Bio

Professional Education


  • Doctor of Philosophy, University of California Irvine (2012)
  • Bachelor of Science, University of California Irvine (2007)

Stanford Advisors


Publications

All Publications


  • Publisher Correction: High-resolution myogenic lineage mapping by single-cell mass cytometry. Nature cell biology Porpiglia, E., Samusik, N., Van Ho, A. T., Cosgrove, B. D., Mai, T., Davis, K. L., Jager, A., Nolan, G. P., Bendall, S. C., Fantl, W. J., Blau, H. M. 2018

    Abstract

    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.

    View details for DOI 10.1038/s41556-018-0043-1

    View details for PubMedID 29507406

  • High-resolution myogenic lineage mapping by single-cell mass cytometry NATURE CELL BIOLOGY Porpiglia, E., Samusik, N., Van Ho, A. T., Cosgrove, B. D., Mai, T., Davis, K. L., Jager, A., Nolan, G. P., Bendall, S. C., Fantl, W. J., Blau, H. M. 2017; 19 (5): 558-?

    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.

    View details for DOI 10.1038/ncb3507

    View details for Web of Science ID 000400376100019

    View details for PubMedID 28414312

Footer Links:

Stanford Medicine Resources: