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Scholz R, Brösamle D, Yuan X, Beyer M, Neher JJ. Epigenetic control of microglial immune responses. Immunol Rev 2024; 323:209-226. [PMID: 38491845 DOI: 10.1111/imr.13317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 03/02/2024] [Indexed: 03/18/2024]
Abstract
Microglia, the major population of brain-resident macrophages, are now recognized as a heterogeneous population comprising several cell subtypes with different (so far mostly supposed) functions in health and disease. A number of studies have performed molecular characterization of these different microglial activation states over the last years making use of "omics" technologies, that is transcriptomics, proteomics and, less frequently, epigenomics profiling. These approaches offer the possibility to identify disease mechanisms, discover novel diagnostic biomarkers, and develop new therapeutic strategies. Here, we focus on epigenetic profiling as a means to understand microglial immune responses beyond what other omics methods can offer, that is, revealing past and present molecular responses, gene regulatory networks and potential future response trajectories, and defining cell subtype-specific disease relevance through mapping non-coding genetic variants. We review the current knowledge in the field regarding epigenetic regulation of microglial identity and function, provide an exemplary analysis that demonstrates the advantages of performing joint transcriptomic and epigenomic profiling of single microglial cells and discuss how comprehensive epigenetic analyses may enhance our understanding of microglial pathophysiology.
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Affiliation(s)
- Rebekka Scholz
- Immunogenomics & Neurodegeneration, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Desirée Brösamle
- Biomedical Center (BMC), Biochemistry, Faculty of Medicine, LMU Munich, Munich, Germany
- Neuroimmunology and Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Xidi Yuan
- Biomedical Center (BMC), Biochemistry, Faculty of Medicine, LMU Munich, Munich, Germany
- Neuroimmunology and Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Marc Beyer
- Immunogenomics & Neurodegeneration, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Systems Medicine, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Platform for Single Cell Genomics and Epigenomics, German Center for Neurodegenerative Diseases (DZNE) and University of Bonn and West German Genome Center, Bonn, Germany
| | - Jonas J Neher
- Biomedical Center (BMC), Biochemistry, Faculty of Medicine, LMU Munich, Munich, Germany
- Neuroimmunology and Neurodegenerative Diseases, German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Department of Cellular Neurology, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
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2
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Daddangadi A, Uppangala S, Kabekkodu SP, Khan G N, Kalthur G, Talevi R, Adiga SK. Advanced Maternal Age Affects the Cryosusceptibility of Ovulated but not In Vitro Matured Mouse Oocytes. Reprod Sci 2024; 31:1420-1428. [PMID: 38294668 DOI: 10.1007/s43032-024-01462-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024]
Abstract
Oocyte cryopreservation is offered to women of various age groups for both health and social reasons. Oocytes derived from either controlled ovarian stimulation or in vitro maturation (IVM) are cryopreserved via vitrification. As maternal age is a significant determinant of oocyte quality, there is limited data on the age-related susceptibility of oocytes to the vitrification-warming procedure alone or in conjunction with IVM. In the present study, metaphase II oocytes obtained from 2, 6, 9, and 12 month old Swiss albino mice either by superovulation or IVM were used. To understand the association between maternal age and oocyte cryotolerance, oocytes were subjected to vitrification-warming and compared to non vitrified sibling oocytes. Survived oocytes were evaluated for mitochondrial potential, spindle integrity, relative expression of spindle checkpoint protein transcripts, and DNA double-strand breaks. Maturation potential and vitrification-warming survival were significantly affected (p < 0.001 and p < 0.05, respectively) in ovulated oocytes from the advanced age group but not in IVM oocytes. Although vitrification-warming significantly increased spindle abnormalities in ovulated oocytes from advanced maternal age (p < 0.01), no significant changes were observed in IVM oocytes. Furthermore, Bub1 and Mad2 transcript levels were significantly higher in vitrified-warmed IVM oocytes (p < 0.05). In conclusion, advanced maternal age can have a negative impact on the cryosusceptibility of ovulated oocytes but not IVM oocytes in mice.
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Affiliation(s)
- Akshatha Daddangadi
- Centre of Excellence in Clinical Embryology, Department of Reproductive Science, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576 104, India
| | - Shubhashree Uppangala
- Division of Reproductive Genetics, Department of Reproductive Science, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576 104, India
| | - Shama Prasada Kabekkodu
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576 104, India
| | - Nadeem Khan G
- Department of Cell and Molecular Biology, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, 576 104, India
| | - Guruprasad Kalthur
- Division of Reproductive Biology, Department of Reproductive Science, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576 104, India
| | - Riccardo Talevi
- Dipartimento Di Biologia, Università Di Napoli "Federico II", Complesso Universitario Di Monte S Angelo, Naples, Italy
| | - Satish Kumar Adiga
- Centre of Excellence in Clinical Embryology, Department of Reproductive Science, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, 576 104, India.
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3
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Duhan L, Kumari D, Naime M, Parmar VS, Chhillar AK, Dangi M, Pasrija R. Single-cell transcriptomics: background, technologies, applications, and challenges. Mol Biol Rep 2024; 51:600. [PMID: 38689046 DOI: 10.1007/s11033-024-09553-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: 02/09/2024] [Accepted: 04/15/2024] [Indexed: 05/02/2024]
Abstract
Single-cell sequencing was developed as a high-throughput tool to elucidate unusual and transient cell states that are barely visible in the bulk. This technology reveals the evolutionary status of cells and differences between populations, helps to identify unique cell subtypes and states, reveals regulatory relationships between genes, targets and molecular mechanisms in disease processes, tumor heterogeneity, the state of the immune environment, etc. However, the high cost and technical limitations of single-cell sequencing initially prevented its widespread application, but with advances in research, numerous new single-cell sequencing techniques have been discovered, lowering the cost barrier. Many single-cell sequencing platforms and bioinformatics methods have recently become commercially available, allowing researchers to make fascinating observations. They are now increasingly being used in various industries. Several protocols have been discovered in this context and each technique has unique characteristics, capabilities and challenges. This review presents the latest advancements in single-cell transcriptomics technologies. This includes single-cell transcriptomics approaches, workflows and statistical approaches to data processing, as well as the potential advances, applications, opportunities and challenges of single-cell transcriptomics technology. You will also get an overview of the entry points for spatial transcriptomics and multi-omics.
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Affiliation(s)
- Lucky Duhan
- Department of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Deepika Kumari
- Department of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Mohammad Naime
- Central Research Institute of Unani Medicine (Under Central Council for Research in Unani Medicine, Ministry of Ayush, Govt of India), Uttar Pradesh, Lucknow, India
| | - Virinder S Parmar
- CUNY-Graduate Center and Departments of Chemistry, Nanoscience Program, City College & Medgar Evers College, The City University of New York, 1638 Bedford Avenue, Brooklyn, NY, 11225, USA
- Institute of Click Chemistry Research and Studies, Amity University, Noida, Uttar Pradesh, 201303, India
| | - Anil K Chhillar
- Centre for Biotechnology, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Mehak Dangi
- Centre for Bioinformatics, Maharshi Dayanand University, Rohtak, Haryana, 124001, India
| | - Ritu Pasrija
- Department of Biochemistry, Maharshi Dayanand University, Rohtak, Haryana, 124001, India.
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4
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Kaur H, Jha P, Ochatt SJ, Kumar V. Single-cell transcriptomics is revolutionizing the improvement of plant biotechnology research: recent advances and future opportunities. Crit Rev Biotechnol 2024; 44:202-217. [PMID: 36775666 DOI: 10.1080/07388551.2023.2165900] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Revised: 11/04/2022] [Accepted: 12/08/2022] [Indexed: 02/14/2023]
Abstract
Single-cell approaches are a promising way to obtain high-resolution transcriptomics data and have the potential to revolutionize the study of plant growth and development. Recent years have seen the advent of unprecedented technological advances in the field of plant biology to study the transcriptional information of individual cells by single-cell RNA sequencing (scRNA-seq). This review focuses on the modern advancements of single-cell transcriptomics in plants over the past few years. In addition, it also offers a new insight of how these emerging methods will expedite advance research in plant biotechnology in the near future. Lastly, the various technological hurdles and inherent limitations of single-cell technology that need to be conquered to develop such outstanding possible knowledge gain is critically analyzed and discussed.
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Affiliation(s)
- Harmeet Kaur
- Division of Research and Development, Plant Biotechnology Lab, Lovely Professional University, Phagwara, Punjab, India
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
| | - Priyanka Jha
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
- Department of Research Facilitation, Division of Research and Development, Lovely Professional University, Phagwara, Punjab, India
| | - Sergio J Ochatt
- Agroécologie, InstitutAgro Dijon, INRAE, Univ. Bourgogne Franche-Comté, Dijon, France
| | - Vijay Kumar
- Division of Research and Development, Plant Biotechnology Lab, Lovely Professional University, Phagwara, Punjab, India
- Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India
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5
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Ali M, Yang T, He H, Zhang Y. Plant biotechnology research with single-cell transcriptome: recent advancements and prospects. PLANT CELL REPORTS 2024; 43:75. [PMID: 38381195 DOI: 10.1007/s00299-024-03168-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 02/05/2024] [Indexed: 02/22/2024]
Abstract
KEY MESSAGE Single-cell transcriptomic techniques have emerged as powerful tools in plant biology, offering high-resolution insights into gene expression at the individual cell level. This review highlights the rapid expansion of single-cell technologies in plants, their potential in understanding plant development, and their role in advancing plant biotechnology research. Single-cell techniques have emerged as powerful tools to enhance our understanding of biological systems, providing high-resolution transcriptomic analysis at the single-cell level. In plant biology, the adoption of single-cell transcriptomics has seen rapid expansion of available technologies and applications. This review article focuses on the latest advancements in the field of single-cell transcriptomic in plants and discusses the potential role of these approaches in plant development and expediting plant biotechnology research in the near future. Furthermore, inherent challenges and limitations of single-cell technology are critically examined to overcome them and enhance our knowledge and understanding.
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Affiliation(s)
- Muhammad Ali
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China
- Peking University-Institute of Advanced Agricultural Sciences, Weifang, China
| | - Tianxia Yang
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China
- State Key Laboratory of Maize Bio-breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding (MOE), China Agricultural University, Beijing, China
| | - Hai He
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China
| | - Yu Zhang
- School of Agriculture, Sun Yat-Sen University, Shenzhen, 518107, China.
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6
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Tijhuis AE, Foijer F. Characterizing chromosomal instability-driven cancer evolution and cell fitness at a glance. J Cell Sci 2024; 137:jcs260199. [PMID: 38224461 DOI: 10.1242/jcs.260199] [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] [Indexed: 01/16/2024] Open
Abstract
Chromosomal instability (CIN), an increased rate of chromosome segregation errors during mitosis, is a hallmark of cancer cells. CIN leads to karyotype differences between cells and thus large-scale heterogeneity among individual cancer cells; therefore, it plays an important role in cancer evolution. Studying CIN and its consequences is technically challenging, but various technologies have been developed to track karyotype dynamics during tumorigenesis, trace clonal lineages and link genomic changes to cancer phenotypes at single-cell resolution. These methods provide valuable insight not only into the role of CIN in cancer progression, but also into cancer cell fitness. In this Cell Science at a Glance article and the accompanying poster, we discuss the relationship between CIN, cancer cell fitness and evolution, and highlight techniques that can be used to study the relationship between these factors. To that end, we explore methods of assessing cancer cell fitness, particularly for chromosomally unstable cancer.
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Affiliation(s)
- Andréa E Tijhuis
- European Research Institute for the Biology of Ageing , University Medical Center Groningen, University of Groningen,9713 AV Groningen, The Netherlands
| | - Floris Foijer
- European Research Institute for the Biology of Ageing , University Medical Center Groningen, University of Groningen,9713 AV Groningen, The Netherlands
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7
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Hu Y, Shen F, Yang X, Han T, Long Z, Wen J, Huang J, Shen J, Guo Q. Single-cell sequencing technology applied to epigenetics for the study of tumor heterogeneity. Clin Epigenetics 2023; 15:161. [PMID: 37821906 PMCID: PMC10568863 DOI: 10.1186/s13148-023-01574-x] [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: 07/16/2023] [Accepted: 09/27/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Previous studies have traditionally attributed the initiation of cancer cells to genetic mutations, considering them as the fundamental drivers of carcinogenesis. However, recent research has shed light on the crucial role of epigenomic alterations in various cell types present within the tumor microenvironment, suggesting their potential contribution to tumor formation and progression. Despite these significant findings, the progress in understanding the epigenetic mechanisms regulating tumor heterogeneity has been impeded over the past few years due to the lack of appropriate technical tools and methodologies. RESULTS The emergence of single-cell sequencing has enhanced our understanding of the epigenetic mechanisms governing tumor heterogeneity by revealing the distinct epigenetic layers of individual cells (chromatin accessibility, DNA/RNA methylation, histone modifications, nucleosome localization) and the diverse omics (transcriptomics, genomics, multi-omics) at the single-cell level. These technologies provide us with new insights into the molecular basis of intratumoral heterogeneity and help uncover key molecular events and driving mechanisms in tumor development. CONCLUSION This paper provides a comprehensive review of the emerging analytical and experimental approaches of single-cell sequencing in various omics, focusing specifically on epigenomics. These approaches have the potential to capture and integrate multiple dimensions of individual cancer cells, thereby revealing tumor heterogeneity and epigenetic features. Additionally, this paper outlines the future trends of these technologies and their current technical limitations.
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Affiliation(s)
- Yuhua Hu
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
- Graduate School, Dalian Medical University, Dalian, 116044, Liaoning, China
| | - Feng Shen
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
- Department of Neurosurgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
| | - Xi Yang
- Department of Radiation Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, 610041, China
| | - Tingting Han
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
- Graduate School, Dalian Medical University, Dalian, 116044, Liaoning, China
| | - Zhuowen Long
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
| | - Jiale Wen
- Graduate School, Dalian Medical University, Dalian, 116044, Liaoning, China
- Department of Cardiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China
| | - Junxing Huang
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
| | - Jiangfeng Shen
- Department of Thoracic Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
| | - Qing Guo
- Department of Oncology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
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8
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Shi Q, Chen X, Zhang Z. Decoding Human Biology and Disease Using Single-cell Omics Technologies. GENOMICS, PROTEOMICS & BIOINFORMATICS 2023; 21:926-949. [PMID: 37739168 PMCID: PMC10928380 DOI: 10.1016/j.gpb.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 05/22/2023] [Accepted: 06/08/2023] [Indexed: 09/24/2023]
Abstract
Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.
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Affiliation(s)
- Qiang Shi
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Xueyan Chen
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China
| | - Zemin Zhang
- Biomedical Pioneering Innovation Center, School of Life Sciences, Peking University, Beijing 100871, China; Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China; Changping Laboratory, Beijing 102206, China.
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9
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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: 89] [Impact Index Per Article: 89.0] [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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10
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Hamashima K, Wong KW, Sam TW, Teo JHJ, Taneja R, Le MTN, Li QJ, Hanna JH, Li H, Loh YH. Single-nucleus multiomic mapping of m 6A methylomes and transcriptomes in native populations of cells with sn-m6A-CT. Mol Cell 2023; 83:S1097-2765(23)00649-4. [PMID: 37657444 PMCID: PMC10895704 DOI: 10.1016/j.molcel.2023.08.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 06/21/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023]
Abstract
N6-methyladenosine (m6A) RNA modification plays important roles in the governance of gene expression and is temporally regulated in different cell states. In contrast to global m6A profiling in bulk sequencing, single-cell technologies for analyzing m6A heterogeneity are not extensively established. Here, we developed single-nucleus m6A-CUT&Tag (sn-m6A-CT) for simultaneous profiling of m6A methylomes and transcriptomes within a single nucleus using mouse embryonic stem cells (mESCs). m6A-CT is capable of enriching m6A-marked RNA molecules in situ, without isolating RNAs from cells. We adapted m6A-CT to the droplet-based single-cell omics platform and demonstrated high-throughput performance in analyzing nuclei isolated from thousands of cells from various cell types. We show that sn-m6A-CT profiling is sufficient to determine cell identity and allows the generation of cell-type-specific m6A methylome landscapes from heterogeneous populations. These indicate that sn-m6A-CT provides additional dimensions to multimodal datasets and insights into epitranscriptomic landscape in defining cell fate identity and states.
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Affiliation(s)
- Kiyofumi Hamashima
- Cell Fate Engineering and Therapeutics Lab, Cell Biology and Therapies Division, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A(∗)STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore.
| | - Ka Wai Wong
- Cell Fate Engineering and Therapeutics Lab, Cell Biology and Therapies Division, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A(∗)STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
| | - Tsz Wing Sam
- Cell Fate Engineering and Therapeutics Lab, Cell Biology and Therapies Division, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A(∗)STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore; Department of Physiology, NUS Yong Loo Lin School of Medicine, 2 Medical Drive, MD9, Singapore, Singapore
| | - Jia Hao Jackie Teo
- Cell Fate Engineering and Therapeutics Lab, Cell Biology and Therapies Division, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A(∗)STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore
| | - Reshma Taneja
- Department of Physiology, NUS Yong Loo Lin School of Medicine, 2 Medical Drive, MD9, Singapore, Singapore
| | - Minh T N Le
- Department of Pharmacology and Institute for Digital Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore; Department of Surgery, Immunology Program, Cancer Program, and Nanomedicine Translational Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
| | - Qi-Jing Li
- Institute of Molecular and Cell Biology, Agency for Science, Technology and Research (A(∗)STAR), Singapore 138673, Singapore; Singapore Immunology Network, Agency for Science, Technology and Research (A(∗)STAR), Singapore 138673, Singapore
| | - Jacob H Hanna
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Yuin-Han Loh
- Cell Fate Engineering and Therapeutics Lab, Cell Biology and Therapies Division, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A(∗)STAR), 61 Biopolis Drive, Proteos, Singapore 138673, Singapore; Department of Physiology, NUS Yong Loo Lin School of Medicine, 2 Medical Drive, MD9, Singapore, Singapore; Department of Biological Sciences, National University of Singapore, Singapore, Singapore; NUS Graduate School's Integrative Sciences and Engineering Programme, National University of Singapore, 28 Medical Drive, Singapore, Singapore.
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11
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Vandereyken K, Sifrim A, Thienpont B, Voet T. Methods and applications for single-cell and spatial multi-omics. Nat Rev Genet 2023; 24:494-515. [PMID: 36864178 PMCID: PMC9979144 DOI: 10.1038/s41576-023-00580-2] [Citation(s) in RCA: 180] [Impact Index Per Article: 180.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2023] [Indexed: 03/04/2023]
Abstract
The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome from single cells is transforming our understanding of cell biology in health and disease. In less than a decade, the field has seen tremendous technological revolutions that enable crucial new insights into the interplay between intracellular and intercellular molecular mechanisms that govern development, physiology and pathogenesis. In this Review, we highlight advances in the fast-developing field of single-cell and spatial multi-omics technologies (also known as multimodal omics approaches), and the computational strategies needed to integrate information across these molecular layers. We demonstrate their impact on fundamental cell biology and translational research, discuss current challenges and provide an outlook to the future.
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Affiliation(s)
- Katy Vandereyken
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Alejandro Sifrim
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Bernard Thienpont
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Thierry Voet
- KU Leuven Institute for Single Cell Omics (LISCO), University of Leuven, KU Leuven, Leuven, Belgium.
- Department of Human Genetics, University of Leuven, KU Leuven, Leuven, Belgium.
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD, USA.
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12
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Fernández-Moya SM, Ganesh AJ, Plass M. Neural cell diversity in the light of single-cell transcriptomics. Transcription 2023; 14:158-176. [PMID: 38229529 PMCID: PMC10807474 DOI: 10.1080/21541264.2023.2295044] [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: 06/27/2023] [Accepted: 11/10/2023] [Indexed: 01/18/2024] Open
Abstract
The development of highly parallel and affordable high-throughput single-cell transcriptomics technologies has revolutionized our understanding of brain complexity. These methods have been used to build cellular maps of the brain, its different regions, and catalog the diversity of cells in each of them during development, aging and even in disease. Now we know that cellular diversity is way beyond what was previously thought. Single-cell transcriptomics analyses have revealed that cell types previously considered homogeneous based on imaging techniques differ depending on several factors including sex, age and location within the brain. The expression profiles of these cells have also been exploited to understand which are the regulatory programs behind cellular diversity and decipher the transcriptional pathways driving them. In this review, we summarize how single-cell transcriptomics have changed our view on the cellular diversity in the human brain, and how it could impact the way we study neurodegenerative diseases. Moreover, we describe the new computational approaches that can be used to study cellular differentiation and gain insight into the functions of individual cell populations under different conditions and their alterations in disease.
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Affiliation(s)
- Sandra María Fernández-Moya
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
| | - Akshay Jaya Ganesh
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
| | - Mireya Plass
- Gene Regulation of Cell Identity, Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL), Barcelona, L’Hospitalet del Llobregat, Spain
- Program for Advancing Clinical Translation of Regenerative Medicine of Catalonia, P- CMR[C], Barcelona, L’Hospitalet del Llobregat, Spain
- Center for Networked Biomedical Research on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
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13
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Kim IS. Single-Cell Molecular Barcoding to Decode Multimodal Information Defining Cell States. Mol Cells 2023; 46:74-85. [PMID: 36859472 PMCID: PMC9982054 DOI: 10.14348/molcells.2023.2168] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 03/03/2023] Open
Abstract
Single-cell research has provided a breakthrough in biology to understand heterogeneous cell groups, such as tissues and organs, in development and disease. Molecular barcoding and subsequent sequencing technology insert a singlecell barcode into isolated single cells, allowing separation cell by cell. Given that multimodal information from a cell defines precise cellular states, recent technical advances in methods focus on simultaneously extracting multimodal data recorded in different biological materials (DNA, RNA, protein, etc.). This review summarizes recently developed singlecell multiomics approaches regarding genome, epigenome, and protein profiles with the transcriptome. In particular, we focus on how to anchor or tag molecules from a cell, improve throughputs with sample multiplexing, and record lineages, and we further discuss the future developments of the technology.
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Affiliation(s)
- Ik Soo Kim
- Department of Microbiology, Gachon University College of Medicine, Incheon 21999, Korea
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14
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Penkov D, Zubkova E, Parfyonova Y. Tn5 DNA Transposase in Multi-Omics Research. Methods Protoc 2023; 6:mps6020024. [PMID: 36961044 PMCID: PMC10037646 DOI: 10.3390/mps6020024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/06/2023] Open
Abstract
Tn5 transposase use in biotechnology has substantially advanced the sequencing applications of genome-wide analysis of cells. This is mainly due to the ability of Tn5 transposase to efficiently transpose DNA essentially randomly into any target DNA without the aid of other factors. This concise review is focused on the advances in Tn5 applications in multi-omics technologies, genome-wide profiling, and Tn5 hybrid molecule creation. The possibilities of other transposase uses are also discussed.
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Affiliation(s)
- Dmitry Penkov
- IRCCS San Raffaele Hospital, 20132 Milan, Italy
- National Medical Research Centre of Cardiology Named after E. I. Chazov, 121552 Moscow, Russia
| | - Ekaterina Zubkova
- National Medical Research Centre of Cardiology Named after E. I. Chazov, 121552 Moscow, Russia
| | - Yelena Parfyonova
- National Medical Research Centre of Cardiology Named after E. I. Chazov, 121552 Moscow, Russia
- Faculty of Medicine, Lomonosov Moscow State University, 119991 Moscow, Russia
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15
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Simultaneous Single-Cell Profiling of the Transcriptome and Accessible Chromatin Using SHARE-seq. Methods Mol Biol 2023; 2611:187-230. [PMID: 36807070 DOI: 10.1007/978-1-0716-2899-7_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
The ability to analyze the transcriptomic and epigenomic states of individual single cells has in recent years transformed our ability to measure and understand biological processes. Recent advancements have focused on increasing sensitivity and throughput to provide richer and deeper biological insights at the cellular level. The next frontier is the development of multiomic methods capable of analyzing multiple features from the same cell, such as the simultaneous measurement of the transcriptome and the chromatin accessibility of candidate regulatory elements. In this chapter, we discuss and describe SHARE-seq (Simultaneous high-throughput ATAC, and RNA expression with sequencing) for carrying out simultaneous chromatin accessibility and transcriptome measurements in single cells, together with the experimental and analytical considerations for achieving optimal results.
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16
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Yi Y, Zeng Y, Sam TW, Hamashima K, Tan RJR, Warrier T, Phua JX, Taneja R, Liou YC, Li H, Xu J, Loh YH. Ribosomal proteins regulate 2-cell-stage transcriptome in mouse embryonic stem cells. Stem Cell Reports 2023; 18:463-474. [PMID: 36638791 PMCID: PMC9968990 DOI: 10.1016/j.stemcr.2022.12.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 12/07/2022] [Accepted: 12/08/2022] [Indexed: 01/14/2023] Open
Abstract
A rare sub-population of mouse embryonic stem cells (mESCs), the 2-cell-like cell, is defined by the expression of MERVL and 2-cell-stage-specific transcript (2C transcript). Here, we report that the ribosomal proteins (RPs) RPL14, RPL18, and RPL23 maintain the identity of mESCs and regulate the expression of 2C transcripts. Disregulation of the RPs induces DUX-dependent expression of 2C transcripts and alters the chromatin landscape. Mechanically, knockdown (KD) of RPs triggers the binding of RPL11 to MDM2, an interaction known to prevent P53 protein degradation. Increased P53 protein upon RP KD further activates its downstream pathways, including DUX. Our study delineates the critical roles of RPs in 2C transcript activation, ascribing a novel function to these essential proteins.
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Affiliation(s)
- Yao Yi
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore; Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Yingying Zeng
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore; School of Biological Sciences, Nanyang Technological University, Singapore 637551, Singapore
| | - Tsz Wing Sam
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore; Department of Physiology, Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore
| | - Kiyofumi Hamashima
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore
| | - Rachel Jun Rou Tan
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore
| | - Tushar Warrier
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore
| | - Jun Xiang Phua
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore
| | - Reshma Taneja
- Department of Physiology, Healthy Longevity Translational Research Program, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore
| | - Yih-Cherng Liou
- Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA
| | - Jian Xu
- Department of Plant Systems Physiology, Institute for Water and Wetland Research, Radboud University, Heyendaalseweg 135, 6525 AJ Nijmegen, the Netherlands; Department of Biological Sciences and Centre for BioImaging Sciences, National University of Singapore, Singapore 117543, Singapore; Joint Center for Single Cell Biology, Shandong Agricultural University, Tai'an, Shandong 271018, China.
| | - Yuin-Han Loh
- Cell Fate Engineering and Therapeutics Laboratory, Division of Cell Biology and Therapies, Institute of Molecular and Cell Biology, A(∗)STAR, Singapore 138673, Singapore; Department of Biological Sciences, National University of Singapore, Singapore 117558, Singapore; NUS Graduate School for Integrative Sciences and Engineering Programme, National University of Singapore, Singapore 119077, Singapore; Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117593, Singapore.
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17
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Zhi Y, Li M, Lv G. Into the multi-omics era: Progress of T cells profiling in the context of solid organ transplantation. Front Immunol 2023; 14:1058296. [PMID: 36798139 PMCID: PMC9927650 DOI: 10.3389/fimmu.2023.1058296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 01/20/2023] [Indexed: 02/04/2023] Open
Abstract
T cells are the common type of lymphocyte to mediate allograft rejection, remaining long-term allograft survival impeditive. However, the heterogeneity of T cells, in terms of differentiation and activation status, the effector function, and highly diverse T cell receptors (TCRs) have thus precluded us from tracking these T cells and thereby comprehending their fate in recipients due to the limitations of traditional detection approaches. Recently, with the widespread development of single-cell techniques, the identification and characterization of T cells have been performed at single-cell resolution, which has contributed to a deeper comprehension of T cell heterogeneity by relevant detections in a single cell - such as gene expression, DNA methylation, chromatin accessibility, surface proteins, and TCR. Although these approaches can provide valuable insights into an individual cell independently, a comprehensive understanding can be obtained when applied joint analysis. Multi-omics techniques have been implemented in characterizing T cells in health and disease, including transplantation. This review focuses on the thesis, challenges, and advances in these technologies and highlights their application to the study of alloreactive T cells to improve the understanding of T cell heterogeneity in solid organ transplantation.
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Affiliation(s)
- Yao Zhi
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Mingqian Li
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
| | - Guoyue Lv
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital of Jilin University, Changchun, China
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18
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Preissl S, Gaulton KJ, Ren B. Characterizing cis-regulatory elements using single-cell epigenomics. Nat Rev Genet 2023; 24:21-43. [PMID: 35840754 PMCID: PMC9771884 DOI: 10.1038/s41576-022-00509-1] [Citation(s) in RCA: 54] [Impact Index Per Article: 54.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/24/2022] [Indexed: 12/24/2022]
Abstract
Cell type-specific gene expression patterns and dynamics during development or in disease are controlled by cis-regulatory elements (CREs), such as promoters and enhancers. Distinct classes of CREs can be characterized by their epigenomic features, including DNA methylation, chromatin accessibility, combinations of histone modifications and conformation of local chromatin. Tremendous progress has been made in cataloguing CREs in the human genome using bulk transcriptomic and epigenomic methods. However, single-cell epigenomic and multi-omic technologies have the potential to provide deeper insight into cell type-specific gene regulatory programmes as well as into how they change during development, in response to environmental cues and through disease pathogenesis. Here, we highlight recent advances in single-cell epigenomic methods and analytical tools and discuss their readiness for human tissue profiling.
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Affiliation(s)
- Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Institute of Experimental and Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
| | - Kyle J Gaulton
- Department of Paediatrics, Paediatric Diabetes Research Center, University of California San Diego, La Jolla, CA, USA.
| | - Bing Ren
- Center for Epigenomics, University of California San Diego, La Jolla, CA, USA.
- Department of Cellular and Molecular Medicine, University of California San Diego, School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
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19
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Highly sensitive single-cell chromatin accessibility assay and transcriptome coassay with METATAC. Proc Natl Acad Sci U S A 2022; 119:e2206450119. [PMID: 36161934 PMCID: PMC9546615 DOI: 10.1073/pnas.2206450119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The thriving field of single-cell genomics allows researchers to dissect the complexity and heterogeneity of tissues at single-cell resolution at large scale, involving transcriptome and epigenome. However, single-cell chromatin accessibility profiling methods exhibit low sensitivity. Here, we increased accessible chromatin detection sensitivity in single cells with METATAC, a single-cell ATAC-seq technique, with the help of META amplification strategy and other biochemical modifications. METATAC reached the highest accessible chromatin region detection efficiency compared with existing techniques, allowing more accurate cis-regulatory element coaccessibility measurement and allele-specific chromatin accessibility analysis in complex tissue samples. In combination with a high-resolution single-cell RNA sequencing assay, we further developed a high-sensitivity joint single-cell ATAC–RNA strategy, which helps us to better resolve gene regulatory programs. Recent advances in single-cell assay for transposase accessible chromatin using sequencing (scATAC-seq) and its coassays have transformed the field of single-cell epigenomics and transcriptomics. However, the low detection efficiency of current methods has limited our understanding of the true complexity of chromatin accessibility and its relationship with gene expression in single cells. Here, we report a high-sensitivity scATAC-seq method, termed multiplexed end-tagging amplification of transposase accessible chromatin (METATAC), which detects a large number of accessible sites per cell and is compatible with automation. Our high detectability and statistical framework allowed precise linking of enhancers to promoters without merging single cells. We systematically investigated allele-specific accessibility in the mouse cerebral cortex, revealing allele-specific accessibility of promotors of certain imprinted genes but biallelic accessibility of their enhancers. Finally, we combined METATAC with our high-sensitivity single-cell RNA sequencing (scRNA-seq) method, multiple annealing and looping based amplification cycles for digital transcriptomics (MALBAC-DT), to develop a joint ATAC–RNA assay, termed METATAC and MALBAC-DT coassay by sequencing (M2C-seq). M2C-seq achieved significant improvements for both ATAC and RNA compared with previous methods, with consistent performance across cell lines and early mouse embryos.
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20
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Xu W, Yang W, Zhang Y, Chen Y, Hong N, Zhang Q, Wang X, Hu Y, Song K, Jin W, Chen X. ISSAAC-seq enables sensitive and flexible multimodal profiling of chromatin accessibility and gene expression in single cells. Nat Methods 2022; 19:1243-1249. [PMID: 36109677 DOI: 10.1038/s41592-022-01601-4] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 08/01/2022] [Indexed: 11/09/2022]
Abstract
Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. Here, we develop in situ sequencing hetero RNA-DNA-hybrid after assay for transposase-accessible chromatin-sequencing (ISSAAC-seq), a highly sensitive and flexible single-cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single nucleus. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from over 10,000 nuclei from the mouse cerebral cortex, we uncovered major and rare cell types and cell-type specific regulatory elements and identified heterogeneity at the chromatin level within established cell types defined by gene expression. Finally, we revealed distinct dynamics and relationships of gene expression and chromatin accessibility during an oligodendrocyte maturation trajectory.
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Affiliation(s)
- Wei Xu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Weilong Yang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yunlong Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yawen Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Qian Zhang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Yukun Hu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Kun Song
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.,Brain Research Center and Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
| | - Xi Chen
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China.
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21
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Ogbeide S, Giannese F, Mincarelli L, Macaulay IC. Into the multiverse: advances in single-cell multiomic profiling. Trends Genet 2022; 38:831-843. [PMID: 35537880 DOI: 10.1016/j.tig.2022.03.015] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 03/23/2022] [Accepted: 03/25/2022] [Indexed: 10/18/2022]
Abstract
Single-cell transcriptomic approaches have revolutionised the study of complex biological systems, with the routine measurement of gene expression in thousands of cells enabling construction of whole-organism cell atlases. However, the transcriptome is just one layer amongst many that coordinate to define cell type and state and, ultimately, function. In parallel with the widespread uptake of single-cell RNA-seq (scRNA-seq), there has been a rapid emergence of methods that enable multiomic profiling of individual cells, enabling parallel measurement of intercellular heterogeneity in the genome, epigenome, transcriptome, and proteomes. Linking measurements from each of these layers has the potential to reveal regulatory and functional mechanisms underlying cell behaviour in healthy development and disease.
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22
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Li Z, Seehawer M, Polyak K. Untangling the web of intratumour heterogeneity. Nat Cell Biol 2022; 24:1192-1201. [PMID: 35941364 DOI: 10.1038/s41556-022-00969-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 06/27/2022] [Indexed: 02/06/2023]
Abstract
Intratumour heterogeneity (ITH) is a hallmark of cancer that drives tumour evolution and disease progression. Technological and computational advances have enabled us to assess ITH at unprecedented depths, yet this accumulating knowledge has not had a substantial clinical impact. This is in part due to a limited understanding of the functional relevance of ITH and the inadequacy of preclinical experimental models to reproduce it. Here, we discuss progress made in these areas and illuminate future directions.
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Affiliation(s)
- Zheqi Li
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Marco Seehawer
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kornelia Polyak
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA. .,Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA. .,Department of Medicine, Harvard Medical School, Boston, MA, USA.
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23
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Pan L, Ku WL, Tang Q, Cao Y, Zhao K. scPCOR-seq enables co-profiling of chromatin occupancy and RNAs in single cells. Commun Biol 2022; 5:678. [PMID: 35804086 PMCID: PMC9270334 DOI: 10.1038/s42003-022-03584-6] [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: 01/25/2022] [Accepted: 06/14/2022] [Indexed: 11/30/2022] Open
Abstract
Cell-to-cell variation in gene expression is a widespread phenomenon, which may play important roles in cellular differentiation, function, and disease development1–9. Chromatin is implicated in contributing to the cellular heterogeneity in gene expression10–16. Fully understanding the mechanisms of cellular heterogeneity requires simultaneous measurement of RNA and occupancy of histone modifications and transcription factors on chromatin due to their critical roles in transcriptional regulation17,18. We generally term the occupancy of histone modifications and transcription factors as Chromatin occupancy. Here, we report a technique, termed scPCOR-seq (single-cell Profiling of Chromatin Occupancy and RNAs Sequencing), for simultaneously profiling genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell. We demonstrated that scPCOR-seq can profile either H3K4me3 or RNAPII and RNAs in a mixture of human H1, GM12878 and 293 T cells at a single-cell resolution and either H3K4me3, RNAPII, or RNA profile can correctly separate the cells. Application of scPCOR-seq to the in vitro differentiation of the erythrocyte precursor CD36 cells from human CD34 stem or progenitor cells revealed that H3K4me3 and RNA exhibit distinct properties in clustering cells during differentiation. Overall, our work provides a promising approach to understand the relationships among different omics layers. scPCOR-seq is a single-cell sequencing technique that enables simultaneous profiling of genome-wide chromatin protein binding or histone modification marks and RNA expression in the same cell.
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Affiliation(s)
- Lixia Pan
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Wai Lim Ku
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Qingsong Tang
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yaqiang Cao
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Keji Zhao
- Laboratory of Epigenome Biology, Systems Biology Center, Division of Intramural Research, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD, USA.
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24
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Källberg J, Xiao W, Van Assche D, Baret JC, Taly V. Frontiers in single cell analysis: multimodal technologies and their clinical perspectives. LAB ON A CHIP 2022; 22:2403-2422. [PMID: 35703438 DOI: 10.1039/d2lc00220e] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Single cell multimodal analysis is at the frontier of single cell research: it defines the roles and functions of distinct cell types through simultaneous analysis to provide unprecedented insight into cellular processes. Current single cell approaches are rapidly moving toward multimodal characterizations. It replaces one-dimensional single cell analysis, for example by allowing for simultaneous measurement of transcription and post-transcriptional regulation, epigenetic modifications and/or surface protein expression. By providing deeper insights into single cell processes, multimodal single cell analyses paves the way to new understandings in various cellular processes such as cell fate decisions, physiological heterogeneity or genotype-phenotype linkages. At the forefront of this, microfluidics is key for high-throughput single cell analysis. Here, we present an overview of the recent multimodal microfluidic platforms having a potential in biomedical research, with a specific focus on their potential clinical applications.
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Affiliation(s)
- Julia Källberg
- Centre de Recherche des Cordeliers, INSERM, CNRS, Université Paris Cité, Sorbonne Université, USPC, Equipe labellisée Ligue Nationale contre le cancer, Paris, France.
| | - Wenjin Xiao
- Centre de Recherche des Cordeliers, INSERM, CNRS, Université Paris Cité, Sorbonne Université, USPC, Equipe labellisée Ligue Nationale contre le cancer, Paris, France.
| | - David Van Assche
- University of Bordeaux, CNRS, Centre de Recherche Paul Pascal, UMR 5031, Pessac 33600, France.
| | - Jean-Christophe Baret
- University of Bordeaux, CNRS, Centre de Recherche Paul Pascal, UMR 5031, Pessac 33600, France.
- Institut Universitaire de France, Paris 75005, France
| | - Valerie Taly
- Centre de Recherche des Cordeliers, INSERM, CNRS, Université Paris Cité, Sorbonne Université, USPC, Equipe labellisée Ligue Nationale contre le cancer, Paris, France.
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25
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Dimitriu MA, Lazar-Contes I, Roszkowski M, Mansuy IM. Single-Cell Multiomics Techniques: From Conception to Applications. Front Cell Dev Biol 2022; 10:854317. [PMID: 35386194 PMCID: PMC8979110 DOI: 10.3389/fcell.2022.854317] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 02/14/2022] [Indexed: 01/16/2023] Open
Abstract
Recent advances in methods for single-cell analyses and barcoding strategies have led to considerable progress in research. The development of multiplexed assays offers the possibility to conduct parallel analyses of multiple factors and processes for comprehensive characterization of cellular and molecular states in health and disease. These technologies have expanded extremely rapidly in the past years and constantly evolve and provide better specificity, precision and resolution. This review summarizes recent progress in single-cell multiomics approaches, and focuses, in particular, on the most innovative techniques that integrate genome, epigenome and transcriptome profiling. It describes the methodologies, discusses their advantages and limitations, and explains how they have been applied to studies on cell heterogeneity and differentiation, and epigenetic reprogramming.
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Affiliation(s)
- Maria A Dimitriu
- Laboratory of Neuroepigenetics, Brain Research Institute, University of Zurich and Institute for Neuroscience, ETH Zurich, Zurich, Switzerland
| | - Irina Lazar-Contes
- Laboratory of Neuroepigenetics, Brain Research Institute, University of Zurich and Institute for Neuroscience, ETH Zurich, Zurich, Switzerland
| | - Martin Roszkowski
- Laboratory of Neuroepigenetics, Brain Research Institute, University of Zurich and Institute for Neuroscience, ETH Zurich, Zurich, Switzerland
| | - Isabelle M Mansuy
- Laboratory of Neuroepigenetics, Brain Research Institute, University of Zurich and Institute for Neuroscience, ETH Zurich, Zurich, Switzerland
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26
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Marand AP, Schmitz RJ. Single-cell analysis of cis-regulatory elements. CURRENT OPINION IN PLANT BIOLOGY 2022; 65:102094. [PMID: 34390932 DOI: 10.1016/j.pbi.2021.102094] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/06/2021] [Accepted: 07/14/2021] [Indexed: 06/13/2023]
Abstract
Plant tissues and organs are composed of functionally discrete cell types that are all defined by the same genome sequence. Cell-type variation in part arises from differential accessibility of cis-regulatory elements that encode the blueprints for transcriptional programs underlying cell identity and function. Owing to technical limitations, the role of cis-regulatory elements in cell identity maintenance, differentiation, and functional specialization has remained relatively unexplored in plant systems. Single-cell profiling has emerged as a powerful tool to circumvent these past obstacles by enabling unbiased charting of transcriptional and cis-regulatory states at the resolution of individual cells. Here, we review state-of-the-art single-cell approaches and analytical frameworks that have paved the way for establishing the link between cellular phenotypic variation and cis-regulatory mechanisms in plants.
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Affiliation(s)
| | - Robert J Schmitz
- Department of Genetics, University of Georgia, Athens, GA 30602, USA.
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27
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Emerging Single-cell Approaches to Understand HIV in the Central Nervous System. Curr HIV/AIDS Rep 2021; 19:113-120. [PMID: 34822063 PMCID: PMC8613726 DOI: 10.1007/s11904-021-00586-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/05/2021] [Indexed: 11/23/2022]
Abstract
Purpose of Review This review highlights emerging single-cell sequencing methods relevant to translational studies of HIV in the central nervous system (CNS), summarizes limited single-cell studies of HIV in the CNS, and discusses opportunities for future HIV translational CNS studies. Recent Findings Innovative methods utilizing single-cell technologies have advanced the study of genomes, proteomes, transcriptomes, and epigenomes at an enhanced resolution and depth. Single-cell analyses of central nervous system tissue, including autopsy brain and CSF cells, may shed light on CNS perturbations in people living with HIV. New strategies can distinguish distinct molecular identifies of rare infected cells at single-cell level, suggesting an opportunity to uncloak the molecular identity of hidden HIV in the CNS reservoir. Summary Adoption of multimodal “omics” analyses to translational HIV studies and tissue compartments beyond blood will be critical to advancing our understanding of viral establishment, persistence, and eradication.
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28
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Single-cell chromatin state analysis with Signac. Nat Methods 2021; 18:1333-1341. [PMID: 34725479 PMCID: PMC9255697 DOI: 10.1038/s41592-021-01282-5] [Citation(s) in RCA: 469] [Impact Index Per Article: 156.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 08/27/2021] [Indexed: 11/08/2022]
Abstract
The recent development of experimental methods for measuring chromatin state at single-cell resolution has created a need for computational tools capable of analyzing these datasets. Here we developed Signac, a comprehensive toolkit for the analysis of single-cell chromatin data. Signac enables an end-to-end analysis of single-cell chromatin data, including peak calling, quantification, quality control, dimension reduction, clustering, integration with single-cell gene expression datasets, DNA motif analysis and interactive visualization. Through its seamless compatibility with the Seurat package, Signac facilitates the analysis of diverse multimodal single-cell chromatin data, including datasets that co-assay DNA accessibility with gene expression, protein abundance and mitochondrial genotype. We demonstrate scaling of the Signac framework to analyze datasets containing over 700,000 cells.
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29
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Stein DF, Chen H, Vinyard ME, Qin Q, Combs RD, Zhang Q, Pinello L. singlecellVR: Interactive Visualization of Single-Cell Data in Virtual Reality. Front Genet 2021; 12:764170. [PMID: 34777482 PMCID: PMC8582280 DOI: 10.3389/fgene.2021.764170] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 09/27/2021] [Indexed: 11/21/2022] Open
Abstract
Single-cell assays have transformed our ability to model heterogeneity within cell populations. As these assays have advanced in their ability to measure various aspects of molecular processes in cells, computational methods to analyze and meaningfully visualize such data have required matched innovation. Independently, Virtual Reality (VR) has recently emerged as a powerful technology to dynamically explore complex data and shows promise for adaptation to challenges in single-cell data visualization. However, adopting VR for single-cell data visualization has thus far been hindered by expensive prerequisite hardware or advanced data preprocessing skills. To address current shortcomings, we present singlecellVR, a user-friendly web application for visualizing single-cell data, designed for cheap and easily available virtual reality hardware (e.g., Google Cardboard, ∼$8). singlecellVR can visualize data from a variety of sequencing-based technologies including transcriptomic, epigenomic, and proteomic data as well as combinations thereof. Analysis modalities supported include approaches to clustering as well as trajectory inference and visualization of dynamical changes discovered through modelling RNA velocity. We provide a companion software package, scvr to streamline data conversion from the most widely-adopted single-cell analysis tools as well as a growing database of pre-analyzed datasets to which users can contribute.
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Affiliation(s)
- David F. Stein
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, United States
| | - Huidong Chen
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, United States
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Michael E. Vinyard
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, United States
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, United States
| | - Qian Qin
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, United States
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Rebecca D. Combs
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
- Winsor School, Boston, MA, United States
| | - Qian Zhang
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, United States
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
| | - Luca Pinello
- Molecular Pathology Unit, Massachusetts General Hospital, Charlestown, MA, United States
- Center for Cancer Research, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Pathology, Harvard Medical School, Boston, MA, United States
- Broad Institute of Harvard and MIT, Cambridge, MA, United States
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30
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Ding J, Alavi A, Ebrahimkhani MR, Bar-Joseph Z. Computational tools for analyzing single-cell data in pluripotent cell differentiation studies. CELL REPORTS METHODS 2021; 1:100087. [PMID: 35474899 PMCID: PMC9017169 DOI: 10.1016/j.crmeth.2021.100087] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
Single-cell technologies are revolutionizing the ability of researchers to infer the causes and results of biological processes. Although several studies of pluripotent cell differentiation have recently utilized single-cell sequencing data, other aspects related to the optimization of differentiation protocols, their validation, robustness, and usage are still not taking full advantage of single-cell technologies. In this review, we focus on computational approaches for the analysis of single-cell omics and imaging data and discuss their use to address many of the major challenges involved in the development, validation, and use of cells obtained from pluripotent cell differentiation.
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Affiliation(s)
- Jun Ding
- Meakins-Christie Laboratories, Department of Medicine, McGill University Health Centre, 1001 Decarie Boulevard, Montreal QC H4A 3J1, Canada
| | - Amir Alavi
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
| | - Mo R. Ebrahimkhani
- Department of Pathology, School of Medicine, University of Pittsburgh, 3550 Terrace Street, Pittsburgh, PA 15261, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
- Machine Learning Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA
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31
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Abstract
It has been just over 10 years since the initial description of transposase-based methods to prepare high-throughput sequencing libraries, or "tagmentation," in which a hyperactive transposase is used to simultaneously fragment target DNA and append universal adapter sequences. Tagmentation effectively replaced a series of processing steps in traditional workflows with one single reaction. It is the simplicity, coupled with the high efficiency of tagmentation, that has made it a favored means of sequencing library construction and fueled a diverse range of adaptations to assay a variety of molecular properties. In recent years, this has been centered in the single-cell space with a catalog of tagmentation-based assays that have been developed, covering a substantial swath of the regulatory landscape. To date, there have been a number of excellent reviews on single-cell technologies structured around the molecular properties that can be profiled. This review is instead framed around the central components and properties of tagmentation and how they have enabled the development of innovative molecular tools to probe the regulatory landscape of single cells. Furthermore, the granular specifics on cell throughput or richness of data provided by the extensive list of individual technologies are not discussed. Such metrics are rapidly changing and highly sample specific and are better left to studies that directly compare technologies for assays against one another in a rigorously controlled framework. The hope for this review is that, in laying out the diversity of molecular techniques at each stage of these assay platforms, new ideas may arise for others to pursue that will further advance the field of single-cell genomics.
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Affiliation(s)
- Andrew C Adey
- Department of Molecular & Medical Genetics, Knight Cancer Institute, Knight Cardiovascular Institute, Cancer Early Detection Advanced Research Center, Oregon Health and Science University, Portland, Oregon 97239, USA
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32
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Gautam P, Hamashima K, Chen Y, Zeng Y, Makovoz B, Parikh BH, Lee HY, Lau KA, Su X, Wong RCB, Chan WK, Li H, Blenkinsop TA, Loh YH. Multi-species single-cell transcriptomic analysis of ocular compartment regulons. Nat Commun 2021; 12:5675. [PMID: 34584087 PMCID: PMC8478974 DOI: 10.1038/s41467-021-25968-8] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Accepted: 09/07/2021] [Indexed: 11/23/2022] Open
Abstract
The retina is a widely profiled tissue in multiple species by single-cell RNA sequencing studies. However, integrative research of the retina across species is lacking. Here, we construct the first single-cell atlas of the human and porcine ocular compartments and study inter-species differences in the retina. In addition to that, we identify putative adult stem cells present in the iris tissue. We also create a disease map of genes involved in eye disorders across compartments of the eye. Furthermore, we probe the regulons of different cell populations, which include transcription factors and receptor-ligand interactions and reveal unique directional signalling between ocular cell types. In addition, we study conservation of regulons across vertebrates and zebrafish to identify common core factors. Here, we show perturbation of KLF7 gene expression during retinal ganglion cells differentiation and conclude that it plays a significant role in the maturation of retinal ganglion cells. A comprehensive analysis of the ocular networks among various tissues is necessary to understand eye physiology in health and disease. Here the authors present a multi-species single-cell transcriptomic atlas consisting of cells of the cornea, iris, ciliary body, neural retina, retinal pigmented epithelium, and choroid.
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Affiliation(s)
- Pradeep Gautam
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore, 117543, Singapore
| | - Kiyofumi Hamashima
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore
| | - Ying Chen
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore.,Department of Biological Sciences, National University of Singapore, Singapore, 117543, Singapore.,Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore
| | - Yingying Zeng
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore.,School of Biological Sciences, Nanyang Technological University, Singapore, 637551, Singapore
| | - Bar Makovoz
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bhav Harshad Parikh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Translational Retinal Research Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore
| | - Hsin Yee Lee
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore
| | - Katherine Anne Lau
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore
| | - Xinyi Su
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.,Translational Retinal Research Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore.,Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Raymond C B Wong
- Centre for Eye Research Australia, Melbourne, Vic, Australia.,Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Vic, Australia.,Shenzhen Eye Hospital, Shenzhen University School of Medicine, Shenzhen, China
| | - Woon-Khiong Chan
- Department of Biological Sciences, National University of Singapore, Singapore, 117543, Singapore.,Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore
| | - Hu Li
- Center for Individualized Medicine, Department of Molecular Pharmacology & Experimental Therapeutics, Mayo Clinic, Rochester, MN, 55905, USA.
| | | | - Yuin-Han Loh
- Cell Fate Engineering and Therapeutics Laboratory, A*STAR Institute of Molecular and Cell Biology, Singapore, 138673, Singapore. .,Department of Biological Sciences, National University of Singapore, Singapore, 117543, Singapore. .,Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, 21 Lower Kent Ridge Road, Singapore, 119077, Singapore. .,Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, 117593, Singapore.
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33
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Li R, Grimm SA, Wade PA. A simple and robust method for simultaneous dual-omics profiling with limited numbers of cells. CELL REPORTS METHODS 2021; 1:100041. [PMID: 34414388 PMCID: PMC8372970 DOI: 10.1016/j.crmeth.2021.100041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 05/04/2021] [Accepted: 05/24/2021] [Indexed: 12/13/2022]
Abstract
Deciphering epigenetic regulation of gene expression requires measuring the epigenome and transcriptome jointly. Single-cell multi-omics technologies have been developed for concurrent profiling of chromatin accessibility and gene expression. However, multi-omics profiling of low-input bulk samples remains challenging. Therefore, we developed low-input ATAC&mRNA-seq, a simple and robust method for studying the role of chromatin structure in gene regulation in a single experiment with thousands of cells, to maximize insights from limited input material by obtaining ATAC-seq and mRNA-seq data simultaneously from the same cells with data quality comparable to that of conventional mono-omics assays. Integrative data analysis revealed similar strong association between promoter accessibility and gene expression when using the data of low-input ATAC&mRNA-seq as when using single-assay data, underscoring the accuracy and reliability of our dual-omics assay to generate both datum types simultaneously with just thousands of cells. We envision our method to be widely applied in many biological disciplines with limited materials.
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Affiliation(s)
- Ruifang Li
- Epigenetics Innovation Lab, Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Sara A. Grimm
- Integrative Bioinformatics Support Group, Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
| | - Paul A. Wade
- Eukaryotic Transcriptional Regulation Group, Epigenetics and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA
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34
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The Trifecta of Single-Cell, Systems-Biology, and Machine-Learning Approaches. Genes (Basel) 2021; 12:genes12071098. [PMID: 34356114 PMCID: PMC8306972 DOI: 10.3390/genes12071098] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 07/13/2021] [Accepted: 07/18/2021] [Indexed: 12/18/2022] Open
Abstract
Together, single-cell technologies and systems biology have been used to investigate previously unanswerable questions in biomedicine with unparalleled detail. Despite these advances, gaps in analytical capacity remain. Machine learning, which has revolutionized biomedical imaging analysis, drug discovery, and systems biology, is an ideal strategy to fill these gaps in single-cell studies. Machine learning additionally has proven to be remarkably synergistic with single-cell data because it remedies unique challenges while capitalizing on the positive aspects of single-cell data. In this review, we describe how systems-biology algorithms have layered machine learning with biological components to provide systems level analyses of single-cell omics data, thus elucidating complex biological mechanisms. Accordingly, we highlight the trifecta of single-cell, systems-biology, and machine-learning approaches and illustrate how this trifecta can significantly contribute to five key areas of scientific research: cell trajectory and identity, individualized medicine, pharmacology, spatial omics, and multi-omics. Given its success to date, the systems-biology, single-cell omics, and machine-learning trifecta has proven to be a potent combination that will further advance biomedical research.
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35
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Seyfferth C, Renema J, Wendrich JR, Eekhout T, Seurinck R, Vandamme N, Blob B, Saeys Y, Helariutta Y, Birnbaum KD, De Rybel B. Advances and Opportunities in Single-Cell Transcriptomics for Plant Research. ANNUAL REVIEW OF PLANT BIOLOGY 2021; 72:847-866. [PMID: 33730513 PMCID: PMC7611048 DOI: 10.1146/annurev-arplant-081720-010120] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Single-cell approaches are quickly changing our view on biological systems by increasing the spatiotemporal resolution of our analyses to the level of the individual cell. The field of plant biology has fully embraced single-cell transcriptomics and is rapidly expanding the portfolio of available technologies and applications. In this review, we give an overview of the main advances in plant single-cell transcriptomics over the past few years and provide the reader with an accessible guideline covering all steps, from sample preparation to data analysis. We end by offering a glimpse of how these technologies will shape and accelerate plant-specific research in the near future.
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Affiliation(s)
- Carolin Seyfferth
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jim Renema
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Jos R Wendrich
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Thomas Eekhout
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
| | - Ruth Seurinck
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
| | - Niels Vandamme
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
| | - Bernhard Blob
- The Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
- Viikki Plant Science Centre, HiLIFE/Organismal and Evolutionary Biology Research Program, Institute of Biotechnology, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, 9052 Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, 9000 Ghent, Belgium
| | - Yrjo Helariutta
- The Sainsbury Laboratory, University of Cambridge, Cambridge CB2 1LR, United Kingdom
- Viikki Plant Science Centre, HiLIFE/Organismal and Evolutionary Biology Research Program, Institute of Biotechnology, Faculty of Biological and Environmental Sciences, University of Helsinki, 00014 Helsinki, Finland
| | - Kenneth D Birnbaum
- Center for Genomics and Systems Biology, Department of Biology, New York University, New York, NY 10003, USA;
| | - Bert De Rybel
- Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;
- VIB Center for Plant Systems Biology, 9052 Ghent, Belgium
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36
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Dorrity MW, Alexandre CM, Hamm MO, Vigil AL, Fields S, Queitsch C, Cuperus JT. The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. Nat Commun 2021; 12:3334. [PMID: 34099698 DOI: 10.1101/2020.07.17.204792] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 05/10/2021] [Indexed: 05/21/2023] Open
Abstract
The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell's regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.
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Affiliation(s)
- Michael W Dorrity
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Morgan O Hamm
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Anna-Lena Vigil
- School of Life Sciences, University of Nevada, Las Vegas, NV, USA
| | - Stanley Fields
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - Christine Queitsch
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Josh T Cuperus
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
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37
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Dorrity MW, Alexandre CM, Hamm MO, Vigil AL, Fields S, Queitsch C, Cuperus JT. The regulatory landscape of Arabidopsis thaliana roots at single-cell resolution. Nat Commun 2021; 12:3334. [PMID: 34099698 PMCID: PMC8184767 DOI: 10.1038/s41467-021-23675-y] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Accepted: 05/10/2021] [Indexed: 02/05/2023] Open
Abstract
The scarcity of accessible sites that are dynamic or cell type-specific in plants may be due in part to tissue heterogeneity in bulk studies. To assess the effects of tissue heterogeneity, we apply single-cell ATAC-seq to Arabidopsis thaliana roots and identify thousands of differentially accessible sites, sufficient to resolve all major cell types of the root. We find that the entirety of a cell's regulatory landscape and its transcriptome independently capture cell type identity. We leverage this shared information on cell identity to integrate accessibility and transcriptome data to characterize developmental progression, endoreduplication and cell division. We further use the combined data to characterize cell type-specific motif enrichments of transcription factor families and link the expression of family members to changing accessibility at specific loci, resolving direct and indirect effects that shape expression. Our approach provides an analytical framework to infer the gene regulatory networks that execute plant development.
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Affiliation(s)
- Michael W. Dorrity
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA USA
| | - Cristina M. Alexandre
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA USA
| | - Morgan O. Hamm
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA USA
| | - Anna-Lena Vigil
- grid.272362.00000 0001 0806 6926School of Life Sciences, University of Nevada, Las Vegas, NV USA
| | - Stanley Fields
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA USA ,grid.34477.330000000122986657Department of Medicine, University of Washington, Seattle, WA USA
| | - Christine Queitsch
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA USA
| | - Josh T. Cuperus
- grid.34477.330000000122986657Department of Genome Sciences, University of Washington, Seattle, WA USA
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38
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Li H, Humphreys BD. Single Cell Technologies: Beyond Microfluidics. KIDNEY360 2021; 2:1196-1204. [PMID: 35368355 PMCID: PMC8786099 DOI: 10.34067/kid.0001822021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2021] [Accepted: 05/13/2021] [Indexed: 02/04/2023]
Abstract
Single-cell RNA-sequencing (scRNA-seq) has been widely adopted in recent years due to standardized protocols and automation, reliability, and standardized bioinformatic pipelines. The most widely adopted platform is the 10× Genomics solution. Although powerful, this system is limited by its high cost, moderate throughput, and the inability to customize due to fixed kit components. This study will cover new approaches that do not rely on microfluidics and thus have low entry costs, are highly customizable, and are within the reach of any laboratory possessing molecular biology expertise.
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Affiliation(s)
| | - Benjamin D. Humphreys
- Division of Nephrology, Washington University in St. Louis School of Medicine, St. Louis, Missouri,Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, Missouri
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Chromatin Regulation in Development: Current Understanding and Approaches. Stem Cells Int 2021; 2021:8817581. [PMID: 33603792 PMCID: PMC7872760 DOI: 10.1155/2021/8817581] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 12/29/2020] [Accepted: 01/21/2021] [Indexed: 11/24/2022] Open
Abstract
The regulation of mammalian stem cell fate during differentiation is complex and can be delineated across many levels. At the chromatin level, the replacement of histone variants by chromatin-modifying proteins, enrichment of specific active and repressive histone modifications, long-range gene interactions, and topological changes all play crucial roles in the determination of cell fate. These processes control regulatory elements of critical transcriptional factors, thereby establishing the networks unique to different cell fates and initiate waves of distinctive transcription events. Due to the technical challenges posed by previous methods, it was difficult to decipher the mechanism of cell fate determination at early embryogenesis through chromatin regulation. Recently, single-cell approaches have revolutionised the field of developmental biology, allowing unprecedented insights into chromatin structure and interactions in early lineage segregation events during differentiation. Here, we review the recent technological advancements and how they have furthered our understanding of chromatin regulation during early differentiation events.
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