33
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Single-cell transcriptome profiling of an adult human cell atlas of 15 major organs

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          As core units of organ tissues, cells of various types play their harmonious rhythms to maintain the homeostasis of the human body. It is essential to identify the characteristics of cells in human organs and their regulatory networks for understanding the biological mechanisms related to health and disease. However, a systematic and comprehensive single-cell transcriptional profile across multiple organs of a normal human adult is missing.

          Results

          We perform single-cell transcriptomes of 84,363 cells derived from 15 tissue organs of one adult donor and generate an adult human cell atlas. The adult human cell atlas depicts 252 subtypes of cells, including major cell types such as T, B, myeloid, epithelial, and stromal cells, as well as novel COCH + fibroblasts and FibSmo cells, each of which is distinguished by multiple marker genes and transcriptional profiles. These collectively contribute to the heterogeneity of major human organs. Moreover, T cell and B cell receptor repertoire comparisons and trajectory analyses reveal direct clonal sharing of T and B cells with various developmental states among different tissues. Furthermore, novel cell markers, transcription factors, and ligand-receptor pairs are identified with potential functional regulations in maintaining the homeostasis of human cells among tissues.

          Conclusions

          The adult human cell atlas reveals the inter- and intra-organ heterogeneity of cell characteristics and provides a useful resource in uncovering key events during the development of human diseases in the context of the heterogeneity of cells and organs.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13059-020-02210-0.

          Related collections

          Most cited references87

          • Record: found
          • Abstract: found
          • Article: not found

          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Integrating single-cell transcriptomic data across different conditions, technologies, and species

              Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.
                Bookmark

                Author and article information

                Contributors
                heshuai@sysucc.org.cn
                wanglh6@mail2.sysu.edu.cn
                liuyang3@sysucc.org.cn
                liyq2@sysucc.org.cn
                chenht3@mail2.sysu.edu.cn
                xujh38@mail2.sysu.edu.cn
                pengwan@sysucc.org.cn
                lingw@sysucc.org.cn
                weipp@sysucc.org.cn
                libo47@mail.sysu.edu.cn
                xiaxj@sysucc.org.cn
                wangdan@sysucc.org.cn
                beijx@sysucc.org.cn
                gdtrc@163.com
                rockyucsf1981@126.com
                Journal
                Genome Biol
                Genome Biol
                Genome Biology
                BioMed Central (London )
                1474-7596
                1474-760X
                7 December 2020
                7 December 2020
                2020
                : 21
                : 294
                Affiliations
                [1 ]Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, 510060 People’s Republic of China
                [2 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Organ Transplant Center, The First Affiliated Hospital, , Sun Yat-sen University, ; Guangzhou, 510080 People’s Republic of China
                [3 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Guangdong Provincial Key Laboratory of Organ Donation and Transplant Immunology, The First Affiliated Hospital, Sun Yat-sen University, ; Guangzhou, 510080 People’s Republic of China
                [4 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Guangdong Provincial International Cooperation Base of Science and Technology (Organ Transplantation), The First Affiliated Hospital, Sun Yat-sen University, ; Guangzhou, 510080 People’s Republic of China
                [5 ]GRID grid.284723.8, ISNI 0000 0000 8877 7471, Department of Laboratory Medicine, Zhujiang Hospital, , Southern Medical University, ; Guangzhou, 510282 People’s Republic of China
                [6 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Department of Biochemistry and Molecular Biology, Zhongshan School of Medicine, , Sun Yat-sen University, ; Guangzhou, 510080 People’s Republic of China
                [7 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, RNA Biomedical Institute, Sun Yat-sen Memorial Hospital, , Sun Yat-sen University, ; Guangzhou, 510120 People’s Republic of China
                [8 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Center for Precision Medicine, , Sun Yat-sen University, ; Guangzhou, 510080 People’s Republic of China
                Author information
                http://orcid.org/0000-0003-1333-407X
                Article
                2210
                10.1186/s13059-020-02210-0
                7720616
                33287869
                06f664da-95c7-4e02-924f-24114b0671f2
                © The Author(s) 2020

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 10 March 2020
                : 20 November 2020
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81471583
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 81570587
                Award ID: 81970564
                Award Recipient :
                Funded by: Guangdong Provincial Key Laboratory Construction Projection on Organ Donation and Transplant Immunology
                Award ID: 2013A061401007
                Award ID: 2017B030314018
                Award Recipient :
                Funded by: Guangdong Provincial Natural Science Funds for Major Basic Science Culture Project
                Award ID: 2015A030308010
                Award Recipient :
                Funded by: Guangdong Provincial Natural Science Funds for Distinguished Young Scholars
                Award ID: 2015A030306025
                Award Recipient :
                Funded by: Special Support Program for Training High Level Talents in Guangdong Province
                Award ID: 2015TQ01R168
                Award Recipient :
                Funded by: Pearl River Nova Program of Guangzhou
                Award ID: 201506010014
                Award Recipient :
                Funded by: Science and Technology Program of Guangzhou
                Award ID: 201704020150
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/100012541, Guangdong Innovative and Entrepreneurial Research Team Program;
                Award ID: 2016ZT06S638
                Award Recipient :
                Funded by: Sun Yat-sen University Young Teacher Key Cultivate Project
                Award ID: 17ykzd29
                Award Recipient :
                Funded by: National Program for Support of Top-Notch Young Professionals
                Funded by: Special Support Program of Guangdong
                Funded by: Chang Jiang Scholars Program
                Categories
                Research
                Custom metadata
                © The Author(s) 2020

                Genetics
                human cell atlas,single-cell rna sequencing,tcr,bcr,transcriptome
                Genetics
                human cell atlas, single-cell rna sequencing, tcr, bcr, transcriptome

                Comments

                Comment on this article

                scite_
                0
                0
                0
                0
                Smart Citations
                0
                0
                0
                0
                Citing PublicationsSupportingMentioningContrasting
                View Citations

                See how this article has been cited at scite.ai

                scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.

                Similar content157

                Cited by83

                Most referenced authors1,953