a multi-batches dataset with imbalanced batches and batch-specific cell types. Moreover, it best preserves the relative gene expression between cell types, yielding superior differential expression analysis results. Finally, in a more complex setting of a Leukemia cohort, our method preserved most of the underlying biological information for each patient while aligning the batches, improving the clustering metrics in the aggregated dataset.
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Affiliation(s)
- Lily Monnier
- Paris-Saclay University, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France
| | - Paul-Henry Cournède
- Paris-Saclay University, CentraleSupélec, Laboratory of Mathematics and Computer Science (MICS), Gif-sur-Yvette, France
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23
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Tyler SR, Lozano-Ojalvo D, Guccione E, Schadt EE. Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq. Nat Commun 2024; 15:699. [PMID: 38267438 PMCID: PMC10808220 DOI: 10.1038/s41467-023-43406-9] [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: 12/13/2022] [Accepted: 11/07/2023] [Indexed: 01/26/2024] Open
Abstract
While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells.
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Affiliation(s)
- Scott R Tyler
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Daniel Lozano-Ojalvo
- Department of Dermatology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ernesto Guccione
- Department of Oncological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Center for Therapeutics Discovery, Department of Oncological Sciences and Pharmacological Sciences, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Bioinformatics for Next Generation Sequencing (BiNGS) Shared Resource Facility, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric E Schadt
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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24
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Roy G, Syed R, Lazaro O, Robertson S, McCabe SD, Rodriguez D, Mawla AM, Johnson TS, Kalwat MA. Identification of type 2 diabetes- and obesity-associated human β-cells using deep transfer learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.18.576260. [PMID: 38328172 PMCID: PMC10849510 DOI: 10.1101/2024.01.18.576260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Diabetes affects >10% of adults worldwide and is caused by impaired production or response to insulin, resulting in chronic hyperglycemia. Pancreatic islet β-cells are the sole source of endogenous insulin and our understanding of β-cell dysfunction and death in type 2 diabetes (T2D) is incomplete. Single-cell RNA-seq data supports heterogeneity as an important factor in β-cell function and survival. However, it is difficult to identify which β-cell phenotypes are critical for T2D etiology and progression. Our goal was to prioritize specific disease-related β-cell subpopulations to better understand T2D pathogenesis and identify relevant genes for targeted therapeutics. To address this, we applied a deep transfer learning tool, DEGAS, which maps disease associations onto single-cell RNA-seq data from bulk expression data. Independent runs of DEGAS using T2D or obesity status identified distinct β-cell subpopulations. A singular cluster of T2D-associated β-cells was identified; however, β-cells with high obese-DEGAS scores contained two subpopulations derived largely from either non-diabetic or T2D donors. The obesity-associated non-diabetic cells were enriched for translation and unfolded protein response genes compared to T2D cells. We selected DLK1 for validation by immunostaining in human pancreas sections from healthy and T2D donors. DLK1 was heterogeneously expressed among β-cells and appeared depleted from T2D islets. In conclusion, DEGAS has the potential to advance our holistic understanding of the β-cell transcriptomic phenotypes, including features that distinguish β-cells in obese non-diabetic or lean T2D states. Future work will expand this approach to additional human islet omics datasets to reveal the complex multicellular interactions driving T2D.
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25
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Sturgill D, Wang L, Arda HE. PancrESS - a meta-analysis resource for understanding cell-type specific expression in the human pancreas. BMC Genomics 2024; 25:76. [PMID: 38238687 PMCID: PMC10797729 DOI: 10.1186/s12864-024-09964-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND The human pancreas is composed of specialized cell types producing hormones and enzymes critical to human health. These specialized functions are the result of cell type-specific transcriptional programs which manifest in cell-specific gene expression. Understanding these programs is essential to developing therapies for pancreatic disorders. Transcription in the human pancreas has been widely studied by single-cell RNA technologies, however the diversity of protocols and analysis methods hinders their interpretability in the aggregate. RESULTS In this work, we perform a meta-analysis of pancreatic single-cell RNA sequencing data. We present a database for reference transcriptome abundances and cell-type specificity metrics. This database facilitates the identification and definition of marker genes within the pancreas. Additionally, we introduce a versatile tool which is freely available as an R package, and should permit integration into existing workflows. Our tool accepts count data files generated by widely-used single-cell gene expression platforms in their original format, eliminating an additional pre-formatting step. Although we designed it to calculate expression specificity of pancreas cell types, our tool is agnostic to the biological source of count data, extending its applicability to other biological systems. CONCLUSIONS Our findings enhance the current understanding of expression specificity within the pancreas, surpassing previous work in terms of scope and detail. Furthermore, our database and tool enable researchers to perform similar calculations in diverse biological systems, expanding the applicability of marker gene identification and facilitating comparative analyses.
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Affiliation(s)
- David Sturgill
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - Li Wang
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA
| | - H Efsun Arda
- Laboratory of Receptor Biology and Gene Expression, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, 20892, USA.
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26
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Jeuken GS, Käll L. Pathway analysis through mutual information. Bioinformatics 2024; 40:btad776. [PMID: 38195928 PMCID: PMC10783954 DOI: 10.1093/bioinformatics/btad776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 12/09/2023] [Accepted: 01/08/2024] [Indexed: 01/11/2024] Open
Abstract
MOTIVATION In pathway analysis, we aim to establish a connection between the activity of a particular biological pathway and a difference in phenotype. There are many available methods to perform pathway analysis, many of them rely on an upstream differential expression analysis, and many model the relations between the abundances of the analytes in a pathway as linear relationships. RESULTS Here, we propose a new method for pathway analysis, MIPath, that relies on information theoretical principles and, therefore, does not model the association between pathway activity and phenotype, resulting in relatively few assumptions. For this, we construct a graph of the data points for each pathway using a nearest-neighbor approach and score the association between the structure of this graph and the phenotype of these same samples using Mutual Information while adjusting for the effects of random chance in each score. The initial nearest neighbor approach evades individual gene-level comparisons, hence making the method scalable and less vulnerable to missing values. These properties make our method particularly useful for single-cell data. We benchmarked our method on several single-cell datasets, comparing it to established and new methods, and found that it produces robust, reproducible, and meaningful scores. AVAILABILITY AND IMPLEMENTATION Source code is available at https://github.com/statisticalbiotechnology/mipath, or through Python Package Index as "mipathway."
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Affiliation(s)
- Gustavo S Jeuken
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm 171 65, Sweden
- Computer Science Department, Vrije Universiteit Amsterdam, Amsterdam 1081 HV, The Netherlands
| | - Lukas Käll
- Science for Life Laboratory, KTH – Royal Institute of Technology, Stockholm 171 65, Sweden
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27
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Cohrs CM, Chen C, Atkinson MA, Drotar DM, Speier S. Bridging the Gap: Pancreas Tissue Slices From Organ and Tissue Donors for the Study of Diabetes Pathogenesis. Diabetes 2024; 73:11-22. [PMID: 38117999 PMCID: PMC10784654 DOI: 10.2337/dbi20-0018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Accepted: 10/14/2023] [Indexed: 12/22/2023]
Abstract
Over the last two decades, increased availability of human pancreatic tissues has allowed for major expansions in our understanding of islet biology in health and disease. Indeed, studies of fixed and frozen pancreatic tissues, as well as efforts using viable isolated islets obtained from organ donors, have provided significant insights toward our understanding of diabetes. However, the procedures associated with islet isolation result in distressed cells that have been removed from any surrounding influence. The pancreas tissue slice technology was developed as an in situ approach to overcome certain limitations associated with studies on isolated islets or fixed tissue. In this Perspective, we discuss the value of this novel platform and review how pancreas tissue slices, within a short time, have been integrated in numerous studies of rodent and human islet research. We show that pancreas tissue slices allow for investigations in a less perturbed organ tissue environment, ranging from cellular processes, over peri-islet modulations, to tissue interactions. Finally, we discuss the considerations and limitations of this technology in its future applications. We believe the pancreas tissue slices will help bridge the gap between studies on isolated islets and cells to the systemic conditions by providing new insight into physiological and pathophysiological processes at the organ level. ARTICLE HIGHLIGHTS Human pancreas tissue slices represent a novel platform to study human islet biology in close to physiological conditions. Complementary to established technologies, such as isolated islets, single cells, and histological sections, pancreas tissue slices help bridge our understanding of islet physiology and pathophysiology from single cell to intact organ. Diverse sources of viable human pancreas tissue, each with distinct characteristics to be considered, are available to use in tissue slices for the study of diabetes pathogenesis.
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Affiliation(s)
- Christian M. Cohrs
- Institute of Physiology, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of Helmholtz Munich at the University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Chunguang Chen
- Institute of Physiology, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of Helmholtz Munich at the University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
| | - Mark A. Atkinson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL
| | - Denise M. Drotar
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida Diabetes Institute, Gainesville, FL
| | - Stephan Speier
- Institute of Physiology, Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- Paul Langerhans Institute Dresden of Helmholtz Munich at the University Hospital and Faculty of Medicine, Technische Universität Dresden, Dresden, Germany
- German Center for Diabetes Research (DZD), München-Neuherberg, Germany
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Paz-Barba M, Muñoz Garcia A, de Winter TJJ, de Graaf N, van Agen M, van der Sar E, Lambregtse F, Daleman L, van der Slik A, Zaldumbide A, de Koning EJP, Carlotti F. Apolipoprotein L genes are novel mediators of inflammation in beta cells. Diabetologia 2024; 67:124-136. [PMID: 37924378 PMCID: PMC10709252 DOI: 10.1007/s00125-023-06033-z] [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: 06/16/2023] [Accepted: 08/22/2023] [Indexed: 11/06/2023]
Abstract
AIMS/HYPOTHESIS Inflammation induces beta cell dysfunction and demise but underlying molecular mechanisms remain unclear. The apolipoprotein L (APOL) family of genes has been associated with innate immunity and apoptosis in non-pancreatic cell types, but also with metabolic syndrome and type 2 diabetes mellitus. Here, we hypothesised that APOL genes play a role in inflammation-induced beta cell damage. METHODS We used single-cell transcriptomics datasets of primary human pancreatic islet cells to study the expression of APOL genes upon specific stress conditions. Validation of the findings was carried out in EndoC-βH1 cells and primary human islets. Finally, we performed loss- and gain-of-function experiments to investigate the role of APOL genes in beta cells. RESULTS APOL genes are expressed in primary human beta cells and APOL1, 2 and 6 are strongly upregulated upon inflammation via the Janus kinase (JAK)-signal transducer and activator of transcription (STAT) pathway. APOL1 overexpression increases endoplasmic reticulum stress while APOL1 knockdown prevents cytokine-induced beta cell death and interferon-associated response. Furthermore, we found that APOL genes are upregulated in beta cells from donors with type 2 diabetes compared with donors without diabetes mellitus. CONCLUSIONS/INTERPRETATION APOLs are novel regulators of islet inflammation and may contribute to beta cell damage during the development of diabetes. DATA AVAILABILITY scRNAseq data generated by our laboratory and used in this study are available in the Gene Expression Omnibus (GEO; www.ncbi.nlm.nih.gov/geo/ ), accession number GSE218316.
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Affiliation(s)
- Miriam Paz-Barba
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Amadeo Muñoz Garcia
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Twan J J de Winter
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Natascha de Graaf
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Maarten van Agen
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Elisa van der Sar
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Ferdy Lambregtse
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Lizanne Daleman
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Arno van der Slik
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Arnaud Zaldumbide
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Eelco J P de Koning
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands
| | - Françoise Carlotti
- Department of Internal Medicine, Leiden University Medical Center, Leiden, the Netherlands.
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Li Y, Ming M, Li C, Liu S, Zhang D, Song T, Tan J, Zhang J. The emerging role of the hedgehog signaling pathway in immunity response and autoimmune diseases. Autoimmunity 2023; 56:2259127. [PMID: 37740690 DOI: 10.1080/08916934.2023.2259127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Accepted: 09/10/2023] [Indexed: 09/25/2023]
Abstract
The Hedgehog (Hh) family is a prototypical morphogen involved in embryonic patterning, multi-lineage differentiation, self-renewal, morphogenesis, and regeneration. There are studies that have demonstrated that the Hh signaling pathway differentiates developing T cells into MHC-restricted self-antigen tolerant T cells in a concentration-dependent manner in the thymus. Whereas Hh signaling pathway is not required in the differentiation of B cells but is indispensable in maintaining the regeneration of hematopoietic stem cells (HSCs) and the viability of germinal centers (GCs) B cells. The Hh signaling pathway exerts both positive and negative effects on immune responses, which involves activating human peripheral CD4+ T cells, regulating the accumulation of natural killer T (NKT) cells, recruiting and activating macrophages, increasing CD4+Foxp3+ regulatory T cells in the inflammation sites to sustain homeostasis. Hedgehog signaling is involved in the patterning of the embryo, as well as homeostasis of adult tissues. Therefore, this review aims to highlight evidence for Hh signaling in the differentiation, function of immune cells and autoimmune disease. Targeting Hh signaling promises to be a novel, alternative or adjunct approach to treating tumors and autoimmune diseases.
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Affiliation(s)
- Yunfei Li
- Department of Immunology, Zunyi Medical University, Zunyi, China
- Department of Respiratory Medicine, The Third Affiliated Hospital of Zunyi Medical University (The First People's Hospital of Zunyi), Zunyi, China
| | - Min Ming
- Department of Immunology, Zunyi Medical University, Zunyi, China
- People's Hospital of Qingbaijiang District, Chengdu, China
| | - Chunyang Li
- Department of Immunology, Zunyi Medical University, Zunyi, China
- Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China
| | - Songpo Liu
- Department of Immunology, Zunyi Medical University, Zunyi, China
- Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China
| | - Dan Zhang
- Zunyi Medical University Library, Zunyi, China
| | - Tao Song
- Department of Immunology, Zunyi Medical University, Zunyi, China
- Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China
- Collaborative Innovation Center of Tissue Damage Repair and Regeneration Medicine, Zunyi Medical University, Zunyi, China
| | - Jun Tan
- Department of Histology and Embryology, Zunyi Medical University, Zunyi, China
| | - Jidong Zhang
- Department of Immunology, Zunyi Medical University, Zunyi, China
- Special Key Laboratory of Gene Detection and Therapy of Guizhou Province, Zunyi Medical University, Zunyi, China
- Collaborative Innovation Center of Tissue Damage Repair and Regeneration Medicine, Zunyi Medical University, Zunyi, China
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30
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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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Doke M, Álvarez-Cubela S, Klein D, Altilio I, Schulz J, Mateus Gonçalves L, Almaça J, Fraker CA, Pugliese A, Ricordi C, Qadir MMF, Pastori RL, Domínguez-Bendala J. Dynamic scRNA-seq of live human pancreatic slices reveals functional endocrine cell neogenesis through an intermediate ducto-acinar stage. Cell Metab 2023; 35:1944-1960.e7. [PMID: 37898119 DOI: 10.1016/j.cmet.2023.10.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 08/23/2023] [Accepted: 10/03/2023] [Indexed: 10/30/2023]
Abstract
Human pancreatic plasticity is implied from multiple single-cell RNA sequencing (scRNA-seq) studies. However, these have been invariably based on static datasets from which fate trajectories can only be inferred using pseudotemporal estimations. Furthermore, the analysis of isolated islets has resulted in a drastic underrepresentation of other cell types, hindering our ability to interrogate exocrine-endocrine interactions. The long-term culture of human pancreatic slices (HPSs) has presented the field with an opportunity to dynamically track tissue plasticity at the single-cell level. Combining datasets from same-donor HPSs at different time points, with or without a known regenerative stimulus (BMP signaling), led to integrated single-cell datasets storing true temporal or treatment-dependent information. This integration revealed population shifts consistent with ductal progenitor activation, blurring of ductal/acinar boundaries, formation of ducto-acinar-endocrine differentiation axes, and detection of transitional insulin-producing cells. This study provides the first longitudinal scRNA-seq analysis of whole human pancreatic tissue, confirming its plasticity in a dynamic fashion.
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Affiliation(s)
- Mayur Doke
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Silvia Álvarez-Cubela
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Dagmar Klein
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Isabella Altilio
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Joseph Schulz
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Luciana Mateus Gonçalves
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Joana Almaça
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Christopher A Fraker
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Alberto Pugliese
- Arthur Riggs Diabetes & Metabolism Research Institute, City of Hope, Duarte, CA 91010, USA
| | - Camillo Ricordi
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mirza M F Qadir
- Tulane University School of Medicine, New Orleans, LA 70112, USA
| | - Ricardo L Pastori
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
| | - Juan Domínguez-Bendala
- Diabetes Research Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA.
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32
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Karin J, Bornfeld Y, Nitzan M. scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching. Nat Biotechnol 2023; 41:1645-1654. [PMID: 36849830 PMCID: PMC10635821 DOI: 10.1038/s41587-023-01663-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Accepted: 01/06/2023] [Indexed: 03/01/2023]
Abstract
Single-cell RNA sequencing has been instrumental in uncovering cellular spatiotemporal context. This task is challenging as cells simultaneously encode multiple, potentially cross-interfering, biological signals. Here we propose scPrisma, a spectral computational method that uses topological priors to decouple, enhance and filter different classes of biological processes in single-cell data, such as periodic and linear signals. We apply scPrisma to the analysis of the cell cycle in HeLa cells, circadian rhythm and spatial zonation in liver lobules, diurnal cycle in Chlamydomonas and circadian rhythm in the suprachiasmatic nucleus in the brain. scPrisma can be used to distinguish mixed cellular populations by specific characteristics such as cell type and uncover regulatory networks and cell-cell interactions specific to predefined biological signals, such as the circadian rhythm. We show scPrisma's flexibility in incorporating prior knowledge, inference of topologically informative genes and generalization to additional diverse templates and systems. scPrisma can be used as a stand-alone workflow for signal analysis and as a prior step for downstream single-cell analysis.
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Affiliation(s)
- Jonathan Karin
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Yonathan Bornfeld
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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33
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Yampolskaya M, Herriges MJ, Ikonomou L, Kotton DN, Mehta P. scTOP: physics-inspired order parameters for cellular identification and visualization. Development 2023; 150:dev201873. [PMID: 37756586 PMCID: PMC10629677 DOI: 10.1242/dev.201873] [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: 04/11/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.
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Affiliation(s)
| | - Michael J. Herriges
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Laertis Ikonomou
- Department of Oral Biology, University at Buffalo, The State University of New York, Buffalo, NY 14215, USA
- Division of Pulmonary, Critical Care and Sleep Medicine, Department of Medicine, University at Buffalo, The State University of New York, Buffalo, NY 14215, USA
| | - Darrell N. Kotton
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- The Pulmonary Center and Department of Medicine, Boston University School of Medicine, Boston, MA 02118, USA
| | - Pankaj Mehta
- Department of Physics, Boston University, Boston, MA 02215, USA
- Center for Regenerative Medicine of Boston University and Boston Medical Center, Boston, MA 02118, USA
- Faculty of Computing and Data Science, Boston University, Boston, MA 02215, USA
- Biological Design Center, Boston University, Boston, MA 02215, USA
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Zhao Z, D’Oliveira Albanus R, Taylor H, Tang X, Han Y, Orchard P, Varshney A, Zhang T, Manickam N, Erdos M, Narisu N, Taylor L, Saavedra X, Zhong A, Li B, Zhou T, Naji A, Liu C, Collins F, Parker SCJ, Chen S. An integrative single-cell multi-omics profiling of human pancreatic islets identifies T1D associated genes and regulatory signals. RESEARCH SQUARE 2023:rs.3.rs-3343318. [PMID: 37886586 PMCID: PMC10602166 DOI: 10.21203/rs.3.rs-3343318/v1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Genome wide association studies (GWAS) have identified over 100 signals associated with type 1 diabetes (T1D). However, translating any given T1D GWAS signal into mechanistic insights, including putative causal variants and the context (cell type and cell state) in which they function, has been limited. Here, we present a comprehensive multi-omic integrative analysis of single-cell/nucleus resolution profiles of gene expression and chromatin accessibility in healthy and autoantibody+ (AAB+) human islets, as well as islets under multiple T1D stimulatory conditions. We broadly nominate effector cell types for all T1D GWAS signals. We further nominated higher-resolution contexts, including effector cell types, regulatory elements, and genes for three independent T1D risk variants acting through islet cells within the pancreas at the DLK1/MEG3, RASGRP1, and TOX loci. Subsequently, we created isogenic gene knockouts DLK1-/-, RASGRP1-/-, and TOX-/-, and the corresponding regulatory region knockout, RASGRP1Δ, and DLK1Δ hESCs. Loss of RASGRP1 or DLK1, as well as knockout of the regulatory region of RASGRP1 or DLK1, increased β cell apoptosis. Additionally, pancreatic β cells derived from isogenic hESCs carrying the risk allele of rs3783355A/A exhibited increased β cell death. Finally, RNA-seq and ATAC-seq identified five genes upregulated in both RASGRP1-/- and DLK1-/- β-like cells, four of which are associated with T1D. Together, this work reports an integrative approach for combining single cell multi-omics, GWAS, and isogenic hESC-derived β-like cells to prioritize the T1D associated signals and their underlying context-specific cell types, genes, SNPs, and regulatory elements, to illuminate biological functions and molecular mechanisms.
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Affiliation(s)
- Zeping Zhao
- Department of Surgery, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
- Center for Genomic Health, Weill Cornell Medicine, 1300 York Ave, New York, NY 15 10065, USA
| | | | - Henry Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xuming Tang
- Department of Surgery, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
- Center for Genomic Health, Weill Cornell Medicine, 1300 York Ave, New York, NY 15 10065, USA
| | - Yuling Han
- Department of Surgery, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
- Center for Genomic Health, Weill Cornell Medicine, 1300 York Ave, New York, NY 15 10065, USA
| | - Peter Orchard
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Arushi Varshney
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Tuo Zhang
- Stem Cell Research Facility, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA
| | - Nandini Manickam
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Mike Erdos
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Narisu Narisu
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Leland Taylor
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Xiaxia Saavedra
- Department of Surgery, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
| | - Aaron Zhong
- Genomic Resource Core Facility, Weill Cornell Medical College, NY 10065, USA
| | - Bo Li
- Department of Surgery, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
| | - Ting Zhou
- Genomic Resource Core Facility, Weill Cornell Medical College, NY 10065, USA
| | - Ali Naji
- Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA19104, USA
| | - Chengyang Liu
- Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA19104, USA
| | - Francis Collins
- Center for Precision Health Research, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen CJ Parker
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Shuibing Chen
- Department of Surgery, Weill Cornell Medicine, 1300 York Ave, New York, NY, 10065, USA
- Center for Genomic Health, Weill Cornell Medicine, 1300 York Ave, New York, NY 15 10065, USA
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35
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Weng C, Gu A, Zhang S, Lu L, Ke L, Gao P, Liu X, Wang Y, Hu P, Plummer D, MacDonald E, Zhang S, Xi J, Lai S, Leskov K, Yuan K, Jin F, Li Y. Single cell multiomic analysis reveals diabetes-associated β-cell heterogeneity driven by HNF1A. Nat Commun 2023; 14:5400. [PMID: 37669939 PMCID: PMC10480445 DOI: 10.1038/s41467-023-41228-3] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 08/29/2023] [Indexed: 09/07/2023] Open
Abstract
Broad heterogeneity in pancreatic β-cell function and morphology has been widely reported. However, determining which components of this cellular heterogeneity serve a diabetes-relevant function remains challenging. Here, we integrate single-cell transcriptome, single-nuclei chromatin accessibility, and cell-type specific 3D genome profiles from human islets and identify Type II Diabetes (T2D)-associated β-cell heterogeneity at both transcriptomic and epigenomic levels. We develop a computational method to explicitly dissect the intra-donor and inter-donor heterogeneity between single β-cells, which reflect distinct mechanisms of T2D pathogenesis. Integrative transcriptomic and epigenomic analysis identifies HNF1A as a principal driver of intra-donor heterogeneity between β-cells from the same donors; HNF1A expression is also reduced in β-cells from T2D donors. Interestingly, HNF1A activity in single β-cells is significantly associated with lower Na+ currents and we nominate a HNF1A target, FXYD2, as the primary mitigator. Our study demonstrates the value of investigating disease-associated single-cell heterogeneity and provides new insights into the pathogenesis of T2D.
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Affiliation(s)
- Chen Weng
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Anniya Gu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- Medical Scientist Training Program (MSTP), School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Shanshan Zhang
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Leina Lu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Luxin Ke
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Peidong Gao
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Xiaoxiao Liu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Yuntong Wang
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Peinan Hu
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Dylan Plummer
- Department of Computer and Data Sciences, School of Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Elise MacDonald
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Saixian Zhang
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Jiajia Xi
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Sisi Lai
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- The Biomedical Sciences Training Program (BSTP), School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Konstantin Leskov
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Kyle Yuan
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
- Department of Biochemistry, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Fulai Jin
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
- Department of Computer and Data Sciences, School of Engineering, Case Western Reserve University, Cleveland, OH, 44106, USA.
- Case Comprehensive Cancer Center, Case Western Reserve University, Cleveland, OH, 44106, USA.
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
| | - Yan Li
- Department of Genetics and Genome Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
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Hrovatin K, Bastidas-Ponce A, Bakhti M, Zappia L, Büttner M, Salinno C, Sterr M, Böttcher A, Migliorini A, Lickert H, Theis FJ. Delineating mouse β-cell identity during lifetime and in diabetes with a single cell atlas. Nat Metab 2023; 5:1615-1637. [PMID: 37697055 PMCID: PMC10513934 DOI: 10.1038/s42255-023-00876-x] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 07/26/2023] [Indexed: 09/13/2023]
Abstract
Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have been generated, a consensus on pancreatic cell states in development, homeostasis and diabetes as well as the value of preclinical animal models is missing. Here, we present an scRNA-seq cross-condition mouse islet atlas (MIA), a curated resource for interactive exploration and computational querying. We integrate over 300,000 cells from nine scRNA-seq datasets consisting of 56 samples, varying in age, sex and diabetes models, including an autoimmune type 1 diabetes model (NOD), a glucotoxicity/lipotoxicity type 2 diabetes model (db/db) and a chemical streptozotocin β-cell ablation model. The β-cell landscape of MIA reveals new cell states during disease progression and cross-publication differences between previously suggested marker genes. We show that β-cells in the streptozotocin model transcriptionally correlate with those in human type 2 diabetes and mouse db/db models, but are less similar to human type 1 diabetes and mouse NOD β-cells. We also report pathways that are shared between β-cells in immature, aged and diabetes models. MIA enables a comprehensive analysis of β-cell responses to different stressors, providing a roadmap for the understanding of β-cell plasticity, compensation and demise.
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Affiliation(s)
- Karin Hrovatin
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany
| | - Aimée Bastidas-Ponce
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Mostafa Bakhti
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Luke Zappia
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Garching, Germany
| | - Maren Büttner
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany
- Genomics and Immunoregulation, Life & Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany
- Systems Medicine, Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Bonn, Germany
| | - Ciro Salinno
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Medical Faculty, Technical University of Munich, Munich, Germany
| | - Michael Sterr
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Anika Böttcher
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Adriana Migliorini
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- McEwen Stem Cell Institute, University Health Network (UHN), Toronto, Ontario, Canada
| | - Heiko Lickert
- Institute of Diabetes and Regeneration Research, Helmholtz Zentrum München, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), Neuherberg, Germany.
- Medical Faculty, Technical University of Munich, Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising, Germany.
- Department of Mathematics, Technical University of Munich, Garching, Germany.
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Vanheer L, Fantuzzi F, To SK, Schiavo A, Van Haele M, Ostyn T, Haesen T, Yi X, Janiszewski A, Chappell J, Rihoux A, Sawatani T, Roskams T, Pattou F, Kerr-Conte J, Cnop M, Pasque V. Inferring regulators of cell identity in the human adult pancreas. NAR Genom Bioinform 2023; 5:lqad068. [PMID: 37435358 PMCID: PMC10331937 DOI: 10.1093/nargab/lqad068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 06/17/2023] [Accepted: 06/28/2023] [Indexed: 07/13/2023] Open
Abstract
Cellular identity during development is under the control of transcription factors that form gene regulatory networks. However, the transcription factors and gene regulatory networks underlying cellular identity in the human adult pancreas remain largely unexplored. Here, we integrate multiple single-cell RNA-sequencing datasets of the human adult pancreas, totaling 7393 cells, and comprehensively reconstruct gene regulatory networks. We show that a network of 142 transcription factors forms distinct regulatory modules that characterize pancreatic cell types. We present evidence that our approach identifies regulators of cell identity and cell states in the human adult pancreas. We predict that HEYL, BHLHE41 and JUND are active in acinar, beta and alpha cells, respectively, and show that these proteins are present in the human adult pancreas as well as in human induced pluripotent stem cell (hiPSC)-derived islet cells. Using single-cell transcriptomics, we found that JUND represses beta cell genes in hiPSC-alpha cells. BHLHE41 depletion induced apoptosis in primary pancreatic islets. The comprehensive gene regulatory network atlas can be explored interactively online. We anticipate our analysis to be the starting point for a more sophisticated dissection of how transcription factors regulate cell identity and cell states in the human adult pancreas.
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Affiliation(s)
- Lotte Vanheer
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
| | - Federica Fantuzzi
- ULB Center for Diabetes Research; Université Libre de Bruxelles; Route de Lennik 808, B-1070 Brussels, Belgium
| | - San Kit To
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
| | - Andrea Schiavo
- ULB Center for Diabetes Research; Université Libre de Bruxelles; Route de Lennik 808, B-1070 Brussels, Belgium
| | - Matthias Van Haele
- Department of Imaging and Pathology; Translational Cell and Tissue Research, KU Leuven and University Hospitals Leuven; Herestraat 49, B-3000 Leuven, Belgium
| | - Tessa Ostyn
- Department of Imaging and Pathology; Translational Cell and Tissue Research, KU Leuven and University Hospitals Leuven; Herestraat 49, B-3000 Leuven, Belgium
| | - Tine Haesen
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
| | - Xiaoyan Yi
- ULB Center for Diabetes Research; Université Libre de Bruxelles; Route de Lennik 808, B-1070 Brussels, Belgium
| | - Adrian Janiszewski
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
| | - Joel Chappell
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
| | - Adrien Rihoux
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
| | - Toshiaki Sawatani
- ULB Center for Diabetes Research; Université Libre de Bruxelles; Route de Lennik 808, B-1070 Brussels, Belgium
| | - Tania Roskams
- Department of Imaging and Pathology; Translational Cell and Tissue Research, KU Leuven and University Hospitals Leuven; Herestraat 49, B-3000 Leuven, Belgium
| | - Francois Pattou
- University of Lille, Inserm, CHU Lille, Institute Pasteur Lille, U1190-EGID, F-59000 Lille, France
- European Genomic Institute for Diabetes, F-59000 Lille, France
- University of Lille, F-59000 Lille, France
| | - Julie Kerr-Conte
- University of Lille, Inserm, CHU Lille, Institute Pasteur Lille, U1190-EGID, F-59000 Lille, France
- European Genomic Institute for Diabetes, F-59000 Lille, France
- University of Lille, F-59000 Lille, France
| | - Miriam Cnop
- ULB Center for Diabetes Research; Université Libre de Bruxelles; Route de Lennik 808, B-1070 Brussels, Belgium
- Division of Endocrinology; Erasmus Hospital, Université Libre de Bruxelles; Route de Lennik 808, B-1070 Brussels, Belgium
| | - Vincent Pasque
- Department of Development and Regeneration; KU Leuven - University of Leuven; Single-cell Omics Institute and Leuven Stem Cell Institute, Herestraat 49, B-3000 Leuven, Belgium
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38
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Oger F, Bourouh C, Friano ME, Courty E, Rolland L, Gromada X, Moreno M, Carney C, Rabhi N, Durand E, Amanzougarene S, Berberian L, Derhourhi M, Blanc E, Hannou SA, Denechaud PD, Benfodda Z, Meffre P, Fajas L, Kerr-Conte J, Pattou F, Froguel P, Pourcet B, Bonnefond A, Collombat P, Annicotte JS. β-Cell-Specific E2f1 Deficiency Impairs Glucose Homeostasis, β-Cell Identity, and Insulin Secretion. Diabetes 2023; 72:1112-1126. [PMID: 37216637 DOI: 10.2337/db22-0604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 05/01/2023] [Indexed: 05/24/2023]
Abstract
The loss of pancreatic β-cell identity has emerged as an important feature of type 2 diabetes development, but the molecular mechanisms are still elusive. Here, we explore the cell-autonomous role of the cell-cycle regulator and transcription factor E2F1 in the maintenance of β-cell identity, insulin secretion, and glucose homeostasis. We show that the β-cell-specific loss of E2f1 function in mice triggers glucose intolerance associated with defective insulin secretion, altered endocrine cell mass, downregulation of many β-cell genes, and concomitant increase of non-β-cell markers. Mechanistically, epigenomic profiling of the promoters of these non-β-cell upregulated genes identified an enrichment of bivalent H3K4me3/H3K27me3 or H3K27me3 marks. Conversely, promoters of downregulated genes were enriched in active chromatin H3K4me3 and H3K27ac histone marks. We find that specific E2f1 transcriptional, cistromic, and epigenomic signatures are associated with these β-cell dysfunctions, with E2F1 directly regulating several β-cell genes at the chromatin level. Finally, the pharmacological inhibition of E2F transcriptional activity in human islets also impairs insulin secretion and the expression of β-cell identity genes. Our data suggest that E2F1 is critical for maintaining β-cell identity and function through sustained control of β-cell and non-β-cell transcriptional programs. ARTICLE HIGHLIGHTS β-Cell-specific E2f1 deficiency in mice impairs glucose tolerance. Loss of E2f1 function alters the ratio of α- to β-cells but does not trigger β-cell conversion into α-cells. Pharmacological inhibition of E2F activity inhibits glucose-stimulated insulin secretion and alters β- and α-cell gene expression in human islets. E2F1 maintains β-cell function and identity through control of transcriptomic and epigenetic programs.
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Affiliation(s)
- Frédérik Oger
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Cyril Bourouh
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Marika Elsa Friano
- INSERM, CNRS, Institut de Biologie Valrose, Université Côte d'Azur, Nice, France
| | - Emilie Courty
- INSERM, U1167 - RID-AGE - Facteurs de Risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Laure Rolland
- INSERM, U1167 - RID-AGE - Facteurs de Risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Xavier Gromada
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Maeva Moreno
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Charlène Carney
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Nabil Rabhi
- Department of Biochemistry, Boston University School of Medicine, Boston, MA
| | - Emmanuelle Durand
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Souhila Amanzougarene
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Lionel Berberian
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Mehdi Derhourhi
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Etienne Blanc
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Sarah Anissa Hannou
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | | | | | | | - Lluis Fajas
- Center for Integrative Genomics, Université de Lausanne, Lausanne, Switzerland
| | - Julie Kerr-Conte
- INSERM, U1190 - EGID, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - François Pattou
- INSERM, U1190 - EGID, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Philippe Froguel
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
- Department of Metabolism, Hammersmith Hospital, Imperial College London, London, U.K
| | - Benoit Pourcet
- INSERM, U1011 - EGID, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
| | - Amélie Bonnefond
- INSERM, U1283 - UMR8199 - European Genomic Institute for Diabetes (EGID), CNRS, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
- Department of Metabolism, Hammersmith Hospital, Imperial College London, London, U.K
| | - Patrick Collombat
- INSERM, CNRS, Institut de Biologie Valrose, Université Côte d'Azur, Nice, France
| | - Jean-Sébastien Annicotte
- INSERM, U1167 - RID-AGE - Facteurs de Risque et Déterminants Moléculaires des Maladies Liées au Vieillissement, Institut Pasteur de Lille, CHU Lille, Université de Lille, Lille, France
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Son J, Accili D. Reversing pancreatic β-cell dedifferentiation in the treatment of type 2 diabetes. Exp Mol Med 2023; 55:1652-1658. [PMID: 37524865 PMCID: PMC10474037 DOI: 10.1038/s12276-023-01043-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 03/29/2023] [Accepted: 04/24/2023] [Indexed: 08/02/2023] Open
Abstract
The maintenance of glucose homeostasis is fundamental for survival and health. Diabetes develops when glucose homeostasis fails. Type 2 diabetes (T2D) is characterized by insulin resistance and pancreatic β-cell failure. The failure of β-cells to compensate for insulin resistance results in hyperglycemia, which in turn drives altered lipid metabolism and β-cell failure. Thus, insulin secretion by pancreatic β-cells is a primary component of glucose homeostasis. Impaired β-cell function and reduced β-cell mass are found in diabetes. Both features stem from a failure to maintain β-cell identity, which causes β-cells to dedifferentiate into nonfunctional endocrine progenitor-like cells or to trans-differentiate into other endocrine cell types. In this regard, one of the key issues in achieving disease modification is how to reestablish β-cell identity. In this review, we focus on the causes and implications of β-cell failure, as well as its potential reversibility as a T2D treatment.
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Affiliation(s)
- Jinsook Son
- Department of Medicine and Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
| | - Domenico Accili
- Department of Medicine and Naomi Berrie Diabetes Center, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA
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40
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Xie B, Gao D, Zhou B, Chen S, Wang L. New discoveries in the field of metabolism by applying single-cell and spatial omics. J Pharm Anal 2023; 13:711-725. [PMID: 37577385 PMCID: PMC10422156 DOI: 10.1016/j.jpha.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 08/15/2023] Open
Abstract
Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.
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Affiliation(s)
- Baocai Xie
- Department of Critical Care Medicine, Shenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, 518060, China
- Department of Respiratory Diseases, The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450014, China
| | - Dengfeng Gao
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Biqiang Zhou
- Department of Geriatric & Spinal Pain Multi-Department Treatment, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen, Guangdong, 518035, China
| | - Shi Chen
- Department of Critical Care Medicine, Shenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, 518060, China
- Department of Gastroenterology, Ministry of Education Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
| | - Lianrong Wang
- Department of Respiratory Diseases, The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450014, China
- Department of Gastroenterology, Ministry of Education Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
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41
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Pettway YD, Saunders DC, Brissova M. The human α cell in health and disease. J Endocrinol 2023; 258:e220298. [PMID: 37114672 PMCID: PMC10428003 DOI: 10.1530/joe-22-0298] [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: 02/27/2023] [Accepted: 04/27/2023] [Indexed: 04/29/2023]
Abstract
In commemoration of 100 years since the discovery of glucagon, we review current knowledge about the human α cell. Alpha cells make up 30-40% of human islet endocrine cells and play a major role in regulating whole-body glucose homeostasis, largely through the direct actions of their main secretory product - glucagon - on peripheral organs. Additionally, glucagon and other secretory products of α cells, namely acetylcholine, glutamate, and glucagon-like peptide-1, have been shown to play an indirect role in the modulation of glucose homeostasis through autocrine and paracrine interactions within the islet. Studies of glucagon's role as a counterregulatory hormone have revealed additional important functions of the α cell, including the regulation of multiple aspects of energy metabolism outside that of glucose. At the molecular level, human α cells are defined by the expression of conserved islet-enriched transcription factors and various enriched signature genes, many of which have currently unknown cellular functions. Despite these common threads, notable heterogeneity exists amongst human α cell gene expression and function. Even greater differences are noted at the inter-species level, underscoring the importance of further study of α cell physiology in the human context. Finally, studies on α cell morphology and function in type 1 and type 2 diabetes, as well as other forms of metabolic stress, reveal a key contribution of α cell dysfunction to dysregulated glucose homeostasis in disease pathogenesis, making targeting the α cell an important focus for improving treatment.
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Affiliation(s)
- Yasminye D. Pettway
- Department of Molecular Physiology and Biophysics, Vanderbilt University School of Medicine, Nashville, Tennessee, 37232, USA
| | - Diane C. Saunders
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA
| | - Marcela Brissova
- Division of Diabetes, Endocrinology and Metabolism, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, 37232, USA
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Clemente-Suárez VJ, Martín-Rodríguez A, Redondo-Flórez L, López-Mora C, Yáñez-Sepúlveda R, Tornero-Aguilera JF. New Insights and Potential Therapeutic Interventions in Metabolic Diseases. Int J Mol Sci 2023; 24:10672. [PMID: 37445852 DOI: 10.3390/ijms241310672] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 06/13/2023] [Accepted: 06/21/2023] [Indexed: 07/15/2023] Open
Abstract
Endocrine homeostasis and metabolic diseases have been the subject of extensive research in recent years. The development of new techniques and insights has led to a deeper understanding of the mechanisms underlying these conditions and opened up new avenues for diagnosis and treatment. In this review, we discussed the rise of metabolic diseases, especially in Western countries, the genetical, psychological, and behavioral basis of metabolic diseases, the role of nutrition and physical activity in the development of metabolic diseases, the role of single-cell transcriptomics, gut microbiota, epigenetics, advanced imaging techniques, and cell-based therapies in metabolic diseases. Finally, practical applications derived from this information are made.
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Affiliation(s)
- Vicente Javier Clemente-Suárez
- Faculty of Sports Sciences, Universidad Europea de Madrid, Tajo Street, s/n, 28670 Madrid, Spain
- Grupo de Investigación en Cultura, Educación y Sociedad, Universidad de la Costa, Barranquilla 080002, Colombia
| | | | - Laura Redondo-Flórez
- Department of Health Sciences, Faculty of Biomedical and Health Sciences, Universidad Europea de Madrid, Tajo Street s/n, 28670 Villaviciosa de Odon, Spain
| | - Clara López-Mora
- Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Valencia, Pg. de l'Albereda, 7, 46010 València, Spain
| | - Rodrigo Yáñez-Sepúlveda
- Faculty of Education and Social Sciences, Universidad Andres Bello, Viña del Mar 2520000, Chile
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Fan Y, Wang Y, Wang F, Huang L, Yang Y, Wong KC, Li X. Reliable Identification and Interpretation of Single-Cell Molecular Heterogeneity and Transcriptional Regulation using Dynamic Ensemble Pruning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023:e2205442. [PMID: 37290050 PMCID: PMC10401140 DOI: 10.1002/advs.202205442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 05/11/2023] [Indexed: 06/10/2023]
Abstract
Unsupervised clustering is an essential step in identifying cell types from single-cell RNA sequencing (scRNA-seq) data. However, a common issue with unsupervised clustering models is that the optimization direction of the objective function and the final generated clustering labels in the absence of supervised information may be inconsistent or even arbitrary. To address this challenge, a dynamic ensemble pruning framework (DEPF) is proposed to identify and interpret single-cell molecular heterogeneity. In particular, a silhouette coefficient-based indicator is developed to determine the optimization direction of the bi-objective function. In addition, a hierarchical autoencoder is employed to project the high-dimensional data onto multiple low-dimensional latent space sets, and then a clustering ensemble is produced in the latent space by the basic clustering algorithm. Following that, a bi-objective fruit fly optimization algorithm is designed to prune dynamically the low-quality basic clustering in the ensemble. Multiple experiments are conducted on 28 real scRNA-seq datasets and one large real scRNA-seq dataset from diverse platforms and species to validate the effectiveness of the DEPF. In addition, biological interpretability and transcriptional and post-transcriptional regulatory are conducted to explore biological patterns from the cell types identified, which could provide novel insights into characterizing the mechanisms.
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Affiliation(s)
- Yi Fan
- School of Artificial Intelligence, Jilin University, Jilin, China
| | - Yunhe Wang
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
| | - Fuzhou Wang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Lei Huang
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Yuning Yang
- Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong
| | - Ka-C Wong
- Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Xiangtao Li
- School of Artificial Intelligence, Jilin University, Jilin, China
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Cephas AT, Hwang WL, Maitra A, Parnas O, DelGiorno KE. It is better to light a candle than to curse the darkness: single-cell transcriptomics sheds new light on pancreas biology and disease. Gut 2023; 72:1211-1219. [PMID: 36997301 PMCID: PMC10988578 DOI: 10.1136/gutjnl-2022-329313] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/19/2023] [Indexed: 04/01/2023]
Abstract
Recent advances in single-cell RNA sequencing and bioinformatics have drastically increased our ability to interrogate the cellular composition of traditionally difficult to study organs, such as the pancreas. With the advent of these technologies and approaches, the field has grown, in just a few years, from profiling pancreas disease states to identifying molecular mechanisms of therapy resistance in pancreatic ductal adenocarcinoma, a particularly deadly cancer. Single-cell transcriptomics and related spatial approaches have identified previously undescribed epithelial and stromal cell types and states, how these populations change with disease progression, and potential mechanisms of action which will serve as the basis for designing new therapeutic strategies. Here, we review the recent literature on how single-cell transcriptomic approaches have changed our understanding of pancreas biology and disease progression.
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Affiliation(s)
- Amelia T Cephas
- Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
| | - William L Hwang
- Center for Systems Biology, Massachusetts General Hospital, Boston, Massachusetts, USA
- Eli and Edythe L Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Anirban Maitra
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
- Sheikh Ahmed Pancreatic Cancer Research Center, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Oren Parnas
- Lautenberg Center for Immunology and Cancer Research, IMRIC, Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Kathleen E DelGiorno
- Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee, USA
- Vanderbilt Digestive Disease Research Center, Vanderbilt University Medical Center, Nashville, Tennessee, USA
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Kang RB, Li Y, Rosselot C, Zhang T, Siddiq M, Rajbhandari P, Stewart AF, Scott DK, Garcia-Ocana A, Lu G. Single-nucleus RNA sequencing of human pancreatic islets identifies novel gene sets and distinguishes β-cell subpopulations with dynamic transcriptome profiles. Genome Med 2023; 15:30. [PMID: 37127706 PMCID: PMC10150516 DOI: 10.1186/s13073-023-01179-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 04/12/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing (scRNA-seq) provides valuable insights into human islet cell types and their corresponding stable gene expression profiles. However, this approach requires cell dissociation that complicates its utility in vivo. On the other hand, single-nucleus RNA sequencing (snRNA-seq) has compatibility with frozen samples, elimination of dissociation-induced transcriptional stress responses, and affords enhanced information from intronic sequences that can be leveraged to identify pre-mRNA transcripts. METHODS We obtained nuclear preparations from fresh human islet cells and generated snRNA-seq datasets. We compared these datasets to scRNA-seq output obtained from human islet cells from the same donor. We employed snRNA-seq to obtain the transcriptomic profile of human islets engrafted in immunodeficient mice. In both analyses, we included the intronic reads in the snRNA-seq data with the GRCh38-2020-A library. RESULTS First, snRNA-seq analysis shows that the top four differentially and selectively expressed genes in human islet endocrine cells in vitro and in vivo are not the canonical genes but a new set of non-canonical gene markers including ZNF385D, TRPM3, LRFN2, PLUT (β-cells); PTPRT, FAP, PDK4, LOXL4 (α-cells); LRFN5, ADARB2, ERBB4, KCNT2 (δ-cells); and CACNA2D3, THSD7A, CNTNAP5, RBFOX3 (γ-cells). Second, by integrating information from scRNA-seq and snRNA-seq of human islet cells, we distinguish three β-cell sub-clusters: an INS pre-mRNA cluster (β3), an intermediate INS mRNA cluster (β2), and an INS mRNA-rich cluster (β1). These display distinct gene expression patterns representing different biological dynamic states both in vitro and in vivo. Interestingly, the INS mRNA-rich cluster (β1) becomes the predominant sub-cluster in vivo. CONCLUSIONS In summary, snRNA-seq and pre-mRNA analysis of human islet cells can accurately identify human islet cell populations, subpopulations, and their dynamic transcriptome profile in vivo.
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Affiliation(s)
- Randy B Kang
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Present address: Department of Molecular and Cellular Endocrinology, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope, 1500 East Duarte Road, Duarte, CA, 91010, USA
| | - Yansui Li
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Carolina Rosselot
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Tuo Zhang
- Genomics Resources Core Facility, Weill Cornell Medicine, New York, NY, 10065, USA
| | - Mustafa Siddiq
- Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Prashant Rajbhandari
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andrew F Stewart
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Donald K Scott
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Adolfo Garcia-Ocana
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Present address: Department of Molecular and Cellular Endocrinology, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope, 1500 East Duarte Road, Duarte, CA, 91010, USA.
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
| | - Geming Lu
- Diabetes, Obesity and Metabolism Institute, and Division of Endocrinology, Diabetes and Bone Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- Present address: Department of Molecular and Cellular Endocrinology, Arthur Riggs Diabetes and Metabolism Research Institute, Beckman Research Institute, City of Hope, 1500 East Duarte Road, Duarte, CA, 91010, USA.
- Department of Pharmacological Sciences and Institute for Systems Biomedicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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Peng H, Zhang K, Miao J, Yang Y, Xu S, Wu T, Tao C, Wang Y, Yang S. SnRNA-Seq of Pancreas Revealed the Dysfunction of Endocrine and Exocrine Cells in Transgenic Pigs with Prediabetes. Int J Mol Sci 2023; 24:ijms24097701. [PMID: 37175407 PMCID: PMC10178631 DOI: 10.3390/ijms24097701] [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: 03/09/2023] [Revised: 04/17/2023] [Accepted: 04/18/2023] [Indexed: 05/15/2023] Open
Abstract
Diabetes poses a significant threat to human health. Exocrine pancreatic dysfunction is related to diabetes, but the exact mechanism is not fully understood. This study aimed to describe the pathological phenotype and pathological mechanisms of the pancreas of transgenic pigs (PIGinH11) that was constructed in our laboratory and to compare it with humans. We established diabetes-susceptible transgenic pigs and subjected them to high-fat and high-sucrose dietary interventions. The damage to the pancreatic endocrine and exocrine was evaluated using histopathology and the involved molecular mechanisms were analyzed using single-nucleus RNA-sequencing (SnRNA-seq). Compared to wild-type (WT) pigs, PIGinH11 pigs showed similar pathological manifestations to type 2 diabetes patients, such as insulin deficiency, fatty deposition, inflammatory infiltration, fibrosis tissue necrosis, double positive cells, endoplasmic reticulum (ER) and mitochondria damage. SnRNA-seq analysis revealed 16 clusters and cell-type-specific gene expression characterization in the pig pancreas. Notably, clusters of Ainar-M and Endocrine-U were observed at the intermediate state between the exocrine and endocrine pancreas. Beta cells of the PIGinH11 group demonstrated the dysfunction with insulin produced and secret decreased and ER stress. Moreover, like clinic patients, acinar cells expressed fewer digestive enzymes and showed organelle damage. We hypothesize that TXNIP that is upregulated by high glucose might play an important role in the dysfunction of endocrine to exocrine cells in PIGinH11 pigs.
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Affiliation(s)
- Huanqi Peng
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Kaiyi Zhang
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Jiakun Miao
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yu Yang
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Shuang Xu
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Tianwen Wu
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Cong Tao
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Yanfang Wang
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
| | - Shulin Yang
- State Key Laboratory of Animal Nutrition, Ministry of Agriculture Key Laboratory of Animal Genetics Breeding and Reproduction, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing 100193, China
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Chung JY, Ma Y, Zhang D, Bickerton HH, Stokes E, Patel SB, Tse HM, Feduska J, Welner RS, Banerjee RR. Pancreatic islet cell type-specific transcriptomic changes during pregnancy and postpartum. iScience 2023; 26:106439. [PMID: 37020962 PMCID: PMC10068570 DOI: 10.1016/j.isci.2023.106439] [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: 11/29/2022] [Revised: 01/11/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023] Open
Abstract
Pancreatic β-cell mass expands during pregnancy and regresses in the postpartum period in conjunction with dynamic metabolic demands on maternal glucose homeostasis. To understand transcriptional changes driving these adaptations in β-cells and other islet cell types, we performed single-cell RNA sequencing on islets from virgin, late gestation, and early postpartum mice. We identified transcriptional signatures unique to gestation and the postpartum in β-cells, including induction of the AP-1 transcription factor subunits and other genes involved in the immediate-early response (IEGs). In addition, we found pregnancy and postpartum-induced changes differed within each endocrine cell type, and in endothelial cells and antigen-presenting cells within islets. Together, our data reveal insights into cell type-specific transcriptional changes responsible for adaptations by islet cells to pregnancy and their resolution postpartum.
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Affiliation(s)
- Jin-Yong Chung
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA
| | - Yongjie Ma
- Department of Pharmacology, the University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Dingguo Zhang
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, The University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Hayden H. Bickerton
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, The University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Eric Stokes
- Department of Pharmacology, University of Colorado Denver/Anschutz, Aurora, CO 80045, USA
| | - Sweta B. Patel
- Division of Hematology and Oncology, Department of Medicine, The University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Hubert M. Tse
- Department of Microbiology, the University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Joseph Feduska
- Department of Microbiology, the University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Rob S. Welner
- Division of Hematology and Oncology, Department of Medicine, The University of Alabama at Birmingham School of Medicine, Birmingham, AL 35294, USA
| | - Ronadip R. Banerjee
- Division of Endocrinology, Diabetes & Metabolism, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21224, USA
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Shen X, Li M, Shao K, Li Y, Ge Z. Post-ischemic inflammatory response in the brain: Targeting immune cell in ischemic stroke therapy. Front Mol Neurosci 2023; 16:1076016. [PMID: 37078089 PMCID: PMC10106693 DOI: 10.3389/fnmol.2023.1076016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 03/13/2023] [Indexed: 04/05/2023] Open
Abstract
An ischemic stroke occurs when the blood supply is obstructed to the vascular basin, causing the death of nerve cells and forming the ischemic core. Subsequently, the brain enters the stage of reconstruction and repair. The whole process includes cellular brain damage, inflammatory reaction, blood–brain barrier destruction, and nerve repair. During this process, the proportion and function of neurons, immune cells, glial cells, endothelial cells, and other cells change. Identifying potential differences in gene expression between cell types or heterogeneity between cells of the same type helps to understand the cellular changes that occur in the brain and the context of disease. The recent emergence of single-cell sequencing technology has promoted the exploration of single-cell diversity and the elucidation of the molecular mechanism of ischemic stroke, thus providing new ideas and directions for the diagnosis and clinical treatment of ischemic stroke.
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Affiliation(s)
- Xueyang Shen
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, China
| | - Mingming Li
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, China
- Gansu Provincial Neurology Clinical Medical Research Center, The Second Hospital of Lanzhou University, Lanzhou, China
- Expert Workstation of Academician Wang Longde, The Second Hospital of Lanzhou University, Lanzhou, China
| | - Kangmei Shao
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, China
| | - Yongnan Li
- Department of Cardiac Surgery, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, China
- Yongnan Li,
| | - Zhaoming Ge
- Department of Neurology, Lanzhou University Second Hospital, Lanzhou University, Lanzhou, China
- Gansu Provincial Neurology Clinical Medical Research Center, The Second Hospital of Lanzhou University, Lanzhou, China
- Expert Workstation of Academician Wang Longde, The Second Hospital of Lanzhou University, Lanzhou, China
- *Correspondence: Zhaoming Ge,
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49
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Rubio-Navarro A, Gómez-Banoy N, Stoll L, Dündar F, Mawla AM, Ma L, Cortada E, Zumbo P, Li A, Reiterer M, Montoya-Oviedo N, Homan EA, Imai N, Gilani A, Liu C, Naji A, Yang B, Chong ACN, Cohen DE, Chen S, Cao J, Pitt GS, Huising MO, Betel D, Lo JC. A beta cell subset with enhanced insulin secretion and glucose metabolism is reduced in type 2 diabetes. Nat Cell Biol 2023; 25:565-578. [PMID: 36928765 PMCID: PMC10449536 DOI: 10.1038/s41556-023-01103-1] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 02/02/2023] [Indexed: 03/18/2023]
Abstract
The pancreatic islets are composed of discrete hormone-producing cells that orchestrate systemic glucose homeostasis. Here we identify subsets of beta cells using a single-cell transcriptomic approach. One subset of beta cells marked by high CD63 expression is enriched for the expression of mitochondrial metabolism genes and exhibits higher mitochondrial respiration compared with CD63lo beta cells. Human and murine pseudo-islets derived from CD63hi beta cells demonstrate enhanced glucose-stimulated insulin secretion compared with pseudo-islets from CD63lo beta cells. We show that CD63hi beta cells are diminished in mouse models of and in humans with type 2 diabetes. Finally, transplantation of pseudo-islets generated from CD63hi but not CD63lo beta cells into diabetic mice restores glucose homeostasis. These findings suggest that loss of a specific subset of beta cells may lead to diabetes. Strategies to reconstitute or maintain CD63hi beta cells may represent a potential anti-diabetic therapy.
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Affiliation(s)
- Alfonso Rubio-Navarro
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Excellence Research Unit "Modeling Nature" (MNat), CTS-963-Center of Biomedical Research (CIBM), University of Granada, Granada, Spain
- Instituto de Investigación Biosanitaria de Granada (ibs.GRANADA), University Hospitals of Granada-University of Granada, Granada, Spain
| | - Nicolás Gómez-Banoy
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Lisa Stoll
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Friederike Dündar
- Department of Physiology and Biophysics, Applied Bioinformatics Core, Weill Cornell Medicine, New York, NY, USA
| | - Alex M Mawla
- Department of Neurobiology, Physiology and Behavior, College of Biological Sciences, University of California, Davis, CA, USA
| | - Lunkun Ma
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Eric Cortada
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Paul Zumbo
- Department of Physiology and Biophysics, Applied Bioinformatics Core, Weill Cornell Medicine, New York, NY, USA
| | - Ang Li
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Moritz Reiterer
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Nathalia Montoya-Oviedo
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Lipids and Diabetes Laboratory, Department of Physiological Sciences, Faculty of Medicine, National University of Colombia, Bogotá, Colombia
| | - Edwin A Homan
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Norihiro Imai
- Division of Gastroenterology and Hepatology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
- Department of Gastroenterology and Hepatology, Nagoya University Graduate School of Medicine, Aichi, Japan
| | - Ankit Gilani
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Chengyang Liu
- Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Ali Naji
- Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Boris Yang
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | | | - David E Cohen
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shuibing Chen
- Department of Surgery, Weill Cornell Medicine, New York, NY, USA
| | - Jingli Cao
- Cardiovascular Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Geoffrey S Pitt
- Cardiovascular Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - Mark O Huising
- Department of Neurobiology, Physiology and Behavior, College of Biological Sciences, University of California, Davis, CA, USA
- Department of Physiology and Membrane Biology, School of Medicine, University of California Davis, Davis, CA, USA
| | - Doron Betel
- Department of Physiology and Biophysics, Applied Bioinformatics Core, Weill Cornell Medicine, New York, NY, USA
- Institute for Computational Biomedicine, Division of Hematology and Medical Oncology, Applied Bioinformatics Core, Weill Cornell Medicine, New York, NY, USA
| | - James C Lo
- Weill Center for Metabolic Health, Cardiovascular Research Institute, Division of Cardiology, Department of Medicine, Weill Cornell Medicine, New York, NY, USA.
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Knight CH, Khan F, Patel A, Gill US, Okosun J, Wang J. IBRAP: integrated benchmarking single-cell RNA-sequencing analytical pipeline. Brief Bioinform 2023; 24:bbad061. [PMID: 36847692 PMCID: PMC10025434 DOI: 10.1093/bib/bbad061] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 12/19/2022] [Accepted: 02/02/2023] [Indexed: 03/01/2023] Open
Abstract
Single-cell ribonucleic acid (RNA)-sequencing (scRNA-seq) is a powerful tool to study cellular heterogeneity. The high dimensional data generated from this technology are complex and require specialized expertise for analysis and interpretation. The core of scRNA-seq data analysis contains several key analytical steps, which include pre-processing, quality control, normalization, dimensionality reduction, integration and clustering. Each step often has many algorithms developed with varied underlying assumptions and implications. With such a diverse choice of tools available, benchmarking analyses have compared their performances and demonstrated that tools operate differentially according to the data types and complexity. Here, we present Integrated Benchmarking scRNA-seq Analytical Pipeline (IBRAP), which contains a suite of analytical components that can be interchanged throughout the pipeline alongside multiple benchmarking metrics that enable users to compare results and determine the optimal pipeline combinations for their data. We apply IBRAP to single- and multi-sample integration analysis using primary pancreatic tissue, cancer cell line and simulated data accompanied with ground truth cell labels, demonstrating the interchangeable and benchmarking functionality of IBRAP. Our results confirm that the optimal pipelines are dependent on individual samples and studies, further supporting the rationale and necessity of our tool. We then compare reference-based cell annotation with unsupervised analysis, both included in IBRAP, and demonstrate the superiority of the reference-based method in identifying robust major and minor cell types. Thus, IBRAP presents a valuable tool to integrate multiple samples and studies to create reference maps of normal and diseased tissues, facilitating novel biological discovery using the vast volume of scRNA-seq data available.
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Affiliation(s)
- Connor H Knight
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ
| | - Faraz Khan
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ
| | - Ankit Patel
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ
| | - Upkar S Gill
- Centre for Immunobiology, Blizard Institute, Faculty of Medicine and Dentistry Medicine & Dentistry, Queen Mary University of London, London E1 2AT, United Kingdom
| | - Jessica Okosun
- Centre for Haemato-Oncology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ
| | - Jun Wang
- Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London EC1M 6BQ
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