Abstract

Here we present a compendium of single-cell transcriptomic data from the model organism Mus musculus that comprises more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations and enable the direct and controlled comparison of gene expression in cell types that are shared between tissues, such as T lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3′-end counting, enabled the survey of thousands of cells at relatively low coverage, whereas the other, full-length transcript analysis based on fluorescence-activated cell sorting, enabled the characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology.

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Data availability

All data, protocols and analysis scripts from the Tabula Muris are shared as a public resource (http://tabula-muris.ds.czbiohub.org/). Gene counts and metadata for FACS (https://doi.org/10.6084/m9.figshare.5829687.v7) and microfluidic droplets (https://doi.org/10.6084/m9.figshare.5968960.v2) from all single cells along with all produced R objects (https://doi.org/10.6084/m9.figshare.5821263.v1), as well as FACS Index data (https://doi.org/10.6084/m9.figshare.5975392) are accessible on Figshare (https://figshare.com/projects/Tabula_Muris_Transcriptomic_characterization_of_20_organs_and_tissues_from_Mus_musculus_at_single_cell_resolution/27733), and raw data are available from the Gene Expression Omnibus (GSE109774).

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Acknowledgements

We thank Sony Biotechnology for making an SH800S instrument available for this project. Some of the cell sorting/flow cytometry analysis for this project was performed using a Sony SH800S instrument in the Stanford Shared FACS Facility. Some FACS experiments used instruments in the VA Flow Cytometry Core, which is supported by the US Department of Veterans Affairs, Palo Alto Veterans Institute for Research and the National Institutes of Health. This work was supported by the Chan Zuckerberg Biohub, NIH Grant DP1 AG053015 and the NOMIS Foundation (T.W.-C.) as well as partly by the Stanford Islet Research Core in the Stanford Diabetes Research Center (P30 DK116074). We thank A. McGeever for contributions to the design of the Tabula Muris web portal.

Author information

Author notes

  1. A list of authors and their affiliations is available online.

Affiliations

  1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, CA, USA

    • Nicholas Schaum
    • , Ankit S. Baghel
    • , Isaac Bakerman
    • , Stephanie D. Conley
    • , Kubilay Demir
    • , Laughing Bear Torrez Dulgeroff
    • , Matt Fish
    • , Benson M. George
    • , Gunsagar S. Gulati
    • , Taichi Isobe
    • , Kevin S. Kao
    • , Aaron M. Kershner
    • , Bernhard M. Kiss
    • , William Kong
    • , Wan-Jin Lu
    • , Anoop Manjunath
    • , Joseph Noh
    • , Nicholas Schaum
    • , Shaheen S. Sikandar
    • , Rahul Sinha
    • , Krzysztof Szade
    • , Linda J. van Weele
    • , Jinyi Xiang
    • , Ankit S. Baghel
    • , Isaac Bakerman
    • , Kubilay Demir
    • , Matt Fish
    • , Benson M. George
    • , Gunsagar S. Gulati
    • , Taichi Isobe
    • , Aaron M. Kershner
    • , Bernhard M. Kiss
    • , William Kong
    • , Wan-Jin Lu
    • , Patricia K. Nguyen
    • , Nicholas Schaum
    • , Shaheen S. Sikandar
    • , Rahul Sinha
    • , Krzysztof Szade
    • , Linda J. van Weele
    • , Jinyi Xiang
    • , Nicholas Schaum
    • , Philip A. Beachy
    • , Benson M. George
    • , Gunsagar S. Gulati
    • , Taichi Isobe
    • , Aaron M. Kershner
    • , Bernhard M. Kiss
    • , William Kong
    • , Wan-Jin Lu
    • , Patricia K. Nguyen
    • , Nicholas Schaum
    • , Shaheen S. Sikandar
    • , Rahul Sinha
    • , Krzysztof Szade
    • , Philip A. Beachy
    • , Michael F. Clarke
    • , Patricia K. Nguyen
    • , Irving L. Weissman
    •  & Sean M. Wu
  2. Chan Zuckerberg Biohub, San Francisco, CA, USA

    • Jim Karkanias
    • , Norma F. Neff
    • , Andrew P. May
    • , Stephen R. Quake
    • , Spyros Darmanis
    • , Joshua Batson
    • , Olga Botvinnik
    • , Steven Chen
    • , Foad Green
    • , Ashley Maynard
    • , Lolita Penland
    • , Angela Oliveira Pisco
    • , Rene V. Sit
    • , James T. Webber
    • , Ishita Bansal
    • , Steven Chen
    • , Min Cho
    • , Giana Cirolia
    • , Spyros Darmanis
    • , Aaron Demers
    • , Tessa Divita
    • , Hamid Ebadi
    • , Foad Green
    • , Geraldine Genetiano
    • , Shayan Hosseinzadeh
    • , Feather Ives
    • , Annie Lo
    • , Andrew P. May
    • , Kaia L. May
    • , Oliver L. May
    • , Ashley Maynard
    • , Marina McKay
    • , Norma F. Neff
    • , Lolita Penland
    • , Robert Puccinelli
    • , Rene V. Sit
    • , Weilun Tan
    • , Cristina Tato
    • , Lucas Waldburger
    • , Justin Youngyunpipatkul
    • , Ishita Bansal
    • , Steven Chen
    • , Min Cho
    • , Giana Cirolia
    • , Spyros Darmanis
    • , Aaron Demers
    • , Tessa Divita
    • , Hamid Ebadi
    • , Geraldine Genetiano
    • , Foad Green
    • , Shayan Hosseinzadeh
    • , Feather Ives
    • , Annie Lo
    • , Andrew P. May
    • , Ashley Maynard
    • , Marina McKay
    • , Norma F. Neff
    • , Lolita Penland
    • , Rene V. Sit
    • , Weilun Tan
    • , Lucas Waldburger
    • , Justin Youngyunpipatkul
    • , Joshua Batson
    • , Olga Botvinnik
    • , Paola Castro
    • , Spyros Darmanis
    • , Joseph L. DeRisi
    • , Jim Karkanias
    • , Angela Oliveira Pisco
    • , James T. Webber
    • , Joshua Batson
    • , Olga Botvinnik
    • , Spyros Darmanis
    • , Hamid Ebadi
    • , Lolita Penland
    • , Joshua Batson
    • , Olga Botvinnik
    • , Steven Chen
    • , Spyros Darmanis
    • , Foad Green
    • , Andrew P. May
    • , Ashley Maynard
    • , Angela Oliveira Pisco
    • , Stephen R. Quake
    • , James T. Webber
    • , Kerwyn Casey Huang
    • , Spyros Darmanis
    • , Kerwyn Casey Huang
    • , Jim Karkanias
    • , Andrew P. May
    • , Norma F. Neff
    • , Justin Sonnenburg
    •  & Stephen R. Quake
  3. Department of Bioengineering, Stanford University, Stanford, CA, USA

    • Stephen R. Quake
    • , Michelle B. Chen
    • , Robert C. Jones
    • , Geoffrey M. Stanley
    • , Fabio Zanini
    • , Michelle B. Chen
    • , Robert C. Jones
    • , Marco Mignardi
    • , Katharine M. Ng
    • , Soso Xue
    • , Fabio Zanini
    • , Derek Croote
    • , Geoffrey M. Stanley
    • , Fabio Zanini
    • , Michelle B. Chen
    • , Katharine M. Ng
    • , Stephen R. Quake
    • , Geoffrey M. Stanley
    • , Fabio Zanini
    • , Kerwyn Casey Huang
    • , Katharine M. Ng
    • , Kerwyn Casey Huang
    •  & Stephen R. Quake
  4. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    • Tony Wyss-Coray
    • , Daniela Berdnik
    • , Biter Bilen
    • , Antoine de Morree
    • , Haley du Bois
    • , Qiang Gan
    • , Albin Huang
    • , Tal Iram
    • , Song E. Lee
    • , Ling Liu
    • , Michael N. Wosczyna
    • , Biter Bilen
    • , Antoine de Morree
    • , Qiang Gan
    • , Albin Huang
    • , Tal Iram
    • , Benoit Lehallier
    • , Ling Liu
    • , Michael N. Wosczyna
    • , Hanadie Yousef
    • , Tony Wyss-Coray
    • , Antoine de Morree
    • , Tal Iram
    • , Ling Liu
    • , Michael N. Wosczyna
    • , Hanadie Yousef
    • , Thomas A. Rando
    •  & Tony Wyss-Coray
  5. Paul F. Glenn Center for the Biology of Aging, Stanford University School of Medicine, Stanford, CA, USA

    • Tony Wyss-Coray
    • , Tony Wyss-Coray
    • , Thomas A. Rando
    •  & Tony Wyss-Coray
  6. Center for Tissue Regeneration, Repair, and Restoration, VA Palo Alto Healthcare System, Palo Alto, CA, USA

    • Tony Wyss-Coray
    • , Davis P. Lee
    • , Macy E. Zardeneta
    • , Tony Wyss-Coray
    • , Thomas A. Rando
    •  & Tony Wyss-Coray
  7. Stanford Cardiovascular Institute, Stanford University School of Medicine, Stanford, CA, USA

    • Isaac Bakerman
    • , Isaac Bakerman
    • , Patricia K. Nguyen
    • , Patricia K. Nguyen
    • , Patricia K. Nguyen
    •  & Sean M. Wu
  8. Department of Medicine, Division of Cardiology, Stanford University School of Medicine, Stanford, CA, USA

    • Isaac Bakerman
    • , Isaac Bakerman
    • , Patricia K. Nguyen
    • , Patricia K. Nguyen
    •  & Patricia K. Nguyen
  9. Department of Biochemistry, Stanford University School of Medicine, Stanford, CA, USA

    • Douglas Brownfield
    • , F. Hernán Espinoza
    • , Astrid Gillich
    • , Wan-Jin Lu
    • , Ahmad N. Nabhan
    • , Kyle J. Travaglini
    • , Douglas Brownfield
    • , F. Hernán Espinoza
    • , Astrid Gillich
    • , Christin S. Kuo
    • , Wan-Jin Lu
    • , Ahmad N. Nabhan
    • , Kyle J. Travaglini
    • , Philip A. Beachy
    • , Wan-Jin Lu
    • , Ahmad N. Nabhan
    • , Kyle J. Travaglini
    • , Philip A. Beachy
    • , Mark A. Krasnow
    • , Christin S. Kuo
    •  & Roel Nusse
  10. Flow Cytometry Core, VA Palo Alto Healthcare System, Palo Alto, CA, USA

    • Corey Cain
  11. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

    • Kubilay Demir
    • , Matt Fish
    • , Kubilay Demir
    • , Matt Fish
    • , Christin S. Kuo
    • , Philip A. Beachy
    • , Philip A. Beachy
    • , Mark A. Krasnow
    • , Christin S. Kuo
    •  & Roel Nusse
  12. Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA

    • Matt Fish
    • , Xueying Gu
    • , Yan Hang
    • , Jonathan Y. Lam
    • , Weng Chuan Peng
    • , Eric J. Rulifson
    • , Krissie Tellez
    • , Matt Fish
    • , Xueying Gu
    • , Yan Hang
    • , Jonathan Y. Lam
    • , Weng Chuan Peng
    • , Eric J. Rulifson
    • , Krissie Tellez
    • , Philip A. Beachy
    • , Yan Hang
    • , Weng Chuan Peng
    • , Eric J. Rulifson
    • , Philip A. Beachy
    • , Seung K. Kim
    •  & Roel Nusse
  13. Department of Medicine and Liver Center, University of California San Francisco, San Francisco, CA, USA

    • Guruswamy Karnam
    • , Rasika Patkar
    • , Guruswamy Karnam
    • , Rasika Patkar
    • , Bruce M. Wang
    • , Bruce M. Wang
    •  & Bruce M. Wang
  14. Department of Urology, Stanford University School of Medicine, Stanford, CA, USA

    • Bernhard M. Kiss
    • , Bernhard M. Kiss
    •  & Bernhard M. Kiss
  15. Sean N. Parker Center for Asthma and Allergy Research, Stanford University School of Medicine, Stanford, CA, USA

    • Maya E. Kumar
    •  & Maya E. Kumar
  16. Department of Medicine, Division of Pulmonary and Critical Care, Stanford University School of Medicine, Stanford, CA, USA

    • Maya E. Kumar
    •  & Maya E. Kumar
  17. Department of Medicine, Division of Cardiovascular Medicine, Stanford University, Stanford, CA, USA

    • Guang Li
    • , Guang Li
    • , Patricia K. Nguyen
    • , Guang Li
    • , Patricia K. Nguyen
    • , Sean M. Wu
    • , Patricia K. Nguyen
    •  & Sean M. Wu
  18. Department of Neurobiology, Stanford University School of Medicine, Stanford, CA, USA

    • Qingyun Li
    • , Lu Zhou
    • , Qingyun Li
    • , Lu Zhou
    • , Qingyun Li
    •  & Ben A. Barres
  19. Vera Moulton Wall Center for Pulmonary and Vascular Disease, Stanford University School of Medicine, Stanford, CA, USA

    • Ross J. Metzger
    • , Fan Zhang
    •  & Ross J. Metzger
  20. Department of Pediatrics, Division of Cardiology, Stanford University School of Medicine, Stanford, CA, USA

    • Ross J. Metzger
    • , Fan Zhang
    •  & Ross J. Metzger
  21. Department of Pediatrics, Pulmonary Medicine, Stanford University School of Medicine, Stanford, CA, USA

    • Dullei Min
    • , Christin S. Kuo
    • , Dullei Min
    • , Kenneth Weinberg
    • , Christin S. Kuo
    •  & Kenneth Weinberg
  22. Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA

    • Rahul Sinha
    • , Rahul Sinha
    • , Serena Y. Tan
    • , Rahul Sinha
    •  & Irving L. Weissman
  23. Ludwig Center for Cancer Stem Cell Research and Medicine, Stanford University School of Medicine, Stanford, CA, USA

    • Rahul Sinha
    • , Rahul Sinha
    • , Rahul Sinha
    •  & Irving L. Weissman
  24. Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA

    • Rahul Sinha
    • , Rahul Sinha
    • , Rahul Sinha
    •  & Irving L. Weissman
  25. Department of Medical Biotechnology, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Kraków, Poland

    • Krzysztof Szade
    • , Krzysztof Szade
    •  & Krzysztof Szade
  26. Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, USA

    • Carolina Tropini
    • , Carolina Tropini
    • , Kerwyn Casey Huang
    • , Carolina Tropini
    • , Kerwyn Casey Huang
    •  & Justin Sonnenburg
  27. Department of Biochemistry and Biophysics, University of California San Francisco, San Francisco, CA, USA

    • Joseph L. DeRisi
  28. Department of Surgery, Division of Plastic and Reconstructive Surgery, Stanford University, Stanford, CA, USA

    • Charles K. F. Chan
    •  & Charles K. F. Chan
  29. Department of Medicine and Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA

    • Seung K. Kim

Consortia

  1. The Tabula Muris Consortium

    1. Overall coordination

    1. Logistical coordination

    1. Organ collection and processing

    1. Library preparation and sequencing

    1. Computational data analysis

    1. Cell type annotation

    1. Writing group

    1. Supplemental text writing group

    1. Principal investigators

    Contributions

    See author list for full contributions.

    Competing interests

    The authors declare no competing interests.

    Corresponding authors

    Correspondence to Stephen R. Quake or Tony Wyss-Coray or Spyros Darmanis.

    Extended data figures and tables

    1. Extended Data Fig. 1 The number and type of FACS cells that compose each organ.

      a, Cells for each organ visualized with t-SNE, coloured by cell type. Cell types were determined by differential gene expression of known markers between clusters. b, Bar plots quantifying the number of each annotated cell type. Cell type colours match their respective t-SNE plot.

    2. Extended Data Fig. 2 The number and type of microfluidic cells that compose each organ.

      a, t-SNE plot of all cells collected by the microfluidic-droplet method, coloured by organ, overlaid with the predominant cell type that composes each cluster. b, Cells for each organ visualized with t-SNE, coloured by cell type. Cell types were determined by differential gene expression of known markers between clusters. c, Bar plots quantifying the number of each annotated cell type. Cell type colours match their respective t-SNE plot.

    3. Extended Data Fig. 3 The number of reads, UMIs and genes detected per cell for each organ.

      a, c, Histograms for each organ of the number of reads per cell (FACS) (a) and UMIs per cell (microfluidic droplet) (c). b, d, Histogram of the number of genes detected per cell for each organ from the FACS method (b), and the microfluidic-droplet method (d).

    4. Extended Data Fig. 4 Graphical representation of cell ontology class representation.

      a, b, Datasets from the FACS method (a) and the microfluidic-droplet method (b), coloured by the relative amount of each cell type in each dataset.

    5. Extended Data Fig. 5 Methodological comparison of detected genes and library saturation.

      a, The number of genes detected (threshold of >0 reads or UMIs per cell) by FACS (red; n = 21,105 individual cells), microfluidic-droplet (green; n = 55,032 individual cells) and microwell-seq (blue; n = 25,891 individual cells) methods20. b, Library saturation fraction for all microfluidic-droplet libraries. Dotted horizontal line demarcates the median saturation (around 0.9). c, Library saturation for all FACS libraries. Saturation was calculated using the number of detected genes while downsampling the number of reads per library. Summary statistics are contained in Supplementary Table 6.

    6. Extended Data Fig. 6 The number of detected genes decreases similarly across organs as the read or UMI threshold is increased.

      Fraction of all detected genes (defined as >0 reads or UMIs) for each cell, across all organs, detected at increasing read or UMI thresholds for FACS (left; n = 44,949 individual cells), microfluidic-droplet (middle; n = 55,656 individual cells), and microwell-seq (right; n = 28,372 individual cells) methods. Summary statistics are contained in Supplementary Table 6.

    7. Extended Data Fig. 7 The number of differentially expressed genes for each cell type that are common between methods.

      Venn diagrams showing the overlap between differentially expressed genes for each common cell type across the three methods (FACS, microfluidic-droplet and microwell-seq). Plotted data are provided in tabular form in Supplementary Table 2.

    8. Extended Data Fig. 8 t-SNE visualization of all FACS cells by cluster ID.

      n = 44,949 individual cells. Clusters are discussed in the text and further analysed in Fig. 3.

    9. Extended Data Fig. 9 Metrics of cluster heterogeneity.

      a, Bar plot showing the heterogeneity score for each cluster containing several cell types. bg, Heat maps showing the average between-cell-type distances within select clusters, normalized so that the average distance between pairs of FACS cells is 1, clipped to a max of 1, for clusters 1 (b), 2 (c), 3 (d), 24 (e), 48 (f) and 53 (g).

    10. Extended Data Fig. 10 Contribution of transcription factors to cell identity.

      a, Tanglegram contrasting the dendrogram obtained using all expressed genes with one obtained using only the expression of transcription factors. The solid lines indicate segments that did not change position during the alignment between the two trees, and the dotted lines correspond to dendrogram branches reordered during the entanglement calculations. The colours indicate the branches for which identical leaves are aligned in both dendrograms. be, t-SNE visualization of epithelial (b), endothelial (c), B cells (d) and T cells (e), coloured by organ. fi, t-SNE visualization of epithelial (f), endothelial (g) B cell (h) and T cell (i) expression of select transcription factors (from grey, low, to red, high). In bi, n = 60 randomly selected cells for each cell type.

    11. Extended Data Fig. 11 Dissociation-induced gene-expression scores for each organ analysed with FACS.

      The dissociation score for each organ represents the magnitude of the first principal component of the 140 dissociation-associated genes from ref. 24. The y axis shows the probability density of the normalized histogram.

    Supplementary information

    1. Supplementary Information

      A detailed discussion of organ cell types.

    2. Reporting Summary

    3. Supplementary Information

      Cell type descriptions and differentially expressed genes analyzed for each organ and tissue.

    4. Supplementary Information

      Organ Annotation Vignette - an annotated example of organ annotation procedure.

    5. Supplementary Table 1

      Number of cells belonging to each annotated cell type across all organs for FACS and microfluidic droplets.

    6. Supplementary Table 2

      Cell type comparisons and lists of differentially expressed genes common between methods (FACS, droplet, microwell-Seq).

    7. Supplementary Table 3

      Random forest results for simultaneously defining all cell types with TF expression. This table consists of 4 tabs. The first tab summarizes the most important variables for the model. The second tab indicates all the unique cell types being classified. To build this model we used a data subset of 10 cells for each of the unique cell types. The third tab contains the classification confusion matrix and the fourth tab the summary of the average classification error per cell type.

    8. Supplementary Table 4

      Random forest results for defining each individual cell type with TF expression. In this model, each unique cell type (112 total) was compared to all other cell types. The numbers are the mean decrease Gini score. The value NA in each cell means that the transcription factor corresponding to that row does not contribute the classification model of the cell corresponding to that column.

    9. Supplementary Table 5

      Literature review of the current successful reprogramming protocols in the mouse and respective comparisons with the TF’s expression in the FACS dataset.

    10. Supplementary Table 6

      Summary statistics for Extended Data Figures 5 and 6.

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    DOI

    https://doi.org/10.1038/s41586-018-0590-4

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