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High-resolution myogenic lineage mapping by single-cell mass cytometry

Nature Cell Biology volume 19, pages 558–567 (2017)Cite this article

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A Publisher Correction to this article was published on 05 March 2018

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Abstract

Muscle regeneration is a dynamic process during which cell state and identity change over time. A major roadblock has been a lack of tools to resolve a myogenic progression in vivo. Here we capitalize on a transformative technology, single-cell mass cytometry (CyTOF), to identify in vivo skeletal muscle stem cell and previously unrecognized progenitor populations that precede differentiation. We discovered two cell surface markers, CD9 and CD104, whose combined expression enabled in vivo identification and prospective isolation of stem and progenitor cells. Data analysis using the X-shift algorithm paired with single-cell force-directed layout visualization defined a molecular signature of the activated stem cell state (CD44+/CD98+/MyoD+) and delineated a myogenic trajectory during recovery from acute muscle injury. Our studies uncover the dynamics of skeletal muscle regeneration in vivo and pave the way for the elucidation of the regulatory networks that underlie cell-state transitions in muscle diseases and ageing.

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Figure 1: Identification of distinct cell surface markers that delineate a myogenic progression in vivo.
Figure 2: Unique strategy for prospective isolation of stem and progenitor cells in vivo in skeletal muscle.
Figure 3: Progenitor cell populations originate from muscle stem cells and exhibit distinct regenerative capacity in vivo.
Figure 4: CyTOF analysis reveals the cellular and molecular dynamics within stem and progenitor cell populations during recovery from acute injury.
Figure 5: High-dimensional analysis of acute muscle injury identifies a molecular signature of the activated stem cell state.
Figure 6: High-dimensional analysis of acute muscle injury uncovers cell-state transitions in vivo.

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  • 05 March 2018

    In the version of this Article originally published, the name of author Andrew Tri Van Ho was coded wrongly, resulting in it being incorrect when exported to citation databases. This has been corrected, though no visible changes will be apparent.

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Acknowledgements

We thank D. Burns and F. Gherardini for valuable discussion; G. Han for help with graphics; M. Kyba for Pax7-ZsGreen transgenic mice and M. A. Rudnicki for Pax7 knockout mice; K. Koleckar, P. Kraft and M. Blake for technical assistance; and the Stanford Shared FACS Facility for technical support. This study was supported by a BD Biosciences Stem Cell grant (E.P.); US National Institutes of Health (NIH) grant K99AG042491 (B.D.C.); Muscular Dystrophy Association (MDA) development grant 217821 (A.T.V.H.), NIH grants NS089533 and AG020961, California Institute for Regenerative Medicine grant RB5-07469 and the Baxter Foundation (H.M.B.).

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Author notes

  1. Benjamin D. Cosgrove, Kara L. Davis & Sean C. Bendall

    Present address: Present addresses: Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York 14853, USA (B.D.C.); Bass Center for Childhood Cancer and Blood Disorders, Lucile Packard Children’s Hospital, Stanford University School of Medicine, Stanford, California 94305, USA (K.L.D.); Department of Pathology, Stanford University, Stanford, California 94305, USA (S.C.B.).,

Authors and Affiliations

  1. Blau Laboratory, Stanford University School of Medicine, Stanford, California 94305, USA

    Ermelinda Porpiglia, Andrew Tri Van Ho, Benjamin D. Cosgrove, Thach Mai & Helen M. Blau

  2. Baxter Laboratory for Stem Cell Biology, Stanford University School of Medicine, Stanford, California 94305, USA

    Ermelinda Porpiglia, Nikolay Samusik, Andrew Tri Van Ho, Benjamin D. Cosgrove, Thach Mai, Kara L. Davis, Astraea Jager, Garry P. Nolan, Sean C. Bendall, Wendy J. Fantl & Helen M. Blau

  3. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University School of Medicine, Stanford, California 94305, USA

    Ermelinda Porpiglia, Andrew Tri Van Ho, Benjamin D. Cosgrove, Thach Mai & Helen M. Blau

  4. Nolan Laboratory, Stanford University School of Medicine, Stanford, California 94305, USA

    Nikolay Samusik, Kara L. Davis, Astraea Jager, Garry P. Nolan & Sean C. Bendall

  5. Stanford Comprehensive Cancer Institute and Department of Obstetrics and Gynecology, Stanford University School of Medicine, Stanford California, California 94305, USA

    Wendy J. Fantl

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Contributions

E.P. and H.M.B. conceived the study. E.P. designed and performed experiments, analysed and interpreted data and wrote the manuscript. N.S. developed the analysis algorithm, and analysed and interpreted data. H.M.B., W.J.F. and A.T.V.H. designed experiments, analysed and interpreted data and wrote the manuscript. T.M., K.L.D., S.C.B., B.D.C. and G.P.N. analysed and interpreted data. A.J. provided technical support with antibody conjugation and CyTOF data acquisition.

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Correspondence to Helen M. Blau.

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Integrated supplementary information

Supplementary Figure 1 (related to Figure 1). CyTOF analysis of skeletal muscle tissue.

(a) Intracellular staining of Pax7 by flow cytometry in sorted muscle stem cells (left panel) and myoblasts (right panel). (b) Muscle cells isolated from Pax-Zs green reporter mice were fixed and permeabilized for intracellular staining. Cells were simultaneously stained with antibodies against Zs-Green and Pax7. Cells that were positive for Pax7 were gated and the fraction of Zs-Green+ cells was quantified to be 90%. (c) CyTOF antibody titration. Isotope-chelated anti-mouse antibodies against the surface marker of muscle stem cells, α7 integrin, and intracellular myogenic transcription factors Pax7, MyoD, Myogenin have been titrated using positive (muscle) and negative (spleen) controls to optimize signal, achieve saturation and minimize background. (d) Gating strategy on CyTOF samples as described in Fig. 2a. Individual contour plots are shown. (e) Histogram plot of CD9 (left panels) and CD104 (right panels) expression in MuSCs (upper panels) and myoblasts (lower panels) compared to the respective isotype control. (f) Screening data were analyzed using the Bland-Altman method to measure significant differences in signal intensity (also known as Median Fluorescence Intensity (MFI)) of individual markers in myoblasts compared to MuSCs. The percentage difference from the average MFI (100 × (Myoblast MFI- MuSC MFI)/Average MFI) is plotted (y axis) as a function of the average MFI ((Myoblast MFI + MuSC MFI)/2) (x axis). (g) PCA plot of Live/Lineage−/α7 integrin+/CD9+ cells by population in uninjured (day 0) samples. Protein expression levels were clustered by their log2 median intensities (representative experiment, n = 3 mice).

Supplementary Figure 2 (related to Figure 2 and 3). Functional characterization of the newly identified progenitor population in skeletal muscle.

(a) Representative biaxial dot plots of CD9 (y axis) by CD104 (x axis) colored by channel, showing MAPKAPK2 phosphorylation in populations SC, P1, P2 and P3 in Pax7−/− muscle (right) and WT control (left), isolated from neonates (upper panels) (n = 3 mice, 2 independent experiments) and 3 weeks old mice (lower panels) (n = 1 Pax7−/−; mean ± SEM from n = 10 WT, 2 independent experiments). (b) Representative biaxial dot plots of CD9 by CD104 as in a colored by Myogenin expression. (c) Representative biaxial dot plots of CD9 by CD104 as in a colored by Pax7 expression. (d) Individual populations were sorted by FACS and cultured in differentiation media for one week. Images were acquired with an AxioPlan2 epifluorescent microscope (Carl Zeiss) with ORCA-ER digital camera (Hamamatsu Photonics). Each population was differentiated to yield fusion competent cells (n = 4, 2 independent experiments). Scale bar, 50 μm. (e) Representative images showing the gating strategy on samples analyzed by flow cytometry at day 0 (upper panels) and day 6 (lower panels) (Figure 3d-f). Live cells are identified based on lack of DAPI staining. Lineage+ cells (CD45+/CD11b+/CD31+/Sca1+) are excluded from the analysis and myogenic cells are enriched by gating on the α7integrin+/CD9+ fraction. A biaxial plot of CD9 (y axis) by CD104 (x axis) (far right) shows populations SC, P1 and P2 (representative images, n = 3 mice per condition).

Supplementary Figure 3 (related to Figure 5). Molecular characterization of stem and progenitor cells during acute muscle injury identifies cell state transitions.

(a) Supervised clustering analysis enforcing a 2-cluster solution in the stem (SC) and progenitor (P1, P2) cell populations during the time course of recovery from acute injury (Day 0 = D0; Day 3 = D3; Day 6 = D6), (representative experiment, n = 3 mice per condition). Dendrograms were fitted using hclust function in R. The distance matrix was calculated through the dist function using euclidean parameters. (b) Representative biaxial dot plot of CD9 by CD104 colored by channel, showing expression of CD9 (upper panel) and CD82 (lower panel), in adult mice (n = 6 mice, 2 independent experiments). CD82 is highly expressed only in a small subset of populations P1 and P2.

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Porpiglia, E., Samusik, N., Ho, A. et al. High-resolution myogenic lineage mapping by single-cell mass cytometry. Nat Cell Biol 19, 558–567 (2017). https://doi.org/10.1038/ncb3507

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