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Hernández-Núñez I, Clark BS. Experimental Framework for Assessing Mouse Retinal Regeneration Through Single-Cell RNA-Sequencing. Methods Mol Biol 2025; 2848:117-134. [PMID: 39240520 DOI: 10.1007/978-1-0716-4087-6_8] [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: 09/07/2024]
Abstract
Retinal degenerative diseases including age-related macular degeneration and glaucoma are estimated to currently affect more than 14 million people in the United States, with an increased prevalence of retinal degenerations in aged individuals. An expanding aged population who are living longer forecasts an increased prevalence and economic burden of visual impairments. Improvements to visual health and treatment paradigms for progressive retinal degenerations slow vision loss. However, current treatments fail to remedy the root cause of visual impairments caused by retinal degenerations-loss of retinal neurons. Stimulation of retinal regeneration from endogenous cellular sources presents an exciting treatment avenue for replacement of lost retinal cells. In multiple species including zebrafish and Xenopus, Müller glial cells maintain a highly efficient regenerative ability to reconstitute lost cells throughout the organism's lifespan, highlighting potential therapeutic avenues for stimulation of retinal regeneration in humans. Here, we describe how the application of single-cell RNA-sequencing (scRNA-seq) has enhanced our understanding of Müller glial cell-derived retinal regeneration, including the characterization of gene regulatory networks that facilitate/inhibit regenerative responses. Additionally, we provide a validated experimental framework for cellular preparation of mouse retinal cells as input into scRNA-seq experiments, including insights into experimental design and analyses of resulting data.
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Affiliation(s)
- Ismael Hernández-Núñez
- John F Hardesty, MD Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO, USA
| | - Brian S Clark
- John F Hardesty, MD Department of Ophthalmology and Visual Sciences, Washington University School of Medicine, St. Louis, MO, USA.
- Department of Developmental Biology, Washington University School of Medicine, St. Louis, MO, USA.
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2
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Dong C, Meng X, Zhang T, Guo Z, Liu Y, Wu P, Chen S, Zhou F, Ma Y, Xiong H, Shu S, He A. Single-cell EpiChem jointly measures drug-chromatin binding and multimodal epigenome. Nat Methods 2024; 21:1624-1633. [PMID: 39025969 PMCID: PMC11399096 DOI: 10.1038/s41592-024-02360-0] [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: 08/13/2023] [Accepted: 06/25/2024] [Indexed: 07/20/2024]
Abstract
Studies of molecular and cellular functions of small-molecule inhibitors in cancer treatment, eliciting effects by targeting genome and epigenome associated proteins, requires measurement of drug-target engagement in single-cell resolution. Here we present EpiChem for in situ single-cell joint mapping of small molecules and multimodal epigenomic landscape. We demonstrate single-cell co-assays of three small molecules together with histone modifications, chromatin accessibility or target proteins in human colorectal cancer (CRC) organoids. Integrated multimodal analysis reveals diverse drug interactions in the context of chromatin states within heterogeneous CRC organoids. We further reveal drug genomic binding dynamics and adaptive epigenome across cell types after small-molecule drug treatment in CRC organoids. This method provides a unique tool to exploit the mechanisms of cell type-specific drug actions.
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Affiliation(s)
- Chao Dong
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Xiaoxuan Meng
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Tong Zhang
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Zhifang Guo
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China
- Peking University International Cancer Institute, Beijing, China
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China
| | - Yaxi Liu
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Peihuang Wu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China
| | - Shiwei Chen
- Peking University International Cancer Institute, Beijing, China
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China
| | - Fanqi Zhou
- State Key Laboratory of Medical Molecular Biology, Haihe laboratory of Cell Ecosystem, Key Laboratory of RNA and Hematopoietic Regulation, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Yanni Ma
- State Key Laboratory of Medical Molecular Biology, Haihe laboratory of Cell Ecosystem, Key Laboratory of RNA and Hematopoietic Regulation, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College, Beijing, China
| | - Haiqing Xiong
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China
- Tianjin Institutes of Health Science, Tianjin, China
| | - Shaokun Shu
- State Key Laboratory of Molecular Oncology, Beijing Key Laboratory of Carcinogenesis and Translational Research, Department of Lymphoma, Peking University Cancer Hospital & Institute, Beijing, China.
- Peking University International Cancer Institute, Beijing, China.
- Peking University-Yunnan Baiyao International Medical Research Center, Beijing, China.
| | - Aibin He
- Institute of Molecular Medicine, National Biomedical Imaging Center, College of Future Technology, Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China.
- Key laboratory of Carcinogenesis and Translational Research of Ministry of Education of China, Peking University Cancer Hospital & Institute, Beijing, China.
- Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies, Chengdu, China.
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3
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Ali A, Manzoor S, Ali T, Asim M, Muhammad G, Ahmad A, Jamaludin MI, Devaraj S, Munawar N. Innovative aspects and applications of single cell technology for different diseases. Am J Cancer Res 2024; 14:4028-4048. [PMID: 39267684 PMCID: PMC11387862 DOI: 10.62347/vufu1836] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Accepted: 08/24/2024] [Indexed: 09/15/2024] Open
Abstract
Recent developments in single-cell technologies have provided valuable insights from cancer genomics to complex microbial communities. Single-cell technologies including the RNA-seq, next-generation sequencing (NGS), epigenomics, genomics, and transcriptomics can be used to uncover the single cell nature and molecular characterization of individual cells. These technologies also reveal the cellular transition states, evolutionary relationships between genes, the complex structure of single-cell populations, cell-to-cell interaction leading to biological discoveries and more reliable than traditional bulk technologies. These technologies are becoming the first choice for the early detection of inflammatory biomarkers affecting the proliferation and progression of tumor cells in the tumor microenvironment and improving the clinical efficacy of patients undergoing immunotherapy. These technologies also hold a central position in the detection of checkpoint inhibitors and thus determining the signaling pathways evoked by tumor invasion. This review addressed the emerging approaches of single cell-based technologies in cancer immunotherapies and different human diseases at cellular and molecular levels and the emerging role of sequencing technologies leading to drug discovery. Advancements in these technologies paved for discovering novel diagnostic markers for better understanding the pathological and biochemical mechanisms also for controlling the rate of different diseases.
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Affiliation(s)
- Ashiq Ali
- Department of Histology and Embryology, Shantou University Medical College Shantou 515041, Guangdong, China
| | - Saba Manzoor
- Department of Zoology, University of Sialkot Sialkot 51310, Pakistan
| | - Tayyab Ali
- Clinico-Molecular Biochemistry Laboratory, Department of Biochemistry, University of Agriculture Faisalabad 38000, Pakistan
| | - Muhammad Asim
- Clinico-Molecular Biochemistry Laboratory, Department of Biochemistry, University of Agriculture Faisalabad 38000, Pakistan
| | - Ghulam Muhammad
- Jinnah Burn and Reconstructive Surgery Centre, Jinnah Hospital, Allama Iqbal Medical College Lahore 54000, Pakistan
| | - Aftab Ahmad
- Biochemistry/Center for Advanced Studies in Agriculture and Food Security (CAS-AFS), University of Agriculture Faisalabad 38040, Pakistan
| | - Mohamad Ikhwan Jamaludin
- BioInspired Device and Tissue Engineering Research Group (BioInspira), Department of Biomedical Engineering and Health Sciences, Faculty of Electrical Engineering, Universiti Teknologi Malaysia Johor Bahru 81310, Johor, Malaysia
| | - Sutha Devaraj
- Graduate School of Medicine, Perdana University Wisma Chase Perdana, Changkat Semantan, Damansara Heights, Kuala Lumpur 50490, Malaysia
| | - Nayla Munawar
- Department of Chemistry, College of Science, United Arab Emirates University Al-Ain 15551, United Arab Emirates
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Sun F, Li H, Sun D, Fu S, Gu L, Shao X, Wang Q, Dong X, Duan B, Xing F, Wu J, Xiao M, Zhao F, Han JDJ, Liu Q, Fan X, Li C, Wang C, Shi T. Single-cell omics: experimental workflow, data analyses and applications. SCIENCE CHINA. LIFE SCIENCES 2024:10.1007/s11427-023-2561-0. [PMID: 39060615 DOI: 10.1007/s11427-023-2561-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Accepted: 04/18/2024] [Indexed: 07/28/2024]
Abstract
Cells are the fundamental units of biological systems and exhibit unique development trajectories and molecular features. Our exploration of how the genomes orchestrate the formation and maintenance of each cell, and control the cellular phenotypes of various organismsis, is both captivating and intricate. Since the inception of the first single-cell RNA technology, technologies related to single-cell sequencing have experienced rapid advancements in recent years. These technologies have expanded horizontally to include single-cell genome, epigenome, proteome, and metabolome, while vertically, they have progressed to integrate multiple omics data and incorporate additional information such as spatial scRNA-seq and CRISPR screening. Single-cell omics represent a groundbreaking advancement in the biomedical field, offering profound insights into the understanding of complex diseases, including cancers. Here, we comprehensively summarize recent advances in single-cell omics technologies, with a specific focus on the methodology section. This overview aims to guide researchers in selecting appropriate methods for single-cell sequencing and related data analysis.
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Affiliation(s)
- Fengying Sun
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China
| | - Haoyan Li
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Dongqing Sun
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Shaliu Fu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Lei Gu
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Shao
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China
| | - Qinqin Wang
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xin Dong
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Bin Duan
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China
| | - Feiyang Xing
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Jun Wu
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Minmin Xiao
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
| | - Fangqing Zhao
- Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Qi Liu
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Translational Medical Center for Stem Cell Therapy and Institute for Regenerative Medicine, Shanghai East Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201210, China.
| | - Xiaohui Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, 310058, China.
- National Key Laboratory of Chinese Medicine Modernization, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing, 314103, China.
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, 310006, China.
| | - Chen Li
- Center for Single-cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Chenfei Wang
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department, Tongji Hospital, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200082, China.
- Frontier Science Center for Stem Cells, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Tieliu Shi
- Department of Clinical Laboratory, the Affiliated Wuhu Hospital of East China Normal University (The Second People's Hospital of Wuhu City), Wuhu, 241000, China.
- Center for Bioinformatics and Computational Biology, Shanghai Key Laboratory of Regulatory Biology, the Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, 200062, China.
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5
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Acharya SN, Nichols RV, Rylaarsdam LE, O'Connell BL, Braun TP, Adey AC. sciMET-cap: high-throughput single-cell methylation analysis with a reduced sequencing burden. Genome Biol 2024; 25:186. [PMID: 38987810 PMCID: PMC11234687 DOI: 10.1186/s13059-024-03306-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 06/11/2024] [Indexed: 07/12/2024] Open
Abstract
DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Accumulated off-target coverage enables genome-wide differentially methylated region (DMR) calling for clusters with as few as 115 cells. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).
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Affiliation(s)
- Sonia N Acharya
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Ruth V Nichols
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Lauren E Rylaarsdam
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Brendan L O'Connell
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA
| | - Theodore P Braun
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Division of Hematology/Medical Oncology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA.
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA.
- Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, USA.
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6
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Qiao Y, Cheng T, Miao Z, Cui Y, Tu J. Recent Innovations and Technical Advances in High-Throughput Parallel Single-Cell Whole-Genome Sequencing Methods. SMALL METHODS 2024:e2400789. [PMID: 38979872 DOI: 10.1002/smtd.202400789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2024] [Indexed: 07/10/2024]
Abstract
Single-cell whole-genome sequencing (scWGS) detects cell heterogeneity at the aspect of genomic variations, which are inheritable and play an important role in life processes such as aging and cancer progression. The recent explosive development of high-throughput single-cell sequencing methods has enabled high-performance heterogeneity detection through a vast number of novel strategies. Despite the limitation on total cost, technical advances in high-throughput single-cell whole-genome sequencing methods are made for higher genome coverage, parallel throughput, and level of integration. This review highlights the technical advancements in high-throughput scWGS in the aspects of strategies design, data efficiency, parallel handling platforms, and their applications on human genome. The experimental innovations, remaining challenges, and perspectives are summarized and discussed.
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Affiliation(s)
- Yi Qiao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Tianguang Cheng
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Zikun Miao
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yue Cui
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Jing Tu
- State Key Laboratory of Digital Medical Engineering, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
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7
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Nanda AS, Wu K, Irkliyenko I, Woo B, Ostrowski MS, Clugston AS, Sayles LC, Xu L, Satpathy AT, Nguyen HG, Alejandro Sweet-Cordero E, Goodarzi H, Kasinathan S, Ramani V. Direct transposition of native DNA for sensitive multimodal single-molecule sequencing. Nat Genet 2024; 56:1300-1309. [PMID: 38724748 PMCID: PMC11176058 DOI: 10.1038/s41588-024-01748-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Accepted: 04/08/2024] [Indexed: 05/23/2024]
Abstract
Concurrent readout of sequence and base modifications from long unamplified DNA templates by Pacific Biosciences of California (PacBio) single-molecule sequencing requires large amounts of input material. Here we adapt Tn5 transposition to introduce hairpin oligonucleotides and fragment (tagment) limiting quantities of DNA for generating PacBio-compatible circular molecules. We developed two methods that implement tagmentation and use 90-99% less input than current protocols: (1) single-molecule real-time sequencing by tagmentation (SMRT-Tag), which allows detection of genetic variation and CpG methylation; and (2) single-molecule adenine-methylated oligonucleosome sequencing assay by tagmentation (SAMOSA-Tag), which uses exogenous adenine methylation to add a third channel for probing chromatin accessibility. SMRT-Tag of 40 ng or more human DNA (approximately 7,000 cell equivalents) yielded data comparable to gold standard whole-genome and bisulfite sequencing. SAMOSA-Tag of 30,000-50,000 nuclei resolved single-fiber chromatin structure, CTCF binding and DNA methylation in patient-derived prostate cancer xenografts and uncovered metastasis-associated global epigenome disorganization. Tagmentation thus promises to enable sensitive, scalable and multimodal single-molecule genomics for diverse basic and clinical applications.
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Affiliation(s)
- Arjun S Nanda
- Gladstone Institute for Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
| | - Ke Wu
- Gladstone Institute for Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Iryna Irkliyenko
- Gladstone Institute for Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Brian Woo
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Helen-Diller Cancer Center, San Francisco, CA, USA
| | - Megan S Ostrowski
- Gladstone Institute for Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA
| | - Andrew S Clugston
- Helen-Diller Cancer Center, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Leanne C Sayles
- Helen-Diller Cancer Center, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Lingru Xu
- Helen-Diller Cancer Center, San Francisco, CA, USA
| | - Ansuman T Satpathy
- Department of Pathology, Stanford University, Stanford, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Gladstone-University of California, San Francisco Institute for Genomic Immunology, Gladstone Institutes, San Francisco, CA, USA
| | - Hao G Nguyen
- Helen-Diller Cancer Center, San Francisco, CA, USA
| | - E Alejandro Sweet-Cordero
- Helen-Diller Cancer Center, San Francisco, CA, USA
- Department of Pediatrics, University of California, San Francisco, San Francisco, CA, USA
| | - Hani Goodarzi
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA
- Helen-Diller Cancer Center, San Francisco, CA, USA
- Parker Institute for Cancer Immunotherapy, San Francisco, CA, USA
- Bakar Computational Health Sciences Institute, San Francisco, CA, USA
| | - Sivakanthan Kasinathan
- Gladstone-University of California, San Francisco Institute for Genomic Immunology, Gladstone Institutes, San Francisco, CA, USA.
- Division of Rheumatology, Department of Pediatrics, Stanford University, Stanford, CA, USA.
| | - Vijay Ramani
- Gladstone Institute for Data Science and Biotechnology, Gladstone Institutes, San Francisco, CA, USA.
- Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA.
- Helen-Diller Cancer Center, San Francisco, CA, USA.
- Bakar Computational Health Sciences Institute, San Francisco, CA, USA.
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8
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Zhang X, Marand AP, Yan H, Schmitz RJ. scifi-ATAC-seq: massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. Genome Biol 2024; 25:90. [PMID: 38589969 PMCID: PMC11003106 DOI: 10.1186/s13059-024-03235-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/01/2024] [Indexed: 04/10/2024] Open
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called scifi-ATAC-seq, single-cell combinatorial fluidic indexing ATAC-sequencing, which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using the 10X Genomics platform. With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples can be indexed in a single emulsion reaction, representing an approximately 20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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Awuah WA, Roy S, Tan JK, Adebusoye FT, Qiang Z, Ferreira T, Ahluwalia A, Shet V, Yee ALW, Abdul‐Rahman T, Papadakis M. Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research. J Cell Mol Med 2024; 28:e18159. [PMID: 38494861 PMCID: PMC10945075 DOI: 10.1111/jcmm.18159] [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: 07/24/2023] [Revised: 12/22/2023] [Accepted: 01/12/2024] [Indexed: 03/19/2024] Open
Abstract
Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.
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Affiliation(s)
| | - Sakshi Roy
- School of MedicineQueen's University BelfastBelfastUK
| | | | | | - Zekai Qiang
- Department of Oncology & MetabolismThe University of SheffieldSheffieldUK
| | - Tomas Ferreira
- Department of Clinical Neurosciences, School of Clinical MedicineUniversity of CambridgeCambridgeUK
| | | | - Vallabh Shet
- Faculty of MedicineBangalore Medical College and Research InstituteBangaloreKarnatakaIndia
| | | | | | - Marios Papadakis
- Department of Surgery II, University Hospital Witten‐HerdeckeUniversity of Witten‐HerdeckeWuppertalGermany
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10
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Zhang H, Mulqueen RM, Iannuzo N, Farrera DO, Polverino F, Galligan JJ, Ledford JG, Adey AC, Cusanovich DA. txci-ATAC-seq: a massive-scale single-cell technique to profile chromatin accessibility. Genome Biol 2024; 25:78. [PMID: 38519979 PMCID: PMC10958877 DOI: 10.1186/s13059-023-03150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
We develop a large-scale single-cell ATAC-seq method by combining Tn5-based pre-indexing with 10× Genomics barcoding, enabling the indexing of up to 200,000 nuclei across multiple samples in a single reaction. We profile 449,953 nuclei across diverse tissues, including the human cortex, mouse brain, human lung, mouse lung, mouse liver, and lung tissue from a club cell secretory protein knockout (CC16-/-) model. Our study of CC16-/- nuclei uncovers previously underappreciated technical artifacts derived from remnant 129 mouse strain genetic material, which cause profound cell-type-specific changes in regulatory elements near many genes, thereby confounding the interpretation of this commonly referenced mouse model.
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Affiliation(s)
- Hao Zhang
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Ryan M Mulqueen
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Natalie Iannuzo
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
| | - Dominique O Farrera
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Francesca Polverino
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
- Division of Pulmonary, Allergy, Critical Care, and Sleep Medicine, University of Arizona, Tucson, AZ, USA
- Banner - University Medicine North, Pulmonary - Clinic F, Tucson, AZ, USA
| | - James J Galligan
- Department of Pharmacology and Toxicology, University of Arizona, Tucson, AZ, USA
| | - Julie G Ledford
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA
| | - Andrew C Adey
- Department of Molecular and Medical Genetics, Oregon Health & Science University, Portland, OR, USA.
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA.
- Oregon Health & Science University, Knight Cancer Institute, Portland, OR, USA.
- Oregon Health & Science University, Knight Cardiovascular Institute, Portland, OR, USA.
| | - Darren A Cusanovich
- Department of Cellular and Molecular Medicine, University of Arizona, Tucson, AZ, USA.
- Asthma & Airway Disease Research Center, University of Arizona, Tucson, AZ, USA.
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11
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Lugagne JB, Blassick CM, Dunlop MJ. Deep model predictive control of gene expression in thousands of single cells. Nat Commun 2024; 15:2148. [PMID: 38459057 PMCID: PMC10923782 DOI: 10.1038/s41467-024-46361-1] [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: 08/09/2023] [Accepted: 02/26/2024] [Indexed: 03/10/2024] Open
Abstract
Gene expression is inherently dynamic, due to complex regulation and stochastic biochemical events. However, the effects of these dynamics on cell phenotypes can be difficult to determine. Researchers have historically been limited to passive observations of natural dynamics, which can preclude studies of elusive and noisy cellular events where large amounts of data are required to reveal statistically significant effects. Here, using recent advances in the fields of machine learning and control theory, we train a deep neural network to accurately predict the response of an optogenetic system in Escherichia coli cells. We then use the network in a deep model predictive control framework to impose arbitrary and cell-specific gene expression dynamics on thousands of single cells in real time, applying the framework to generate complex time-varying patterns. We also showcase the framework's ability to link expression patterns to dynamic functional outcomes by controlling expression of the tetA antibiotic resistance gene. This study highlights how deep learning-enabled feedback control can be used to tailor distributions of gene expression dynamics with high accuracy and throughput without expert knowledge of the biological system.
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Affiliation(s)
- Jean-Baptiste Lugagne
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215, USA.
- Biological Design Center, Boston University, Boston, Massachusetts, 02215, USA.
| | - Caroline M Blassick
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215, USA
- Biological Design Center, Boston University, Boston, Massachusetts, 02215, USA
| | - Mary J Dunlop
- Department of Biomedical Engineering, Boston University, Boston, Massachusetts, 02215, USA.
- Biological Design Center, Boston University, Boston, Massachusetts, 02215, USA.
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12
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Schneider MP, Cullen AE, Pangonyte J, Skelton J, Major H, Van Oudenhove E, Garcia MJ, Chaves Urbano B, Piskorz AM, Brenton JD, Macintyre G, Markowetz F. scAbsolute: measuring single-cell ploidy and replication status. Genome Biol 2024; 25:62. [PMID: 38438920 PMCID: PMC10910719 DOI: 10.1186/s13059-024-03204-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 02/22/2024] [Indexed: 03/06/2024] Open
Abstract
Cancer cells often exhibit DNA copy number aberrations and can vary widely in their ploidy. Correct estimation of the ploidy of single-cell genomes is paramount for downstream analysis. Based only on single-cell DNA sequencing information, scAbsolute achieves accurate and unbiased measurement of single-cell ploidy and replication status, including whole-genome duplications. We demonstrate scAbsolute's capabilities using experimental cell multiplets, a FUCCI cell cycle expression system, and a benchmark against state-of-the-art methods. scAbsolute provides a robust foundation for single-cell DNA sequencing analysis across different technologies and has the potential to enable improvements in a number of downstream analyses.
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Affiliation(s)
- Michael P Schneider
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Amy E Cullen
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Justina Pangonyte
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Jason Skelton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Harvey Major
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Elke Van Oudenhove
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Maria J Garcia
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | | | - Anna M Piskorz
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - James D Brenton
- University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK
| | - Geoff Macintyre
- Spanish National Cancer Research Centre (CNIO), Madrid, Spain
| | - Florian Markowetz
- University of Cambridge, Cambridge, UK.
- Cancer Research UK Cambridge Institute, Robinson Way, Cambridge, UK.
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13
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Zhang X, Marand AP, Yan H, Schmitz RJ. Massive-scale single-cell chromatin accessibility sequencing using combinatorial fluidic indexing. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.17.558155. [PMID: 37786710 PMCID: PMC10541611 DOI: 10.1101/2023.09.17.558155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/04/2023]
Abstract
Single-cell ATAC-seq has emerged as a powerful approach for revealing candidate cis-regulatory elements genome-wide at cell-type resolution. However, current single-cell methods suffer from limited throughput and high costs. Here, we present a novel technique called single-cell combinatorial fluidic indexing ATAC-sequencing ("scifi-ATAC-seq"), which combines a barcoded Tn5 pre-indexing step with droplet-based single-cell ATAC-seq using a widely commercialized microfluidics platform (10X Genomics). With scifi-ATAC-seq, up to 200,000 nuclei across multiple samples in a single emulsion reaction can be indexed, representing a ~20-fold increase in throughput compared to the standard 10X Genomics workflow.
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Affiliation(s)
- Xuan Zhang
- Department of Genetics, University of Georgia, Athens, GA, USA
| | - Alexandre P Marand
- Department of Genetics, University of Georgia, Athens, GA, USA
- Current address: Department of Molecular, Cellular, and Development Biology, University of Michigan, Ann Arbor, MI, USA
| | - Haidong Yan
- Department of Genetics, University of Georgia, Athens, GA, USA
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14
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Ye F, Wang J, Li J, Mei Y, Guo G. Mapping Cell Atlases at the Single-Cell Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2305449. [PMID: 38145338 PMCID: PMC10885669 DOI: 10.1002/advs.202305449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 12/01/2023] [Indexed: 12/26/2023]
Abstract
Recent advancements in single-cell technologies have led to rapid developments in the construction of cell atlases. These atlases have the potential to provide detailed information about every cell type in different organisms, enabling the characterization of cellular diversity at the single-cell level. Global efforts in developing comprehensive cell atlases have profound implications for both basic research and clinical applications. This review provides a broad overview of the cellular diversity and dynamics across various biological systems. In addition, the incorporation of machine learning techniques into cell atlas analyses opens up exciting prospects for the field of integrative biology.
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Affiliation(s)
- Fang Ye
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jingjing Wang
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
| | - Jiaqi Li
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Yuqing Mei
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
| | - Guoji Guo
- Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative MedicineZhejiang University School of MedicineHangzhouZhejiang310000China
- Liangzhu LaboratoryZhejiang UniversityHangzhouZhejiang311121China
- Zhejiang Provincial Key Lab for Tissue Engineering and Regenerative MedicineDr. Li Dak Sum & Yip Yio Chin Center for Stem Cell and Regenerative MedicineHangzhouZhejiang310058China
- Institute of HematologyZhejiang UniversityHangzhouZhejiang310000China
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15
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Mihai IS, Chafle S, Henriksson J. Representing and extracting knowledge from single-cell data. Biophys Rev 2024; 16:29-56. [PMID: 38495441 PMCID: PMC10937862 DOI: 10.1007/s12551-023-01091-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Accepted: 06/28/2023] [Indexed: 03/19/2024] Open
Abstract
Single-cell analysis is currently one of the most high-resolution techniques to study biology. The large complex datasets that have been generated have spurred numerous developments in computational biology, in particular the use of advanced statistics and machine learning. This review attempts to explain the deeper theoretical concepts that underpin current state-of-the-art analysis methods. Single-cell analysis is covered from cell, through instruments, to current and upcoming models. The aim of this review is to spread concepts which are not yet in common use, especially from topology and generative processes, and how new statistical models can be developed to capture more of biology. This opens epistemological questions regarding our ontology and models, and some pointers will be given to how natural language processing (NLP) may help overcome our cognitive limitations for understanding single-cell data.
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Affiliation(s)
- Ionut Sebastian Mihai
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
- Industrial Doctoral School, Umeå University, Umeå, Sweden
| | - Sarang Chafle
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
| | - Johan Henriksson
- The Laboratory for Molecular Infection Medicine Sweden (MIMS), Umeå, Sweden
- Umeå Centre for Microbial Research (UCMR), Department of Molecular Biology, Umeå University, Umeå, Sweden
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16
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Han Y, Huang C, Pan Y, Gu X. Single Cell Sequencing Technology and Its Application in Alzheimer's Disease. J Alzheimers Dis 2024; 97:1033-1050. [PMID: 38217599 DOI: 10.3233/jad-230861] [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: 01/15/2024]
Abstract
Alzheimer's disease (AD) involves degeneration of cells in the brain. Due to insidious onset and slow progression, AD is often not diagnosed until it gets progressed to a more severe stage. The diagnosis and treatment of AD has been a challenge. In recent years, high-throughput sequencing technologies have exhibited advantages in exploring the pathogenesis of diseases. However, the types of cells of the central nervous system are complex and traditional bulk sequencing cannot reflect their heterogeneity. Single-cell sequencing technology enables study at the individual cell level and has an irreplaceable advantage in the study of complex diseases. In recent years, this field has expanded rapidly and several types of single-cell sequencing technologies have emerged, including transcriptomics, epigenomics, genomics and proteomics. This review article provides an overview of these single-cell sequencing technologies and their application in AD.
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Affiliation(s)
- Yuru Han
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Congying Huang
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuhui Pan
- Center for Disease Control and Prevention of Harbin, Harbin, China
| | - Xuefeng Gu
- Shanghai Key Laboratory of Molecular Imaging, Zhoupu Hospital, Shanghai University of Medicine and Health Sciences, Shanghai, China
- School of Pharmacy, Shanghai University of Medicine & Health Sciences, Shanghai, China
- School of Health Sciences and Engineering, University of Shanghai for Science and Technology, Shanghai, China
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17
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Russell AJC, Weir JA, Nadaf NM, Shabet M, Kumar V, Kambhampati S, Raichur R, Marrero GJ, Liu S, Balderrama KS, Vanderburg CR, Shanmugam V, Tian L, Iorgulescu JB, Yoon CH, Wu CJ, Macosko EZ, Chen F. Slide-tags enables single-nucleus barcoding for multimodal spatial genomics. Nature 2024; 625:101-109. [PMID: 38093010 PMCID: PMC10764288 DOI: 10.1038/s41586-023-06837-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 11/06/2023] [Indexed: 12/17/2023]
Abstract
Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed1-6. However, missing from these measurements is the ability to routinely and easily spatially localize these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are tagged with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as an input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 μm spatial resolution and delivered whole-transcriptome data that are indistinguishable in quality from ordinary single-nucleus RNA-sequencing data. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualized receptor-ligand interactions driving B cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to almost any single-cell measurement technology. As a proof of principle, we performed multiomic measurements of open chromatin, RNA and T cell receptor (TCR) sequences in the same cells from metastatic melanoma, identifying transcription factor motifs driving cancer cell state transitions in spatially distinct microenvironments. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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Affiliation(s)
- Andrew J C Russell
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Jackson A Weir
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biological and Biomedical Sciences Program, Harvard University, Cambridge, MA, USA
| | - Naeem M Nadaf
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Vipin Kumar
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Sandeep Kambhampati
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard University, Boston, MA, USA
| | - Ruth Raichur
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | | | - Sophia Liu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Biophysics Program, Harvard University, Boston, MA, USA
- Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | | | - Vignesh Shanmugam
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Luyi Tian
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Guangzhou Laboratory, Guangdong, China
| | - J Bryan Iorgulescu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA
- Molecular Diagnostics Laboratory, Department of Hematopathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles H Yoon
- Department of Surgical Oncology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Catherine J Wu
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Stem Cell Transplantation and Cellular Therapies, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Evan Z Macosko
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
| | - Fei Chen
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.
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18
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Mukamel EA, Liu H, Behrens MM, Ecker JR. Cell type-specific enrichment of somatic aneuploidy in the mammalian brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572285. [PMID: 38187559 PMCID: PMC10769240 DOI: 10.1101/2023.12.18.572285] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Somatic mutations alter the genomes of a subset of an individual's brain cells1-3, impacting gene regulation and contributing to disease processes4,5. Mosaic single nucleotide variants have been characterized with single-cell resolution in the brain2,3, but we have limited information about large-scale structural variation, including whole-chromosome duplication or loss1,6,7. We used a dataset of over 415,000 single-cell DNA methylation and chromatin conformation profiles across the adult mouse brain to identify aneuploid cells comprehensively. Whole-chromosome loss or duplication occurred in <1% of cells, with rates up to 1.8% in non-neuronal cell types, including oligodendrocyte precursors and pericytes. Among all aneuploidies, we observed a strong enrichment of trisomy on chromosome 16, which is syntenic with human chromosome 21 and constitutively trisomic in Down syndrome. Chromosome 16 trisomy occurred in multiple cell types and across brain regions, suggesting that nondisjunction is a recurrent feature of somatic variation in the brain.
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Affiliation(s)
- Eran A. Mukamel
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA 92037, USA
| | - Hanqing Liu
- Genomic Analysis Laboratory, University of California, San Diego, La Jolla, CA 92037, USA
| | - M. Margarita Behrens
- Computational Neurobiology Laboratory, University of California, San Diego, La Jolla, CA 92037, USA
| | - Joseph R. Ecker
- Genomic Analysis Laboratory, University of California, San Diego, La Jolla, CA 92037, USA
- Howard Hughes Medical Institute, Salk Institute, La Jolla, CA 92037, USA
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19
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Song B, Liu D, Dai W, McMyn N, Wang Q, Yang D, Krejci A, Vasilyev A, Untermoser N, Loregger A, Song D, Williams B, Rosen B, Cheng X, Chao L, Kale HT, Zhang H, Diao Y, Bürckstümmer T, Siliciano JM, Li JJ, Siliciano R, Huangfu D, Li W. Decoding Heterogenous Single-cell Perturbation Responses. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.30.564796. [PMID: 37961332 PMCID: PMC10635009 DOI: 10.1101/2023.10.30.564796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Understanding diverse responses of individual cells to the same perturbation is central to many biological and biomedical problems. Current methods, however, do not precisely quantify the strength of perturbation responses and, more importantly, reveal new biological insights from heterogeneity in responses. Here we introduce the perturbation-response score (PS), based on constrained quadratic optimization, to quantify diverse perturbation responses at a single-cell level. Applied to single-cell transcriptomes of large-scale genetic perturbation datasets (e.g., Perturb-seq), PS outperforms existing methods for quantifying partial gene perturbation responses. In addition, PS presents two major advances. First, PS enables large-scale, single-cell-resolution dosage analysis of perturbation, without the need to titrate perturbation strength. By analyzing the dose-response patterns of over 2,000 essential genes in Perturb-seq, we identify two distinct patterns, depending on whether a moderate reduction in their expression induces strong downstream expression alterations. Second, PS identifies intrinsic and extrinsic biological determinants of perturbation responses. We demonstrate the application of PS in contexts such as T cell stimulation, latent HIV-1 expression, and pancreatic cell differentiation. Notably, PS unveiled a previously unrecognized, cell-type-specific role of coiled-coil domain containing 6 (CCDC6) in guiding liver and pancreatic lineage decisions, where CCDC6 knockouts drive the endoderm cell differentiation towards liver lineage, rather than pancreatic lineage. The PS approach provides an innovative method for dose-to-function analysis and will enable new biological discoveries from single-cell perturbation datasets. One sentence summary We present a method to quantify diverse perturbation responses and discover novel biological insights in single-cell perturbation datasets.
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20
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Queitsch K, Moore TW, O'Connell BL, Nichols RV, Muschler JL, Keith D, Lopez C, Sears RC, Mills GB, Yardımcı GG, Adey AC. Accessible high-throughput single-cell whole-genome sequencing with paired chromatin accessibility. CELL REPORTS METHODS 2023; 3:100625. [PMID: 37918402 PMCID: PMC10694488 DOI: 10.1016/j.crmeth.2023.100625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 08/29/2023] [Accepted: 10/11/2023] [Indexed: 11/04/2023]
Abstract
Single-cell whole-genome sequencing (scWGS) enables the assessment of genome-level molecular differences between individual cells with particular relevance to genetically diverse systems like solid tumors. The application of scWGS was limited due to a dearth of accessible platforms capable of producing high-throughput profiles. We present a technique that leverages nucleosome disruption methodologies with the widely adopted 10× Genomics ATAC-seq workflow to produce scWGS profiles for high-throughput copy-number analysis without new equipment or custom reagents. We further demonstrate the use of commercially available indexed transposase complexes from ScaleBio for sample multiplexing, reducing the per-sample preparation costs. Finally, we demonstrate that sequential indexed tagmentation with an intervening nucleosome disruption step allows for the generation of both ATAC and WGS data from the same cell, producing comparable data to the unimodal assays. By exclusively utilizing accessible commercial reagents, we anticipate that these scWGS and scWGS+ATAC methods can be broadly adopted by the research community.
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Affiliation(s)
- Konstantin Queitsch
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Travis W Moore
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Brendan L O'Connell
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Ruth V Nichols
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - John L Muschler
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Department of Biomedical Engineering, Oregon Health & Science University, Portland, OR, USA; Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Dove Keith
- Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Charles Lopez
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Rosalie C Sears
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Brenden-Colson Center for Pancreatic Care, Oregon Health & Science University, Portland, OR, USA
| | - Gordon B Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Department of Cell, Developmental and Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Galip Gürkan Yardımcı
- Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Andrew C Adey
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA; Cancer Early Detection Advanced Research Center, Oregon Health & Science University, Portland, OR, USA; Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA; Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR, USA.
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21
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Tang L, Huang ZP, Mei H, Hu Y. Insights gained from single-cell analysis of chimeric antigen receptor T-cell immunotherapy in cancer. Mil Med Res 2023; 10:52. [PMID: 37941075 PMCID: PMC10631149 DOI: 10.1186/s40779-023-00486-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Accepted: 10/10/2023] [Indexed: 11/10/2023] Open
Abstract
Advances in chimeric antigen receptor (CAR)-T cell therapy have significantly improved clinical outcomes of patients with relapsed or refractory hematologic malignancies. However, progress is still hindered as clinical benefit is only available for a fraction of patients. A lack of understanding of CAR-T cell behaviors in vivo at the single-cell level impedes their more extensive application in clinical practice. Mounting evidence suggests that single-cell sequencing techniques can help perfect the receptor design, guide gene-based T cell modification, and optimize the CAR-T manufacturing conditions, and all of them are essential for long-term immunosurveillance and more favorable clinical outcomes. The information generated by employing these methods also potentially informs our understanding of the numerous complex factors that dictate therapeutic efficacy and toxicities. In this review, we discuss the reasons why CAR-T immunotherapy fails in clinical practice and what this field has learned since the milestone of single-cell sequencing technologies. We further outline recent advances in the application of single-cell analyses in CAR-T immunotherapy. Specifically, we provide an overview of single-cell studies focusing on target antigens, CAR-transgene integration, and preclinical research and clinical applications, and then discuss how it will affect the future of CAR-T cell therapy.
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Affiliation(s)
- Lu Tang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China
| | - Zhong-Pei Huang
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China
| | - Heng Mei
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China.
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Yu Hu
- Institute of Hematology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Medical Center of Cell Therapy for Neoplastic Disease, Wuhan, 430022, China.
- Key Laboratory of Biological Targeted Therapy, The Ministry of Education, Wuhan, 430022, China.
- Hubei Key Laboratory of Biological Targeted Therapy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
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22
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Zhang X, Cao Q, Rajachandran S, Grow EJ, Evans M, Chen H. Dissecting mammalian reproduction with spatial transcriptomics. Hum Reprod Update 2023; 29:794-810. [PMID: 37353907 PMCID: PMC10628492 DOI: 10.1093/humupd/dmad017] [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: 03/19/2023] [Revised: 05/15/2023] [Indexed: 06/25/2023] Open
Abstract
BACKGROUND Mammalian reproduction requires the fusion of two specialized cells: an oocyte and a sperm. In addition to producing gametes, the reproductive system also provides the environment for the appropriate development of the embryo. Deciphering the reproductive system requires understanding the functions of each cell type and cell-cell interactions. Recent single-cell omics technologies have provided insights into the gene regulatory network in discrete cellular populations of both the male and female reproductive systems. However, these approaches cannot examine how the cellular states of the gametes or embryos are regulated through their interactions with neighboring somatic cells in the native tissue environment owing to tissue disassociations. Emerging spatial omics technologies address this challenge by preserving the spatial context of the cells to be profiled. These technologies hold the potential to revolutionize our understanding of mammalian reproduction. OBJECTIVE AND RATIONALE We aim to review the state-of-the-art spatial transcriptomics (ST) technologies with a focus on highlighting the novel biological insights that they have helped to reveal about the mammalian reproductive systems in the context of gametogenesis, embryogenesis, and reproductive pathologies. We also aim to discuss the current challenges of applying ST technologies in reproductive research and provide a sneak peek at what the field of spatial omics can offer for the reproduction community in the years to come. SEARCH METHODS The PubMed database was used in the search for peer-reviewed research articles and reviews using combinations of the following terms: 'spatial omics', 'fertility', 'reproduction', 'gametogenesis', 'embryogenesis', 'reproductive cancer', 'spatial transcriptomics', 'spermatogenesis', 'ovary', 'uterus', 'cervix', 'testis', and other keywords related to the subject area. All relevant publications until April 2023 were critically evaluated and discussed. OUTCOMES First, an overview of the ST technologies that have been applied to studying the reproductive systems was provided. The basic design principles and the advantages and limitations of these technologies were discussed and tabulated to serve as a guide for researchers to choose the best-suited technologies for their own research. Second, novel biological insights into mammalian reproduction, especially human reproduction revealed by ST analyses, were comprehensively reviewed. Three major themes were discussed. The first theme focuses on genes with non-random spatial expression patterns with specialized functions in multiple reproductive systems; The second theme centers around functionally interacting cell types which are often found to be spatially clustered in the reproductive tissues; and the thrid theme discusses pathological states in reproductive systems which are often associated with unique cellular microenvironments. Finally, current experimental and computational challenges of applying ST technologies to studying mammalian reproduction were highlighted, and potential solutions to tackle these challenges were provided. Future directions in the development of spatial omics technologies and how they will benefit the field of human reproduction were discussed, including the capture of cellular and tissue dynamics, multi-modal molecular profiling, and spatial characterization of gene perturbations. WIDER IMPLICATIONS Like single-cell technologies, spatial omics technologies hold tremendous potential for providing significant and novel insights into mammalian reproduction. Our review summarizes these novel biological insights that ST technologies have provided while shedding light on what is yet to come. Our review provides reproductive biologists and clinicians with a much-needed update on the state of art of ST technologies. It may also facilitate the adoption of cutting-edge spatial technologies in both basic and clinical reproductive research.
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Affiliation(s)
- Xin Zhang
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Qiqi Cao
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shreya Rajachandran
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward J Grow
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Melanie Evans
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Haiqi Chen
- Cecil H. and Ida Green Center for Reproductive Biology Sciences, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Obstetrics and Gynecology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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23
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Li Z, Gu H, Xu X, Tian Y, Huang X, Du Y. Unveiling the novel immune and molecular signatures of ovarian cancer: insights and innovations from single-cell sequencing. Front Immunol 2023; 14:1288027. [PMID: 38022625 PMCID: PMC10654630 DOI: 10.3389/fimmu.2023.1288027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Ovarian cancer is a highly heterogeneous and lethal malignancy with limited treatment options. Over the past decade, single-cell sequencing has emerged as an advanced biological technology capable of decoding the landscape of ovarian cancer at the single-cell resolution. It operates at the level of genes, transcriptomes, proteins, epigenomes, and metabolisms, providing detailed information that is distinct from bulk sequencing methods, which only offer average data for specific lesions. Single-cell sequencing technology provides detailed insights into the immune and molecular mechanisms underlying tumor occurrence, development, drug resistance, and immune escape. These insights can guide the development of innovative diagnostic markers, therapeutic strategies, and prognostic indicators. Overall, this review provides a comprehensive summary of the diverse applications of single-cell sequencing in ovarian cancer. It encompasses the identification and characterization of novel cell subpopulations, the elucidation of tumor heterogeneity, the investigation of the tumor microenvironment, the analysis of mechanisms underlying metastasis, and the integration of innovative approaches such as organoid models and multi-omics analysis.
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Affiliation(s)
- Zhongkang Li
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Haihan Gu
- Department of Pharmacy, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Xiaotong Xu
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yanpeng Tian
- Department of Obstetrics and Gynecology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Xianghua Huang
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yanfang Du
- Department of Obstetrics and Gynecology, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
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24
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Baysoy A, Bai Z, Satija R, Fan R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol 2023; 24:695-713. [PMID: 37280296 PMCID: PMC10242609 DOI: 10.1038/s41580-023-00615-w] [Citation(s) in RCA: 150] [Impact Index Per Article: 150.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/08/2023]
Abstract
Single-cell multi-omics technologies and methods characterize cell states and activities by simultaneously integrating various single-modality omics methods that profile the transcriptome, genome, epigenome, epitranscriptome, proteome, metabolome and other (emerging) omics. Collectively, these methods are revolutionizing molecular cell biology research. In this comprehensive Review, we discuss established multi-omics technologies as well as cutting-edge and state-of-the-art methods in the field. We discuss how multi-omics technologies have been adapted and improved over the past decade using a framework characterized by optimization of throughput and resolution, modality integration, uniqueness and accuracy, and we also discuss multi-omics limitations. We highlight the impact that single-cell multi-omics technologies have had in cell lineage tracing, tissue-specific and cell-specific atlas production, tumour immunology and cancer genetics, and in mapping of cellular spatial information in fundamental and translational research. Finally, we discuss bioinformatics tools that have been developed to link different omics modalities and elucidate functionality through the use of better mathematical modelling and computational methods.
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Affiliation(s)
- Alev Baysoy
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Zhiliang Bai
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Rahul Satija
- New York Genome Center, New York, NY, USA
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Rong Fan
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
- Yale Stem Cell Center and Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA.
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25
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Lu Z, Zhang M, Lee J, Sziraki A, Anderson S, Zhang Z, Xu Z, Jiang W, Ge S, Nelson PT, Zhou W, Cao J. Tracking cell-type-specific temporal dynamics in human and mouse brains. Cell 2023; 186:4345-4364.e24. [PMID: 37774676 PMCID: PMC10545416 DOI: 10.1016/j.cell.2023.08.042] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 05/28/2023] [Accepted: 08/30/2023] [Indexed: 10/01/2023]
Abstract
Progenitor cells are critical in preserving organismal homeostasis, yet their diversity and dynamics in the aged brain remain underexplored. We introduced TrackerSci, a single-cell genomic method that combines newborn cell labeling and combinatorial indexing to characterize the transcriptome and chromatin landscape of proliferating progenitor cells in vivo. Using TrackerSci, we investigated the dynamics of newborn cells in mouse brains across various ages and in a mouse model of Alzheimer's disease. Our dataset revealed diverse progenitor cell types in the brain and their epigenetic signatures. We further quantified aging-associated shifts in cell-type-specific proliferation and differentiation and deciphered the associated molecular programs. Extending our study to the progenitor cells in the aged human brain, we identified conserved genetic signatures across species and pinpointed region-specific cellular dynamics, such as the reduced oligodendrogenesis in the cerebellum. We anticipate that TrackerSci will be broadly applicable to unveil cell-type-specific temporal dynamics in diverse systems.
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Affiliation(s)
- Ziyu Lu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA; The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Melissa Zhang
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Jasper Lee
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Andras Sziraki
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA; The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Sonya Anderson
- Department of Pathology and Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Zehao Zhang
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA; The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Zihan Xu
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA; The David Rockefeller Graduate Program in Bioscience, The Rockefeller University, New York, NY, USA
| | - Weirong Jiang
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Shaoyu Ge
- Department of Neurobiology & Behavior, SUNY at Stony Brook, Stony Brook, NY, USA
| | - Peter T Nelson
- Department of Pathology and Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, USA
| | - Wei Zhou
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
| | - Junyue Cao
- Laboratory of Single Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA.
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26
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Jing K, Xu Y, Yang Y, Yin P, Ning D, Huang G, Deng Y, Chen G, Li G, Tian SZ, Zheng M. ScSmOP: a universal computational pipeline for single-cell single-molecule multiomics data analysis. Brief Bioinform 2023; 24:bbad343. [PMID: 37779245 DOI: 10.1093/bib/bbad343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 06/24/2023] [Accepted: 09/10/2023] [Indexed: 10/03/2023] Open
Abstract
Single-cell multiomics techniques have been widely applied to detect the key signature of cells. These methods have achieved a single-molecule resolution and can even reveal spatial localization. These emerging methods provide insights elucidating the features of genomic, epigenomic and transcriptomic heterogeneity in individual cells. However, they have given rise to new computational challenges in data processing. Here, we describe Single-cell Single-molecule multiple Omics Pipeline (ScSmOP), a universal pipeline for barcode-indexed single-cell single-molecule multiomics data analysis. Essentially, the C language is utilized in ScSmOP to set up spaced-seed hash table-based algorithms for barcode identification according to ligation-based barcoding data and synthesis-based barcoding data, followed by data mapping and deconvolution. We demonstrate high reproducibility of data processing between ScSmOP and published pipelines in comprehensive analyses of single-cell omics data (scRNA-seq, scATAC-seq, scARC-seq), single-molecule chromatin interaction data (ChIA-Drop, SPRITE, RD-SPRITE), single-cell single-molecule chromatin interaction data (scSPRITE) and spatial transcriptomic data from various cell types and species. Additionally, ScSmOP shows more rapid performance and is a versatile, efficient, easy-to-use and robust pipeline for single-cell single-molecule multiomics data analysis.
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Affiliation(s)
- Kai Jing
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yewen Xu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yang Yang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Pengfei Yin
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Duo Ning
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guangyu Huang
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yuqing Deng
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Gengzhan Chen
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Guoliang Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China
- Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
| | - Simon Zhongyuan Tian
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Meizhen Zheng
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
- Department of Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
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27
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Yi D, Nam JW, Jeong H. Toward the functional interpretation of somatic structural variations: bulk- and single-cell approaches. Brief Bioinform 2023; 24:bbad297. [PMID: 37587831 PMCID: PMC10516374 DOI: 10.1093/bib/bbad297] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/05/2023] [Accepted: 07/23/2023] [Indexed: 08/18/2023] Open
Abstract
Structural variants (SVs) are genomic rearrangements that can take many different forms such as copy number alterations, inversions and translocations. During cell development and aging, somatic SVs accumulate in the genome with potentially neutral, deleterious or pathological effects. Generation of somatic SVs is a key mutational process in cancer development and progression. Despite their importance, the detection of somatic SVs is challenging, making them less studied than somatic single-nucleotide variants. In this review, we summarize recent advances in whole-genome sequencing (WGS)-based approaches for detecting somatic SVs at the tissue and single-cell levels and discuss their advantages and limitations. First, we describe the state-of-the-art computational algorithms for somatic SV calling using bulk WGS data and compare the performance of somatic SV detectors in the presence or absence of a matched-normal control. We then discuss the unique features of cutting-edge single-cell-based techniques for analyzing somatic SVs. The advantages and disadvantages of bulk and single-cell approaches are highlighted, along with a discussion of their sensitivity to copy-neutral SVs, usefulness for functional inferences and experimental and computational costs. Finally, computational approaches for linking somatic SVs to their functional readouts, such as those obtained from single-cell transcriptome and epigenome analyses, are illustrated, with a discussion of the promise of these approaches in health and diseases.
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Affiliation(s)
- Dohun Yi
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Jin-Wu Nam
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Research Institute for Convergence of Basic Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
| | - Hyobin Jeong
- Department of Life Science, College of Natural Sciences, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Bio-BigData Center, Hanyang Institute of Bioscience and Biotechnology, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
- Hanyang Institute of Advanced BioConvergence, Hanyang University, Wangsimni-ro 222, Seongdong-gu, Seoul 04763, Republic of Korea
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28
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Wang K, Kumar T, Wang J, Minussi DC, Sei E, Li J, Tran TM, Thennavan A, Hu M, Casasent AK, Xiao Z, Bai S, Yang L, King LM, Shah V, Kristel P, van der Borden CL, Marks JR, Zhao Y, Zurita AJ, Aparicio A, Chapin B, Ye J, Zhang J, Gibbons DL, Sawyer E, Thompson AM, Futreal A, Hwang ES, Wesseling J, Lips EH, Navin NE. Archival single-cell genomics reveals persistent subclones during DCIS progression. Cell 2023; 186:3968-3982.e15. [PMID: 37586362 DOI: 10.1016/j.cell.2023.07.024] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2023] [Revised: 05/09/2023] [Accepted: 07/17/2023] [Indexed: 08/18/2023]
Abstract
Ductal carcinoma in situ (DCIS) is a common precursor of invasive breast cancer. Our understanding of its genomic progression to recurrent disease remains poor, partly due to challenges associated with the genomic profiling of formalin-fixed paraffin-embedded (FFPE) materials. Here, we developed Arc-well, a high-throughput single-cell DNA-sequencing method that is compatible with FFPE materials. We validated our method by profiling 40,330 single cells from cell lines, a frozen tissue, and 27 FFPE samples from breast, lung, and prostate tumors stored for 3-31 years. Analysis of 10 patients with matched DCIS and cancers that recurred 2-16 years later show that many primary DCIS had already undergone whole-genome doubling and clonal diversification and that they shared genomic lineages with persistent subclones in the recurrences. Evolutionary analysis suggests that most DCIS cases in our cohort underwent an evolutionary bottleneck, and further identified chromosome aberrations in the persistent subclones that were associated with recurrence.
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Affiliation(s)
- Kaile Wang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tapsi Kumar
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Junke Wang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Darlan Conterno Minussi
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Emi Sei
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianzhuo Li
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Tuan M Tran
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Aatish Thennavan
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Min Hu
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Anna K Casasent
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Zhenna Xiao
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Shanshan Bai
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lei Yang
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA
| | - Lorraine M King
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Vandna Shah
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London WC2R 2LS, UK
| | - Petra Kristel
- Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Carolien L van der Borden
- Division of Molecular Pathology, the Netherlands Cancer Institute, Amsterdam 1066 CX, the Netherlands
| | - Jeffrey R Marks
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Yuehui Zhao
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Amado J Zurita
- Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Ana Aparicio
- Department of Genitourinary Medical Oncology, UT MD Anderson Cancer Center, Houston, TX, USA
| | - Brian Chapin
- Department of Urology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jie Ye
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Jianjun Zhang
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Don L Gibbons
- Department of Thoracic/Head and Neck Medical Oncology, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Ellinor Sawyer
- School of Cancer and Pharmaceutical Sciences, Faculty of Life Sciences and Medicine, Guy's Cancer Centre, King's College London, London WC2R 2LS, UK
| | - Alastair M Thompson
- Department of Surgery, Dan L Duncan Comprehensive Cancer Center, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andrew Futreal
- Department of Genomic Medicine, UT MD Anderson Cancer Center, Houston, TX 77030, USA
| | - E Shelley Hwang
- Department of Surgery, Duke University School of Medicine, Durham, NC 27707, USA
| | - Jelle Wesseling
- Department of Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden 2333 ZC, the Netherlands
| | - Esther H Lips
- Department of Pathology, the Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam 1066 CX, the Netherlands; Department of Pathology, Leiden University Medical Center, Leiden 2333 ZC, the Netherlands
| | - Nicholas E Navin
- Department of Systems Biology, UT MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Genetics, UT MD Anderson Cancer Center, Houston, TX 77030, USA; MD Anderson UTHealth Graduate School of Biomedical Sciences, Houston, TX 77030, USA; Department of Bioinformatics, UT MD Anderson Cancer Center, Houston, TX 77030, USA.
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29
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Kim J, Kim S, Yeom H, Song SW, Shin K, Bae S, Ryu HS, Kim JY, Choi A, Lee S, Ryu T, Choi Y, Kim H, Kim O, Jung Y, Kim N, Han W, Lee HB, Lee AC, Kwon S. Barcoded multiple displacement amplification for high coverage sequencing in spatial genomics. Nat Commun 2023; 14:5261. [PMID: 37644058 PMCID: PMC10465490 DOI: 10.1038/s41467-023-41019-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023] Open
Abstract
Determining mutational landscapes in a spatial context is essential for understanding genetically heterogeneous cell microniches. Current approaches, such as Multiple Displacement Amplification (MDA), offer high genome coverage but limited multiplexing, which hinders large-scale spatial genomic studies. Here, we introduce barcoded MDA (bMDA), a technique that achieves high-coverage genomic analysis of low-input DNA while enhancing the multiplexing capabilities. By incorporating cell barcodes during MDA, bMDA streamlines library preparation in one pot, thereby overcoming a key bottleneck in spatial genomics. We apply bMDA to the integrative spatial analysis of triple-negative breast cancer tissues by examining copy number alterations, single nucleotide variations, structural variations, and kataegis signatures for each spatial microniche. This enables the assessment of subclonal evolutionary relationships within a spatial context. Therefore, bMDA has emerged as a scalable technology with the potential to advance the field of spatial genomics significantly.
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Affiliation(s)
- Jinhyun Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sungsik Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Huiran Yeom
- Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong, 18323, Republic of Korea
| | - Seo Woo Song
- Basic Science and Engineering Initiative, Children's Heart Center, Stanford University, Stanford, CA, USA
| | - Kyoungseob Shin
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sangwook Bae
- Renal Division and Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Han Suk Ryu
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Department of Pathology, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Ji Young Kim
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
| | - Ahyoun Choi
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea
| | - Taehoon Ryu
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yeongjae Choi
- School of Materials Science and Engineering, Gwangju Institute of Science and Technology (GIST), Gwangju, 61005, Republic of Korea
| | - Hamin Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Okju Kim
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Yushin Jung
- ATG Lifetech Inc., Seoul, 08507, Republic of Korea
| | - Namphil Kim
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - Wonshik Han
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea
| | - Han-Byoel Lee
- Cancer Research Institute, Seoul National University, Seoul, 03080, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
| | - Amos C Lee
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Meteor Biotech, Co. Ltd., Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Biomedical Research Institute, Seoul National University Hospital, Seoul, 03080, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Inter-University Semiconductor Research Center, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
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30
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Hård J, Mold JE, Eisfeldt J, Tellgren-Roth C, Häggqvist S, Bunikis I, Contreras-Lopez O, Chin CS, Nordlund J, Rubin CJ, Feuk L, Michaëlsson J, Ameur A. Long-read whole-genome analysis of human single cells. Nat Commun 2023; 14:5164. [PMID: 37620373 PMCID: PMC10449900 DOI: 10.1038/s41467-023-40898-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 08/07/2023] [Indexed: 08/26/2023] Open
Abstract
Long-read sequencing has dramatically increased our understanding of human genome variation. Here, we demonstrate that long-read technology can give new insights into the genomic architecture of individual cells. Clonally expanded CD8+ T-cells from a human donor were subjected to droplet-based multiple displacement amplification (dMDA) to generate long molecules with reduced bias. PacBio sequencing generated up to 40% genome coverage per single-cell, enabling detection of single nucleotide variants (SNVs), structural variants (SVs), and tandem repeats, also in regions inaccessible by short reads. 28 somatic SNVs were detected, including one case of mitochondrial heteroplasmy. 5473 high-confidence SVs/cell were discovered, a sixteen-fold increase compared to Illumina-based results from clonally related cells. Single-cell de novo assembly generated a genome size of up to 598 Mb and 1762 (12.8%) complete gene models. In summary, our work shows the promise of long-read sequencing toward characterization of the full spectrum of genetic variation in single cells.
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Affiliation(s)
- Joanna Hård
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden.
- Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
- ETH AI Center, ETH Zurich, Zurich, Switzerland.
| | - Jeff E Mold
- Department of Cell and Molecular Biology, Karolinska Institutet, Stockholm, Sweden
| | - Jesper Eisfeldt
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden
| | - Christian Tellgren-Roth
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Susana Häggqvist
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Ignas Bunikis
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | | | | | - Jessica Nordlund
- Science for Life Laboratory, Department of Medical Sciences, Uppsala University, Uppsala, Sweden
| | - Carl-Johan Rubin
- Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Lars Feuk
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden
| | - Jakob Michaëlsson
- Center for Infectious Medicine, Department of Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Adam Ameur
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Uppsala University, Uppsala, Sweden.
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31
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Acharya SN, Nichols RV, Rylaarsdam LE, O’Connell BL, Braun TP, Adey AC. sciMET-cap: High-throughput single-cell methylation analysis with a reduced sequencing burden. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.12.548718. [PMID: 37502923 PMCID: PMC10369954 DOI: 10.1101/2023.07.12.548718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
DNA methylation is a key component of the mammalian epigenome, playing a regulatory role in development, disease, and other processes. Robust, high-throughput single-cell DNA methylation assays are now possible (sciMET); however, the genome-wide nature of DNA methylation results in a high sequencing burden per cell. Here, we leverage target enrichment with sciMET to capture sufficient information per cell for cell type assignment using substantially fewer sequence reads (sciMET-cap). Sufficient off-target coverage further enables the production of near-complete methylomes for individual cell types. We characterize sciMET-cap on human PBMCs and brain (middle frontal gyrus).
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Affiliation(s)
- Sonia N. Acharya
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Ruth V. Nichols
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Lauren E. Rylaarsdam
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
| | - Brendan L. O’Connell
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA
| | - Theodore P. Braun
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Division of Hematology/Medical Oncology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Andrew C. Adey
- Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR, USA
- Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, OR, USA
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, USA
- Knight Cardiovascular Institute, Oregon Health and Science University, Portland, OR, USA
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32
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Xie B, Gao D, Zhou B, Chen S, Wang L. New discoveries in the field of metabolism by applying single-cell and spatial omics. J Pharm Anal 2023; 13:711-725. [PMID: 37577385 PMCID: PMC10422156 DOI: 10.1016/j.jpha.2023.06.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 05/29/2023] [Accepted: 06/02/2023] [Indexed: 08/15/2023] Open
Abstract
Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.
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Affiliation(s)
- Baocai Xie
- Department of Critical Care Medicine, Shenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, 518060, China
- Department of Respiratory Diseases, The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450014, China
| | - Dengfeng Gao
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Biqiang Zhou
- Department of Geriatric & Spinal Pain Multi-Department Treatment, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen, Guangdong, 518035, China
| | - Shi Chen
- Department of Critical Care Medicine, Shenzhen Institute of Translational Medicine, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, Guangdong, 518060, China
- Department of Gastroenterology, Ministry of Education Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
| | - Lianrong Wang
- Department of Respiratory Diseases, The Research and Application Center of Precision Medicine, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, 450014, China
- Department of Gastroenterology, Ministry of Education Key Laboratory of Combinatorial Biosynthesis and Drug Discovery, Zhongnan Hospital of Wuhan University, School of Pharmaceutical Sciences, Wuhan University, Wuhan, 430071, China
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33
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Fischer J, Schulz MH. Efficiently quantifying DNA methylation for bulk- and single-cell bisulfite data. Bioinformatics 2023; 39:btad386. [PMID: 37326968 PMCID: PMC10310462 DOI: 10.1093/bioinformatics/btad386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 05/17/2023] [Accepted: 06/14/2023] [Indexed: 06/17/2023] Open
Abstract
MOTIVATION DNA CpG methylation (CpGm) has proven to be a crucial epigenetic factor in the mammalian gene regulatory system. Assessment of DNA CpG methylation values via whole-genome bisulfite sequencing (WGBS) is, however, computationally extremely demanding. RESULTS We present FAst MEthylation calling (FAME), the first approach to quantify CpGm values directly from bulk or single-cell WGBS reads without intermediate output files. FAME is very fast but as accurate as standard methods, which first produce BS alignment files before computing CpGm values. We present experiments on bulk and single-cell bisulfite datasets in which we show that data analysis can be significantly sped-up and help addressing the current WGBS analysis bottleneck for large-scale datasets without compromising accuracy. AVAILABILITY AND IMPLEMENTATION An implementation of FAME is open source and licensed under GPL-3.0 at https://github.com/FischerJo/FAME.
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Affiliation(s)
- Jonas Fischer
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, United States
- Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken 66123, Germany
| | - Marcel H Schulz
- Department for Computational Biology and Applied Algorithmics, Max Planck Institute for Informatics, Saarbrücken 66123, Germany
- Institute of Cardiovascular Regeneration, Department of Medicine, Goethe University, Frankfurt am Main 60590, Germany
- Cardio-Pulmonary Institute, Goethe University, Frankfurt am Main, Germany
- German Centre for Cardiovascular Research, Partner Site Rhein-Main, Frankfurt am Main 60590, Germany
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34
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Russell AJC, Weir JA, Nadaf NM, Shabet M, Kumar V, Kambhampati S, Raichur R, Marrero GJ, Liu S, Balderrama KS, Vanderburg CR, Shanmugam V, Tian L, Wu CJ, Yoon CH, Macosko EZ, Chen F. Slide-tags: scalable, single-nucleus barcoding for multi-modal spatial genomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.01.535228. [PMID: 37066158 PMCID: PMC10103946 DOI: 10.1101/2023.04.01.535228] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Recent technological innovations have enabled the high-throughput quantification of gene expression and epigenetic regulation within individual cells, transforming our understanding of how complex tissues are constructed. Missing from these measurements, however, is the ability to routinely and easily spatially localise these profiled cells. We developed a strategy, Slide-tags, in which single nuclei within an intact tissue section are 'tagged' with spatial barcode oligonucleotides derived from DNA-barcoded beads with known positions. These tagged nuclei can then be used as input into a wide variety of single-nucleus profiling assays. Application of Slide-tags to the mouse hippocampus positioned nuclei at less than 10 micron spatial resolution, and delivered whole-transcriptome data that was indistinguishable in quality from ordinary snRNA-seq. To demonstrate that Slide-tags can be applied to a wide variety of human tissues, we performed the assay on brain, tonsil, and melanoma. We revealed cell-type-specific spatially varying gene expression across cortical layers and spatially contextualised receptor-ligand interactions driving B-cell maturation in lymphoid tissue. A major benefit of Slide-tags is that it is easily adaptable to virtually any single-cell measurement technology. As proof of principle, we performed multiomic measurements of open chromatin, RNA, and T-cell receptor sequences in the same cells from metastatic melanoma. We identified spatially distinct tumour subpopulations to be differentially infiltrated by an expanded T-cell clone and undergoing cell state transition driven by spatially clustered accessible transcription factor motifs. Slide-tags offers a universal platform for importing the compendium of established single-cell measurements into the spatial genomics repertoire.
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35
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Wirth J, Huber N, Yin K, Brood S, Chang S, Martinez-Jimenez CP, Meier M. Spatial transcriptomics using multiplexed deterministic barcoding in tissue. Nat Commun 2023; 14:1523. [PMID: 36934108 PMCID: PMC10024691 DOI: 10.1038/s41467-023-37111-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 03/02/2023] [Indexed: 03/20/2023] Open
Abstract
Spatially resolved transcriptomics of tissue sections enables advances in fundamental and applied biomedical research. Here, we present Multiplexed Deterministic Barcoding in Tissue (xDBiT) to acquire spatially resolved transcriptomes of nine tissue sections in parallel. New microfluidic chips were developed to spatially encode mRNAs over a total tissue area of 1.17 cm2 with a 50 µm resolution. Optimization of the biochemical protocol increased read and gene counts per spot by one order of magnitude compared to previous reports. Furthermore, the introduction of alignment markers allowed seamless registration of images and spatial transcriptomic spots. Together with technological advances, we provide an open-source computational pipeline to prepare raw sequencing data for downstream analysis. The functionality of xDBiT was demonstrated by acquiring 16 spatially resolved transcriptomic datasets from five different murine organs, including the cerebellum, liver, kidney, spleen, and heart. Factor analysis and deconvolution of spatial transcriptomes allowed for in-depth characterization of the murine kidney.
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Affiliation(s)
- Johannes Wirth
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany
| | - Nina Huber
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany
| | - Kelvin Yin
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany
| | - Sophie Brood
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany
| | - Simon Chang
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany
| | - Celia P Martinez-Jimenez
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany.
- TUM School of Medicine, Technical University of Munich, Munich, Germany.
| | - Matthias Meier
- Helmholtz Pioneer Campus, Helmholtz Munich, Munich, Germany.
- Center for Biotechnology and Biomedicine, University of Leipzig, Leipzig, Germany.
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36
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Olsen TR, Talla P, Furnari J, Bruce JN, Canoll P, Zha S, Sims PA. Scalable co-sequencing of RNA and DNA from individual nuclei. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.09.527940. [PMID: 36798358 PMCID: PMC9934633 DOI: 10.1101/2023.02.09.527940] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
The ideal technology for directly investigating the relationship between genotype and phenotype would analyze both RNA and DNA genome-wide and with single-cell resolution. However, existing tools lack the throughput required for comprehensive analysis of complex tumors and tissues. We introduce a highly scalable method for jointly profiling DNA and expression following nucleosome depletion (DEFND-seq). In DEFND-seq, nuclei are nucleosome-depleted, tagmented, and separated into individual droplets for mRNA and genomic DNA barcoding. Once nuclei have been depleted of nucleosomes, subsequent steps can be performed using the widely available 10x Genomics droplet microfluidic technology and commercial kits without experimental modification. We demonstrate the production of high-complexity mRNA and gDNA sequencing libraries from thousands of individual nuclei from both cell lines and archived surgical specimens for associating gene expression phenotypes with both copy number and single nucleotide variants.
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Affiliation(s)
- Timothy R Olsen
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
| | - Pranay Talla
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
| | - Julia Furnari
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032
| | - Jeffrey N Bruce
- Department of Neurological Surgery, Columbia University Irving Medical Center, New York, NY 10032
| | - Peter Canoll
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032
| | - Shan Zha
- Department of Pathology & Cell Biology, Columbia University Irving Medical Center, New York, NY, 10032
- Institute for Cancer Genetics, Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, 10032
| | - Peter A Sims
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032
- Sulzberger Columbia Genome Center, Columbia University Irving Medical Center, New York, NY, 10032
- Department of Biochemistry & Molecular Biophysics, Columbia University Irving Medical Center, New York, NY, 10032
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37
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Leonaviciene G, Mazutis L. RNA cytometry of single-cells using semi-permeable microcapsules. Nucleic Acids Res 2023; 51:e2. [PMID: 36268865 PMCID: PMC9841424 DOI: 10.1093/nar/gkac918] [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: 07/31/2022] [Revised: 09/23/2022] [Accepted: 10/07/2022] [Indexed: 01/29/2023] Open
Abstract
Analytical tools for gene expression profiling of individual cells are critical for studying complex biological systems. However, the techniques enabling rapid measurements of gene expression on thousands of single-cells are lacking. Here, we report a high-throughput RNA cytometry for digital profiling of single-cells isolated in liquid droplets enveloped by a thin semi-permeable membrane (microcapsules). Due to the selective permeability of the membrane, the desirable enzymes and reagents can be loaded, or replaced, in the microcapsule at any given step by simply changing the reaction buffer in which the microcapsules are dispersed. Therefore, complex molecular biology workflows can be readily adapted to conduct nucleic acid analysis on encapsulated mammalian cells, or other biological species. The microcapsules support sequential multi-step enzymatic reactions and remain intact under different biochemical conditions, freezing, thawing, and thermocycling. Combining microcapsules with conventional FACS provides a high-throughput approach for conducting RNA cytometry of individual cells based on their digital gene expression signature.
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Affiliation(s)
- Greta Leonaviciene
- Institute of Biotechnology, Life Sciences Centre, Vilnius University, 7 Sauletekio av., Vilnius, LT-10257, Lithuania
| | - Linas Mazutis
- Institute of Biotechnology, Life Sciences Centre, Vilnius University, 7 Sauletekio av., Vilnius, LT-10257, Lithuania
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38
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Kong S, Li R, Tian Y, Zhang Y, Lu Y, Ou Q, Gao P, Li K, Zhang Y. Single-cell omics: A new direction for functional genetic research in human diseases and animal models. Front Genet 2023; 13:1100016. [PMID: 36685871 PMCID: PMC9846559 DOI: 10.3389/fgene.2022.1100016] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 12/16/2022] [Indexed: 01/06/2023] Open
Abstract
Over the past decade, with the development of high-throughput single-cell sequencing technology, single-cell omics has been emerged as a powerful tool to understand the molecular basis of cellular mechanisms and refine our knowledge of diverse cell states. They can reveal the heterogeneity at different genetic layers and elucidate their associations by multiple omics analysis, providing a more comprehensive genetic map of biological regulatory networks. In the post-GWAS era, the molecular biological mechanisms influencing human diseases will be further elucidated by single-cell omics. This review mainly summarizes the development and trend of single-cell omics. This involves single-cell omics technologies, single-cell multi-omics technologies, multiple omics data integration methods, applications in various human organs and diseases, classic laboratory cell lines, and animal disease models. The review will reveal some perspectives for elucidating human diseases and constructing animal models.
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Affiliation(s)
- Siyuan Kong
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Rongrong Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yunhan Tian
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
- College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
| | - Yaqiu Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yuhui Lu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Qiaoer Ou
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Peiwen Gao
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Kui Li
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
| | - Yubo Zhang
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China; College of Animal Science and Technology, Qingdao Agricultural University, Qingdao, China
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-omics of MARA, Animal Functional Genomics Group, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
- College of Life Science and Engineering, Foshan University, Foshan, China
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39
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Sun H, Campoy JA, Schneeberger K. Gamete Binning to Achieve Haplotype-Resolved Genome Assembly. Methods Mol Biol 2023; 2590:201-218. [PMID: 36335501 DOI: 10.1007/978-1-0716-2819-5_13] [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: 06/16/2023]
Abstract
Haplotype-resolved genome assemblies remain a challenge in practice. Here, we provide a step-by-step guide on gamete binning, a method to generate haplotype-resolved genome assemblies for diploid species. The protocol starts by phasing heterozygous variants to individual haplotypes of specific chromosomes using the genome information of individual haploid gametes of the focal individual. Using phased variants, the whole-genome sequencing reads from the diploid genome can be genotyped and assigned into groups, which represent the individual haplotypes of each of the chromosomes. Finally, haplotype-specific chromosomes can be assembled independently using standard assembly tools. First applications of gamete binning revealed a haplotyping accuracy over 99%, which outperformed sequence-only or Hi-C-based haplotype-resolved genome assemblies.Availability: github.com/schneebergerlab/GameteBinning_prac .
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Affiliation(s)
- Hequan Sun
- Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - José A Campoy
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany
| | - Korbinian Schneeberger
- Faculty of Biology, LMU Munich, Planegg-Martinsried, Germany.
- Department of Chromosome Biology, Max Planck Institute for Plant Breeding Research, Cologne, Germany.
- Cluster of Excellence on Plant Sciences, Heinrich-Heine University, Düsseldorf, Germany.
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40
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Martin BK, Qiu C, Nichols E, Phung M, Green-Gladden R, Srivatsan S, Blecher-Gonen R, Beliveau BJ, Trapnell C, Cao J, Shendure J. Optimized single-nucleus transcriptional profiling by combinatorial indexing. Nat Protoc 2023; 18:188-207. [PMID: 36261634 PMCID: PMC9839601 DOI: 10.1038/s41596-022-00752-0] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Accepted: 06/30/2022] [Indexed: 01/17/2023]
Abstract
Single-cell combinatorial indexing RNA sequencing (sci-RNA-seq) is a powerful method for recovering gene expression data from an exponentially scalable number of individual cells or nuclei. However, sci-RNA-seq is a complex protocol that has historically exhibited variable performance on different tissues, as well as lower sensitivity than alternative methods. Here, we report a simplified, optimized version of the sci-RNA-seq protocol with three rounds of split-pool indexing that is faster, more robust and more sensitive and has a higher yield than the original protocol, with reagent costs on the order of 1 cent per cell or less. The total hands-on time from nuclei isolation to final library preparation takes 2-3 d, depending on the number of samples sharing the experiment. The improvements also allow RNA profiling from tissues rich in RNases like older mouse embryos or adult tissues that were problematic for the original method. We showcase the optimized protocol via whole-organism analysis of an E16.5 mouse embryo, profiling ~380,000 nuclei in a single experiment. Finally, we introduce a 'Tiny-Sci' protocol for experiments in which input material is very limited.
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Affiliation(s)
- Beth K. Martin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Chengxiang Qiu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Eva Nichols
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Melissa Phung
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Department of Biology, Case Western Reserve University, Cleveland, OH, USA
| | - Rula Green-Gladden
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Division of Hematology/Oncology, Seattle Children’s Hospital, Seattle, WA, USA
| | - Sanjay Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Ronnie Blecher-Gonen
- The Crown Genomics Institute of the Nancy and Stephen Grand Israel National Center for Personalized Medicine, Weizmann Institute of Science, Israel
| | - Brian J. Beliveau
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.,Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.,Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Junyue Cao
- Laboratory of Single-Cell Genomics and Population Dynamics, The Rockefeller University, New York, NY, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA. .,Brotman Baty Institute for Precision Medicine, Seattle, WA, USA. .,Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA. .,Howard Hughes Medical Institute, Seattle, WA, USA.
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41
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Nichols RV, O’Connell BL, Mulqueen RM, Thomas J, Woodfin AR, Acharya S, Mandel G, Pokholok D, Steemers FJ, Adey AC. High-throughput robust single-cell DNA methylation profiling with sciMETv2. Nat Commun 2022; 13:7627. [PMID: 36494343 PMCID: PMC9734657 DOI: 10.1038/s41467-022-35374-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Accepted: 11/28/2022] [Indexed: 12/13/2022] Open
Abstract
DNA methylation is a key epigenetic property that drives gene regulatory programs in development and disease. Current single-cell methods that produce high quality methylomes are expensive and low throughput without the aid of extensive automation. We previously described a proof-of-principle technique that enabled high cell throughput; however, it produced only low-coverage profiles and was a difficult protocol that required custom sequencing primers and recipes and frequently produced libraries with excessive adapter contamination. Here, we describe a greatly improved version that generates high-coverage profiles (~15-fold increase) using a robust protocol that does not require custom sequencing capabilities, includes multiple stopping points, and exhibits minimal adapter contamination. We demonstrate two versions of sciMETv2 on primary human cortex, a high coverage and rapid version, identifying distinct cell types using CH methylation patterns. These datasets are able to be directly integrated with one another as well as with existing snmC-seq2 datasets with little discernible bias. Finally, we demonstrate the ability to determine cell types using CG methylation alone, which is the dominant context for DNA methylation in most cell types other than neurons and the most applicable analysis outside of brain tissue.
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Affiliation(s)
- Ruth V. Nichols
- grid.5288.70000 0000 9758 5690Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - Brendan L. O’Connell
- grid.5288.70000 0000 9758 5690Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR USA ,grid.5288.70000 0000 9758 5690Cancer Early Detection Advanced Research Institute, Oregon Health & Science University, Portland, OR USA
| | - Ryan M. Mulqueen
- grid.5288.70000 0000 9758 5690Cancer Early Detection Advanced Research Institute, Oregon Health & Science University, Portland, OR USA
| | | | | | - Sonia Acharya
- grid.5288.70000 0000 9758 5690Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR USA
| | - Gail Mandel
- grid.5288.70000 0000 9758 5690Vollum Institute for Neuroscience, Oregon Health & Science University, Portland, OR USA
| | | | | | - Andrew C. Adey
- grid.5288.70000 0000 9758 5690Department of Molecular & Medical Genetics, Oregon Health & Science University, Portland, OR USA ,grid.5288.70000 0000 9758 5690Cancer Early Detection Advanced Research Institute, Oregon Health & Science University, Portland, OR USA ,grid.5288.70000 0000 9758 5690Knight Cancer Institute, Oregon Health & Science University, Portland, OR USA ,grid.5288.70000 0000 9758 5690Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR USA
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42
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Xu X, Zhang Q, Li M, Lin S, Liang S, Cai L, Zhu H, Su R, Yang C. Microfluidic single‐cell multiomics analysis. VIEW 2022. [DOI: 10.1002/viw.20220034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Affiliation(s)
- Xing Xu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Qiannan Zhang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Mingyin Li
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Shiyan Lin
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Shanshan Liang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Linfeng Cai
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Huanghuang Zhu
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Rui Su
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
| | - Chaoyong Yang
- Department of Chemical Biology, College of Chemistry and Chemical Engineering The First Affiliated Hospital of Xiamen UniversityXiamen University Xiamen China
- Institute of Molecular Medicine Renji Hospital Shanghai Jiao Tong University School of Medicine Shanghai China
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43
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Battenberg K, Kelly ST, Ras RA, Hetherington NA, Hayashi M, Minoda A. A flexible cross-platform single-cell data processing pipeline. Nat Commun 2022; 13:6847. [PMID: 36369450 PMCID: PMC9652453 DOI: 10.1038/s41467-022-34681-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 11/02/2022] [Indexed: 11/13/2022] Open
Abstract
Single-cell RNA-sequencing analysis to quantify the RNA molecules in individual cells has become popular, as it can obtain a large amount of information from each experiment. We introduce UniverSC ( https://github.com/minoda-lab/universc ), a universal single-cell RNA-seq data processing tool that supports any unique molecular identifier-based platform. Our command-line tool, docker image, and containerised graphical application enables consistent and comprehensive integration, comparison, and evaluation across data generated from a wide range of platforms. We also provide a cross-platform application to run UniverSC via a graphical user interface, available for macOS, Windows, and Linux Ubuntu, negating one of the bottlenecks with single-cell RNA-seq analysis that is data processing for researchers who are not bioinformatically proficient.
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Affiliation(s)
- Kai Battenberg
- Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
- Center for Sustainable Resource Science, RIKEN, Yokohama, Japan
| | - S Thomas Kelly
- Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | - Radu Abu Ras
- Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
- Faculty of Automatics, Computers and Electronics, University of Craiova, Craiova, Romania
| | - Nicola A Hetherington
- Department of Cell Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands
| | - Makoto Hayashi
- Center for Sustainable Resource Science, RIKEN, Yokohama, Japan
| | - Aki Minoda
- Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan.
- Department of Cell Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Radboud University, Nijmegen, The Netherlands.
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44
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Liu Z, Li H, Dang Q, Weng S, Duo M, Lv J, Han X. Integrative insights and clinical applications of single-cell sequencing in cancer immunotherapy. Cell Mol Life Sci 2022; 79:577. [DOI: 10.1007/s00018-022-04608-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 10/12/2022] [Accepted: 10/20/2022] [Indexed: 11/03/2022]
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45
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Qin R, Zhao H, He Q, Li F, Li Y, Zhao H. Advances in single-cell sequencing technology in the field of hepatocellular carcinoma. Front Genet 2022; 13:996890. [PMID: 36303541 PMCID: PMC9592975 DOI: 10.3389/fgene.2022.996890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 09/28/2022] [Indexed: 11/13/2022] Open
Abstract
Tumors are a class of diseases characterized by altered genetic information and uncontrolled growth. Sequencing technology provide researchers with a better way to explore specific tumor pathogenesis. In recent years, single-cell sequencing technology has shone in tumor research, especially in the study of liver cancer, revealing phenomena that were unexplored by previous studies. Single-cell sequencing (SCS) is a technique for sequencing the cellular genome, transcriptome, epigenome, proteomics, or metabolomics after dissociation of tissues into single cells. Compared with traditional bulk sequencing, single-cell sequencing can dissect human tumors at single-cell resolution, finely delineate different cell types, and reveal the heterogeneity of tumor cells. In view of the diverse pathological types and complex pathogenesis of hepatocellular carcinoma (HCC), the study of the heterogeneity among tumor cells can help improve its clinical diagnosis, treatment and prognostic judgment. On this basis, SCS has revolutionized our understanding of tumor heterogeneity, tumor immune microenvironment, and clonal evolution of tumor cells. This review summarizes the basic process and development of single-cell sequencing technology and its increasing role in the field of hepatocellular carcinoma.
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Affiliation(s)
- Rongyi Qin
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Haichao Zhao
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
| | - Qizu He
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Feng Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
| | - Yanjun Li
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Yanjun Li, ; Haoliang Zhao,
| | - Haoliang Zhao
- Third Hospital of Shanxi Medical University, Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Taiyuan, China
- Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, Taiyuan, China
- *Correspondence: Yanjun Li, ; Haoliang Zhao,
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46
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Vergara X, Schep R, Medema RH, van Steensel B. From fluorescent foci to sequence: Illuminating DNA double strand break repair by high-throughput sequencing technologies. DNA Repair (Amst) 2022; 118:103388. [PMID: 36037787 DOI: 10.1016/j.dnarep.2022.103388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 11/25/2022]
Abstract
Technologies to study DNA double-strand break (DSB) repair have traditionally mostly relied on fluorescence read-outs, either by microscopy or flow cytometry. The advent of high throughput sequencing (HTS) has created fundamentally new opportunities to study the mechanisms underlying DSB repair. Here, we review the suite of HTS-based assays that are used to study three different aspects of DNA repair: detection of broken ends, protein recruitment and pathway usage. We highlight new opportunities that HTS technology offers towards a better understanding of the DSB repair process.
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Affiliation(s)
- Xabier Vergara
- Division of Gene Regulation, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Division of Cell Biology, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, the Netherlands
| | - Ruben Schep
- Division of Gene Regulation, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, the Netherlands
| | - René H Medema
- Division of Cell Biology, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, the Netherlands.
| | - Bas van Steensel
- Division of Gene Regulation, Netherlands Cancer Institute, 1066 CX Amsterdam, the Netherlands; Oncode Institute, the Netherlands; Department of Cell Biology, Erasmus University Medical Centre, the Netherlands.
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47
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Chen F, Xue J, Bai M, Fan C, Zhao Y. Lighting Up Nucleic Acid Modifications in Single Cells with DNA-Encoded Amplification. Acc Chem Res 2022; 55:2248-2259. [PMID: 35904502 DOI: 10.1021/acs.accounts.2c00269] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Nucleic acids are naturally decorated with various chemical modifications at nucleobases. Most nucleic acid modifications (NAMs) do not alter Watson-Crick base pairing but can regulate gene expression known as "epigenetics". Their abundances present a very wide range, approximately 10-2 to 10-6 of total bases. Different NAMs may coexist in spatial proximity (e.g., <20 nm) in the crowded intracellular environment. Considering the highly dynamic chromatin accessibility (physical access to DNA), the NAMs in inaccessible DNA probably plays different roles. These multilayered features of NAMs vary from cell to cell. Our understanding of the function and mechanism of NAMs in biological processes and disease states has largely been driven by the expanding array of sequencing-based methodologies. However, an underexplored aspect is the measurement of the subcellular distribution, spatial proximity, and inaccessibility of NAMs in single cells. In recent years, we have developed new approaches that light up single-cell NAMs with single-site sensitivity. These methods are mainly based on the integration of chemical or chemoenzymatic tools, DNA amplification and nanotechnology, and/or microfluidics. An overview of these methods together with conventional methods such as immunofluorescence (IF) and fluorescence in situ hybridization (FISH) is provided in this Account.Our laboratory has proposed DNA-encoded amplification (DEA) as the main strategy for developing a set of single-cell NAM imaging methods. In brief, DEA transforms the different features of NAMs into unique DNA primers for rolling circle amplification (RCA) followed by FISH imaging. The first method is base-encoded amplifying FISH (BEA-FISH), in which we convert individual NAMs into RCA primers via chemoselective labeling and click bioconjugation. It enables the in situ visualization of low-abundance NAMs (e.g., 5hmU), which is impracticable by conventional methods. We subsequently developed pairwise proximity-differentiated amplifying FISH (PPDA-FISH), which integrates BEA-FISH with DNA nanotechnology. PPDA-FISH utilizes proximity ligation and toehold strand displacement to label the adjacent site of two different NAMs (one-to-one proximity) and their respective residual sites with three unique RCA probes. It achieves simultaneous counting of the above-mentioned three types of modified sites in the same cells. The third method is cellular macromolecule-tethered DNA walking indexing (Cell-TALKING) to probe more than two NAMs within the same nanoenvironments. Cell-TALKING uses dynamic DNA proximity cleavage to continuously activate different preblocked RCA primers (for each NAM) near one walking probe (for one target molecule). We have explored three NAMs around one histone (one-to-many proximity) in different cancer cell lines and clinical specimens. Then, we describe a single-cell hydrogel encoding amplification (scHEA) method by integrating droplet microfluidics with BEA-FISH. This method generates hydrogel beads that encapsulate single cells and their genomic DNA after cell lysis. It realizes the analysis of global (accessible and inaccessible) DNA from the same cells. We find that the global levels of both 5hmC and 5hmU in single cells can distinguish different breast cancer cells. Finally, the current limitations of these strategies and the future development directions are also discussed. We hope that this Account can spark new ideas and invite new efforts from different disciplines for single-cell NAM analysis.
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Affiliation(s)
- Feng Chen
- Institute of Analytical Chemistry and Instrument for Life Science, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Jing Xue
- Institute of Analytical Chemistry and Instrument for Life Science, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Min Bai
- Institute of Analytical Chemistry and Instrument for Life Science, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
| | - Chunhai Fan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules and National Center for Translational Medicine, Shanghai Jiao Tong University, Shanghai 200240, P. R. China
| | - Yongxi Zhao
- Institute of Analytical Chemistry and Instrument for Life Science, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi 710049, P. R. China
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48
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Zhang Y, Xu S, Wen Z, Gao J, Li S, Weissman SM, Pan X. Sample-multiplexing approaches for single-cell sequencing. Cell Mol Life Sci 2022; 79:466. [PMID: 35927335 PMCID: PMC11073057 DOI: 10.1007/s00018-022-04482-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 06/25/2022] [Accepted: 07/11/2022] [Indexed: 12/12/2022]
Abstract
Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.
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Affiliation(s)
- Yulong Zhang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Siwen Xu
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
- SequMed BioTechnology, Inc., Guangzhou, Guangdong, China
| | - Zebin Wen
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Jinyu Gao
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China
| | - Shuang Li
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China
| | - Sherman M Weissman
- Department of Genetics, Yale University School of Medicine, New Haven, CT, 06520-8005, USA
| | - Xinghua Pan
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Southern Medical University, Guangzhou, Guangdong, 510515, China.
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, China.
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49
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Small noncoding RNAs play superior roles in maintaining hematopoietic stem cell homeostasis. BLOOD SCIENCE 2022; 4:125-132. [DOI: 10.1097/bs9.0000000000000123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 05/31/2022] [Indexed: 11/25/2022] Open
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50
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Zhang X, Qiu H, Zhang F, Ding S. Advances in Single-Cell Multi-Omics and Application in Cardiovascular Research. Front Cell Dev Biol 2022; 10:883861. [PMID: 35733851 PMCID: PMC9207481 DOI: 10.3389/fcell.2022.883861] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 05/23/2022] [Indexed: 12/30/2022] Open
Abstract
With the development of ever more powerful and versatile high-throughput sequencing techniques and innovative ways to capture single cells, mapping the multicellular tissues at the single-cell level is becoming routine practice. However, it is still challenging to depict the epigenetic landscape of a single cell, especially the genome-wide chromatin accessibility, histone modifications, and DNA methylation. We summarize the most recent methodologies to profile these epigenetic marks at the single-cell level. We also discuss the development and advancement of several multi-omics sequencing technologies from individual cells. Advantages and limitations of various methods to compare and integrate datasets obtained from different sources are also included with specific practical notes. Understanding the heart tissue at single-cell resolution and multi-modal levels will help to elucidate the cell types and states involved in physiological and pathological events during heart development and disease. The rich information produced from single-cell multi-omics studies will also promote the research of heart regeneration and precision medicine on heart diseases.
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Affiliation(s)
- Xingwu Zhang
- Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China
| | - Hui Qiu
- Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China
- School of Life Sciences, Tsinghua University, Beijing, China
| | - Fengzhi Zhang
- First Hospital of Tsinghua University, Beijing, China
| | - Shuangyuan Ding
- Center for Stem Cell Biology and Regenerative Medicine, School of Medicine, Tsinghua University, Beijing, China
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