1
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Mimpen JY, Baldwin MJ, Paul C, Ramos-Mucci L, Kurjan A, Cohen CJ, Sharma S, Chevalier Florquin MSN, Hulley PA, McMaster J, Titchener A, Martin A, Costa ML, Gwilym SE, Cribbs AP, Snelling SJB. Exploring cellular changes in ruptured human quadriceps tendons at single-cell resolution. J Physiol 2025. [PMID: 40232153 DOI: 10.1113/jp287812] [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: 10/04/2024] [Accepted: 02/21/2025] [Indexed: 04/16/2025] Open
Abstract
Tendon ruptures in humans have often been studied during the chronic phase of injury, particularly in the context of rotator cuff disease. However, the early response to acute tendon ruptures remains less investigated. Quadriceps tendons, which require prompt surgical treatment, offer a model to investigate this early response. Therefore, this study aimed to explore the early cellular changes in ruptured compared to healthy human quadriceps tendons. Quadriceps tendon samples were collected from patients undergoing fracture repair (healthy) or tendon repair surgery (collected 7-8 days post-injury). Nuclei were isolated for single-nucleus RNA sequencing, and comprehensive transcriptomic analysis was conducted. The transcriptomes of 12,808 nuclei (7268 from healthy and 5540 from ruptured quadriceps tendons) were profiled, revealing 12 major cell types and several cell subtypes and states. Rupture samples showed increased expression of genes related to extracellular matrix organisation and cell cycle signalling, and a decrease in expression of genes in lipid metabolism pathways. These changes were predominantly driven by gene expression changes in the fibroblast, vascular endothelial cell (VEC), mural cell, and macrophage populations: fibroblasts shift to an activated phenotype upon rupture and there is an increase in the proportion of capillary and dividing VECs. A diverse immune environment was observed, with a shift from homeostatic to activated macrophages following rupture. Cell-cell interactions increased in number and diversity in rupture, and primarily involved fibroblast and VEC populations. Collectively, this transcriptomic analysis suggests that fibroblasts and endothelial cells are key orchestrators of the early injury response within ruptured quadriceps tendon. KEY POINTS: Tendon ruptures in humans have regularly been studied during the chronic phase of injury, but less is known about the early injury response after acute tendon ruptures. This study explored the early cellular changes in ruptured compared to healthy human quadriceps tendons at single-cell resolution. Fibroblasts and endothelial cells seem to be the key orchestrators of the early injury response within ruptured quadriceps tendon. Therefore, these cell types are obvious targets for interventions to enhance tendon healing. Overall, this study highlights that the development of more effective therapeutic options for tendon injury requires better understanding of the cellular, extracellular, and mechanical landscape of tendon tissue.
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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
- Kennedy Institute of Rheumatology, 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
| | - Claudia Paul
- 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
| | - Alina Kurjan
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Carla J Cohen
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Centre for Computational Biology, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | - Shreeya Sharma
- 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
| | - John McMaster
- Oxford University Hospital NHS Foundation Trust, Oxford, UK
| | | | | | - Matthew L Costa
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Oxford University Hospital NHS Foundation Trust, Oxford, UK
| | - Stephen E Gwilym
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Oxford University Hospital NHS Foundation Trust, Oxford, UK
| | - Adam P Cribbs
- The Botnar Institute of Musculoskeletal Sciences, Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
- Oxford Centre for Translational Myeloma Research 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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2
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Kumar S, Wu CC, Wulandari FS, Chiao CC, Ko CC, Lin HY, Ngadio JL, Rebecca C, Xuan DTM, Solomon DD, Michael M, Kristiani L, Chuang JY, Tsai MC, Wang CY. Integration of multi-omics and single-cell transcriptome reveals mitochondrial outer membrane protein-2 (MTX-2) as a prognostic biomarker and characterizes ubiquinone metabolism in lung adenocarcinoma. J Cancer 2025; 16:2401-2420. [PMID: 40302794 PMCID: PMC12036103 DOI: 10.7150/jca.106902] [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: 11/13/2024] [Accepted: 03/26/2025] [Indexed: 05/02/2025] Open
Abstract
Lung adenocarcinoma (LUAD) remains to be one of the most prevalent and highly invasive forms of cancer. Mitochondrial outer membrane protein-2 or Metaxin-2 (MTX2), a key regulator of mitochondrial function, has been linked to cellular bioenergetics and stress response mechanisms. However, its roles in the progression and prognosis of LUAD remain largely unexplored. This study, employed a multi-omics approach, integrating transcriptomic and clinical patient data from public databases, to evaluate the expression and prognostic relevance of MTX2 in LUAD. Single-cell RNA sequencing was utilized to further explore MTX2's role in immune infiltration and interactions within the tumor microenvironment. Additionally, we validated these findings through a series of molecular biology and functional assays. Our results demonstrated that MTX2 expression was higher in LUAD tissues compared to normal lung tissues. Elevated MTX2 levels were significantly associated with poorer overall survival in LUAD patients. Functional analyses revealed that MTX2 regulates mitochondrial bioenergetics and facilitates tumor cell proliferation. Additionally, MTX2 expression was associated with increased immune cell infiltration. A pathway analysis identified cell metabolic and tumor growth pathways regulated by MTX2, supporting its role in tumor progression. Our research identifies MTX2 as a promising prognostic biomarker and therapeutic target for LUAD. Increased expression of MTX2 promotes tumor growth by altering metabolic pathways and modulating the immune response, underscoring its potential as a new target for LUAD treatment.
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Affiliation(s)
- Sachin Kumar
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Faculty of Applied Sciences and Biotechnology, Shoolini University of Biotechnology and Management Sciences, Himachal Pradesh, 173229, India
| | - Chung-Che Wu
- Department of Neurosurgery, Taipei Medical University Hospital, Taipei, Taiwan
- Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Fitria Sari Wulandari
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Department of Surgery, Division of Neurosurgery, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
| | - Chung-Chieh Chiao
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
| | - Ching-Chung Ko
- Department of Medical Imaging, Chi-Mei Medical Center, Tainan, Taiwan
- Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
- School of Medicine, College of Medicine, National Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Hung-Yun Lin
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Cancer Center, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 11031, Taiwan
- Traditional Herbal Medicine Research Center of Taipei Medical University Hospital, Taipei Medical University, Taipei 11031, Taiwan
- Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences, Rensselaer, NY 12144, USA
| | - Juan Lorell Ngadio
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Department of Bioinformatics, School of Life Sciences, Indonesia International Institute for Life Sciences, Jl. Pulomas Barat Kav 88, Jakarta Timur, 13210, Indonesia
| | - Cathleen Rebecca
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Department of Pharmacy, School of Medicine and Health Sciences, Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia
| | - Do Thi Minh Xuan
- Faculty of Pharmacy, Van Lang University, 69/68 Dang Thuy Tram Street, Ward 13, Binh Thanh District, Ho Chi Minh City 70000, Vietnam.
| | - Dahlak Daniel Solomon
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Yogananda School of AI Computers and Data Sciences, Shoolini University, Solan 173229, India
| | - Michael Michael
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Department of Biomedicine, School of Life Sciences, Indonesia International Institute for Life Sciences, Jl Pulomas Barat Kav 88, Jakarta Timur, 13210, Indonesia
| | - Lidya Kristiani
- Department of Biomedicine, School of Life Sciences, Indonesia International Institute for Life Sciences, Jl Pulomas Barat Kav 88, Jakarta Timur, 13210, Indonesia
| | - Jian-Ying Chuang
- The Ph.D. Program for Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Cell Physiology and Molecular Image Research Center, Wan Fang Hospital, Taipei Medical University, Taipei 11031, Taiwan
- Department of Biomedical Science and Environmental Biology, Kaohsiung Medical University, Kaohsiung 80708, Taiwan
| | - Ming-Cheng Tsai
- School of Medicine, Fu Jen Catholic University, New Taipei City 242, Taiwan
- Department of Neurosurgery, Shin-Kong Wu Ho-Su Memorial Hospital, 95 Wen-Chang Road, Shih-Lin District, Taipei 111045, Taiwan
| | - Chih-Yang Wang
- Graduate Institute of Cancer Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- Ph.D. Program for Cancer Molecular Biology and Drug Discovery, College of Medical Science and Technology, Taipei Medical University, Taipei 11031, Taiwan
- TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taipei 11031, Taiwan
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3
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Qaqorh T, Takahashi Y, Sameshima K, Otani K, Yazawa I, Nishida Y, Tonai K, Fujihara Y, Honda M, Oki S, Ohkawa Y, Thorburn DR, Frazier AE, Takeda A, Ikeda Y, Sakaguchi H, Watanabe T, Fukushima N, Tsukamoto Y, Makita N, Yamaguchi O, Murayama K, Ohtake A, Okazaki Y, Kimura T, Kato H, Inoue H, Matsuoka K, Takashima S, Shintani Y. Atf3 controls transitioning in female mitochondrial cardiomyopathy as identified by spatial and single-cell transcriptomics. SCIENCE ADVANCES 2025; 11:eadq1575. [PMID: 40184463 PMCID: PMC11970478 DOI: 10.1126/sciadv.adq1575] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2024] [Accepted: 02/28/2025] [Indexed: 04/06/2025]
Abstract
Oxidative phosphorylation defects result in now intractable mitochondrial diseases (MD) with cardiac involvement markedly affecting prognosis. The mechanisms underlying the transition from compensation to dysfunction in response to metabolic deficiency remain unclear. Here, we used spatially resolved transcriptomics and single-nucleus RNA sequencing (snRNA-seq) on the heart of a patient with mitochondrial cardiomyopathy (MCM), combined with an MCM mouse model with cardiac-specific Ndufs6 knockdown (FS6KD). Cardiomyocytes demonstrated the most heterogeneous expression landscape among cell types caused by metabolic perturbation, and pseudotime trajectory analysis revealed dynamic cellular states transitioning from compensation to severe compromise. This progression coincided with the transient up-regulation of a transcription factor, ATF3. Genetic ablation of Atf3 in FS6KD corroborated its pivotal role, effectively delaying cardiomyopathy progression in a female-specific manner. Our findings highlight a fate-determining role of ATF3 in female MCM progression and that the latest transcriptomic analysis will help decipher the mechanisms underlying MD progression.
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Affiliation(s)
- Tasneem Qaqorh
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
- Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biosciences, Suita, Osaka, Japan
| | - Yusuke Takahashi
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Kohei Sameshima
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Kentaro Otani
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Issei Yazawa
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Yuya Nishida
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Kohei Tonai
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Yoshitaka Fujihara
- Department of Advanced Medical Technologies, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Mizuki Honda
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Shinya Oki
- Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine, Kyoto, Japan
| | - Yasuyuki Ohkawa
- Division of Transcriptomics, Medical Institute of Bioregulation, Kyushu University, Fukuoka, Japan
| | - David R. Thorburn
- Murdoch Children’s Research Institute, Royal Children’s Hospital, and University of Melbourne, Department of Paediatrics, Parkville, Victoria, Australia
- Victorian Clinical Genetics Services, Royal Children’s Hospital, Parkville, Victoria, Australia
| | - Ann E. Frazier
- Murdoch Children’s Research Institute, Royal Children’s Hospital, and University of Melbourne, Department of Paediatrics, Parkville, Victoria, Australia
| | - Atsuhito Takeda
- Department of Pediatrics, Faculty of Medicine, Hokkaido University, Sapporo, Japan
| | - Yoshihiko Ikeda
- Department of Pathology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Heima Sakaguchi
- Department of Pediatric Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Takuya Watanabe
- Department of Transplant Medicine, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Norihide Fukushima
- Department of Transplant Medicine, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
- Senri Kinran University, Suita, Osaka, Japan
| | - Yasumasa Tsukamoto
- Department of Transplant Medicine, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Naomasa Makita
- Omics Research Center, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
- Department of Cardiology, Sapporo Teishinkai Hospital, Sapporo, Japan
| | - Osamu Yamaguchi
- Omics Research Center, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
| | - Kei Murayama
- Department of Metabolism, Chiba Children’s Hospital, Chiba, Japan
- Diagnostics and Therapeutic of Intractable Diseases, Intractable Disease Research Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Akira Ohtake
- Department of Pediatrics and Clinical Genomics, Saitama Medical University, Moroyama, Saitama, Japan
| | - Yasushi Okazaki
- Diagnostics and Therapeutic of Intractable Diseases, Intractable Disease Research Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Takanari Kimura
- Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biosciences, Suita, Osaka, Japan
| | - Hisakazu Kato
- Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biosciences, Suita, Osaka, Japan
| | - Hijiri Inoue
- Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biosciences, Suita, Osaka, Japan
| | - Ken Matsuoka
- Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biosciences, Suita, Osaka, Japan
| | - Seiji Takashima
- Department of Medical Biochemistry, Osaka University Graduate School of Frontier Biosciences, Suita, Osaka, Japan
| | - Yasunori Shintani
- Department of Molecular Pharmacology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan
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4
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Muncie-Vasic JM, Sinha T, Clark AP, Brower EF, Saucerman JJ, Black BL, Bruneau BG. MEF2C controls segment-specific gene regulatory networks that direct heart tube morphogenesis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2024.11.01.621613. [PMID: 39554149 PMCID: PMC11566030 DOI: 10.1101/2024.11.01.621613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
The gene regulatory networks (GRNs) that control early heart formation are beginning to be understood, but lineage-specific GRNs remain largely undefined. We investigated networks controlled by the vital transcription factor MEF2C, with a time course of single-nucleus RNA- and ATAC-sequencing in wild-type and Mef2c -null embryos. We identified a "posteriorized" cardiac gene signature and chromatin landscape in the absence of MEF2C. Integrating our multiomics data in a deep learning-based model, we constructed developmental trajectories for each of the outflow tract, ventricular, and inflow tract segments, and alterations of these in Mef2c -null embryos. We computationally identified segment-specific MEF2C-dependent enhancers, with activity in the developing zebrafish heart. Finally, using inferred GRNs we discovered that the Mef2c -null heart malformations are partly driven by increased activity of the nuclear hormone receptor NR2F2. Our results delineate lineage-specific GRNs in the early heart tube and provide a generalizable framework for dissecting transcriptional networks governing developmental processes.
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5
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Hackenberg M, Brunn N, Vogel T, Binder H. Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets. Commun Biol 2025; 8:414. [PMID: 40069486 PMCID: PMC11897155 DOI: 10.1038/s42003-025-07872-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 03/03/2025] [Indexed: 03/15/2025] Open
Abstract
Dimensionality reduction greatly facilitates the exploration of cellular heterogeneity in single-cell RNA sequencing data. While most of such approaches are data-driven, it can be useful to incorporate biologically plausible assumptions about the underlying structure or the experimental design. We propose the boosting autoencoder (BAE) approach, which combines the advantages of unsupervised deep learning for dimensionality reduction and boosting for formalizing assumptions. Specifically, our approach selects small sets of genes that explain latent dimensions. As illustrative applications, we explore the diversity of neural cell identities and temporal patterns of embryonic development.
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Grants
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 322977937, GRK 2344
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 322977937, GRK 2344 ; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 499552394, SFB 1597
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 322977937, GRK 2344; Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project-ID 499552394, SFB 1597
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Affiliation(s)
- Maren Hackenberg
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
- Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany.
| | - Niklas Brunn
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
- Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany.
| | - Tanja Vogel
- Institute of Anatomy and Cell Biology, Department Molecular Embryology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Harald Binder
- Institute of Medical Biometry and Statistics (IMBI), Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany
- Freiburg Center for Data Analysis, Modeling and AI, University of Freiburg, Freiburg, Germany
- Centre for Integrative Biological Signaling Studies (CIBSS), University of Freiburg, Freiburg, Germany
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6
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Agboraw E, Haese-Hill W, Hentzschel F, Briggs E, Aghabi D, Heawood A, Harding CR, Shiels B, Crouch K, Somma D, Otto TD. paraCell: a novel software tool for the interactive analysis and visualization of standard and dual host-parasite single-cell RNA-seq data. Nucleic Acids Res 2025; 53:gkaf091. [PMID: 39988320 PMCID: PMC11840555 DOI: 10.1093/nar/gkaf091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2024] [Revised: 01/21/2025] [Accepted: 02/03/2025] [Indexed: 02/25/2025] Open
Abstract
Advances in sequencing technology have led to a dramatic increase in the number of single-cell transcriptomic datasets. In the field of parasitology, these datasets typically describe the gene expression patterns of a given parasite species at the single-cell level under experimental conditions, in specific hosts or tissues, or at different life cycle stages. However, while this wealth of available data represents a significant resource, analysing these datasets often requires expert computational skills, preventing a considerable proportion of the parasitology community from meaningfully integrating existing single-cell data into their work. Here, we present paraCell, a novel software tool that allows the user to visualize and analyse pre-loaded single-cell data without requiring any programming ability. The source code is free to allow remote installation. On our web server, we demonstrated how to visualize and re-analyse published Plasmodium and Trypanosoma datasets. We have also generated Toxoplasma-mouse and Theileria-cow scRNA-seq datasets to highlight the functionality of paraCell for pathogen-host interaction. The analysis of the data highlights the impact of the host interferon-γ response and gene expression profiles associated with disease susceptibility by these intracellular parasites, respectively.
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Affiliation(s)
- Edward Agboraw
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
| | - William Haese-Hill
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
- MVLS SRF, Research Software Engineering, University of Glasgow, G12 8QQ Glasgow, United Kingdom
| | - Franziska Hentzschel
- Centre for Infectious Diseases, Heidelberg University Medical Faculty, 69120 Heidelberg, Germany
| | - Emma Briggs
- Institute for Immunology and Infection Research, School of Biological Sciences, University of Edinburgh, EH4 2JP Edinburgh, United Kingdom
| | - Dana Aghabi
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
| | - Anna Heawood
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
| | - Clare R Harding
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
| | - Brian Shiels
- School of Biodiversity, One Health & Veterinary Medicine, University of Glasgow, G61 1QH Glasgow, United Kingdom
| | - Kathryn Crouch
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
| | - Domenico Somma
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
| | - Thomas D Otto
- School of Infection & Immunity, University of Glasgow, G12 8TA Glasgow, United Kingdom
- LPHI, CNRS, INSERM, Université de Montpellier, 34090 Montpellier, France
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7
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Stier MT, Sewell AE, Mwizerwa EL, Sim CY, Tanner SM, Nichols CM, Durai HH, Jennings EQ, Lindau P, Wilfong EM, Newcomb DC, Bastarache JA, Ware LB, Rathmell JC. Metabolic Adaptations Rewire CD4 T Cells in a Subset-Specific Manner in Human Critical Illness with and without Sepsis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.01.27.635146. [PMID: 39975258 PMCID: PMC11838299 DOI: 10.1101/2025.01.27.635146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/21/2025]
Abstract
Host immunity in sepsis has features of hyperinflammation together with progressive immunosuppression, particularly among CD4 T cells, that can predispose to secondary infections and ineffectual organ recovery. Metabolic and immunologic dysfunction are archetypal findings in critically ill patients with sepsis, but whether these factors are mechanistically linked remains incompletely defined. We characterized functional metabolic properties of human CD4 T cells from critically ill patients with and without sepsis and healthy adults. CD4 T cells in critical illness showed increased subset-specific metabolic plasticity, with regulatory T cells (Tregs) acquiring glycolytic capacity that stabilized suppressive markers FOXP3 and TIGIT and correlated with clinical illness severity. Single-cell transcriptomics identified differential kynurenine metabolism in Tregs, which was validated ex vivo as a mechanism of Treg glycolytic adaptation and suppressive rewiring. These findings underscore immunometabolic dysfunction as a driver of CD4 T cell remodeling in sepsis and suggest therapeutic avenues to restore an effective immune response.
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Affiliation(s)
- Matthew T. Stier
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Allison E. Sewell
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Erin L. Mwizerwa
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Chooi Ying Sim
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Samantha M. Tanner
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Casey M. Nichols
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Heather H. Durai
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Erin Q. Jennings
- Division of Hematology and Oncology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Paul Lindau
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Erin M. Wilfong
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Division of Rheumatology and Immunology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dawn C. Newcomb
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Julie A. Bastarache
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, United States
| | - Lorraine B. Ware
- Division of Allergy, Pulmonary & Critical Care Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jeffrey C. Rathmell
- Vanderbilt Center for Immunobiology, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TN, United States
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8
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Frey Y, Haj M, Ziv Y, Elkon R, Shiloh Y. Broad repression of DNA repair genes in senescent cells identified by integration of transcriptomic data. Nucleic Acids Res 2025; 53:gkae1257. [PMID: 39739833 PMCID: PMC11724277 DOI: 10.1093/nar/gkae1257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 11/19/2024] [Accepted: 12/06/2024] [Indexed: 01/02/2025] Open
Abstract
Cellular senescence plays a significant role in tissue aging. Senescent cells, which resist apoptosis while remaining metabolically active, generate endogenous DNA-damaging agents, primarily reactive oxygen species. Efficient DNA repair is therefore crucial in these cells, especially when they undergo senescence escape, resuming DNA replication and cellular proliferation. To investigate whether senescent cell transcriptomes reflect adequate DNA repair capacity, we conducted a comprehensive meta-analysis of 60 transcriptomic datasets comparing senescent to proliferating cells. Our analysis revealed a striking downregulation of genes encoding essential components across DNA repair pathways in senescent cells. This includes pathways active in different cell cycle phases such as nucleotide excision repair, base excision repair, nonhomologous end joining and homologous recombination repair of double-strand breaks, mismatch repair and interstrand crosslink repair. The downregulation observed suggests a significant accumulation of DNA lesions. Experimental monitoring of DNA repair readouts in cells that underwent radiation-induced senescence supported this conclusion. This phenomenon was consistent across various senescence triggers and was also observed in primary cell lines from aging individuals. These findings highlight the potential of senescent cells as 'ticking bombs' in aging-related diseases and tumors recurring following therapy-induced senescence.
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Affiliation(s)
- Yann Frey
- The David and Inez Myers Laboratory for Cancer Research, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Majd Haj
- The David and Inez Myers Laboratory for Cancer Research, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yael Ziv
- The David and Inez Myers Laboratory for Cancer Research, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Ran Elkon
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
| | - Yosef Shiloh
- The David and Inez Myers Laboratory for Cancer Research, Tel Aviv University, Tel Aviv 6997801, Israel
- Department of Human Molecular Genetics and Biochemistry, School of Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
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9
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CZI Cell Science Program, Abdulla S, Aevermann B, Assis P, Badajoz S, Bell SM, Bezzi E, Cakir B, Chaffer J, Chambers S, Cherry J, Chi T, Chien J, Dorman L, Garcia-Nieto P, Gloria N, Hastie M, Hegeman D, Hilton J, Huang T, Infeld A, Istrate AM, Jelic I, Katsuya K, Kim YJ, Liang K, Lin M, Lombardo M, Marshall B, Martin B, McDade F, Megill C, Patel N, Predeus A, Raymor B, Robatmili B, Rogers D, Rutherford E, Sadgat D, Shin A, Small C, Smith T, Sridharan P, Tarashansky A, Tavares N, Thomas H, Tolopko A, Urisko M, Yan J, Yeretssian G, Zamanian J, Mani A, Cool J, Carr A. CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. Nucleic Acids Res 2025; 53:D886-D900. [PMID: 39607691 PMCID: PMC11701654 DOI: 10.1093/nar/gkae1142] [Citation(s) in RCA: 35] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Revised: 10/28/2024] [Accepted: 11/01/2024] [Indexed: 11/29/2024] Open
Abstract
Hundreds of millions of single cells have been analyzed using high-throughput transcriptomic methods. The cumulative knowledge within these datasets provides an exciting opportunity for unlocking insights into health and disease at the level of single cells. Meta-analyses that span diverse datasets building on recent advances in large language models and other machine-learning approaches pose exciting new directions to model and extract insight from single-cell data. Despite the promise of these and emerging analytical tools for analyzing large amounts of data, the sheer number of datasets, data models and accessibility remains a challenge. Here, we present CZ CELLxGENE Discover (cellxgene.cziscience.com), a data platform that provides curated and interoperable single-cell data. Available via a free-to-use online data portal, CZ CELLxGENE hosts a growing corpus of community-contributed data of over 93 million unique cells. Curated, standardized and associated with consistent cell-level metadata, this collection of single-cell transcriptomic data is the largest of its kind and growing rapidly via community contributions. A suite of tools and features enables accessibility and reusability of the data via both computational and visual interfaces to allow researchers to explore individual datasets, perform cross-corpus analysis, and run meta-analyses of tens of millions of cells across studies and tissues at the resolution of single cells.
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Affiliation(s)
| | - Shibla Abdulla
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Brian Aevermann
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Pedro Assis
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Seve Badajoz
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Sidney M Bell
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Emanuele Bezzi
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Batuhan Cakir
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Jim Chaffer
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Signe Chambers
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - J Michael Cherry
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Tiffany Chi
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Jennifer Chien
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Leah Dorman
- Chan Zuckerberg, Biohub, SF, 499 Illinois St, San Francisco, CA 94158, USA
| | - Pablo Garcia-Nieto
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Nayib Gloria
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Mim Hastie
- Clever Canary, 850 Front St. #1491, Santa Cruz, CA, USA
| | - Daniel Hegeman
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Jason Hilton
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Timmy Huang
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Amanda Infeld
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Ana-Maria Istrate
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Ivana Jelic
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Kuni Katsuya
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Yang Joon Kim
- Chan Zuckerberg, Biohub, SF, 499 Illinois St, San Francisco, CA 94158, USA
| | - Karen Liang
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Mike Lin
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | | | - Bailey Marshall
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Bruce Martin
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Fran McDade
- Clever Canary, 850 Front St. #1491, Santa Cruz, CA, USA
| | - Colin Megill
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Nikhil Patel
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Alexander Predeus
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton CB10 1SA, UK
| | - Brian Raymor
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Behnam Robatmili
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Dave Rogers
- Clever Canary, 850 Front St. #1491, Santa Cruz, CA, USA
| | - Erica Rutherford
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Dana Sadgat
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Andrew Shin
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Corinn Small
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Trent Smith
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Prathap Sridharan
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | | | - Norbert Tavares
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Harley Thomas
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Andrew Tolopko
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Meghan Urisko
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Joyce Yan
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Garabet Yeretssian
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Jennifer Zamanian
- Department of Genetics, Stanford University School of Medicine, 291 Campus Drive, Li Ka Shing Building, Stanford, CA 94305, USA
| | - Arathi Mani
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Jonah Cool
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
| | - Ambrose Carr
- Chan Zuckerberg Initiative, 1180 Main Street, Redwood City, CA 94063, USA
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10
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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 2025; 68:5-102. [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] [MESH Headings] [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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11
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Chehimi SN. Dissection of Gene Expression at the Single-Cell Level: scRNA-seq. Methods Mol Biol 2025; 2866:159-173. [PMID: 39546202 DOI: 10.1007/978-1-0716-4192-7_9] [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] [Indexed: 11/17/2024]
Abstract
Sequencing approaches that allowed for a better resolution of the transcriptome have been a major goal in the transcriptomics field since the development of RNA-seq techniques. While RNA-seq provides gene expression data from one entire sample in bulk, single-cell analysis allows for a better characterization of gene expression associated to specific cell types. Single-cell RNA-seq (scRNA-seq) is a reliable technique to unravel transcriptomic features of the tissues of interest dissociated at a single-cell level. The main feature of the single-cell technique is its ability to generate barcoded individual cells that allow for tracking the origin of thousands to millions of transcripts and reveal new cell types associated to diseases and different cell types and states. In this chapter, we discuss how scRNA-seq has become the gold standard to deepen the understanding of the gene expression with single-cell resolution.
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12
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Wang K, Gong Y, Yan Z, Dang Z, Wang J, Wu M, Zhang Y. Protocol for analyzing functional gene module perturbation during the progression of diseases using a single-cell Bayesian biclustering framework. STAR Protoc 2024; 5:103349. [PMID: 39352811 PMCID: PMC11472622 DOI: 10.1016/j.xpro.2024.103349] [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: 05/19/2024] [Revised: 07/26/2024] [Accepted: 09/06/2024] [Indexed: 10/04/2024] Open
Abstract
The pathogenesis of complex diseases involves intricate gene regulation across cell types, necessitating a comprehensive analysis approach. Here, we present a protocol for analyzing functional gene module (FGM) perturbation during the progression of diseases using a single-cell Bayesian biclustering (scBC) framework. We describe steps for setting up the scBC workspace, preparing and exploring input data, training the model, and reconstructing the data matrix. We then detail procedures for Bayesian biclustering, exploring biclustering results, and uncovering pathway perturbations. For complete details on the use and execution of this protocol, please refer to Gong et al.1.
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Affiliation(s)
- Kunyue Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Yuqiao Gong
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Zixin Yan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Zhiyuan Dang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Junhao Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China
| | - Maoying Wu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China.
| | - Yue Zhang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, 800 Dongchuan Road, Minhang District, Shanghai 200240, China; SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China; Center for Biomedical Data Science, Translational Science Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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13
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Zhang H, Zhang W, Zhao S, Xu G, Shen Y, Jiang F, Qin A, Cui L. easySCF: a tool for enhancing interoperability between R and Python for efficient single-cell data analysis. Bioinformatics 2024; 40:btae710. [PMID: 39585309 PMCID: PMC11634540 DOI: 10.1093/bioinformatics/btae710] [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: 08/29/2024] [Revised: 11/08/2024] [Accepted: 11/22/2024] [Indexed: 11/26/2024] Open
Abstract
SUMMARY This study introduces easySCF, a tool designed to enhance the interoperability of single-cell data between the two major bioinformatics platforms, R and Python. By supporting seamless data exchange, easySCF improves the efficiency and accuracy of single-cell data analysis. AVAILABILITY AND IMPLEMENTATION easySCF utilizes a unified data format (.h5 format) to facilitate data transfer between R and Python platforms. The tool has been evaluated for data processing speed, memory efficiency, and disk usage, as well as its capability to handle large-scale single-cell datasets. easySCF is available as an open-source package, with implementation details and documentation accessible at https://github.com/xleizi/easySCF.
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Affiliation(s)
- Haoyun Zhang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Wentao Zhang
- School of Mathematical Sciences, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Shuai Zhao
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, 530021, China
| | - Guangyu Xu
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, 530021, China
| | - Yi Shen
- Jiangsu Key Laboratory of Marine Pharmaceutical Compound Screening, College of Pharmacy, Jiangsu Ocean University, Lianyungang 222005, China
| | - Feng Jiang
- Shanghai Research and Development Center, UxBioInfo, Shanghai, 201100, China
| | - An Qin
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125, China
| | - Lei Cui
- Collaborative Innovation Centre of Regenerative Medicine and Medical BioResource Development and Application Co-constructed by the Province and Ministry, Guangxi Medical University, Nanning, 530021, China
- Shanghai Key Laboratory of Orthopaedic Implants, Department of Orthopaedics, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200125, China
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14
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Nazaret A, Fan JL, Lavallée VP, Burdziak C, Cornish AE, Kiseliovas V, Bowman RL, Masilionis I, Chun J, Eisman SE, Wang J, Hong J, Shi L, Levine RL, Mazutis L, Blei D, Pe’er D, Azizi E. Joint representation and visualization of derailed cell states with Decipher. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.11.566719. [PMID: 38014231 PMCID: PMC10680623 DOI: 10.1101/2023.11.11.566719] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Biological insights often depend on comparing conditions such as disease and health, yet we lack effective computational tools for integrating single-cell genomics data across conditions or characterizing transitions from normal to deviant cell states. Here, we present Decipher, a deep generative model that characterizes derailed cell-state trajectories. Decipher jointly models and visualizes gene expression and cell state from normal and perturbed single-cell RNA-seq data, revealing shared and disrupted dynamics. We demonstrate its superior performance across diverse contexts, including in pancreatitis with oncogene mutation, acute myeloid leukemia, and gastric cancer.
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Affiliation(s)
- Achille Nazaret
- Department of Computer Science, Columbia University, New York, NY 10027, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
| | - Joy Linyue Fan
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
| | - Vincent-Philippe Lavallée
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Centre Hospitalier Universitaire Sainte-Justine Research Center, Montréal, QC, Canada
- Department of Pediatrics, Université de Montréal, Montréal, QC, Canada
| | - Cassandra Burdziak
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Andrew E. Cornish
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Immunology Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Vaidotas Kiseliovas
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Robert L. Bowman
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of Cancer Biology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ignas Masilionis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jaeyoung Chun
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Shira E. Eisman
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - James Wang
- Department of Computer Science, Columbia University, New York, NY 10027, USA
| | - Justin Hong
- Department of Computer Science, Columbia University, New York, NY 10027, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
| | - Lingting Shi
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
| | - Ross L. Levine
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Linas Mazutis
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Alan and Sandra Gerry Metastasis and Tumor Ecosystems Center, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Institute of Biotechnology Vilnius University, Life Sciences Centre, Vilnius 02158, Lithuania
| | - David Blei
- Department of Computer Science, Columbia University, New York, NY 10027, USA
- Department of Statistics, Columbia University, New York, NY 10027, USA
- Data Science Institute, Columbia University, New York, NY 10027, USA
| | - Dana Pe’er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York 10027, NY 10065, USA
| | - Elham Azizi
- Department of Computer Science, Columbia University, New York, NY 10027, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, USA
- Department of Biomedical Engineering, Columbia University, New York, NY 10027, USA
- Data Science Institute, Columbia University, New York, NY 10027, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, USA
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15
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Seege M, Schöls E, Barquist L. s CIRCLE-An interactive visual exploration tool for single cell RNA-Seq data. NAR Genom Bioinform 2024; 6:lqae084. [PMID: 39022325 PMCID: PMC11252841 DOI: 10.1093/nargab/lqae084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 06/17/2024] [Accepted: 07/04/2024] [Indexed: 07/20/2024] Open
Abstract
sCIRCLE (single-Cell Interactive Real-time Computer visualization for Low-dimensional Exploration) is a tool for exploratory analysis of single cell RNA-seq (scRNA-seq) data sets, with a focus on bacterial scRNA-seq. The software takes an information design perspective to re-envision visually and interactively exploring low dimensional representations of scRNA-Seq data. Users can project cells in various 3D and 2D spaces and interactively query and paint cells using rich metadata sets reporting on cell cluster, gene function, and gene expression. As a standalone application it contains, among other features, options for dimensionality reduction, navigation and interaction with data in 3d and 2d space, gene filtering, fold change and metacell computation as well as various capabilities for visualization, data import and export.
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Affiliation(s)
- Maximilian Seege
- Technical University of Applied Sciences Würzburg-Schweinfurt, Faculty of Design, 97070 Würzburg, Germany
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection Research, 97080 Würzburg, Germany
| | - Erich Schöls
- Technical University of Applied Sciences Würzburg-Schweinfurt, Faculty of Design, 97070 Würzburg, Germany
| | - Lars Barquist
- Helmholtz Institute for RNA-based Infection Research, Helmholtz Centre for Infection Research, 97080 Würzburg, Germany
- University of Würzburg, Faculty of Medicine, 97080 Würzburg, Germany
- Department of Biology, University of Toronto Mississauga, Mississauga, ON, L5L 1C6, Canada
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16
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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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17
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Waichman TV, Vercesi ML, Berardino AA, Beckel MS, Giacomini D, Rasetto NB, Herrero M, Di Bella DJ, Arlotta P, Schinder AF, Chernomoretz A. scX: A user-friendly tool for scRNA-seq exploration. ARXIV 2024:arXiv:2311.00012v2. [PMID: 37961742 PMCID: PMC10635291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an interactive graphic interface, implemented as a web application, scX provides easy access to key scRNAseq analyses, including marker identification, gene expression profiling, and differential gene expression analysis. Additionally, scX seamlessly integrates with commonly used single-cell Seurat and Single-CellExperiment R objects, resulting in efficient processing and visualization of varied datasets. Overall, scX serves as a valuable and user-friendly tool for effortless exploration and sharing of single-cell data, simplifying some of the complexities inherent in scRNAseq analysis.
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Affiliation(s)
- Tomás Vega Waichman
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
| | - M Luz Vercesi
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
| | - Ariel A Berardino
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
- Instituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina
| | - Maximiliano S Beckel
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
- Instituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina
| | - Damiana Giacomini
- Instituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
| | - Natalí B Rasetto
- Instituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
| | - Magalí Herrero
- Instituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
| | - Daniela J Di Bella
- Dept. of Stem Cells and Regenerative Biology, Harvard University & Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Paola Arlotta
- Dept. of Stem Cells and Regenerative Biology, Harvard University & Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alejandro F Schinder
- Instituto de Investigaciones Bioquímicas de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, C1425 FQB, Argentina
- Laboratory of Neuronal Plasticity, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
| | - Ariel Chernomoretz
- Integrative Systems Biology Lab, Leloir Institute, Buenos Aires, C1405 BWE, Argentina
- Departamento de Física, Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Instituto de Física de Buenos Aires, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
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18
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Xia L, Lee C, Li JJ. Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. Nat Commun 2024; 15:1753. [PMID: 38409103 PMCID: PMC10897166 DOI: 10.1038/s41467-024-45891-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: 04/10/2023] [Accepted: 02/06/2024] [Indexed: 02/28/2024] Open
Abstract
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
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Affiliation(s)
- Lucy Xia
- Department of ISOM, School of Business and Management, Hong Kong University of Science and Technology, Clear Water Bay, Hong Kong, China
| | - Christy Lee
- Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Radcliffe Institute of Advanced Study, Harvard University, Cambridge, MA, USA.
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19
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Kondoh H. Multiple Cell Lineages Give Rise to a Cell Type. Results Probl Cell Differ 2024; 72:83-104. [PMID: 38509253 DOI: 10.1007/978-3-031-39027-2_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
Abstract
It has long been assumed that a specific cell type arises following stepwise specification of cells corresponding to the branching of cell lineages. However, accumulating evidence indicates that multiple and even remote cell lineages can lead to the development of the same cells. Four examples giving different yet new insights will be discussed: skeletal muscle development from precursors with distinct initial histories of transcriptional regulation, lens cell development from remote lineages yet sharing basic transcription factors, blood cell development under intersectional pathways, and neural tissue development from cardiac precursors through the manipulation of just one component of epigenetic regulation. These examples provide flexible and nondogmatic perspectives on developmental cell regulation, fundamentally revising the old model relying on cell lineages.
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Affiliation(s)
- Hisato Kondoh
- Osaka University, Suita, Osaka, Japan
- Biohistory Research Hall, Takatsuki, Osaka, Japan
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20
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Xia L, Lee C, Li JJ. scDEED: a statistical method for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.21.537839. [PMID: 37163087 PMCID: PMC10168265 DOI: 10.1101/2023.04.21.537839] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-SNE and UMAP are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embedding might not reliably inform the similarities among cell clusters. Motivated by this challenge, we developed a statistical method, scDEED, for detecting dubious cell embeddings output by any 2D-embedding method. By calculating a reliability score for every cell embedding, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. Applied to multiple scRNA-seq datasets, scDEED demonstrates its effectiveness for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
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21
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Zilbauer M, James KR, Kaur M, Pott S, Li Z, Burger A, Thiagarajah JR, Burclaff J, Jahnsen FL, Perrone F, Ross AD, Matteoli G, Stakenborg N, Sujino T, Moor A, Bartolome-Casado R, Bækkevold ES, Zhou R, Xie B, Lau KS, Din S, Magness ST, Yao Q, Beyaz S, Arends M, Denadai-Souza A, Coburn LA, Gaublomme JT, Baldock R, Papatheodorou I, Ordovas-Montanes J, Boeckxstaens G, Hupalowska A, Teichmann SA, Regev A, Xavier RJ, Simmons A, Snyder MP, Wilson KT. A Roadmap for the Human Gut Cell Atlas. Nat Rev Gastroenterol Hepatol 2023; 20:597-614. [PMID: 37258747 PMCID: PMC10527367 DOI: 10.1038/s41575-023-00784-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 04/14/2023] [Indexed: 06/02/2023]
Abstract
The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human Cell Atlas. Based on the consensus opinion of experts from across the globe, we outline the main requirements for the first complete HGCA by summarizing existing data sets and highlighting anatomical regions and/or tissues with limited coverage. We provide recommendations for future studies and discuss key methodologies and the importance of integrating the healthy gut atlas with related diseases and gut organoids. Importantly, we critically overview the computational tools available and provide recommendations to overcome key challenges.
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Affiliation(s)
- Matthias Zilbauer
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK.
- University Department of Paediatrics, University of Cambridge, Cambridge, UK.
- Department of Paediatric Gastroenterology, Hepatology and Nutrition, Cambridge University Hospitals, Cambridge, UK.
| | - Kylie R James
- Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
- School of Biomedical Sciences, University of New South Wales, Sydney, NSW, Australia
| | - Mandeep Kaur
- School of Molecular and Cell Biology, University of the Witwatersrand, Johannesburg, South Africa
| | - Sebastian Pott
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Zhixin Li
- Dana-Farber Cancer Institute, Boston, MA, USA
| | - Albert Burger
- Department of Computer Science, Heriot-watt University, Edinburgh, UK
| | - Jay R Thiagarajah
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph Burclaff
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Frode L Jahnsen
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Francesca Perrone
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Alexander D Ross
- Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
- University Department of Paediatrics, University of Cambridge, Cambridge, UK
- University Department of Medical Genetics, University of Cambridge, Cambridge, UK
| | - Gianluca Matteoli
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Nathalie Stakenborg
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Tomohisa Sujino
- Center for the Diagnostic and Therapeutic Endoscopy, School of Medicine, Keio University, Tokyo, Japan
| | - Andreas Moor
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland
| | - Raquel Bartolome-Casado
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
- Wellcome Sanger Institute, Hinxton, UK
| | - Espen S Bækkevold
- Department of Pathology, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Ran Zhou
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Bingqing Xie
- Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Ken S Lau
- Epithelial Biology Center and Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Shahida Din
- Edinburgh IBD Unit, Western General Hospital, NHS Lothian, Edinburgh, UK
| | - Scott T Magness
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University', Chapel Hill, NC, USA
- Center for Gastrointestinal Biology and Disease, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Qiuming Yao
- Department of Computer Science and Engineering, University of Nebraska Lincoln, Lincoln, NE, USA
| | - Semir Beyaz
- Cold Spring Harbour Laboratory, Cold Spring Harbour, New York, NY, USA
| | - Mark Arends
- Division of Pathology, Centre for Comparative Pathology, Cancer Research UK Edinburgh Centre, Institute of Cancer and Genetics, University of Edinburgh, Edinburgh, UK
| | - Alexandre Denadai-Souza
- Laboratory of Mucosal Biology, Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | - Lori A Coburn
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
| | | | | | - Irene Papatheodorou
- European Molecular Biology Laboratory, European Bioinformatics Institute, EMBL-EBI, Wellcome Genome Campus, Hinxton, UK
| | - Jose Ordovas-Montanes
- Division of Gastroenterology, Hepatology and Nutrition, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Guy Boeckxstaens
- Translational Research Center for Gastrointestinal Disorders (TARGID), Department of Chronic Diseases, Metabolism and Ageing, KU Leuven, Leuven, Belgium
| | | | - Sarah A Teichmann
- Wellcome Sanger Institute, Hinxton, UK
- Theory of Condensed Matter Group, Cavendish Laboratory/Department of Physics, University of Cambridge, Cambridge, UK
| | - Aviv Regev
- Genentech, San Francisco, CA, USA
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Ramnik J Xavier
- Broad Institute and Department of Molecular Biology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Alison Simmons
- MRC Human Immunology Unit, MRC Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK
| | | | - Keith T Wilson
- Vanderbilt University Medical Center, Nashville, TN, USA
- Veterans Affairs Tennessee Valley Healthcare System, Nashville, TN, USA
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22
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Daniels RR, Taylor RS, Robledo D, Macqueen DJ. Single cell genomics as a transformative approach for aquaculture research and innovation. REVIEWS IN AQUACULTURE 2023; 15:1618-1637. [PMID: 38505116 PMCID: PMC10946576 DOI: 10.1111/raq.12806] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Revised: 02/16/2023] [Accepted: 02/16/2023] [Indexed: 03/21/2024]
Abstract
Single cell genomics encompasses a suite of rapidly maturing technologies that measure the molecular profiles of individual cells within target samples. These approaches provide a large up-step in biological information compared to long-established 'bulk' methods that profile the average molecular profiles of all cells in a sample, and have led to transformative advances in understanding of cellular biology, particularly in humans and model organisms. The application of single cell genomics is fast expanding to non-model taxa, including aquaculture species, where numerous research applications are underway with many more envisaged. In this review, we highlight the potential transformative applications of single cell genomics in aquaculture research, considering barriers and potential solutions to the broad uptake of these technologies. Focusing on single cell transcriptomics, we outline considerations for experimental design, including the essential requirement to obtain high quality cells/nuclei for sequencing in ectothermic aquatic species. We further outline data analysis and bioinformatics considerations, tailored to studies with the under-characterized genomes of aquaculture species, where our knowledge of cellular heterogeneity and cell marker genes is immature. Overall, this review offers a useful source of knowledge for researchers aiming to apply single cell genomics to address biological challenges faced by the global aquaculture sector though an improved understanding of cell biology.
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Affiliation(s)
- Rose Ruiz Daniels
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| | - Richard S. Taylor
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| | - Diego Robledo
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
| | - Daniel J. Macqueen
- The Roslin Institute and Royal (Dick) School of Veterinary StudiesThe University of EdinburghMidlothianUK
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23
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Lim J, Chin V, Fairfax K, Moutinho C, Suan D, Ji H, Powell JE. Transitioning single-cell genomics into the clinic. Nat Rev Genet 2023:10.1038/s41576-023-00613-w. [PMID: 37258725 DOI: 10.1038/s41576-023-00613-w] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2023] [Indexed: 06/02/2023]
Abstract
The use of genomics is firmly established in clinical practice, resulting in innovations across a wide range of disciplines such as genetic screening, rare disease diagnosis and molecularly guided therapy choice. This new field of genomic medicine has led to improvements in patient outcomes. However, most clinical applications of genomics rely on information generated from bulk approaches, which do not directly capture the genomic variation that underlies cellular heterogeneity. With the advent of single-cell technologies, research is rapidly uncovering how genomic data at cellular resolution can be used to understand disease pathology and mechanisms. Both DNA-based and RNA-based single-cell technologies have the potential to improve existing clinical applications and open new application spaces for genomics in clinical practice, with oncology, immunology and haematology poised for initial adoption. However, challenges in translating cellular genomics from research to a clinical setting must first be overcome.
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Affiliation(s)
- Jennifer Lim
- Cellular Science, Garvan Institute of Medical Research, Sydney, NSW, Australia
- Department of Oncology, St George Hospital, Sydney, NSW, Australia
- The Kinghorn Cancer Centre, St Vincent's Hospital, Sydney, NSW, Australia
- Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Venessa Chin
- Cellular Science, Garvan Institute of Medical Research, Sydney, NSW, Australia
- The Kinghorn Cancer Centre, St Vincent's Hospital, Sydney, NSW, Australia
- Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - Kirsten Fairfax
- School of Medicine, University of Tasmania, Hobart, Australia
| | - Catia Moutinho
- Cellular Science, Garvan Institute of Medical Research, Sydney, NSW, Australia
| | - Dan Suan
- Cellular Science, Garvan Institute of Medical Research, Sydney, NSW, Australia
- Westmead Clinical School, University of Sydney, Sydney, NSW, Australia
| | - Hanlee Ji
- School of Medicine, Stanford University, Palo Alto, CA, USA
- Stanford Genome Technology Center, Stanford University, Palo Alto, CA, USA
| | - Joseph E Powell
- Cellular Science, Garvan Institute of Medical Research, Sydney, NSW, Australia.
- Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
- UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia.
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24
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Patil AR, Kumar G, Zhou H, Warren L. scViewer: An Interactive Single-Cell Gene Expression Visualization Tool. Cells 2023; 12:1489. [PMID: 37296611 PMCID: PMC10253102 DOI: 10.3390/cells12111489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/09/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023] Open
Abstract
Single-cell RNA sequencing (scRNA-seq) is an attractive technology for researchers to gain valuable insights into the cellular processes and cell type diversity present in all tissues. The data generated by the scRNA-seq experiment are high-dimensional and complex in nature. Several tools are now available to analyze the raw scRNA-seq data from public databases; however, simple and easy-to-explore single-cell gene expression visualization tools focusing on differential expression and co-expression are lacking. Here, we present scViewer, an interactive graphical user interface (GUI) R/Shiny application designed to facilitate the visualization of scRNA-seq gene expression data. With the processed Seurat RDS object as input, scViewer utilizes several statistical approaches to provide detailed information on the loaded scRNA-seq experiment and generates publication-ready plots. The major functionalities of scViewer include exploring cell-type-specific gene expression, co-expression analysis of two genes, and differential expression analysis with different biological conditions considering both cell-level and subject-level variations using negative binomial mixed modeling. We utilized a publicly available dataset (brain cells from a study of Alzheimer's disease to demonstrate the utility of our tool. scViewer can be downloaded from GitHub as a Shiny app with local installation. Overall, scViewer is a user-friendly application that will allow researchers to visualize and interpret the scRNA-seq data efficiently for multi-condition comparison by performing gene-level differential expression and co-expression analysis on the fly. Considering the functionalities of this Shiny app, scViewer can be a great resource for collaboration between bioinformaticians and wet lab scientists for faster data visualizations.
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Affiliation(s)
- Abhijeet R. Patil
- Global Statistical and Data Sciences, Teva Pharmaceuticals, West Chester, PA 19380, USA
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25
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Li K, Sun YH, Ouyang Z, Negi S, Gao Z, Zhu J, Wang W, Chen Y, Piya S, Hu W, Zavodszky MI, Yalamanchili H, Cao S, Gehrke A, Sheehan M, Huh D, Casey F, Zhang X, Zhang B. scRNASequest: an ecosystem of scRNA-seq analysis, visualization, and publishing. BMC Genomics 2023; 24:228. [PMID: 37131143 PMCID: PMC10155351 DOI: 10.1186/s12864-023-09332-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/25/2023] [Indexed: 05/04/2023] Open
Abstract
BACKGROUND Single-cell RNA sequencing is a state-of-the-art technology to understand gene expression in complex tissues. With the growing amount of data being generated, the standardization and automation of data analysis are critical to generating hypotheses and discovering biological insights. RESULTS Here, we present scRNASequest, a semi-automated single-cell RNA-seq (scRNA-seq) data analysis workflow which allows (1) preprocessing from raw UMI count data, (2) harmonization by one or multiple methods, (3) reference-dataset-based cell type label transfer and embedding projection, (4) multi-sample, multi-condition single-cell level differential gene expression analysis, and (5) seamless integration with cellxgene VIP for visualization and with CellDepot for data hosting and sharing by generating compatible h5ad files. CONCLUSIONS We developed scRNASequest, an end-to-end pipeline for single-cell RNA-seq data analysis, visualization, and publishing. The source code under MIT open-source license is provided at https://github.com/interactivereport/scRNASequest . We also prepared a bookdown tutorial for the installation and detailed usage of the pipeline: https://interactivereport.github.io/scRNAsequest/tutorial/docs/ . Users have the option to run it on a local computer with a Linux/Unix system including MacOS, or interact with SGE/Slurm schedulers on high-performance computing (HPC) clusters.
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Affiliation(s)
- Kejie Li
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yu H Sun
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | | | - Soumya Negi
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Zhen Gao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Jing Zhu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wanli Wang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Yirui Chen
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Sarbottam Piya
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Wenxing Hu
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Maria I Zavodszky
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Hima Yalamanchili
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Shaolong Cao
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Andrew Gehrke
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Mark Sheehan
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Dann Huh
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Fergal Casey
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA
| | - Xinmin Zhang
- Data Science, BioInfoRx Inc., Madison, WI, 53719, USA
| | - Baohong Zhang
- Research Data Sciences, Translational Biology, Biogen Inc., Cambridge, MA, 02142, USA.
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Choi Y, Li R, Quon G. siVAE: interpretable deep generative models for single-cell transcriptomes. Genome Biol 2023; 24:29. [PMID: 36803416 PMCID: PMC9940350 DOI: 10.1186/s13059-023-02850-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 01/06/2023] [Indexed: 02/22/2023] Open
Abstract
Neural networks such as variational autoencoders (VAE) perform dimensionality reduction for the visualization and analysis of genomic data, but are limited in their interpretability: it is unknown which data features are represented by each embedding dimension. We present siVAE, a VAE that is interpretable by design, thereby enhancing downstream analysis tasks. Through interpretation, siVAE also identifies gene modules and hubs without explicit gene network inference. We use siVAE to identify gene modules whose connectivity is associated with diverse phenotypes such as iPSC neuronal differentiation efficiency and dementia, showcasing the wide applicability of interpretable generative models for genomic data analysis.
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Affiliation(s)
- Yongin Choi
- Graduate Group in Biomedical Engineering, University of California, Davis, Davis, CA, USA
- Genome Center, University of California, Davis, Davis, CA, USA
| | - Ruoxin Li
- Genome Center, University of California, Davis, Davis, CA, USA
- Graduate Group in Biostatistics, University of California, Davis, Davis, CA, USA
| | - Gerald Quon
- Graduate Group in Biomedical Engineering, University of California, Davis, Davis, CA, USA.
- Genome Center, University of California, Davis, Davis, CA, USA.
- Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA, USA.
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Cheng F, Keller MS, Qu H, Gehlenborg N, Wang Q. Polyphony: an Interactive Transfer Learning Framework for Single-Cell Data Analysis. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:591-601. [PMID: 36155452 PMCID: PMC10039961 DOI: 10.1109/tvcg.2022.3209408] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Reference-based cell-type annotation can significantly reduce time and effort in single-cell analysis by transferring labels from a previously-annotated dataset to a new dataset. However, label transfer by end-to-end computational methods is challenging due to the entanglement of technical (e.g., from different sequencing batches or techniques) and biological (e.g., from different cellular microenvironments) variations, only the first of which must be removed. To address this issue, we propose Polyphony, an interactive transfer learning (ITL) framework, to complement biologists' knowledge with advanced computational methods. Polyphony is motivated and guided by domain experts' needs for a controllable, interactive, and algorithm-assisted annotation process, identified through interviews with seven biologists. We introduce anchors, i.e., analogous cell populations across datasets, as a paradigm to explain the computational process and collect user feedback for model improvement. We further design a set of visualizations and interactions to empower users to add, delete, or modify anchors, resulting in refined cell type annotations. The effectiveness of this approach is demonstrated through quantitative experiments, two hypothetical use cases, and interviews with two biologists. The results show that our anchor-based ITL method takes advantage of both human and machine intelligence in annotating massive single-cell datasets.
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Chuwdhury GS, Ng IOL, Ho DWH. scAnalyzeR: A Comprehensive Software Package With Graphical User Interface for Single-Cell RNA Sequencing Analysis and its Application on Liver Cancer. Technol Cancer Res Treat 2022; 21:15330338221142729. [PMID: 36476060 PMCID: PMC9742707 DOI: 10.1177/15330338221142729] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Introduction: The application of single-cell RNA sequencing to delineate tissue heterogeneity and complexity has become increasingly popular. Given its tremendous resolution and high-dimensional capacity for in-depth investigation, single-cell RNA sequencing offers an unprecedented research power. Although some popular software packages are available for single-cell RNA sequencing data analysis and visualization, it is still a big challenge for their usage, as they provide only a command-line interface and require significant level of bioinformatics skills. Methods: We have developed scAnalyzeR, which is a single-cell RNA sequencing analysis pipeline with an interactive and user-friendly graphical interface for analyzing and visualizing single-cell RNA sequencing data. It accepts single-cell RNA sequencing data from various technology platforms and different model organisms (human and mouse) and allows flexibility in input file format. It provides functionalities for data preprocessing, quality control, basic summary statistics, dimension reduction, unsupervised clustering, differential gene expression, gene set enrichment analysis, correlation analysis, pseudotime cell trajectory inference, and various visualization plots. It also provides default parameters for easy usage and allows a wide range of flexibility and optimization by accepting user-defined options. It has been developed as a docker image that can be run in any docker-supported environment including Linux, Mac, and Windows, without installing any dependencies. Results: We compared the performance of scAnalyzeR with 2 other graphical tools that are popular for analyzing single-cell RNA sequencing data. The comparison was based on the comprehensiveness of functionalities, ease of usage and flexibility, and execution time. In general, scAnalyzeR outperformed the other tested counterparts in various aspects, demonstrating its superior overall performance. To illustrate the usefulness of scAnalyzeR in cancer research, we have analyzed the in-house liver cancer single-cell RNA sequencing dataset. Liver cancer tumor cells were revealed to have multiple subpopulations with distinctive gene expression signatures. Conclusion: scAnalyzeR has comprehensive functionalities and demonstrated usability. We anticipate more functionalities to be adopted in the future development.
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Affiliation(s)
- GS Chuwdhury
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
| | - Irene Oi-Lin Ng
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong
| | - Daniel Wai-Hung Ho
- Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong,Daniel Ho, Department of Pathology and State Key Laboratory of Liver Research, The University of Hong Kong, Hong Kong.
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29
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Prieto C, Barrios D, Villaverde A. SingleCAnalyzer: Interactive Analysis of Single Cell RNA-Seq Data on the Cloud. FRONTIERS IN BIOINFORMATICS 2022; 2:793309. [PMID: 36304292 PMCID: PMC9580930 DOI: 10.3389/fbinf.2022.793309] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 05/09/2022] [Indexed: 12/13/2022] Open
Abstract
Single-cell RNA sequencing (scRNA-Seq) enables researchers to quantify the transcriptomes of individual cells. The capacity of researchers to perform this type of analysis has allowed researchers to undertake new scientific goals. The usefulness of scRNA-Seq has depended on the development of new computational biology methods, which have been designed to meeting challenges associated with scRNA-Seq analysis. However, the proper application of these computational methods requires extensive bioinformatics expertise. Otherwise, it is often difficult to obtain reliable and reproducible results. We have developed SingleCAnalyzer, a cloud platform that provides a means to perform full scRNA-Seq analysis from FASTQ within an easy-to-use and self-exploratory web interface. Its analysis pipeline includes the demultiplexing and alignment of FASTQ files, read trimming, sample quality control, feature selection, empty droplets detection, dimensional reduction, cellular type prediction, unsupervised clustering of cells, pseudotime/trajectory analysis, expression comparisons between groups, functional enrichment of differentially expressed genes and gene set expression analysis. Results are presented with interactive graphs, which provide exploratory and analytical features. SingleCAnalyzer is freely available at https://singleCAnalyzer.eu.
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Cai G, Yu X, Youn C, Zhou J, Xiao F. SCANNER: a web platform for annotation, visualization and sharing of single cell RNA-seq data. Database (Oxford) 2022; 2022:6520818. [PMID: 35134150 PMCID: PMC9246089 DOI: 10.1093/database/baab086] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/01/2021] [Accepted: 12/27/2021] [Indexed: 01/07/2023]
Abstract
In recent years, efficient scRNA-seq methods have been developed, enabling the transcriptome profiling of single cells massively in parallel. Meanwhile, its high dimensionality and complexity bring challenges to the data analysis and require extensive collaborations between biologists and bioinformaticians and/or biostatisticians. The communication between these two units demands a platform for easy data sharing and exploration. Here we developed Single-Cell Transcriptomics Annotated Viewer (SCANNER), as a public web resource for the scientific community, for sharing and analyzing scRNA-seq data in a collaborative manner. It is easy-to-use without requiring special software or extensive coding skills. Moreover, it equipped a real-time database for secure data management and enables an efficient investigation of the activation of gene sets on a single-cell basis. Currently, SCANNER hosts a database of 19 types of cancers and COVID-19, as well as healthy samples from lungs of smokers and non-smokers, human brain cells and peripheral blood mononuclear cells (PBMC). The database will be frequently updated with datasets from new studies. Using SCANNER, we identified a larger proportion of cancer-associated fibroblasts cells and more active fibroblast growth-related genes in melanoma tissues in female patients compared to male patients. Moreover, we found ACE2 is mainly expressed in lung pneumocytes, secretory cells and ciliated cells and differentially expressed in lungs of smokers and never smokers.
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Affiliation(s)
- Guoshuai Cai
- *Correspondence may also be addressed to Guoshuai Cai. Tel: +803-777-4120; Fax: +803-777-3391; and Feifei Xiao. Tel: +803-777-8936; Fax: +803-777-2524;
| | - Xuanxuan Yu
- Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC 29208, USA
| | - Choonhan Youn
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, USA
| | - Jun Zhou
- Research Computing Group, University of South Carolina, Columbia, SC 29208, USA
| | - Feifei Xiao
- *Correspondence may also be addressed to Guoshuai Cai. Tel: +803-777-4120; Fax: +803-777-3391; and Feifei Xiao. Tel: +803-777-8936; Fax: +803-777-2524;
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31
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Elorbany R, Popp JM, Rhodes K, Strober BJ, Barr K, Qi G, Gilad Y, Battle A. Single-cell sequencing reveals lineage-specific dynamic genetic regulation of gene expression during human cardiomyocyte differentiation. PLoS Genet 2022; 18:e1009666. [PMID: 35061661 PMCID: PMC8809621 DOI: 10.1371/journal.pgen.1009666] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 02/02/2022] [Accepted: 12/21/2021] [Indexed: 12/13/2022] Open
Abstract
Dynamic and temporally specific gene regulatory changes may underlie unexplained genetic associations with complex disease. During a dynamic process such as cellular differentiation, the overall cell type composition of a tissue (or an in vitro culture) and the gene regulatory profile of each cell can both experience significant changes over time. To identify these dynamic effects in high resolution, we collected single-cell RNA-sequencing data over a differentiation time course from induced pluripotent stem cells to cardiomyocytes, sampled at 7 unique time points in 19 human cell lines. We employed a flexible approach to map dynamic eQTLs whose effects vary significantly over the course of bifurcating differentiation trajectories, including many whose effects are specific to one of these two lineages. Our study design allowed us to distinguish true dynamic eQTLs affecting a specific cell lineage from expression changes driven by potentially non-genetic differences between cell lines such as cell composition. Additionally, we used the cell type profiles learned from single-cell data to deconvolve and re-analyze data from matched bulk RNA-seq samples. Using this approach, we were able to identify a large number of novel dynamic eQTLs in single cell data while also attributing dynamic effects in bulk to a particular lineage. Overall, we found that using single cell data to uncover dynamic eQTLs can provide new insight into the gene regulatory changes that occur among heterogeneous cell types during cardiomyocyte differentiation.
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Affiliation(s)
- Reem Elorbany
- Interdisciplinary Scientist Training Program, University of Chicago, Chicago, Illinois, United States of America
| | - Joshua M. Popp
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Katherine Rhodes
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Benjamin J. Strober
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Kenneth Barr
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
| | - Guanghao Qi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Yoav Gilad
- Department of Human Genetics, University of Chicago, Chicago, Illinois, United States of America
- Department of Medicine, University of Chicago, Chicago, Illinois, United States of America
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Computer Science, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Genetic Medicine, Johns Hopkins University, Baltimore, Maryland, United States of America
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32
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Medini H, Zirman A, Mishmar D. Immune system cells from COVID-19 patients display compromised mitochondrial-nuclear expression co-regulation and rewiring toward glycolysis. iScience 2021; 24:103471. [PMID: 34812416 PMCID: PMC8599136 DOI: 10.1016/j.isci.2021.103471] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/15/2021] [Accepted: 11/14/2021] [Indexed: 01/06/2023] Open
Abstract
Mitochondria are pivotal for bioenergetics, as well as in cellular response to viral infections. Nevertheless, their role in COVID-19 was largely overlooked. Here, we analyzed available bulk RNA-seq datasets from COVID-19 patients and corresponding healthy controls (three blood datasets, N = 48 healthy, 119 patients; two respiratory tract datasets, N = 157 healthy, 524 patients). We found significantly reduced mtDNA gene expression in blood, but not in respiratory tract samples from patients. Next, analysis of eight single-cells RNA-seq datasets from peripheral blood mononuclear cells, nasopharyngeal samples, and Bronchoalveolar lavage fluid (N = 1,192,243 cells), revealed significantly reduced mtDNA gene expression especially in immune system cells from patients. This is associated with elevated expression of nuclear DNA-encoded OXPHOS subunits, suggesting compromised mitochondrial-nuclear co-regulation. This, together with elevated expression of ROS-response genes and glycolysis enzymes in patients, suggest rewiring toward glycolysis, thus generating beneficial conditions for SARS-CoV-2 replication. Our findings underline the centrality of mitochondrial dysfunction in COVID-19. mtDNA gene expression is downregulated in COVID-19 blood, but not in respiratory tract Decreased mtDNA gene expression disrupts mito-nuclear coordination mtDNA is downregulated and rewired toward glycolysis especially in immune system cells Mitochondrial dysfunction is central to the etiology of COVID19
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Affiliation(s)
- Hadar Medini
- Department of Life Sciences, Ben-Gurion University of the Negev, Building 40, Room 009, Beer-Sheva 84105, Israel
| | - Amit Zirman
- Department of Life Sciences, Ben-Gurion University of the Negev, Building 40, Room 009, Beer-Sheva 84105, Israel
| | - Dan Mishmar
- Department of Life Sciences, Ben-Gurion University of the Negev, Building 40, Room 009, Beer-Sheva 84105, Israel
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Speir ML, Bhaduri A, Markov NS, Moreno P, Nowakowski TJ, Papatheodorou I, Pollen AA, Raney BJ, Seninge L, Kent WJ, Haeussler M. UCSC Cell Browser: visualize your single-cell data. Bioinformatics 2021; 37:4578-4580. [PMID: 34244710 PMCID: PMC8652023 DOI: 10.1093/bioinformatics/btab503] [Citation(s) in RCA: 148] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Accepted: 07/05/2021] [Indexed: 02/07/2023] Open
Abstract
SUMMARY As the use of single-cell technologies has grown, so has the need for tools to explore these large, complicated datasets. The UCSC Cell Browser is a tool that allows scientists to visualize gene expression and metadata annotation distribution throughout a single-cell dataset or multiple datasets. AVAILABILITY AND IMPLEMENTATION We provide the UCSC Cell Browser as a free website where scientists can explore a growing collection of single-cell datasets and a freely available python package for scientists to create stable, self-contained visualizations for their own single-cell datasets. Learn more at https://cells.ucsc.edu. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matthew L Speir
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Aparna Bhaduri
- Department of Biological Chemistry, University of California, Los Angeles, CA 90095, USA
| | - Nikolay S Markov
- Division of Pulmonary and Critical Care, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Pablo Moreno
- EMBL-EBI European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Tomasz J Nowakowski
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Anatomy, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Psychiatry and Behavioral Sciences, University of California San Francisco, San Francisco, CA 94143, USA
- Chan Zuckerberg Biohub, San Francisco, CA 94158, USA
| | - Irene Papatheodorou
- EMBL-EBI European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | - Alex A Pollen
- The Eli and Edythe Broad Center of Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA 94143, USA
- Department of Neurology, University of California San Francisco, San Francisco, CA 94143, USA
| | - Brian J Raney
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Lucas Seninge
- Genomics Institute, University of California Santa Cruz, Santa Cruz, CA 95064, USA
- Department of Biomolecular Engineering, 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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34
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Stephenson M, Nip KM, HafezQorani S, Gagalova KK, Yang C, Warren RL, Birol I. RNA-Scoop: interactive visualization of transcripts in single-cell transcriptomes. NAR Genom Bioinform 2021; 3:lqab105. [PMID: 34859209 PMCID: PMC8633890 DOI: 10.1093/nargab/lqab105] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 08/21/2021] [Accepted: 11/26/2021] [Indexed: 11/12/2022] Open
Abstract
Recent advances in single-cell RNA sequencing technologies have made detection of transcripts in single cells possible. The level of resolution provided by these technologies can be used to study changes in transcript usage across cell populations and help investigate new biology. Here, we introduce RNA-Scoop, an interactive cell cluster and transcriptome visualization tool to analyze transcript usage across cell categories and clusters. The tool allows users to examine differential transcript expression across clusters and investigate how usage of specific transcript expression mechanisms varies across cell groups.
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Affiliation(s)
- Maria Stephenson
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
- Computer Science Co-op Program, University of British Columbia, Vancouver, BC V6T 1Z4, Canada
| | - Ka Ming Nip
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V5Z 4S6, Canada
| | - Saber HafezQorani
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V5Z 4S6, Canada
| | - Kristina K Gagalova
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V5Z 4S6, Canada
| | - Chen Yang
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
- Bioinformatics Graduate Program, University of British Columbia, Vancouver, BC V5Z 4S6, Canada
| | - René L Warren
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
| | - Inanc Birol
- Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC V5Z 4S6, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, BC V6H 3N1, Canada
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35
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Legetth O, Rodhe J, Lang S, Dhapola P, Wallergård M, Soneji S. CellexalVR: A virtual reality platform to visualize and analyze single-cell omics data. iScience 2021; 24:103251. [PMID: 34849461 PMCID: PMC8609247 DOI: 10.1016/j.isci.2021.103251] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 09/15/2021] [Accepted: 10/07/2021] [Indexed: 12/20/2022] Open
Abstract
Single-cell RNAseq is a routinely used method to explore heterogeneity within cell populations. Data from these experiments are often visualized using dimension reduction methods such as UMAP and tSNE, where each cell is projected in two or three dimensional space. Three-dimensional projections can be more informative for larger and complex datasets because they are less prone to merging and flattening similar cell-types/clusters together. However, visualizing and cross-comparing 3D projections using current software on conventional flat-screen displays is far from optimal as they are still essentially 2D, and lack meaningful interaction between the user and the data. Here we present CellexalVR (www.cellexalvr.med.lu.se), a feature-rich, fully interactive virtual reality environment for the visualization and analysis of single-cell experiments that allows researchers to intuitively and collaboratively gain an understanding of their data. Single-cell experiments are often visualized when embedded into three dimensions CellexalVR is a virtual reality environment to visualize all data simultaneously Teams can analyze single-cell experiments together in VR regardless of location
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Affiliation(s)
- Oscar Legetth
- Division of Molecular Hematology, BMC, Lund University, 22690 Lund, Sweden.,Lund Stem Cell Center, Lund University, 22184 Lund, Sweden
| | - Johan Rodhe
- Division of Molecular Hematology, BMC, Lund University, 22690 Lund, Sweden.,Lund Stem Cell Center, Lund University, 22184 Lund, Sweden
| | - Stefan Lang
- Division of Molecular Hematology, BMC, Lund University, 22690 Lund, Sweden.,Lund Stem Cell Center, Lund University, 22184 Lund, Sweden
| | - Parashar Dhapola
- Division of Molecular Hematology, BMC, Lund University, 22690 Lund, Sweden.,Lund Stem Cell Center, Lund University, 22184 Lund, Sweden
| | | | - Shamit Soneji
- Division of Molecular Hematology, BMC, Lund University, 22690 Lund, Sweden.,Lund Stem Cell Center, Lund University, 22184 Lund, Sweden
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36
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Roma S, Carpen L, Raveane A, Bertolini F. The Dual Role of Innate Lymphoid and Natural Killer Cells in Cancer. from Phenotype to Single-Cell Transcriptomics, Functions and Clinical Uses. Cancers (Basel) 2021; 13:cancers13205042. [PMID: 34680190 PMCID: PMC8533946 DOI: 10.3390/cancers13205042] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 10/05/2021] [Accepted: 10/06/2021] [Indexed: 02/07/2023] Open
Abstract
Simple Summary Innate lymphoid cells (ILCs), a family of innate immune cells including natural killers (NKs), play a multitude of roles in first-line cancer control, in escape from immunity and in cancer progression. In this review, we summarize preclinical and clinical data on ILCs and NK cells concerning their phenotype, function and clinical applications in cellular therapy trials. We also describe how single-cell transcriptome sequencing has been used and forecast how it will be used to better understand ILC and NK involvement in cancer control and progression as well as their therapeutic potential. Abstract The role of innate lymphoid cells (ILCs), including natural killer (NK) cells, is pivotal in inflammatory modulation and cancer. Natural killer cell activity and count have been demonstrated to be regulated by the expression of activating and inhibitory receptors together with and as a consequence of different stimuli. The great majority of NK cell populations have an anti-tumor activity due to their cytotoxicity, and for this reason have been used for cellular therapies in cancer patients. On the other hand, the recently classified helper ILCs are fundamentally involved in inflammation and they can be either helpful or harmful in cancer development and progression. Tissue niche seems to play an important role in modulating ILC function and conversion, as observed at the transcriptional level. In the past, these cell populations have been classified by the presence of specific cellular receptor markers; more recently, due to the advent of single-cell RNA sequencing (scRNA-seq), it has been possible to also explore them at the transcriptomic level. In this article we review studies on ILC (and NK cell) classification, function and their involvement in cancer. We also summarize the potential application of NK cells in cancer therapy and give an overview of the most recent studies involving ILCs and NKs at scRNA-seq, focusing on cancer. Finally, we provide a resource for those who wish to start single-cell transcriptomic analysis on the context of these innate lymphoid cell populations.
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Wang W, Wang L, She J, Zhu J. Examining heterogeneity of stromal cells in tumor microenvironment based on pan-cancer single-cell RNA sequencing data. Cancer Biol Med 2021; 19:j.issn.2095-3941.2020.0762. [PMID: 34398535 PMCID: PMC8763007 DOI: 10.20892/j.issn.2095-3941.2020.0762] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 02/05/2021] [Indexed: 11/28/2022] Open
Abstract
Tumor tissues contain both tumor and non-tumor cells, which include infiltrated immune cells and stromal cells, collectively called the tumor microenvironment (TME). Single-cell RNA sequencing (scRNAseq) enables the examination of heterogeneity of tumor cells and TME. In this review, we examined scRNAseq datasets for multiple cancer types and evaluated the heterogeneity of major cell type composition in different cancer types. We further showed that endothelial cells and fibroblasts/myofibroblasts in different cancer types can be classified into common subtypes, and the subtype composition is clearly associated with cancer characteristic and therapy response.
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Affiliation(s)
- Wenhui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Li Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Junjun She
- First Affiliate Hospital of Xi’an Jiaotong University, Xi’an 710061, China
| | - Jun Zhu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
- First Affiliate Hospital of Xi’an Jiaotong University, Xi’an 710061, China
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
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38
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Djekidel MN, Rosikiewicz W, Peng JC, Kanneganti TD, Hui Y, Jin H, Hedges D, Schreiner P, Fan Y, Wu G, Xu B. CovidExpress: an interactive portal for intuitive investigation on SARS-CoV-2 related transcriptomes. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2021:2021.05.14.444026. [PMID: 34075382 PMCID: PMC8168395 DOI: 10.1101/2021.05.14.444026] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in humans could cause coronavirus disease 2019 (COVID-19). Since its first discovery in Dec 2019, SARS-CoV-2 has become a global pandemic and caused 3.3 million direct/indirect deaths (2021 May). Amongst the scientific community's response to COVID-19, data sharing has emerged as an essential aspect of the combat against SARS-CoV-2. Despite the ever-growing studies about SARS-CoV-2 and COVID-19, to date, only a few databases were curated to enable access to gene expression data. Furthermore, these databases curated only a small set of data and do not provide easy access for investigators without computational skills to perform analyses. To fill this gap and advance open-access to the growing gene expression data on this deadly virus, we collected about 1,500 human bulk RNA-seq datasets from publicly available resources, developed a database and visualization tool, named CovidExpress (https://stjudecab.github.io/covidexpress). This open access database will allow research investigators to examine the gene expression in various tissues, cell lines, and their response to SARS-CoV-2 under different experimental conditions, accelerating the understanding of the etiology of this disease to inform the drug and vaccine development. Our integrative analysis of this big dataset highlights a set of commonly regulated genes in SARS-CoV-2 infected lung and Rhinovirus infected nasal tissues, including OASL that were under-studied in COVID-19 related reports. Our results also suggested a potential FURIN positive feedback loop that might explain the evolutional advantage of SARS-CoV-2.
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Affiliation(s)
- Mohamed Nadhir Djekidel
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
- These authors contributed equally to this study
| | - Wojciech Rosikiewicz
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
- These authors contributed equally to this study
| | - Jamy C. Peng
- Department of Developmental Neurobiology, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | | | - Yawei Hui
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Dale Hedges
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Patrick Schreiner
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Yiping Fan
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
| | - Beisi Xu
- Center for Applied Bioinformatics, St. Jude Children’s Research Hospital, Memphis, Tennessee, 38105, USA
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Orvis J, Gottfried B, Kancherla J, Adkins RS, Song Y, Dror AA, Olley D, Rose K, Chrysostomou E, Kelly MC, Milon B, Matern MS, Azaiez H, Herb B, Colantuoni C, Carter RL, Ament SA, Kelley MW, White O, Bravo HC, Mahurkar A, Hertzano R. gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration. Nat Methods 2021; 18:843-844. [PMID: 34172972 PMCID: PMC8996439 DOI: 10.1038/s41592-021-01200-9] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Joshua Orvis
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brian Gottfried
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jayaram Kancherla
- Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Ricky S Adkins
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Yang Song
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Amiel A Dror
- Department of Otolaryngology-Head and Neck Surgery, Galilee Medical Center, Nahariya, Israel
- Faculty of Medicine in the Galilee, Bar-Ilan University, Safed, Israel
| | - Dustin Olley
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kevin Rose
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Elena Chrysostomou
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Michael C Kelly
- Laboratory of Cochlear Development, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
| | - Beatrice Milon
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Maggie S Matern
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hela Azaiez
- Molecular Otolaryngology and Renal Research Laboratories, Department of Otolaryngology, University of Iowa, Iowa City, IA, USA
| | - Brian Herb
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Carlo Colantuoni
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert L Carter
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Seth A Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Matthew W Kelley
- Laboratory of Cochlear Development, National Institute on Deafness and Other Communication Disorders, National Institutes of Health, Bethesda, MD, USA
| | - Owen White
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Hector Corrada Bravo
- Institute for Advanced Computer Studies, University of Maryland, College Park, MD, USA
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD, USA
- Department of Computer Science, University of Maryland, College Park, MD, USA
| | - Anup Mahurkar
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ronna Hertzano
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Otorhinolaryngology Head and Neck Surgery, University of Maryland School of Medicine, Baltimore, MD, USA.
- Department of Anatomy and Neurobiology, University of Maryland School of Medicine, Baltimore, MD, USA.
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40
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Kim HK, Ha TW, Lee MR. Single-Cell Transcriptome Analysis as a Promising Tool to Study Pluripotent Stem Cell Reprogramming. Int J Mol Sci 2021; 22:ijms22115988. [PMID: 34206025 PMCID: PMC8198005 DOI: 10.3390/ijms22115988] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/26/2021] [Accepted: 05/31/2021] [Indexed: 12/15/2022] Open
Abstract
Cells are the basic units of all organisms and are involved in all vital activities, such as proliferation, differentiation, senescence, and apoptosis. A human body consists of more than 30 trillion cells generated through repeated division and differentiation from a single-cell fertilized egg in a highly organized programmatic fashion. Since the recent formation of the Human Cell Atlas consortium, establishing the Human Cell Atlas at the single-cell level has been an ongoing activity with the goal of understanding the mechanisms underlying diseases and vital cellular activities at the level of the single cell. In particular, transcriptome analysis of embryonic stem cells at the single-cell level is of great importance, as these cells are responsible for determining cell fate. Here, we review single-cell analysis techniques that have been actively used in recent years, introduce the single-cell analysis studies currently in progress in pluripotent stem cells and reprogramming, and forecast future studies.
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Marín-Sedeño E, de Morentin XM, Pérez-Pomares JM, Gómez-Cabrero D, Ruiz-Villalba A. Understanding the Adult Mammalian Heart at Single-Cell RNA-Seq Resolution. Front Cell Dev Biol 2021; 9:645276. [PMID: 34055776 PMCID: PMC8149764 DOI: 10.3389/fcell.2021.645276] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 04/09/2021] [Indexed: 12/24/2022] Open
Abstract
During the last decade, extensive efforts have been made to comprehend cardiac cell genetic and functional diversity. Such knowledge allows for the definition of the cardiac cellular interactome as a reasonable strategy to increase our understanding of the normal and pathologic heart. Previous experimental approaches including cell lineage tracing, flow cytometry, and bulk RNA-Seq have often tackled the analysis of cardiac cell diversity as based on the assumption that cell types can be identified by the expression of a single gene. More recently, however, the emergence of single-cell RNA-Seq technology has led us to explore the diversity of individual cells, enabling the cardiovascular research community to redefine cardiac cell subpopulations and identify relevant ones, and even novel cell types, through their cell-specific transcriptomic signatures in an unbiased manner. These findings are changing our understanding of cell composition and in consequence the identification of potential therapeutic targets for different cardiac diseases. In this review, we provide an overview of the continuously changing cardiac cellular landscape, traveling from the pre-single-cell RNA-Seq times to the single cell-RNA-Seq revolution, and discuss the utilities and limitations of this technology.
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Affiliation(s)
- Ernesto Marín-Sedeño
- Department of Animal Biology, Faculty of Sciences, Instituto Malagueño de Biomedicina, University of Málaga, Málaga, Spain
- BIONAND, Centro Andaluz de Nanomedicina y Biotecnología, Junta de Andalucía, Universidad de Málaga, Málaga, Spain
| | - Xabier Martínez de Morentin
- Traslational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Universidad Pública de Navarra, Pamplona, Spain
| | - Jose M. Pérez-Pomares
- Department of Animal Biology, Faculty of Sciences, Instituto Malagueño de Biomedicina, University of Málaga, Málaga, Spain
- BIONAND, Centro Andaluz de Nanomedicina y Biotecnología, Junta de Andalucía, Universidad de Málaga, Málaga, Spain
| | - David Gómez-Cabrero
- Traslational Bioinformatics Unit, Navarrabiomed, Complejo Hospitalario de Navarra, Instituto de Investigación Sanitaria de Navarra (IdiSNA), Universidad Pública de Navarra, Pamplona, Spain
- Centre of Host-Microbiome Interactions, Faculty of Dentistry, Oral & Craniofacial Sciences, King’s College London, London, United Kingdom
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Adrián Ruiz-Villalba
- Department of Animal Biology, Faculty of Sciences, Instituto Malagueño de Biomedicina, University of Málaga, Málaga, Spain
- BIONAND, Centro Andaluz de Nanomedicina y Biotecnología, Junta de Andalucía, Universidad de Málaga, Málaga, Spain
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43
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Guruprasad P, Lee YG, Kim KH, Ruella M. The current landscape of single-cell transcriptomics for cancer immunotherapy. J Exp Med 2021; 218:e20201574. [PMID: 33601414 PMCID: PMC7754680 DOI: 10.1084/jem.20201574] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 11/28/2020] [Accepted: 12/02/2020] [Indexed: 12/28/2022] Open
Abstract
Immunotherapies such as immune checkpoint blockade and adoptive cell transfer have revolutionized cancer treatment, but further progress is hindered by our limited understanding of tumor resistance mechanisms. Emerging technologies now enable the study of tumors at the single-cell level, providing unprecedented high-resolution insights into the genetic makeup of the tumor microenvironment and immune system that bulk genomics cannot fully capture. Here, we highlight the recent key findings of the use of single-cell RNA sequencing to deconvolute heterogeneous tumors and immune populations during immunotherapy. Single-cell RNA sequencing has identified new crucial factors and cellular subpopulations that either promote tumor progression or leave tumors vulnerable to immunotherapy. We anticipate that the strategic use of single-cell analytics will promote the development of the next generation of successful, rationally designed immunotherapeutics.
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Affiliation(s)
- Puneeth Guruprasad
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
- Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Department of Medicine, Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Yong Gu Lee
- Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Department of Medicine, Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Ki Hyun Kim
- Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Department of Medicine, Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| | - Marco Ruella
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA
- Center for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Department of Medicine, Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA
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Abstract
Dimensionality reduction is a crucial step in essentially every single-cell RNA-sequencing (scRNA-seq) analysis. In this chapter, we describe the typical dimensionality reduction workflow that is used for scRNA-seq datasets, specifically highlighting the roles of principal component analysis, t-distributed stochastic neighborhood embedding, and uniform manifold approximation and projection in this setting. We particularly emphasize efficient computation; the software implementations used in this chapter can scale to datasets with millions of cells.
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45
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Ferrero R, Rainer P, Deplancke B. Toward a Consensus View of Mammalian Adipocyte Stem and Progenitor Cell Heterogeneity. Trends Cell Biol 2020; 30:937-950. [PMID: 33148396 DOI: 10.1016/j.tcb.2020.09.007] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Revised: 09/04/2020] [Accepted: 09/09/2020] [Indexed: 12/31/2022]
Abstract
White adipose tissue (WAT) is a cellularly heterogeneous endocrine organ that not only serves as an energy reservoir, but also actively participates in metabolic homeostasis. Among the main constituents of adipose tissue are adipocytes, which arise from adipose stem and progenitor cells (ASPCs). While it is well known that these ASPCs reside in the stromal vascular fraction (SVF) of adipose tissue, their molecular heterogeneity and functional diversity is still poorly understood. Driven by the resolving power of single-cell transcriptomics, several recent studies provided new insights into the cellular complexity of ASPCs among different mammalian fat depots. In this review, we present current knowledge on ASPCs, their population structure, hierarchy, fat depot-specific nature, function, and regulatory mechanisms, and discuss not only the similarities, but also the differences between mouse and human ASPC biology.
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Affiliation(s)
- Radiana Ferrero
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Pernille Rainer
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Bart Deplancke
- Laboratory of Systems Biology and Genetics, Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland.
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