1
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Subedi S, Sumida TS, Park YP. A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection. Life Sci Alliance 2024; 7:e202402713. [PMID: 39107066 PMCID: PMC11303850 DOI: 10.26508/lsa.202402713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 07/29/2024] [Accepted: 07/30/2024] [Indexed: 08/09/2024] Open
Abstract
Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.
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Affiliation(s)
- Sishir Subedi
- https://ror.org/03rmrcq20Bioinformatics Graduate Program, University of British Columbia, Vancouver, Canada
- BC Cancer Research, Vancouver, Canada
| | - Tomokazu S Sumida
- Neurology, Program for Neuroinflammation, Yale School of Medicine, New Haven, CT, USA
| | - Yongjin P Park
- BC Cancer Research, Vancouver, Canada
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
- Department of Statistics, University of British Columbia, Vancouver, Canada
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2
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Karmaus PWF. Application of Single Cell Methods in Immunometabolism and Immunotoxicology. CURRENT OPINION IN TOXICOLOGY 2024; 39:100488. [PMID: 39091379 PMCID: PMC11290472 DOI: 10.1016/j.cotox.2024.100488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Recent developments of novel single-cell analysis techniques have rapidly advanced the fields of immunotoxicology and immunometabolism. Single-cell analyses enable the characterization of immune cells, unraveling heterogeneity, and population dynamics in response to cellular perturbations, including toxicant insults and changes in cellular metabolism. This review provides an overview of current technologies and recent discoveries, illustrating an emerging role of single-cell analyses in the field of immunotoxicology and immunometabolism. Various single-cell techniques, including flow cytometry, mass cytometry, multiplexed imaging, and sequencing, together with their applications to studying immunotoxicology and immunometabolism are discussed. This review emphasizes the potential for single-cell analyses to revolutionize our understanding of immune cell heterogeneity, uncover novel cellular therapeutic targets, and pave the way for novel mechanistic insights.
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Affiliation(s)
- Peer W F Karmaus
- National Institute of Environmental Health Sciences, US
- Institute for Integrative Toxicology, Michigan State University, US
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3
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Xu L, Yu D, Xu M, Liu Y, Yang LX, Zou QC, Feng XL, Li MH, Sheng N, Yao YG. Primate-specific BTN3A2 protects against SARS-CoV-2 infection by interacting with and reducing ACE2. EBioMedicine 2024; 107:105281. [PMID: 39142074 DOI: 10.1016/j.ebiom.2024.105281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/24/2024] [Accepted: 07/30/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Coronavirus disease 2019 (COVID-19) is an immune-related disorder caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The complete pathogenesis of the virus remains to be determined. Unraveling the molecular mechanisms governing SARS-CoV-2 interactions with host cells is crucial for the formulation of effective prophylactic measures and the advancement of COVID-19 therapeutics. METHODS We analyzed human lung single-cell RNA sequencing dataset to discern the association of butyrophilin subfamily 3 member A2 (BTN3A2) expression with COVID-19. The BTN3A2 gene edited cell lines and transgenic mice were infected by live SARS-CoV-2 in a biosafety level 3 (BSL-3) laboratory. Immunoprecipitation, flow cytometry, biolayer interferometry and competition ELISA assays were performed in BTN3A2 gene edited cells. We performed quantitative real-time PCR, histological and/or immunohistochemical analyses for tissue samples from mice with or without SARS-CoV-2 infection. FINDINGS The BTN3A2 mRNA level was correlated with COVID-19 severity. BTN3A2 expression was predominantly identified in epithelial cells, elevated in pathological epithelial cells from COVID-19 patients and co-occurred with ACE2 expression in the same lung cell subtypes. BTN3A2 targeted the early stage of the viral life cycle by inhibiting SARS-CoV-2 attachment through interactions with the receptor-binding domain (RBD) of the Spike protein and ACE2. BTN3A2 inhibited ACE2-mediated SARS-CoV-2 infection by reducing ACE2 in vitro and in vivo. INTERPRETATION These results reveal a key role of BTN3A2 in the fight against COVID-19. Identifying potential monoclonal antibodies which mimic BTN3A2 may facilitate disruption of SARS-CoV-2 infection, providing a therapeutic avenue for COVID-19. FUNDING This study was supported by the National Natural Science Foundation of China (32070569, U1902215, and 32371017), the CAS "Light of West China" Program, and Yunnan Province (202305AH340006).
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Affiliation(s)
- Ling Xu
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China; Kunming National High-Level Biosafety Research Center for Non-Human Primates, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China.
| | - Dandan Yu
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China; Kunming National High-Level Biosafety Research Center for Non-Human Primates, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China
| | - Min Xu
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Yamin Liu
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China
| | - Lu-Xiu Yang
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China
| | - Qing-Cui Zou
- Kunming National High-Level Biosafety Research Center for Non-Human Primates, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China
| | - Xiao-Li Feng
- Kunming National High-Level Biosafety Research Center for Non-Human Primates, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China
| | - Ming-Hua Li
- Kunming National High-Level Biosafety Research Center for Non-Human Primates, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China
| | - Nengyin Sheng
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China.
| | - Yong-Gang Yao
- Key Laboratory of Genetic Evolution and Animal Models, Key Laboratory of Animal Models and Human Disease Mechanisms of Yunnan Province, and KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650204, China; Kunming College of Life Science, University of Chinese Academy of Sciences, Kunming, 650204, China; Kunming National High-Level Biosafety Research Center for Non-Human Primates, Center for Biosafety Mega-Science, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China; National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), National Resource Center for Non-Human Primates, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650107, China.
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4
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Korb A, Tajbakhsh S, Comai GE. Functional specialisation and coordination of myonuclei. Biol Rev Camb Philos Soc 2024; 99:1164-1195. [PMID: 38477382 DOI: 10.1111/brv.13063] [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: 04/10/2023] [Revised: 01/30/2024] [Accepted: 02/02/2024] [Indexed: 03/14/2024]
Abstract
Myofibres serve as the functional unit for locomotion, with the sarcomere as fundamental subunit. Running the entire length of this structure are hundreds of myonuclei, located at the periphery of the myofibre, juxtaposed to the plasma membrane. Myonuclear specialisation and clustering at the centre and ends of the fibre are known to be essential for muscle contraction, yet the molecular basis of this regionalisation has remained unclear. While the 'myonuclear domain hypothesis' helped explain how myonuclei can independently govern large cytoplasmic territories, novel technologies have provided granularity on the diverse transcriptional programs running simultaneously within the syncytia and added a new perspective on how myonuclei communicate. Building upon this, we explore the critical cellular and molecular sources of transcriptional and functional heterogeneity within myofibres, discussing the impact of intrinsic and extrinsic factors on myonuclear programs. This knowledge provides new insights for understanding muscle development, repair, and disease, but also opens avenues for the development of novel and precise therapeutic approaches.
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Affiliation(s)
- Amaury Korb
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
| | - Shahragim Tajbakhsh
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
| | - Glenda E Comai
- Institut Pasteur, Université Paris Cité, CNRS UMR 3738, Stem Cells & Development Unit, 25 rue du Dr. Roux, Institut Pasteur, Paris, F-75015, France
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5
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Hou W, Ji Z. Assessing GPT-4 for cell type annotation in single-cell RNA-seq analysis. Nat Methods 2024; 21:1462-1465. [PMID: 38528186 PMCID: PMC11310073 DOI: 10.1038/s41592-024-02235-4] [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: 04/16/2023] [Accepted: 03/05/2024] [Indexed: 03/27/2024]
Abstract
Here we demonstrate that the large language model GPT-4 can accurately annotate cell types using marker gene information in single-cell RNA sequencing analysis. When evaluated across hundreds of tissue and cell types, GPT-4 generates cell type annotations exhibiting strong concordance with manual annotations. This capability can considerably reduce the effort and expertise required for cell type annotation. Additionally, we have developed an R software package GPTCelltype for GPT-4's automated cell type annotation.
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Affiliation(s)
- Wenpin Hou
- Department of Biostatistics, Columbia University Mailman School of Public Health, New York City, NY, USA.
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA.
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6
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Hie BL, Kim S, Rando TA, Bryson B, Berger B. Scanorama: integrating large and diverse single-cell transcriptomic datasets. Nat Protoc 2024; 19:2283-2297. [PMID: 38844552 DOI: 10.1038/s41596-024-00991-3] [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: 05/29/2023] [Accepted: 01/11/2024] [Indexed: 08/07/2024]
Abstract
Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.
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Affiliation(s)
- Brian L Hie
- Department of Chemical Engineering, Stanford University School, Stanford, CA, USA.
- Stanford Data Science, Stanford University, Stanford, CA, USA.
- Arc Institute, Palo Alto, CA, USA.
| | - Soochi Kim
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA
| | - Thomas A Rando
- Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA
- Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA
- Department of Neurology, UCLA, Los Angeles, CA, USA
- Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, UCLA, Los Angeles, CA, USA
| | - Bryan Bryson
- Department of Biological Engineering, MIT, Cambridge, MA, USA.
- Ragon Institute of Mass General, MIT and Harvard, Cambridge, MA, USA.
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
- Department of Mathematics, MIT, Cambridge, MA, USA.
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7
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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8
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Liang SQ, Navia AW, Ramseier M, Zhou X, Martinez M, Lee C, Zhou C, Wu J, Xie J, Su Q, Wang D, Flotte TR, Anderson DG, Tarantal AF, Shalek AK, Gao G, Xue W. AAV5 Delivery of CRISPR/Cas9 Mediates Genome Editing in the Lungs of Young Rhesus Monkeys. Hum Gene Ther 2024. [PMID: 38767512 DOI: 10.1089/hum.2024.035] [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] [Indexed: 05/22/2024] Open
Abstract
Genome editing has the potential to treat genetic diseases in a variety of tissues, including the lung. We have previously developed and validated a dual adeno-associated virus (AAV) CRISPR platform that supports effective editing in the airways of mice. To validate this delivery vehicle in a large animal model, we have shown that intratracheal instillation of CRISPR/Cas9 in AAV5 can edit a housekeeping gene or a disease-related gene in the lungs of young rhesus monkeys. We observed up to 8% editing of angiotensin-converting enzyme 2 (ACE2) in lung lobes after single-dose administration. Single-nuclear RNA sequencing revealed that AAV5 transduces multiple cell types in the caudal lung lobes, including alveolar cells, macrophages, fibroblasts, endothelial cells, and B cells. These results demonstrate that AAV5 is efficient in the delivery of CRISPR/Cas9 in the lung lobes of young rhesus monkeys.
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Affiliation(s)
- Shun-Qing Liang
- RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Andrew W Navia
- Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, USA
| | - Michelle Ramseier
- Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, USA
| | - Xuntao Zhou
- RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Michele Martinez
- Departments of Pediatrics and Cell Biology and Human Anatomy, School of Medicine, and California National Primate Research Center, University of California, Davis, California, USA
| | - Charles Lee
- Departments of Pediatrics and Cell Biology and Human Anatomy, School of Medicine, and California National Primate Research Center, University of California, Davis, California, USA
| | - Chen Zhou
- Horae Gene Therapy Center and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Joae Wu
- Department of Pediatrics, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Jun Xie
- Horae Gene Therapy Center and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Viral Vector Core, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Qin Su
- Horae Gene Therapy Center and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Viral Vector Core, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Dan Wang
- Horae Gene Therapy Center and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Terence R Flotte
- Horae Gene Therapy Center and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Daniel G Anderson
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Harvard and MIT Division of Health Science and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Alice F Tarantal
- Departments of Pediatrics and Cell Biology and Human Anatomy, School of Medicine, and California National Primate Research Center, University of California, Davis, California, USA
| | - Alex K Shalek
- Institute for Medical Engineering and Science, Department of Chemistry, and Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, Massachusetts, USA
| | - Guangping Gao
- Horae Gene Therapy Center and Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Viral Vector Core, University of Massachusetts Medical School, Worcester, Massachusetts, USA
| | - Wen Xue
- RNA Therapeutics Institute, University of Massachusetts Medical School, Worcester, Massachusetts, USA
- Li Weibo Institute for Rare Diseases Research, University of Massachusetts Medical School, Worcester, Massachusetts, USA
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9
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Rupp BT, Cook CD, Purcell EA, Pop M, Radomski AE, Mesyngier N, Bailey RC, Nagrath S. CellMag-CARWash: A High Throughput Droplet Microfluidic Device for Live Cell Isolation and Single Cell Applications. Adv Biol (Weinh) 2024; 8:e2400066. [PMID: 38741244 DOI: 10.1002/adbi.202400066] [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: 04/04/2024] [Indexed: 05/16/2024]
Abstract
The recent push toward understanding an individual cell's behavior and identifying cellular heterogeneity has created an unmet need for technologies that can probe live cells at the single-cell level. Cells within a population are known to exhibit heterogeneous responses to environmental cues. These differences can lead to varied cellular states, behavior, and responses to therapeutics. Techniques are needed that are not only capable of processing and analyzing cellular populations at the single cell level, but also have the ability to isolate specific cell populations from a complex sample at high throughputs. The new CellMag-Coalesce-Attract-Resegment Wash (CellMag-CARWash) system combines positive magnetic selection with droplet microfluidic devices to isolate cells of interest from a mixture with >93% purity and incorporate treatments within individual droplets to observe single cell biological responses. This workflow is shown to be capable of probing the single cell extracellular vesicle (EV) secretion of MCF7 GFP cells. This article reports the first measurement of β-Estradiol's effect on EV secretion from MCF7 cells at the single cell level. Single cell processing revealed that MCF7 GFP cells possess a heterogeneous response to β-Estradiol stimulation with a 1.8-fold increase relative to the control.
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Affiliation(s)
- Brittany T Rupp
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Claire D Cook
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Emma A Purcell
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Matei Pop
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Abigail E Radomski
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Nicolas Mesyngier
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Ryan C Bailey
- Department of Chemistry, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Sunitha Nagrath
- Department of Chemical Engineering, University of Michigan, Ann Arbor, MI, 48109, USA
- Biointerfaces Institute, University of Michigan, Ann Arbor, MI, 48109, USA
- Rogel Cancer Center, University of Michigan, Ann Arbor, MI, 48109, USA
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10
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Xie J, Ruan S, Tu M, Yuan Z, Hu J, Li H, Li S. Clustering single-cell RNA sequencing data via iterative smoothing and self-supervised discriminative embedding. Oncogene 2024; 43:2279-2292. [PMID: 38834657 DOI: 10.1038/s41388-024-03074-5] [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: 01/21/2024] [Revised: 05/22/2024] [Accepted: 05/28/2024] [Indexed: 06/06/2024]
Abstract
Single-cell transcriptome sequencing (scRNA-seq) is a high-throughput technique used to study gene expression at the single-cell level. Clustering analysis is a commonly used method in scRNA-seq data analysis, helping researchers identify cell types and uncover interactions between cells. However, the choice of a robust similarity metric in the clustering procedure is still an open challenge due to the complex underlying structures of the data and the inherent noise in data acquisition. Here, we propose a deep clustering method for scRNA-seq data called scRISE (scRNA-seq Iterative Smoothing and self-supervised discriminative Embedding model) to resolve this challenge. The model consists of two main modules: an iterative smoothing module based on graph autoencoders designed to denoise the data and refine the pairwise similarity in turn to gradually incorporate cell structural features and enrich the data information; and a self-supervised discriminative embedding module with adaptive similarity threshold for partitioning samples into correct clusters. Our approach has shown improved quality of data representation and clustering on seventeen scRNA-seq datasets against a number of state-of-the-art deep learning clustering methods. Furthermore, utilizing the scRISE method in biological analysis against the HNSCC dataset has unveiled 62 informative genes, highlighting their potential roles as therapeutic targets and biomarkers.
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Affiliation(s)
- Jinxin Xie
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Shanshan Ruan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Mingyan Tu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Zhen Yuan
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Jianguo Hu
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
| | - Honglin Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai, 200062, China.
- Lingang Laboratory, Shanghai, 200031, China.
| | - Shiliang Li
- Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Innovation Center for AI and Drug Discovery, School of Pharmacy, East China Normal University, Shanghai, 200062, China.
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11
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Frenkel M, Raman S. Discovering mechanisms of human genetic variation and controlling cell states at scale. Trends Genet 2024; 40:587-600. [PMID: 38658256 DOI: 10.1016/j.tig.2024.03.010] [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: 02/24/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/26/2024]
Abstract
Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, USA; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA; Department of Bacteriology, University of Wisconsin, Madison, WI, USA; Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA.
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12
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Nishide M, Shimagami H, Kumanogoh A. Single-cell analysis in rheumatic and allergic diseases: insights for clinical practice. Nat Rev Immunol 2024:10.1038/s41577-024-01043-3. [PMID: 38914790 DOI: 10.1038/s41577-024-01043-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/26/2024]
Abstract
Since the advent of single-cell RNA sequencing (scRNA-seq) methodology, single-cell analysis has become a powerful tool for exploration of cellular networks and dysregulated immune responses in disease pathogenesis. Advanced bioinformatics tools have enabled the combined analysis of scRNA-seq data and information on various cell properties, such as cell surface molecular profiles, chromatin accessibility and spatial information, leading to a deeper understanding of pathology. This Review provides an overview of the achievements in single-cell analysis applied to clinical samples of rheumatic and allergic diseases, including rheumatoid arthritis, systemic lupus erythematosus, systemic sclerosis, allergic airway diseases and atopic dermatitis, with an expanded scope beyond peripheral blood cells to include local diseased tissues. Despite the valuable insights that single-cell analysis has provided into disease pathogenesis, challenges remain in translating single-cell findings into clinical practice and developing personalized treatment strategies. Beyond understanding the atlas of cellular diversity, we discuss the application of data obtained in each study to clinical practice, with a focus on identifying biomarkers and therapeutic targets.
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Affiliation(s)
- Masayuki Nishide
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
| | - Hiroshi Shimagami
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan
- Department of Advanced Clinical and Translational Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan
| | - Atsushi Kumanogoh
- Department of Respiratory Medicine and Clinical Immunology, Graduate School of Medicine, Osaka University, Suita, Osaka, Japan.
- Department of Immunopathology, World Premier International Research Center Initiative (WPI), Immunology Frontier Research Center (IFReC), Osaka University, Suita, Osaka, Japan.
- Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives (OTRI), Osaka University, Suita, Osaka, Japan.
- Center for Infectious Diseases for Education and Research (CiDER), Osaka University, Suita, Osaka, Japan.
- Center for Advanced Modalities and DDS (CAMaD), Osaka University, Suita, Osaka, Japan.
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13
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Jiang J, Hiron TK, Agbaedeng TA, Malhotra Y, Drydale E, Bancroft J, Ng E, Reschen ME, Davison LJ, O’Callaghan CA. A Novel Macrophage Subpopulation Conveys Increased Genetic Risk of Coronary Artery Disease. Circ Res 2024; 135:6-25. [PMID: 38747151 PMCID: PMC11191562 DOI: 10.1161/circresaha.123.324172] [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: 12/21/2023] [Revised: 04/26/2024] [Accepted: 05/01/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND Coronary artery disease (CAD), the leading cause of death worldwide, is influenced by both environmental and genetic factors. Although over 250 genetic risk loci have been identified through genome-wide association studies, the specific causal variants and their regulatory mechanisms are still largely unknown, particularly in disease-relevant cell types such as macrophages. METHODS We utilized single-cell RNA-seq and single-cell multiomics approaches in primary human monocyte-derived macrophages to explore the transcriptional regulatory network involved in a critical pathogenic event of coronary atherosclerosis-the formation of lipid-laden foam cells. The relative genetic contribution to CAD was assessed by partitioning disease heritability across different macrophage subpopulations. Meta-analysis of single-cell RNA-seq data sets from 38 human atherosclerotic samples was conducted to provide high-resolution cross-referencing to macrophage subpopulations in vivo. RESULTS We identified 18 782 cis-regulatory elements by jointly profiling the gene expression and chromatin accessibility of >5000 macrophages. Integration with CAD genome-wide association study data prioritized 121 CAD-related genetic variants and 56 candidate causal genes. We showed that CAD heritability was not uniformly distributed and was particularly enriched in the gene programs of a novel CD52-hi lipid-handling macrophage subpopulation. These CD52-hi macrophages displayed significantly less lipoprotein accumulation and were also found in human atherosclerotic plaques. We investigated the cis-regulatory effect of a risk variant rs10488763 on FDX1, implicating the recruitment of AP-1 and C/EBP-β in the causal mechanisms at this locus. CONCLUSIONS Our results provide genetic evidence of the divergent roles of macrophage subsets in atherogenesis and highlight lipid-handling macrophages as a key subpopulation through which genetic variants operate to influence disease. These findings provide an unbiased framework for functional fine-mapping of genome-wide association study results using single-cell multiomics and offer new insights into the genotype-environment interactions underlying atherosclerotic disease.
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Affiliation(s)
- Jiahao Jiang
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas K. Hiron
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Thomas A. Agbaedeng
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Yashaswat Malhotra
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Edward Drydale
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - James Bancroft
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
| | - Esther Ng
- Nuffield Department of Orthopaedics, Kennedy Institute of Rheumatology, Rheumatology and Musculoskeletal Sciences (E.N.), University of Oxford, United Kingdom
| | - Michael E. Reschen
- Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom (M.E.R.)
| | - Lucy J. Davison
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
- Department of Clinical Science and Services, Royal Veterinary College, Hatfield, United Kingdom (L.J.D.)
| | - Chris A. O’Callaghan
- Nuffield Department of Medicine, Wellcome Centre for Human Genetics (J.J., T.K.H., T.A.A., Y.M., E.D., J.B., L.J.D., C.A.O.), University of Oxford, United Kingdom
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14
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García-González J, Garcia-Gonzalez S, Liou L, O'Reilly PF. The Gene Expression Landscape of Disease Genes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.20.24309121. [PMID: 38947033 PMCID: PMC11213058 DOI: 10.1101/2024.06.20.24309121] [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
Fine-mapping and gene-prioritisation techniques applied to the latest Genome-Wide Association Study (GWAS) results have prioritised hundreds of genes as causally associated with disease. Here we leverage these recently compiled lists of high-confidence causal genes to interrogate where in the body disease genes operate. Specifically, we combine GWAS summary statistics, gene prioritisation results and gene expression RNA-seq data from 46 tissues and 204 cell types in relation to 16 major diseases (including 8 cancers). In tissues and cell types with well-established relevance to the disease, the prioritised genes typically have higher absolute and relative (i.e. tissue/cell specific) expression compared to non-prioritised 'control' genes. Examples include brain tissues in psychiatric disorders (P-value < 1×10-7), microglia cells in Alzheimer's Disease (P-value = 9.8×10-3) and colon mucosa in colorectal cancer (P-value < 1×10-3). We also observe significantly higher expression for disease genes in multiple tissues and cell types with no established links to the corresponding disease. While some of these results may be explained by cell types that span multiple tissues, such as macrophages in brain, blood, lung and spleen in relation to Alzheimer's disease (P-values < 1×10-3), the cause for others is unclear and motivates further investigation that may provide novel insights into disease etiology. For example, mammary tissue in Type 2 Diabetes (P-value < 1×10-7); reproductive tissues such as breast, uterus, vagina, and prostate in Coronary Artery Disease (P-value < 1×10-4); and motor neurons in psychiatric disorders (P-value < 3×10-4). In the GTEx dataset, tissue type is the major predictor of gene expression but the contribution of each predictor (tissue, sample, subject, batch) varies widely among disease-associated genes. Finally, we highlight genes with the highest levels of gene expression in relevant tissues to guide functional follow-up studies. Our results could offer novel insights into the tissues and cells involved in disease initiation, inform drug target and delivery strategies, highlighting potential off-target effects, and exemplify the relative performance of different statistical tests for linking disease genes with tissue and cell type gene expression.
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Affiliation(s)
- Judit García-González
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
| | - Saul Garcia-Gonzalez
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
- Center for Excellence in Youth Education, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
| | - Lathan Liou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
| | - Paul F O'Reilly
- Department of Genetics and Genomic Sciences, Icahn School of Medicine, Mount Sinai, New York City, NY 10029, USA
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15
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Li C, Shao X, Zhang S, Wang Y, Jin K, Yang P, Lu X, Fan X, Wang Y. scRank infers drug-responsive cell types from untreated scRNA-seq data using a target-perturbed gene regulatory network. Cell Rep Med 2024; 5:101568. [PMID: 38754419 PMCID: PMC11228399 DOI: 10.1016/j.xcrm.2024.101568] [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: 05/05/2023] [Revised: 12/27/2023] [Accepted: 04/21/2024] [Indexed: 05/18/2024]
Abstract
Cells respond divergently to drugs due to the heterogeneity among cell populations. Thus, it is crucial to identify drug-responsive cell populations in order to accurately elucidate the mechanism of drug action, which is still a great challenge. Here, we address this problem with scRank, which employs a target-perturbed gene regulatory network to rank drug-responsive cell populations via in silico drug perturbations using untreated single-cell transcriptomic data. We benchmark scRank on simulated and real datasets, which shows the superior performance of scRank over existing methods. When applied to medulloblastoma and major depressive disorder datasets, scRank identifies drug-responsive cell types that are consistent with the literature. Moreover, scRank accurately uncovers the macrophage subpopulation responsive to tanshinone IIA and its potential targets in myocardial infarction, with experimental validation. In conclusion, scRank enables the inference of drug-responsive cell types using untreated single-cell data, thus providing insights into the cellular-level impacts of therapeutic interventions.
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Affiliation(s)
- Chengyu Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
| | - Shujing Zhang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Yingchao Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Kaiyu Jin
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Penghui Yang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China
| | - Xiaoyan Lu
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; Innovation Institute for Artificial Intelligence in Medicine, Zhejiang University, Hangzhou, China
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China; Jinhua Institute of Zhejiang University, Jinhua 321299, China; Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou 310006, China.
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China; National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314103, China.
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16
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Xue Y, Friedl V, Ding H, Wong CK, Stuart JM. Single-cell signatures identify microenvironment factors in tumors associated with patient outcomes. CELL REPORTS METHODS 2024; 4:100799. [PMID: 38889686 PMCID: PMC11228369 DOI: 10.1016/j.crmeth.2024.100799] [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: 11/09/2023] [Revised: 04/30/2024] [Accepted: 05/21/2024] [Indexed: 06/20/2024]
Abstract
The cellular components of tumors and their microenvironment play pivotal roles in tumor progression, patient survival, and the response to cancer treatments. Unveiling a comprehensive cellular profile within bulk tumors via single-cell RNA sequencing (scRNA-seq) data is crucial, as it unveils intrinsic tumor cellular traits that elude identification through conventional cancer subtyping methods. Our contribution, scBeacon, is a tool that derives cell-type signatures by integrating and clustering multiple scRNA-seq datasets to extract signatures for deconvolving unrelated tumor datasets on bulk samples. Through the employment of scBeacon on the The Cancer Genome Atlas (TCGA) cohort, we find cellular and molecular attributes within specific tumor categories, many with patient outcome relevance. We developed a tumor cell-type map to visually depict the relationships among TCGA samples based on the cell-type inferences.
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Affiliation(s)
- Yuanqing Xue
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Verena Friedl
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Hongxu Ding
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Christopher K Wong
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA
| | - Joshua M Stuart
- UC Santa Cruz Department, Biomolecular Engineering, Genomics Institute, Santa Cruz, CA, USA.
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17
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McLean AK, Reynolds G, Pratt AG. Leveraging Multi-Tissue, Single-Cell Atlases as Tools to Elucidate Shared Mechanisms of Immune-Mediated Inflammatory Diseases. Biomedicines 2024; 12:1297. [PMID: 38927506 PMCID: PMC11201400 DOI: 10.3390/biomedicines12061297] [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: 05/13/2024] [Revised: 06/05/2024] [Accepted: 06/08/2024] [Indexed: 06/28/2024] Open
Abstract
The observation that certain therapeutic strategies for targeting inflammation benefit patients with distinct immune-mediated inflammatory diseases (IMIDs) is exemplified by the success of TNF blockade in conditions including rheumatoid arthritis, ulcerative colitis, and skin psoriasis, albeit only for subsets of individuals with each condition. This suggests intersecting "nodes" in inflammatory networks at a molecular and cellular level may drive and/or maintain IMIDs, being "shared" between traditionally distinct diagnoses without mapping neatly to a single clinical phenotype. In line with this proposition, integrative tumour tissue analyses in oncology have highlighted novel cell states acting across diverse cancers, with important implications for precision medicine. Drawing upon advances in the oncology field, this narrative review will first summarise learnings from the Human Cell Atlas in health as a platform for interrogating IMID tissues. It will then review cross-disease studies to date that inform this endeavour before considering future directions in the field.
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Affiliation(s)
- Anthony K. McLean
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
| | - Gary Reynolds
- Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Arthur G. Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Musculoskeletal Unit, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
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18
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Curion F, Theis FJ. Machine learning integrative approaches to advance computational immunology. Genome Med 2024; 16:80. [PMID: 38862979 PMCID: PMC11165829 DOI: 10.1186/s13073-024-01350-3] [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: 06/29/2023] [Accepted: 05/23/2024] [Indexed: 06/13/2024] Open
Abstract
The study of immunology, traditionally reliant on proteomics to evaluate individual immune cells, has been revolutionized by single-cell RNA sequencing. Computational immunologists play a crucial role in analysing these datasets, moving beyond traditional protein marker identification to encompass a more detailed view of cellular phenotypes and their functional roles. Recent technological advancements allow the simultaneous measurements of multiple cellular components-transcriptome, proteome, chromatin, epigenetic modifications and metabolites-within single cells, including in spatial contexts within tissues. This has led to the generation of complex multiscale datasets that can include multimodal measurements from the same cells or a mix of paired and unpaired modalities. Modern machine learning (ML) techniques allow for the integration of multiple "omics" data without the need for extensive independent modelling of each modality. This review focuses on recent advancements in ML integrative approaches applied to immunological studies. We highlight the importance of these methods in creating a unified representation of multiscale data collections, particularly for single-cell and spatial profiling technologies. Finally, we discuss the challenges of these holistic approaches and how they will be instrumental in the development of a common coordinate framework for multiscale studies, thereby accelerating research and enabling discoveries in the computational immunology field.
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Affiliation(s)
- Fabiola Curion
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Center Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.
- School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
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19
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Fowler H, Clifford RE, Bowden D, Sutton PA, Govindarajah N, Fok M, Glenn M, Wall M, Rubbi C, Buczacki SJA, Mandal A, Francies H, Hughes J, Parsons JL, Vimalachandran D. Myoferlin: A Potential Marker of Response to Radiation Therapy and Survival in Locally Advanced Rectal Cancer. Int J Radiat Oncol Biol Phys 2024:S0360-3016(24)00696-5. [PMID: 38866213 DOI: 10.1016/j.ijrobp.2024.05.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Revised: 05/24/2024] [Accepted: 05/31/2024] [Indexed: 06/14/2024]
Abstract
PURPOSE Patients with locally advanced rectal cancer often require neoadjuvant chemoradiation therapy to downstage the disease, but the response is variable with no predictive biomarkers. We have previously revealed through proteomic profiling that myoferlin is associated with response to radiation therapy. The aims of this study were to further validate this finding and explore the potential for myoferlin to act as a prognostic and/or therapeutic target. METHODS AND MATERIALS Immunohistochemical analysis of a tissue microarray (TMA) for 111 patients was used to validate the initial proteomic findings. Manipulation of myoferlin was achieved using small interfering RNA, a small molecular inhibitor (wj460), and a CRISPR-Cas9 knockout cell line. Radiosensitization after treatment was assessed using 2-dimensional clonogenic assays, 3-dimensional spheroid models, and patient-derived organoids. Underlying mechanisms were investigated using electrophoresis, immunofluorescence, and immunoblotting. RESULTS Analysis of both the diagnostic biopsy and tumor resection samples confirmed that low myoferlin expression correlated with a good response to neoadjuvant long-course chemoradiation therapy. High myoferlin expression was associated with spread to local lymph nodes and worse 5-year survival (P = .01; hazard ratio, 3.5; 95% CI, 1.27-10.04). This was externally validated using the Stratification in Colorectal Cancer database. Quantification of myoferlin using immunoblotting in immortalized colorectal cancer cell lines and organoids demonstrated that high myoferlin expression was associated with increased radioresistance. Biological and pharmacologic manipulation of myoferlin resulted in significantly increased radiosensitivity across all cell lines in 2-dimensional and 3-dimensional models. After irradiation, myoferlin knockdown cells had a significantly impaired ability to repair DNA double-strand breaks. This appeared to be mediated via nonhomologous end-joining. CONCLUSIONS We have confirmed that high expression of myoferlin in rectal cancer is associated with poor response to neoadjuvant therapy and worse long-term survival. Furthermore, the manipulation of myoferlin led to increased radiosensitivity in vitro. This suggests that myoferlin could be targeted to enhance the sensitivity of patients with rectal cancer to radiation therapy, and further work is required.
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Affiliation(s)
- Hayley Fowler
- Department of Colorectal Surgery, Countess of Chester National Health Service Foundation Trust, Chester, United Kingdom; Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom.
| | - Rachael E Clifford
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom
| | - David Bowden
- Department of Colorectal Surgery, Countess of Chester National Health Service Foundation Trust, Chester, United Kingdom
| | - Paul A Sutton
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom
| | - Naren Govindarajah
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom
| | - Matthew Fok
- Department of Colorectal Surgery, Countess of Chester National Health Service Foundation Trust, Chester, United Kingdom; Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom
| | - Mark Glenn
- Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom
| | - Michael Wall
- Department of Colorectal Surgery, Countess of Chester National Health Service Foundation Trust, Chester, United Kingdom
| | - Carlos Rubbi
- Faculty of Health, Social Care & Medicine, Edge Hill University, St Helens Road, Ormskirk, Lancashire, L39 4QP
| | - Simon J A Buczacki
- Nuffield Department of Surgical Sciences (NDS), University of Oxford, Oxford, United Kingdom
| | - Amit Mandal
- Nuffield Department of Surgical Sciences (NDS), University of Oxford, Oxford, United Kingdom
| | - Hayley Francies
- Wellcome-Medical Research Council Cambridge Stem Cell Institute, University of Cambridge, Cambridge, United Kingdom
| | - Jonathan Hughes
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Jason L Parsons
- Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom
| | - Dale Vimalachandran
- Department of Colorectal Surgery, Countess of Chester National Health Service Foundation Trust, Chester, United Kingdom; Department of Molecular and Clinical Cancer Medicine, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, William Henry Duncan Building, Liverpool, United Kingdom
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20
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Tune T, Kooiker KB, Davis J, Daniel T, Moussavi-Harami F. Identifying Mechanisms and Therapeutic Targets in Muscle using Bayesian Parameter Estimation with Conditional Variational Autoencoders. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.08.593035. [PMID: 38766103 PMCID: PMC11100674 DOI: 10.1101/2024.05.08.593035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Cardiomyopathies, often caused by mutations in genes encoding muscle proteins, are traditionally treated by phenotyping hearts and addressing symptoms post irreversible damage. With advancements in genotyping, early diagnosis is now possible, potentially preventing such damage. However, the intricate structure of muscle and its myriad proteins make treatment predictions challenging. Here we approach the problem of estimating therapeutic targets for a mutation in mouse muscle using a spatially explicit half sarcomere muscle model. We selected 9 rate parameters in our model linked to both small molecules and cardiomyopathy-causing mutations. We then randomly varied these rate parameters and simulated an isometric twitch for each combination to generate a large training dataset. We used this dataset to train a Conditional Variational Autoencoder (CVAE), a technique used in Bayesian parameter estimation. Given simulated or experimental isometric twitches, this machine learning model is able to then predict the set of rate parameters which are most likely to yield that result. We then predict the set of rate parameters associated with both control and the cardiac Troponin C (cTnC) I61Q variant in mouse trabeculae and model parameters that recover the abnormal I61Q cTnC twitches. SIGNIFICANCE Machine learning techniques have potential to accelerate discoveries in biologically complex systems. However, they require large data sets and can be challenging in high dimensional systems such as cardiac muscle. In this study, we combined experimental measures of cardiac muscle twitch forces with mechanistic simulations and a newly developed mixture of Bayesian inference with neural networks (in autoencoders) to solve the inverse problem of determining the underlying kinetics for observed force generation by cardiac muscle. The autoencoders are trained on millions of simulations spanning parameter spaces that correspond to the mechanochemistry of cardiac sarcomeres. We apply the trained model to experimental data in order to infer parameters that can explain a diseased twitch and ways to recover it.
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21
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Kong Y, Börner K. Publication, funding, and experimental data in support of Human Reference Atlas construction and usage. Sci Data 2024; 11:574. [PMID: 38834597 PMCID: PMC11150433 DOI: 10.1038/s41597-024-03416-8] [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: 01/31/2024] [Accepted: 05/24/2024] [Indexed: 06/06/2024] Open
Abstract
Experts from 18 consortia are collaborating on the Human Reference Atlas (HRA) which aims to map the 37 trillion cells in the healthy human body. Information relevant for HRA construction and usage is held by experts, published in scholarly papers, and captured in experimental data. However, these data sources use different metadata schemas and cannot be cross-searched efficiently. This paper documents the compilation of a dataset, named HRAlit, that links the 136 HRA v1.4 digital objects (31 organs with 4,279 anatomical structures, 1,210 cell types, 2,089 biomarkers) to 583,117 experts; 7,103,180 publications; 896,680 funded projects, and 1,816 experimental datasets. The resulting HRAlit has 22 tables with 20,939,937 records including 6 junction tables with 13,170,651 relationships. The HRAlit can be mined to identify leading experts, major papers, funding trends, or alignment with existing ontologies in support of systematic HRA construction and usage.
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Affiliation(s)
- Yongxin Kong
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
- School of Information Management, Sun Yat-sen University, Guangzhou, 510006, China.
| | - Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47408, USA.
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22
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Mimpen JY, Ramos-Mucci L, Paul C, Kurjan A, Hulley PA, Ikwuanusi CT, Cohen CJ, Gwilym SE, Baldwin MJ, Cribbs AP, Snelling SJB. Single nucleus and spatial transcriptomic profiling of healthy human hamstring tendon. FASEB J 2024; 38:e23629. [PMID: 38742770 DOI: 10.1096/fj.202300601rrr] [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: 03/29/2023] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 05/16/2024]
Abstract
The molecular and cellular basis of health in human tendons remains poorly understood. Among human tendons, hamstring tendon has markedly low pathology and can provide a prototypic healthy tendon reference. The aim of this study was to determine the transcriptomes and location of all cell types in healthy hamstring tendon. Using single nucleus RNA sequencing, we profiled the transcriptomes of 10 533 nuclei from four healthy donors and identified 12 distinct cell types. We confirmed the presence of two fibroblast cell types, endothelial cells, mural cells, and immune cells, and identified cell types previously unreported in tendons, including different skeletal muscle cell types, satellite cells, adipocytes, and undefined nervous system cells. The location of these cell types within tendon was defined using spatial transcriptomics and imaging, and potential transcriptional networks and cell-cell interactions were analyzed. We demonstrate that fibroblasts have the highest number of potential cell-cell interactions in our dataset, are present throughout the tendon, and play an important role in the production and organization of extracellular matrix, thus confirming their role as key regulators of hamstring tendon homeostasis. Overall, our findings underscore the complexity of the cellular networks that underpin healthy human tendon function and the central role of fibroblasts as key regulators of hamstring tendon tissue homeostasis.
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Affiliation(s)
- Jolet Y Mimpen
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Lorenzo Ramos-Mucci
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Claudia Paul
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Alina Kurjan
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Philippa A Hulley
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | | | - Carla J Cohen
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom of Great Britain and Northern Ireland
| | - Stephen E Gwilym
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Mathew J Baldwin
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Adam P Cribbs
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Sarah J B Snelling
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
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23
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Tung KF, Pan CY, Lin WC. Housekeeping protein-coding genes interrogated with tissue and individual variations. Sci Rep 2024; 14:12454. [PMID: 38816574 PMCID: PMC11139953 DOI: 10.1038/s41598-024-63269-4] [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: 03/18/2024] [Accepted: 05/27/2024] [Indexed: 06/01/2024] Open
Abstract
Housekeeping protein-coding genes are stably expressed genes in cells and tissues that are thought to be engaged in fundamental cellular biological functions. They are often utilized as normalization references in molecular biology research and are especially important in integrated bioinformatic investigations. Prior studies have examined human housekeeping protein-coding genes by analyzing various gene expression datasets. The inclusion of different tissue types significantly impacted the discovery of housekeeping genes. In this report, we investigated particularly individual human subject expression differences in protein-coding genes across different tissue types. We used GTEx V8 gene expression datasets obtained from more than 16,000 human normal tissue samples. Furthermore, the Gini index is utilized to investigate the expression variations of protein-coding genes between tissue and individual donor subjects. Housekeeping protein-coding genes found using Gini index profiles may vary depending on the tissue subtypes investigated, particularly given the diverse sample size collections across the GTEx tissue subtypes. We subsequently selected major tissues and identified subsets of housekeeping genes with stable expression levels among human donors within those tissues. In this work, we provide alternative sets of housekeeping protein-coding genes that show more consistent expression patterns in human subjects across major solid organs. Weblink: https://hpsv.ibms.sinica.edu.tw .
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Affiliation(s)
- Kuo-Feng Tung
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C
| | - Chao-Yu Pan
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C
| | - Wen-Chang Lin
- Institute of Biomedical Sciences, Academia Sinica, Taipei, 115, Taiwan, R.O.C..
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24
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Lee EJ, Suh M, Choi H, Choi Y, Hwang DW, Bae S, Lee DS. Spatial transcriptomic brain imaging reveals the effects of immunomodulation therapy on specific regional brain cells in a mouse dementia model. BMC Genomics 2024; 25:516. [PMID: 38796425 PMCID: PMC11128132 DOI: 10.1186/s12864-024-10434-8] [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: 02/18/2024] [Accepted: 05/20/2024] [Indexed: 05/28/2024] Open
Abstract
Increasing evidence of brain-immune crosstalk raises expectations for the efficacy of novel immunotherapies in Alzheimer's disease (AD), but the lack of methods to examine brain tissues makes it difficult to evaluate therapeutics. Here, we investigated the changes in spatial transcriptomic signatures and brain cell types using the 10x Genomics Visium platform in immune-modulated AD models after various treatments. To proceed with an analysis suitable for barcode-based spatial transcriptomics, we first organized a workflow for segmentation of neuroanatomical regions, establishment of appropriate gene combinations, and comprehensive review of altered brain cell signatures. Ultimately, we investigated spatial transcriptomic changes following administration of immunomodulators, NK cell supplements and an anti-CD4 antibody, which ameliorated behavior impairment, and designated brain cells and regions showing probable associations with behavior changes. We provided the customized analytic pipeline into an application named STquantool. Thus, we anticipate that our approach can help researchers interpret the real action of drug candidates by simultaneously investigating the dynamics of all transcripts for the development of novel AD therapeutics.
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Affiliation(s)
- Eun Ji Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea
| | - Minseok Suh
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
| | - Hongyoon Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yoori Choi
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea
- Cliniclal Research Institute, Seoul National University Hospital, Seoul, Republic of Korea
| | - Do Won Hwang
- Research and Development Center, THERABEST Inc., Seocho-daero 40-gil, Seoul, 06657, Republic of Korea
| | - Sungwoo Bae
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
| | - Dong Soo Lee
- Department of Nuclear Medicine, Seoul National University College of Medicine, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, Republic of Korea.
- Department of Nuclear Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
- Institute of Radiation Medicine, Medical Research Center, Seoul National University, 103 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
- Medical Science and Engineering, School of Convergence Science and Technology, POSTECH, Pohang, Republic of Korea.
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25
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Börner K, Blood PD, Silverstein JC, Ruffalo M, Teichmann SA, Pryhuber G, Misra R, Purkerson J, Fan J, Hickey JW, Molla G, Xu C, Zhang Y, Weber G, Jain Y, Qaurooni D, Kong Y, Bueckle A, Herr BW. Human BioMolecular Atlas Program (HuBMAP): 3D Human Reference Atlas Construction and Usage. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.27.587041. [PMID: 38826261 PMCID: PMC11142047 DOI: 10.1101/2024.03.27.587041] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Abstract
The Human BioMolecular Atlas Program (HuBMAP) aims to construct a reference 3D structural, cellular, and molecular atlas of the healthy adult human body. The HuBMAP Data Portal (https://portal.hubmapconsortium.org) serves experimental datasets and supports data processing, search, filtering, and visualization. The Human Reference Atlas (HRA) Portal (https://humanatlas.io) provides open access to atlas data, code, procedures, and instructional materials. Experts from more than 20 consortia are collaborating to construct the HRA's Common Coordinate Framework (CCF), knowledge graphs, and tools that describe the multiscale structure of the human body (from organs and tissues down to cells, genes, and biomarkers) and to use the HRA to understand changes that occur at each of these levels with aging, disease, and other perturbations. The 6th release of the HRA v2.0 covers 36 organs with 4,499 unique anatomical structures, 1,195 cell types, and 2,089 biomarkers (e.g., genes, proteins, lipids) linked to ontologies. In addition, three workflows were developed to map new experimental data into the HRA's CCF. This paper describes the HRA user stories, terminology, data formats, ontology validation, unified analysis workflows, user interfaces, instructional materials, application programming interface (APIs), flexible hybrid cloud infrastructure, and demonstrates first atlas usage applications and previews.
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Affiliation(s)
- Katy Börner
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
- CIFAR MacMillan Multiscale Human program, CIFAR, Toronto, ON, Canada
| | - Philip D. Blood
- Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Jonathan C. Silverstein
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Matthew Ruffalo
- Ray and Stephanie Lane Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Sarah A. Teichmann
- CIFAR MacMillan Multiscale Human program, CIFAR, Toronto, ON, Canada
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | | | - Ravi Misra
- University of Rochester Medical Center, Rochester, NY, USA
| | | | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore MD, USA
| | - John W. Hickey
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Yun Zhang
- J. Craig Venter Institute, La Jolla, CA, USA
| | - Griffin Weber
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Danial Qaurooni
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Yongxin Kong
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | | | - Andreas Bueckle
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
| | - Bruce W. Herr
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA
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26
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Li X, Turaga D, Li RG, Tsai CR, Quinn JN, Zhao Y, Wilson R, Carlson K, Wang J, Spinner JA, Hickey EJ, Adachi I, Martin JF. The Macrophage Landscape Across the Lifespan of a Human Cardiac Allograft. Circulation 2024; 149:1650-1666. [PMID: 38344825 PMCID: PMC11105989 DOI: 10.1161/circulationaha.123.065294] [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: 04/25/2023] [Accepted: 01/16/2024] [Indexed: 05/22/2024]
Abstract
BACKGROUND Much of our knowledge of organ rejection after transplantation is derived from rodent models. METHODS We used single-nucleus RNA sequencing to investigate the inflammatory myocardial microenvironment in human pediatric cardiac allografts at different stages after transplantation. We distinguished donor- from recipient-derived cells using naturally occurring genetic variants embedded in single-nucleus RNA sequencing data. RESULTS Donor-derived tissue resident macrophages, which accompany the allograft into the recipient, are lost over time after transplantation. In contrast, monocyte-derived macrophages from the recipient populate the heart within days after transplantation and form 2 macrophage populations: recipient MP1 and recipient MP2. Recipient MP2s have cell signatures similar to donor-derived resident macrophages; however, they lack signatures of pro-reparative phagocytic activity typical of donor-derived resident macrophages and instead express profibrotic genes. In contrast, recipient MP1s express genes consistent with hallmarks of cellular rejection. Our data suggest that recipient MP1s activate a subset of natural killer cells, turning them into a cytotoxic cell population through feed-forward signaling between recipient MP1s and natural killer cells. CONCLUSIONS Our findings reveal an imbalance of donor-derived and recipient-derived macrophages in the pediatric cardiac allograft that contributes to allograft failure.
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Affiliation(s)
- Xiao Li
- The Texas Heart Institute, Houston, TX, USA
| | - Diwakar Turaga
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Division of Critical Care Medicine, Texas Children’s Hospital, Houston TX, USA
| | - Rich G. Li
- The Texas Heart Institute, Houston, TX, USA
| | - Chang-Ru Tsai
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Julianna N. Quinn
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, TX, USA
| | - Yi Zhao
- The Texas Heart Institute, Houston, TX, USA
| | | | - Katherine Carlson
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Jun Wang
- Department of Pediatrics, McGovern Medical School, The University of Texas Health Science Center at Houston, TX, USA
| | - Joseph A. Spinner
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Division of Cardiology, Texas Children’s Hospital, Houston, TX, USA
| | - Edward J. Hickey
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
- Division of Congenital Heart Surgery, Texas Children’s Hospital, Houston, TX, USA
| | - Iki Adachi
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
- Division of Congenital Heart Surgery, Texas Children’s Hospital, Houston, TX, USA
| | - James F. Martin
- The Texas Heart Institute, Houston, TX, USA
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
- Center for Organ Repair and Renewal, Baylor College of Medicine, Houston, TX, USA
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27
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Hong SC, Muyas F, Cortés-Ciriano I, Hormoz S. scAI-SNP: a method for inferring ancestry from single-cell data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594208. [PMID: 38798590 PMCID: PMC11118306 DOI: 10.1101/2024.05.14.594208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Collaborative efforts, such as the Human Cell Atlas, are rapidly accumulating large amounts of single-cell data. To ensure that single-cell atlases are representative of human genetic diversity, we need to determine the ancestry of the donors from whom single-cell data are generated. Self-reporting of race and ethnicity, although important, can be biased and is not always available for the datasets already collected. Here, we introduce scAI-SNP, a tool to infer ancestry directly from single-cell genomics data. To train scAI-SNP, we identified 4.5 million ancestry-informative single-nucleotide polymorphisms (SNPs) in the 1000 Genomes Project dataset across 3201 individuals from 26 population groups. For a query single-cell data set, scAI-SNP uses these ancestry-informative SNPs to compute the contribution of each of the 26 population groups to the ancestry of the donor from whom the cells were obtained. Using diverse single-cell data sets with matched whole-genome sequencing data, we show that scAI-SNP is robust to the sparsity of single-cell data, can accurately and consistently infer ancestry from samples derived from diverse types of tissues and cancer cells, and can be applied to different modalities of single-cell profiling assays, such as single-cell RNA-seq and single-cell ATAC-seq. Finally, we argue that ensuring that single-cell atlases represent diverse ancestry, ideally alongside race and ethnicity, is ultimately important for improved and equitable health outcomes by accounting for human diversity.
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Affiliation(s)
- Sung Chul Hong
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge CB10 1SD, UK
| | - Sahand Hormoz
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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28
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Zhang S, Shu H, Zhou J, Rubin-Sigler J, Yang X, Liu Y, Cooper-Knock J, Monte E, Zhu C, Tu S, Li H, Tong M, Ecker JR, Ichida JK, Shen Y, Zeng J, Tsao PS, Snyder MP. Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.14.594252. [PMID: 38798507 PMCID: PMC11118500 DOI: 10.1101/2024.05.14.594252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.
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Affiliation(s)
- Sai Zhang
- Department of Epidemiology, University of Florida, Gainesville, FL, USA
- Departments of Biostatistics & Biomedical Engineering, Genetics Institute, McKnight Brain Institute, University of Florida, Gainesville, FL, USA
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Hantao Shu
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jingtian Zhou
- Arc Institute, Palo Alto, CA, USA
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla, CA, USA
- These authors contributed equally: Sai Zhang, Hantao Shu, and Jingtian Zhou
| | - Jasper Rubin-Sigler
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Xiaoyu Yang
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Yuxi Liu
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Johnathan Cooper-Knock
- Sheffield Institute for Translational Neuroscience, University of Sheffield, Sheffield, UK
| | - Emma Monte
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Chenchen Zhu
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Sharon Tu
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Han Li
- Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China
| | - Mingming Tong
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Howard Hughes Medical Institute, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Justin K. Ichida
- Department of Stem Cell Biology and Regenerative Medicine, Eli and Edythe Broad Center for Regenerative Medicine and Stem Cell Research, University of Southern California, Los Angeles, CA, USA
| | - Yin Shen
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
- Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Jianyang Zeng
- School of Engineering, Research Center for Industries of the Future, Westlake University, Hangzhou, Zhejiang, China
| | - Philip S. Tsao
- VA Palo Alto Healthcare System, Palo Alto, CA, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Michael P. Snyder
- Department of Genetics, Center for Genomics and Personalized Medicine, Stanford University School of Medicine, Stanford, CA, USA
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29
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Muyas F, Sauer CM, Valle-Inclán JE, Li R, Rahbari R, Mitchell TJ, Hormoz S, Cortés-Ciriano I. De novo detection of somatic mutations in high-throughput single-cell profiling data sets. Nat Biotechnol 2024; 42:758-767. [PMID: 37414936 PMCID: PMC11098751 DOI: 10.1038/s41587-023-01863-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 06/07/2023] [Indexed: 07/08/2023]
Abstract
Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.
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Affiliation(s)
- Francesc Muyas
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Carolin M Sauer
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Jose Espejo Valle-Inclán
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK
| | - Ruoyan Li
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Raheleh Rahbari
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
| | - Thomas J Mitchell
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, UK
- Cambridge University Hospitals NHS Foundation Trust and NIHR Cambridge Biomedical Research Centre, Cambridge, UK
- Department of Surgery, University of Cambridge, Cambridge, UK
| | - Sahand Hormoz
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Isidro Cortés-Ciriano
- European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, Cambridge, UK.
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30
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Dou J, Tan Y, Kock KH, Wang J, Cheng X, Tan LM, Han KY, Hon CC, Park WY, Shin JW, Jin H, Wang Y, Chen H, Ding L, Prabhakar S, Navin N, Chen R, Chen K. Single-nucleotide variant calling in single-cell sequencing data with Monopogen. Nat Biotechnol 2024; 42:803-812. [PMID: 37592035 PMCID: PMC11098741 DOI: 10.1038/s41587-023-01873-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 06/21/2023] [Indexed: 08/19/2023]
Abstract
Single-cell omics technologies enable molecular characterization of diverse cell types and states, but how the resulting transcriptional and epigenetic profiles depend on the cell's genetic background remains understudied. We describe Monopogen, a computational tool to detect single-nucleotide variants (SNVs) from single-cell sequencing data. Monopogen leverages linkage disequilibrium from external reference panels to identify germline SNVs and detects putative somatic SNVs using allele cosegregating patterns at the cell population level. It can identify 100 K to 3 M germline SNVs achieving a genotyping accuracy of 95%, together with hundreds of putative somatic SNVs. Monopogen-derived genotypes enable global and local ancestry inference and identification of admixed samples. It identifies variants associated with cardiomyocyte metabolic levels and epigenomic programs. It also improves putative somatic SNV detection that enables clonal lineage tracing in primary human clonal hematopoiesis. Monopogen brings together population genetics, cell lineage tracing and single-cell omics to uncover genetic determinants of cellular processes.
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Affiliation(s)
- Jinzhuang Dou
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yukun Tan
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kian Hong Kock
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Jun Wang
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Xuesen Cheng
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Le Min Tan
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Kyung Yeon Han
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Chung-Chau Hon
- Laboratory for Genome Information Analysis, RIKEN center for Integrative Medical Sciences, Graduate School of Integrated Sciences for Life, Hiroshima University, Higashi-Hiroshima, Japan
| | - Woong-Yang Park
- Samsung Genome Institute, Samsung Medical Center, Seoul, South Korea
| | - Jay W Shin
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
- Laboratory for Advanced Genomics Circuit, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Haijing Jin
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yujia Wang
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Han Chen
- Human Genetics Center, Department of Epidemiology, Human Genetics and Environmental Sciences, School of Public Health, The University of Texas Health Science Center, Houston, TX, USA
- Center for Precision Health, McWilliams School of Biomedical Informatics, The University of Texas Health Science Center, Houston, TX, USA
| | - Li Ding
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO, USA
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA
- Siteman Cancer Institute, Washington University School of Medicine, St. Louis, MO, USA
| | - Shyam Prabhakar
- Genome Institute of Singapore (GIS), Agency for Science, Technology and Research (A*STAR), Singapore, Republic of Singapore
| | - Nicholas Navin
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rui Chen
- Human Genome Sequencing Center, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Ken Chen
- Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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31
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Lai Y, Ramírez-Pardo I, Isern J, An J, Perdiguero E, Serrano AL, Li J, García-Domínguez E, Segalés J, Guo P, Lukesova V, Andrés E, Zuo J, Yuan Y, Liu C, Viña J, Doménech-Fernández J, Gómez-Cabrera MC, Song Y, Liu L, Xu X, Muñoz-Cánoves P, Esteban MA. Multimodal cell atlas of the ageing human skeletal muscle. Nature 2024; 629:154-164. [PMID: 38649488 PMCID: PMC11062927 DOI: 10.1038/s41586-024-07348-6] [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: 08/24/2022] [Accepted: 03/25/2024] [Indexed: 04/25/2024]
Abstract
Muscle atrophy and functional decline (sarcopenia) are common manifestations of frailty and are critical contributors to morbidity and mortality in older people1. Deciphering the molecular mechanisms underlying sarcopenia has major implications for understanding human ageing2. Yet, progress has been slow, partly due to the difficulties of characterizing skeletal muscle niche heterogeneity (whereby myofibres are the most abundant) and obtaining well-characterized human samples3,4. Here we generate a single-cell/single-nucleus transcriptomic and chromatin accessibility map of human limb skeletal muscles encompassing over 387,000 cells/nuclei from individuals aged 15 to 99 years with distinct fitness and frailty levels. We describe how cell populations change during ageing, including the emergence of new populations in older people, and the cell-specific and multicellular network features (at the transcriptomic and epigenetic levels) associated with these changes. On the basis of cross-comparison with genetic data, we also identify key elements of chromatin architecture that mark susceptibility to sarcopenia. Our study provides a basis for identifying targets in the skeletal muscle that are amenable to medical, pharmacological and lifestyle interventions in late life.
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Affiliation(s)
- Yiwei Lai
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Ignacio Ramírez-Pardo
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Joan Isern
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Juan An
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China
- School of Life Sciences, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
| | - Eusebio Perdiguero
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Antonio L Serrano
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA
| | - Jinxiu Li
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Esther García-Domínguez
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia and CIBERFES, Fundación Investigación Hospital Clínico Universitario/INCLIVA, Valencia, Spain
| | - Jessica Segalés
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Pengcheng Guo
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Jilin, China
| | - Vera Lukesova
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Eva Andrés
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain
| | - Jing Zuo
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Yue Yuan
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - Chuanyu Liu
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
| | - José Viña
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia and CIBERFES, Fundación Investigación Hospital Clínico Universitario/INCLIVA, Valencia, Spain
| | - Julio Doménech-Fernández
- Servicio de Cirugía Ortopédica y Traumatología, Hospital Arnau de Vilanova y Hospital de Liria and Health Care Department Arnau-Lliria, Valencia, Spain
- Department of Orthopedic Surgery, Clinica Universidad de Navarra, Pamplona, Spain
| | - Mari Carmen Gómez-Cabrera
- Freshage Research Group, Department of Physiology, Faculty of Medicine, University of Valencia and CIBERFES, Fundación Investigación Hospital Clínico Universitario/INCLIVA, Valencia, Spain
| | - Yancheng Song
- Department of Orthopedics, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
| | - Longqi Liu
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xun Xu
- BGI Research, Hangzhou, China
- BGI Research, Shenzhen, China
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Pura Muñoz-Cánoves
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- Altos Labs, San Diego Institute of Science, San Diego, CA, USA.
- ICREA, Barcelona, Spain.
| | - Miguel A Esteban
- BGI Research, Hangzhou, China.
- BGI Research, Shenzhen, China.
- Laboratory of Integrative Biology, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou, China.
- State Key Laboratory for Diagnosis and Treatment of Severe Zoonotic Infectious Diseases, Key Laboratory for Zoonosis Research of the Ministry of Education, Institute of Zoonosis, College of Veterinary Medicine, Jilin University, Jilin, China.
- The Fifth Affiliated Hospital of Guangzhou Medical University-BGI Research Center for Integrative Biology, The Fifth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
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32
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Hu P, Rychik J, Zhao J, Bai H, Bauer A, Yu W, Rand EB, Dodds KM, Goldberg DJ, Tan K, Wilkins BJ, Pei L. Single-cell multiomics guided mechanistic understanding of Fontan-associated liver disease. Sci Transl Med 2024; 16:eadk6213. [PMID: 38657025 PMCID: PMC11103255 DOI: 10.1126/scitranslmed.adk6213] [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: 08/31/2023] [Accepted: 04/02/2024] [Indexed: 04/26/2024]
Abstract
The Fontan operation is the current standard of care for single-ventricle congenital heart disease. Individuals with a Fontan circulation (FC) exhibit central venous hypertension and face life-threatening complications of hepatic fibrosis, known as Fontan-associated liver disease (FALD). The fundamental biology and mechanisms of FALD are little understood. Here, we generated a transcriptomic and epigenomic atlas of human FALD at single-cell resolution using multiomic snRNA-ATAC-seq. We found profound cell type-specific transcriptomic and epigenomic changes in FC livers. Central hepatocytes (cHep) exhibited the most substantial changes, featuring profound metabolic reprogramming. These cHep changes preceded substantial activation of hepatic stellate cells and liver fibrosis, suggesting cHep as a potential first "responder" in the pathogenesis of FALD. We also identified a network of ligand-receptor pairs that transmit signals from cHep to hepatic stellate cells, which may promote their activation and liver fibrosis. We further experimentally demonstrated that activins A and B promote fibrotic activation in vitro and identified mechanisms of activin A's transcriptional activation in FALD. Together, our single-cell transcriptomic and epigenomic atlas revealed mechanistic insights into the pathogenesis of FALD and may aid identification of potential therapeutic targets.
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Affiliation(s)
- Po Hu
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Jack Rychik
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Juanjuan Zhao
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Huajun Bai
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Aidan Bauer
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Wenbao Yu
- Center for Childhood Cancer Research, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Elizabeth B. Rand
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Kathryn M. Dodds
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- School of Nursing, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - David J. Goldberg
- Department of Pediatrics, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Kai Tan
- Center for Childhood Cancer Research, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
| | - Benjamin J. Wilkins
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
| | - Liming Pei
- Center for Mitochondrial and Epigenomic Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia; Philadelphia, PA 19104, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
- Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania; Philadelphia, PA 19104, USA
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33
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Ding J, Garber JJ, Uchida A, Lefkovith A, Carter GT, Vimalathas P, Canha L, Dougan M, Staller K, Yarze J, Delorey TM, Rozenblatt-Rosen O, Ashenberg O, Graham DB, Deguine J, Regev A, Xavier RJ. An esophagus cell atlas reveals dynamic rewiring during active eosinophilic esophagitis and remission. Nat Commun 2024; 15:3344. [PMID: 38637492 PMCID: PMC11026436 DOI: 10.1038/s41467-024-47647-0] [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: 09/08/2023] [Accepted: 04/09/2024] [Indexed: 04/20/2024] Open
Abstract
Coordinated cell interactions within the esophagus maintain homeostasis, and disruption can lead to eosinophilic esophagitis (EoE), a chronic inflammatory disease with poorly understood pathogenesis. We profile 421,312 individual cells from the esophageal mucosa of 7 healthy and 15 EoE participants, revealing 60 cell subsets and functional alterations in cell states, compositions, and interactions that highlight previously unclear features of EoE. Active disease displays enrichment of ALOX15+ macrophages, PRDM16+ dendritic cells expressing the EoE risk gene ATP10A, and cycling mast cells, with concomitant reduction of TH17 cells. Ligand-receptor expression uncovers eosinophil recruitment programs, increased fibroblast interactions in disease, and IL-9+IL-4+IL-13+ TH2 and endothelial cells as potential mast cell interactors. Resolution of inflammation-associated signatures includes mast and CD4+ TRM cell contraction and cell type-specific downregulation of eosinophil chemoattractant, growth, and survival factors. These cellular alterations in EoE and remission advance our understanding of eosinophilic inflammation and opportunities for therapeutic intervention.
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Affiliation(s)
- Jiarui Ding
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Department of Computer Science, University of British Columbia, Vancouver, BC, V6T 1Z4, Canada
| | - John J Garber
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.
- Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
| | - Amiko Uchida
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Ariel Lefkovith
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Grace T Carter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Praveen Vimalathas
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
- Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Lauren Canha
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Michael Dougan
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Kyle Staller
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Joseph Yarze
- Gastrointestinal Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA
| | - Toni M Delorey
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Genentech, South San Francisco, CA, 94080, USA
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Daniel B Graham
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
- Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - Jacques Deguine
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, 02142, USA.
- Genentech, South San Francisco, CA, 94080, USA.
| | - Ramnik J Xavier
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA.
- Center for the Study of Inflammatory Bowel Disease, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
- Center for Computational and Integrative Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
- Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA.
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34
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Sud A, Parry EM, Wu CJ. The molecular map of CLL and Richter's syndrome. Semin Hematol 2024; 61:73-82. [PMID: 38368146 DOI: 10.1053/j.seminhematol.2024.01.009] [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: 10/31/2023] [Revised: 01/16/2024] [Accepted: 01/20/2024] [Indexed: 02/19/2024]
Abstract
Clonal expansion of B-cells, from the early stages of monoclonal B-cell lymphocytosis through to chronic lymphocytic leukemia (CLL), and then in some cases to Richter's syndrome (RS) provides a comprehensive model of cancer evolution, notable for the marked morphological transformation and distinct clinical phenotypes. High-throughput sequencing of large cohorts of patients and single-cell studies have generated a molecular map of CLL and more recently, of RS, yielding fundamental insights into these diseases and of clonal evolution. A selection of CLL driver genes have been functionally interrogated to yield novel insights into the biology of CLL. Such findings have the potential to impact patient care through risk stratification, treatment selection and drug discovery. However, this molecular map remains incomplete, with extant questions concerning the origin of the B-cell clone, the role of the TME, inter- and intra-compartmental heterogeneity and of therapeutic resistance mechanisms. Through the application of multi-modal single-cell technologies across tissues, disease states and clinical contexts, these questions can now be addressed with the answers holding great promise of generating translatable knowledge to improve patient care.
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Affiliation(s)
- Amit Sud
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Immuno-Oncology, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Erin M Parry
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA.
| | - Catherine J Wu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA; Harvard Medical School, Boston, MA; Broad Institute of MIT and Harvard, Cambridge, MA; Department of Medicine, Brigham and Women's Hospital, Boston, MA
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35
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Tsagiopoulou M, Rashmi S, Aguilar-Fernandez S, Nieto J, Gut IG. Multi-organ single-cell transcriptomics of immune cells uncovered organ-specific gene expression and functions. Sci Data 2024; 11:316. [PMID: 38538617 PMCID: PMC10973478 DOI: 10.1038/s41597-024-03152-z] [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: 06/27/2023] [Accepted: 03/18/2024] [Indexed: 04/01/2024] Open
Abstract
Despite the wealth of publicly available single-cell datasets, our understanding of distinct resident immune cells and their unique features in diverse human organs remains limited. To address this, we compiled a meta-analysis dataset of 114,275 CD45+ immune cells sourced from 14 organs in healthy donors. While the transcriptome of immune cells remains relatively consistent across organs, our analysis has unveiled organ-specific gene expression differences (GTPX3 in kidney, DNTT and ACVR2B in thymus). These alterations are linked to different transcriptional factor activities and pathways including metabolism. TNF-α signaling through the NFkB pathway was found in several organs and immune compartments. The presence of distinct expression profiles for NFkB family genes and their target genes, including cytokines, underscores their pivotal role in cell positioning. Taken together, immune cells serve a dual role: safeguarding the organs and dynamically adjusting to the intricacies of the host organ environment, thereby actively contributing to its functionality and overall homeostasis.
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Affiliation(s)
| | - Sonal Rashmi
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
| | | | - Juan Nieto
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain
| | - Ivo G Gut
- Centro Nacional de Analisis Genomico (CNAG), Barcelona, Spain.
- Universitat de Barcelona (UB), Barcelona, Spain.
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36
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Chamberlin JT, Lee Y, Marth GT, Quinlan AR. Differences in molecular sampling and data processing explain variation among single-cell and single-nucleus RNA-seq experiments. Genome Res 2024; 34:179-188. [PMID: 38355308 PMCID: PMC10984380 DOI: 10.1101/gr.278253.123] [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: 07/07/2023] [Accepted: 02/01/2024] [Indexed: 02/16/2024]
Abstract
A mechanistic understanding of the biological and technical factors that impact transcript measurements is essential to designing and analyzing single-cell and single-nucleus RNA sequencing experiments. Nuclei contain the same pre-mRNA population as cells, but they contain a small subset of the mRNAs. Nonetheless, early studies argued that single-nucleus analysis yielded results comparable to cellular samples if pre-mRNA measurements were included. However, typical workflows do not distinguish between pre-mRNA and mRNA when estimating gene expression, and variation in their relative abundances across cell types has received limited attention. These gaps are especially important given that incorporating pre-mRNA has become commonplace for both assays, despite known gene length bias in pre-mRNA capture. Here, we reanalyze public data sets from mouse and human to describe the mechanisms and contrasting effects of mRNA and pre-mRNA sampling on gene expression and marker gene selection in single-cell and single-nucleus RNA-seq. We show that pre-mRNA levels vary considerably among cell types, which mediates the degree of gene length bias and limits the generalizability of a recently published normalization method intended to correct for this bias. As an alternative, we repurpose an existing post hoc gene length-based correction method from conventional RNA-seq gene set enrichment analysis. Finally, we show that inclusion of pre-mRNA in bioinformatic processing can impart a larger effect than assay choice itself, which is pivotal to the effective reuse of existing data. These analyses advance our understanding of the sources of variation in single-cell and single-nucleus RNA-seq experiments and provide useful guidance for future studies.
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Affiliation(s)
- John T Chamberlin
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA
| | - Younghee Lee
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA
- Seoul National University, College of Veterinary Medicine, Seoul, 08826, South Korea
| | - Gabor T Marth
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah 84112, USA
| | - Aaron R Quinlan
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah 84108, USA;
- Department of Human Genetics, Utah Center for Genetic Discovery, University of Utah, Salt Lake City, Utah 84112, USA
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Lu Y, Oliva M, Pierce BL, Liu J, Chen LS. Integrative cross-omics and cross-context analysis elucidates molecular links underlying genetic effects on complex traits. Nat Commun 2024; 15:2383. [PMID: 38493154 PMCID: PMC10944527 DOI: 10.1038/s41467-024-46675-0] [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: 10/06/2022] [Accepted: 03/06/2024] [Indexed: 03/18/2024] Open
Abstract
Genetic effects on functionally related 'omic' traits often co-occur in relevant cellular contexts, such as tissues. Motivated by the multi-tissue methylation quantitative trait loci (mQTLs) and expression QTLs (eQTLs) analysis, we propose X-ING (Cross-INtegrative Genomics) for cross-omics and cross-context integrative analysis. X-ING takes as input multiple matrices of association statistics, each obtained from different omics data types across multiple cellular contexts. It models the latent binary association status of each statistic, captures the major association patterns among omics data types and contexts, and outputs the posterior mean and probability for each input statistic. X-ING enables the integration of effects from different omics data with varying effect distributions. In the multi-tissue cis-association analysis, X-ING shows improved detection and replication of mQTLs by integrating eQTL maps. In the trans-association analysis, X-ING reveals an enrichment of trans-associations in many disease/trait-relevant tissues.
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Affiliation(s)
- Yihao Lu
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Meritxell Oliva
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
- Genomics Research Center, AbbVie, North Chicago, IL, USA
| | - Brandon L Pierce
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA
| | - Jin Liu
- School of Data Science, The Chinese University of Hong Kong-Shenzhen, Shenzhen, China.
| | - Lin S Chen
- Department of Public Health Sciences, The University of Chicago, Chicago, IL, USA.
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Shi R, Ma X, Zhou M, Xie X, Xu L. Integrated analysis reveals the dysfunction of intercellular communication and metabolic signals in dilated cardiomyopathy. Heliyon 2024; 10:e26803. [PMID: 38434389 PMCID: PMC10907783 DOI: 10.1016/j.heliyon.2024.e26803] [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: 05/13/2023] [Revised: 01/23/2024] [Accepted: 02/20/2024] [Indexed: 03/05/2024] Open
Abstract
Aims Dilated cardiomyopathy refers to a heart muscle condition characterized by structural and functional irregularities in the myocardium that are not related to ischemia. Due to diverse etiologies such as genetic mutations, infections, and exposure to toxins, dilated cardiomyopathy can lead to substantial morbidity and mortality despite advances in the management of heart failure in dilated cardiomyopathy patients. We sought to analyze the characteristics of cell-cell communication and the metabolic signaling pathways in dilated cardiomyopathy. Methods and results The single-nucleus sequencing data of left ventricle samples were acquired from two donor datasets and two dilated cardiomyopathy datasets. Three dilated cardiomyopathy bulk-sequencing datasets were included to determine the shared dilated cardiomyopathy-specific alterations in differentially expressed genes and signaling pathways. Using "CellChat," we analyzed intercellular communication to grasp how cell clusters interact and to map out the impaired signaling pathways in both donor and dilated cardiomyopathy conditions. Gene set enrichment analysis was applied to compare the metabolic signaling before and after dilated cardiomyopathy. We showcased how cell clusters exhibited abnormal cell-to-cell signaling transduction and how each cell type displayed dysfunctional metabolic signaling pathways through the integration of various datasets. The crucial ligand-receptor signaling contributing to outgoing or incoming signaling of dilated cardiomyopathy was identified in a cell-type dependent way, and the cell-specific metabolic alterations in glucose, lipid and amino acid were determined. The expression of gene pairs in BMP and NOTCH signal, as well as the gene expression in the arginine metabolism was validated. Conclusions We reveal the key signals and metabolic pathways for dilated cardiomyopathy adaptation and maintenance, providing potential targets for dilated cardiomyopathy interference.
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Affiliation(s)
- Rui Shi
- Department of Obstetrics and Gynecology, East Hospital, Tongji University School of Medicine, Shanghai, China
| | - Xiue Ma
- Key Laboratory of Arrhythmias of the Ministry of Education of China, Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
| | - Mi Zhou
- Department of Cardiovascular Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xin Xie
- Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
| | - Liang Xu
- Key Laboratory of Arrhythmias of the Ministry of Education of China, Research Center for Translational Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
- Department of Cardiology, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China
- Department of Cardiology, Shanghai General Hospital, Shanghai Jiaotong University School of Medicine, 201620, China
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Li B, Zhang C, Zhao L, Chen N, Hu Y, Li Z, Kang S, Blake A, Xiao S. Diverse clinical presentations of pseudomyogenic hemangioendothelioma associated with EGFL7::FOSB fusion: a second case. Histopathology 2024; 84:708-712. [PMID: 38012540 DOI: 10.1111/his.15108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/08/2023] [Accepted: 11/10/2023] [Indexed: 11/29/2023]
Affiliation(s)
- Bin Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Changliang Zhang
- Molecular Genetics Laboratory, Suzhou Sano Precision Medicine Ltd, Suzhou, China
| | - Lina Zhao
- Molecular Genetics Laboratory, Suzhou Sano Precision Medicine Ltd, Suzhou, China
| | - Nan Chen
- Molecular Genetics Laboratory, Suzhou Sano Precision Medicine Ltd, Suzhou, China
| | - Yongbin Hu
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, China
| | - Zhiyuan Li
- Department of Oncology, Xiangya Hospital, Central South University, Changsha, China
| | - Suya Kang
- Department of Pathology, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Angella Blake
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sheng Xiao
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Van Melkebeke L, Verbeek J, Bihary D, Boesch M, Boeckx B, Feio-Azevedo R, Smets L, Wallays M, Claus E, Bonne L, Maleux G, Govaere O, Korf H, Lambrechts D, van der Merwe S. Comparison of the single-cell and single-nucleus hepatic myeloid landscape within decompensated cirrhosis patients. Front Immunol 2024; 15:1346520. [PMID: 38380322 PMCID: PMC10878168 DOI: 10.3389/fimmu.2024.1346520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 01/22/2024] [Indexed: 02/22/2024] Open
Abstract
Background and aims A complete understanding of disease pathophysiology in advanced liver disease is hampered by the challenges posed by clinical specimen collection. Notably, in these patients, a transjugular liver biopsy (TJB) is the only safe way to obtain liver tissue. However, it remains unclear whether successful sequencing of this extremely small and fragile tissue can be achieved for downstream characterization of the hepatic landscape. Methods Here we leveraged in-house available single-cell RNA-sequencing (scRNA-seq) and single-nucleus (snRNA-seq) technologies and accompanying tissue processing protocols and performed an in-patient comparison on TJB's from decompensated cirrhosis patients (n = 3). Results We confirmed a high concordance between nuclear and whole cell transcriptomes and captured 31,410 single nuclei and 6,152 single cells, respectively. The two platforms revealed similar diversity since all 8 major cell types could be identified, albeit with different cellular proportions thereof. Most importantly, hepatocytes were most abundant in snRNA-seq, while lymphocyte frequencies were elevated in scRNA-seq. We next focused our attention on hepatic myeloid cells due to their key role in injury and repair during chronic liver disease. Comparison of their transcriptional signatures indicated that these were largely overlapping between the two platforms. However, the scRNA-seq platform failed to recover sufficient Kupffer cell numbers, and other monocytes/macrophages featured elevated expression of stress-related parameters. Conclusion Our results indicate that single-nucleus transcriptome sequencing provides an effective means to overcome complications associated with clinical specimen collection and could sufficiently profile all major hepatic cell types including all myeloid cell subsets.
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Affiliation(s)
- Lukas Van Melkebeke
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
| | - Jef Verbeek
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
| | - Dora Bihary
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, Leuven, Belgium
| | - Markus Boesch
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Bram Boeckx
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, Leuven, Belgium
| | - Rita Feio-Azevedo
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Lena Smets
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Marie Wallays
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Eveline Claus
- Department of Interventional Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Lawrence Bonne
- Department of Interventional Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Geert Maleux
- Department of Interventional Radiology, University Hospitals Leuven, Leuven, Belgium
| | - Olivier Govaere
- Department of Imaging and Pathology, Translational Cell and Tissue Research, KU Leuven and University Hospitals Leuven, Leuven, Belgium
| | - Hannelie Korf
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium
- VIB Center for Cancer Biology, Leuven, Belgium
| | - Schalk van der Merwe
- Laboratory of Hepatology, Department of Chronic Diseases and Metabolism, KU Leuven, Leuven, Belgium
- Department of Gastroenterology and Hepatology, University Hospitals Leuven, Leuven, Belgium
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Aparicio L, Crowley L, Christin JR, Laplaca CJ, Hibshoosh H, Rabadan R, Shen MM. Meta-analyses of mouse and human prostate single-cell transcriptomes reveal widespread epithelial plasticity in tissue regression, regeneration, and cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.30.578066. [PMID: 38352515 PMCID: PMC10862785 DOI: 10.1101/2024.01.30.578066] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Recent advances in single-cell RNA-sequencing (scRNA-seq) technology have facilitated studies of cell states and plasticity in tissue maintenance and cancer, including in the prostate. Here we present meta-analyses of multiple new and published scRNA-seq datasets to establish reference cell type classifications for the normal mouse and human prostate. Our analyses demonstrate transcriptomic similarities between epithelial cell states in the normal prostate, in the regressed prostate after androgen-deprivation, and in primary prostate tumors. During regression in the mouse prostate, all epithelial cells shift their expression profiles towards a proximal periurethral (PrU) state, demonstrating an androgen-dependent plasticity that is restored to normal during androgen restoration and regeneration. In the human prostate, we find progressive rewiring of transcriptional programs across epithelial cell types in benign prostate hyperplasia and treatment-naïve prostate cancer. Notably, we detect copy number variants predominantly within Luminal Acinar cells in prostate tumors, suggesting a bias in their cell type of origin, as well as a larger field of transcriptomic alterations in non-tumor cells. Finally, we observe that Luminal Acinar tumor cells in treatment-naïve prostate cancer display heterogeneous androgen receptor (AR) signaling activity, including a split between high-AR and low-AR profiles with similarity to PrU-like states. Taken together, our analyses of cellular heterogeneity and plasticity provide important translational insights into the origin and treatment response of prostate cancer.
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Affiliation(s)
- Luis Aparicio
- Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Laura Crowley
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - John R. Christin
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Caroline J. Laplaca
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Hanina Hibshoosh
- Department of Pathology and Cell Biology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Raul Rabadan
- Program for Mathematical Genomics, Columbia University Irving Medical Center, New York, NY
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
| | - Michael M. Shen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY
- Department of Medicine, Columbia University Irving Medical Center, New York, NY
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, NY
- Department of Urology, Columbia University Irving Medical Center, New York, NY
- Department of Herbert Irving Comprehensive Cancer Center, Columbia University Irving Medical Center, New York, NY
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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43
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Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol 2024; 42:293-304. [PMID: 37231261 PMCID: PMC10928517 DOI: 10.1038/s41587-023-01767-y] [Citation(s) in RCA: 226] [Impact Index Per Article: 226.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/28/2023] [Indexed: 05/27/2023]
Abstract
Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce 'bridge integration', a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a 'dictionary', which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.
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Affiliation(s)
- Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Tim Stuart
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Madeline H Kowalski
- New York Genome Center, New York, NY, USA
- Institute for System Genetics, NYU Langone Medical Center, New York, NY, USA
| | - Saket Choudhary
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Paul Hoffman
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Austin Hartman
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Avi Srivastava
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | | | - Shaista Madad
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Carlos Fernandez-Granda
- Center for Data Science, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA.
- New York Genome Center, New York, NY, USA.
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Chen H, Fang X, Shao J, Zhang Q, Xu L, Chen J, Mei Y, Jiang M, Wang Y, Li Z, Chen Z, Chen Y, Yu C, Ma L, Zhang P, Zhang T, Liao Y, Lv Y, Wang X, Yang L, Fu Y, Chen D, Jiang L, Yan F, Lu W, Chen G, Shen H, Wang J, Wang C, Liang T, Han X, Wang Y, Guo G. Pan-Cancer Single-Nucleus Total RNA Sequencing Using snHH-Seq. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2304755. [PMID: 38010945 PMCID: PMC10837386 DOI: 10.1002/advs.202304755] [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: 07/13/2023] [Revised: 10/11/2023] [Indexed: 11/29/2023]
Abstract
Tumor heterogeneity and its drivers impair tumor progression and cancer therapy. Single-cell RNA sequencing is used to investigate the heterogeneity of tumor ecosystems. However, most methods of scRNA-seq amplify the termini of polyadenylated transcripts, making it challenging to perform total RNA analysis and somatic mutation analysis.Therefore, a high-throughput and high-sensitivity method called snHH-seq is developed, which combines random primers and a preindex strategy in the droplet microfluidic platform. This innovative method allows for the detection of total RNA in single nuclei from clinically frozen samples. A robust pipeline to facilitate the analysis of full-length RNA-seq data is also established. snHH-seq is applied to more than 730 000 single nuclei from 32 patients with various tumor types. The pan-cancer study enables it to comprehensively profile data on the tumor transcriptome, including expression levels, mutations, splicing patterns, clone dynamics, etc. New malignant cell subclusters and exploring their specific function across cancers are identified. Furthermore, the malignant status of epithelial cells is investigated among different cancer types with respect to mutation and splicing patterns. The ability to detect full-length RNA at the single-nucleus level provides a powerful tool for studying complex biological systems and has broad implications for understanding tumor pathology.
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Affiliation(s)
- Haide Chen
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- M20 Genomics, Hangzhou, 311121, China
| | - Xiunan Fang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, 999077, China
| | - Jikai Shao
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
| | - Qi Zhang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310006, China
- The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Liwei Xu
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
| | | | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Mengmeng Jiang
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
| | - Yuting Wang
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Zhouyang Li
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Zihang Chen
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310009, China
| | - Yang Chen
- Zhejiang Key Laboratory of Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, 310022, China
- The Second Clinical Medical College of Zhejiang Chinese Medical University Hangzhou, Hangzhou, 310053, China
| | - Chengxuan Yu
- Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang Provincial Clinical Research Center for Cancer, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Lifeng Ma
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Peijing Zhang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, 310058, China
| | | | - Yuan Liao
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- M20 Genomics, Hangzhou, 311121, China
| | | | - Xueyi Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Lei Yang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yuting Fu
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Daobao Chen
- Department of Breast Surgery, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Liming Jiang
- Department of Radiology, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, 310022, China
| | - Feng Yan
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310009, China
| | - Wei Lu
- Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, Zhejiang Provincial Clinical Research Center for Cancer, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Gao Chen
- Department of Neurosurgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- Key Laboratory of Precise Treatment and Clinical Translational Research of Neurological Diseases, Hangzhou, 310009, China
| | - Huahao Shen
- Key Laboratory of Respiratory Disease of Zhejiang Province, Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
- State Key Laboratory of Respiratory Disease, Guangzhou, 510120, China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
| | - Changchun Wang
- Department of Thoracic Surgery, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, 310022, China
- Zhejiang Key Laboratory of Diagnosis and Treatment Technology on Thoracic Oncology, Hangzhou, 310022, China
| | - Tingbo Liang
- Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China
- Zhejiang Clinical Research Center of Hepatobiliary and Pancreatic Diseases, Hangzhou, 310006, China
- The Innovation Center for the Study of Pancreatic Diseases of Zhejiang Province, Hangzhou, 310006, China
- Cancer Center, Zhejiang University, Hangzhou, 310058, China
| | - Xiaoping Han
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Yongcheng Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, 310058, China
- Liangzhu Laboratory, Zhejiang University, Hangzhou, 311121, China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative Medicine, Dr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative Medicine, Hangzhou, 310058, China
- Institute of Hematology, Zhejiang University, Hangzhou, 310006, China
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45
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Tadros HJ, Turaga D, Zhao Y, Chang-Ru T, Adachi IA, Li X, Martin JF. Activated fibroblasts drive cellular interactions in end-stage pediatric hypertrophic cardiomyopathy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.25.577226. [PMID: 38352607 PMCID: PMC10862753 DOI: 10.1101/2024.01.25.577226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/20/2024]
Abstract
Hypertrophic cardiomyopathy (HCM) is a relatively rare but debilitating diagnosis in the pediatric population and patients with end-stage HCM require heart transplantation. In this study, we performed single-nucleus RNA sequencing on pediatric HCM and control myocardium. We identified distinct underling cellular processes in pediatric, end-stage HCM in cardiomyocytes, fibroblasts, endothelial cells, and myeloid cells, compared to controls. Pediatric HCM was enriched in cardiomyocytes exhibiting "stressed" myocardium gene signatures and underlying pathways associated with cardiac hypertrophy. Cardiac fibroblasts exhibited clear activation signatures and heightened downstream processes associated with fibrosis, more so than adult counterparts. There was notable depletion of tissue-resident macrophages, and increased vascular remodeling in endothelial cells. Our analysis provides the first single nuclei analysis focused on end-stage pediatric HCM.
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Affiliation(s)
- Hanna J Tadros
- Department of Pediatrics, Section of Pediatric Cardiology, Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA
| | - Diwakar Turaga
- Department of Pediatrics, Baylor College of Medicine, Houston, Texas, USA
- Division of Critical Care Medicine, Texas Children's Hospital, Houston TX, USA
| | - Yi Zhao
- The Texas Heart Institute, Houston, TX, USA
| | - Tsai Chang-Ru
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
| | - Iki A Adachi
- Department of Surgery, Baylor College of Medicine, Houston, Texas, USA
- Division of Congenital Heart Surgery, Texas Children's Hospital, Houston, TX, USA
| | - Xiao Li
- The Texas Heart Institute, Houston, TX, USA
| | - James F Martin
- The Texas Heart Institute, Houston, TX, USA
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX, USA
- Center for Organ Repair and Renewal, Baylor College of Medicine, Houston, TX, USA
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46
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Patel AG, Ashenberg O, Collins NB, Segerstolpe Å, Jiang S, Slyper M, Huang X, Caraccio C, Jin H, Sheppard H, Xu K, Chang TC, Orr BA, Shirinifard A, Chapple RH, Shen A, Clay MR, Tatevossian RG, Reilly C, Patel J, Lupo M, Cline C, Dionne D, Porter CBM, Waldman J, Bai Y, Zhu B, Barrera I, Murray E, Vigneau S, Napolitano S, Wakiro I, Wu J, Grimaldi G, Dellostritto L, Helvie K, Rotem A, Lako A, Cullen N, Pfaff KL, Karlström Å, Jané-Valbuena J, Todres E, Thorner A, Geeleher P, Rodig SJ, Zhou X, Stewart E, Johnson BE, Wu G, Chen F, Yu J, Goltsev Y, Nolan GP, Rozenblatt-Rosen O, Regev A, Dyer MA. A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574538. [PMID: 38260392 PMCID: PMC10802404 DOI: 10.1101/2024.01.07.574538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.
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Affiliation(s)
- Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- These authors contributed equally
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally
| | - Natalie B Collins
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- These authors contributed equally
| | - Åsa Segerstolpe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Huang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chiara Caraccio
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Heather Sheppard
- Comparative Pathology Core, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ke Xu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ti-Cheng Chang
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Brent A Orr
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Abbas Shirinifard
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Amber Shen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael R Clay
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ruth G Tatevossian
- Cancer Biomarkers Laboratory, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Colleen Reilly
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jaimin Patel
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Marybeth Lupo
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Cynthia Cline
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caroline B M Porter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sébastien Vigneau
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Napolitano
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Isaac Wakiro
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jingyi Wu
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Grace Grimaldi
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Dellostritto
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Asaf Rotem
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kathleen L Pfaff
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Åsa Karlström
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Judit Jané-Valbuena
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ellen Todres
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron Thorner
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Elizabeth Stewart
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yury Goltsev
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
- Lead contacts
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Lead contacts
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47
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Hekselman I, Vital A, Ziv-Agam M, Kerber L, Yairi I, Yeger-Lotem E. Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data. eLife 2024; 13:e84613. [PMID: 38197427 PMCID: PMC10830129 DOI: 10.7554/elife.84613] [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: 11/01/2022] [Accepted: 12/08/2023] [Indexed: 01/11/2024] Open
Abstract
Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.
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Affiliation(s)
- Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Assaf Vital
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Maya Ziv-Agam
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Lior Kerber
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Ido Yairi
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Ben-Gurion University of the NegevBe’er ShevaIsrael
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the NegevBe’er ShevaIsrael
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48
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Hamel AR, Yan W, Rouhana JM, Monovarfeshani A, Jiang X, Mehta PA, Advani J, Luo Y, Liang Q, Rajasundaram S, Shrivastava A, Duchinski K, Mantena S, Wang J, van Zyl T, Pasquale LR, Swaroop A, Gharahkhani P, Khawaja AP, MacGregor S, Chen R, Vitart V, Sanes JR, Wiggs JL, Segrè AV. Integrating genetic regulation and single-cell expression with GWAS prioritizes causal genes and cell types for glaucoma. Nat Commun 2024; 15:396. [PMID: 38195602 PMCID: PMC10776627 DOI: 10.1038/s41467-023-44380-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: 05/11/2022] [Accepted: 12/12/2023] [Indexed: 01/11/2024] Open
Abstract
Primary open-angle glaucoma (POAG), characterized by retinal ganglion cell death, is a leading cause of irreversible blindness worldwide. However, its molecular and cellular causes are not well understood. Elevated intraocular pressure (IOP) is a major risk factor, but many patients have normal IOP. Colocalization and Mendelian randomization analysis of >240 POAG and IOP genome-wide association study (GWAS) loci and overlapping expression and splicing quantitative trait loci (e/sQTLs) in 49 GTEx tissues and retina prioritizes causal genes for 60% of loci. These genes are enriched in pathways implicated in extracellular matrix organization, cell adhesion, and vascular development. Analysis of single-nucleus RNA-seq of glaucoma-relevant eye tissues reveals that the POAG and IOP colocalizing genes and genome-wide associations are enriched in specific cell types in the aqueous outflow pathways, retina, optic nerve head, peripapillary sclera, and choroid. This study nominates IOP-dependent and independent regulatory mechanisms, genes, and cell types that may contribute to POAG pathogenesis.
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Affiliation(s)
- Andrew R Hamel
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Wenjun Yan
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - John M Rouhana
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Aboozar Monovarfeshani
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xinyi Jiang
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Molecular Medicine, The University of Edinburgh, Edinburgh, UK
| | - Puja A Mehta
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jayshree Advani
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Yuyang Luo
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Qingnan Liang
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Skanda Rajasundaram
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Centre for Evidence-Based Medicine, University of Oxford, Oxford, UK
- Faculty of Medicine, Imperial College London, London, UK
| | - Arushi Shrivastava
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Katherine Duchinski
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Bioinformatics and Integrative Genomics (BIG) PhD Program, Harvard Medical School, Boston, MA, USA
| | - Sreekar Mantena
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Harvard/MIT MD-PhD Program, Harvard Medical School, Boston, MA, USA
| | - Jiali Wang
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Tavé van Zyl
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
- Department of Ophthalmology and Visual Sciences, Yale School of Medicine, New Haven, CT, USA
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Anand Swaroop
- Neurobiology, Neurodegeneration and Repair Laboratory, National Eye Institute, National Institutes of Health, Bethesda, MA, USA
| | - Puya Gharahkhani
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK
| | - Stuart MacGregor
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4029, Australia
| | - Rui Chen
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Veronique Vitart
- MRC Human Genetics Unit, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, UK
| | - Joshua R Sanes
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Janey L Wiggs
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ayellet V Segrè
- Ocular Genomics Institute, Department of Ophthalmology, Massachusetts Eye and Ear, Boston, MA, USA.
- Department of Ophthalmology, Harvard Medical School, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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49
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Li Y, Girard R, Srinath A, Cruz DV, Ciszewski C, Chen C, Lightle R, Romanos S, Sone JY, Moore T, DeBiasse D, Stadnik A, Lee JJ, Shenkar R, Koskimäki J, Lopez-Ramirez MA, Marchuk DA, Ginsberg MH, Kahn ML, Shi C, Awad IA. Transcriptomic signatures of individual cell types in cerebral cavernous malformation. Cell Commun Signal 2024; 22:23. [PMID: 38195510 PMCID: PMC10775676 DOI: 10.1186/s12964-023-01301-2] [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: 07/19/2023] [Accepted: 08/30/2023] [Indexed: 01/11/2024] Open
Abstract
Cerebral cavernous malformation (CCM) is a hemorrhagic neurovascular disease with no currently available therapeutics. Prior evidence suggests that different cell types may play a role in CCM pathogenesis. The contribution of each cell type to the dysfunctional cellular crosstalk remains unclear. Herein, RNA-seq was performed on fluorescence-activated cell sorted endothelial cells (ECs), pericytes, and neuroglia from CCM lesions and non-lesional brain tissue controls. Differentially Expressed Gene (DEG), pathway and Ligand-Receptor (LR) analyses were performed to characterize the dysfunctional genes of respective cell types within CCMs. Common DEGs among all three cell types were related to inflammation and endothelial-to-mesenchymal transition (EndMT). DEG and pathway analyses supported a role of lesional ECs in dysregulated angiogenesis and increased permeability. VEGFA was particularly upregulated in pericytes. Further pathway and LR analyses identified vascular endothelial growth factor A/ vascular endothelial growth factor receptor 2 signaling in lesional ECs and pericytes that would result in increased angiogenesis. Moreover, lesional pericytes and neuroglia predominantly showed DEGs and pathways mediating the immune response. Further analyses of cell specific gene alterations in CCM endorsed potential contribution to EndMT, coagulation, and a hypoxic microenvironment. Taken together, these findings motivate mechanistic hypotheses regarding non-endothelial contributions to lesion pathobiology and may lead to novel therapeutic targets. Video Abstract.
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Affiliation(s)
- Ying Li
- Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Romuald Girard
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Abhinav Srinath
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Diana Vera Cruz
- Center for Research Informatics, The University of Chicago, Chicago, IL, USA
| | - Cezary Ciszewski
- Human Disease and Immune Discovery Core, The University of Chicago, Chicago, IL, USA
| | - Chang Chen
- Center for Research Informatics, The University of Chicago, Chicago, IL, USA
| | - Rhonda Lightle
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Sharbel Romanos
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Je Yeong Sone
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Thomas Moore
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Dorothy DeBiasse
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Agnieszka Stadnik
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Justine J Lee
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Robert Shenkar
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA
| | - Janne Koskimäki
- Department of Neurosurgery, Division of Clinical Neurosciences, Turku University Hospital and University of Turku, Turku, Finland
- Department of Neurosurgery, Oulu University Hospital, Neurocenter, Oulu, Finland
| | - Miguel A Lopez-Ramirez
- Department of Medicine, University of California, La Jolla, San Diego, CA, USA
- Department of Pharmacology, University of California, La Jolla, San Diego, CA, USA
| | - Douglas A Marchuk
- Department of Molecular Genetics and Microbiology, Duke University School of Medicine, Durham, NC, USA
| | - Mark H Ginsberg
- Department of Medicine, University of California, La Jolla, San Diego, CA, USA
| | - Mark L Kahn
- Department of Medicine and Cardiovascular Institute, University of Pennsylvania, Philadelphia, PA, USA
| | - Changbin Shi
- Department of Neurosurgery, First Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang, China
| | - Issam A Awad
- Department of Neurological Surgery, Neurovascular Surgery Program, The University of Chicago, Chicago, IL, USA.
- Department of Neurological Surgery, University of Chicago Medicine, 5841 S Maryland, MC3026/Neurosurgery J341, Chicago, IL, 60637, USA.
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50
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Raney BJ, Barber GP, Benet-Pagès A, Casper J, Clawson H, Cline M, Diekhans M, Fischer C, Navarro Gonzalez J, Hickey G, Hinrichs A, Kuhn R, Lee B, Lee C, Le Mercier P, Miga K, Nassar L, Nejad P, Paten B, Perez G, Schmelter D, Speir M, Wick B, Zweig A, Haussler D, Kent W, Haeussler M. The UCSC Genome Browser database: 2024 update. Nucleic Acids Res 2024; 52:D1082-D1088. [PMID: 37953330 PMCID: PMC10767968 DOI: 10.1093/nar/gkad987] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/06/2023] [Accepted: 10/17/2023] [Indexed: 11/14/2023] Open
Abstract
The UCSC Genome Browser (https://genome.ucsc.edu) is a web-based genomic visualization and analysis tool that serves data to over 7,000 distinct users per day worldwide. It provides annotation data on thousands of genome assemblies, ranging from human to SARS-CoV2. This year, we have introduced new data from the Human Pangenome Reference Consortium and on viral genomes including SARS-CoV2. We have added 1,200 new genomes to our GenArk genome system, increasing the overall diversity of our genomic representation. We have added support for nine new user-contributed track hubs to our public hub system. Additionally, we have released 29 new tracks on the human genome and 11 new tracks on the mouse genome. Collectively, these new features expand both the breadth and depth of the genomic knowledge that we share publicly with users worldwide.
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Affiliation(s)
- Brian J Raney
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Galt P Barber
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Anna Benet-Pagès
- Institute of Neurogenomics, Helmholtz Zentrum München GmbH - German Research Center for Environmental Health, 85764 Neuherberg, Germany
- Medical Genetics Center (Medizinisch Genetisches Zentrum), Munich 80335, Germany
| | - Jonathan Casper
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Hiram Clawson
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Melissa S Cline
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Mark Diekhans
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Clayton Fischer
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - Glenn Hickey
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Angie S Hinrichs
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | | | - Brian T Lee
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Christopher M Lee
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Phillipe Le Mercier
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, CMU, 1 Michel Servet, 1211 Geneva 4, Switzerland
| | - Karen H Miga
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Luis R Nassar
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Parisa Nejad
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Benedict Paten
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Gerardo Perez
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Daniel Schmelter
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Matthew L Speir
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Brittney D Wick
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ann S Zweig
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - David Haussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - W James Kent
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Maximilian Haeussler
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
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