Honors & Awards


  • Bio-X SIGF Fellowship, Stanford University (2018)
  • Student Travel Award, ISAC 33rd International Congress (2018)
  • CERSI Scholar, UCSF-Stanford CERSI (2017)
  • Travel Grant, Stanford Biosciences Office of Graduate Education (2017)
  • Departmental Honors in Biology, University of Texas at Austin (2015)
  • Honorable Mention in Engineering, National Collegiate Research Conference, Harvard University (2015)
  • Stanford Graduate Fellowship, Stanford University (2015)
  • International Education Fee Scholarship, University of Texas at Austin (2014)
  • Undergraduate Research Conference Travel Scholarship, University of Texas at Austin (2014)

Education & Certifications


  • PhD, Stanford University, Computational and Systems Immunology (2020)
  • BS, University of Texas at Austin, Cell and Molecular Biology (2015)
  • BMus, Texas State University, Sound Recording Techology (2007)

Stanford Advisors


Current Research and Scholarly Interests


My research aims to comprehensively characterize B cells in health and disease through the application of multi-omic single cell technologies. I am also developing new computational methods to analyze single cell datasets with applications in hematopoietic cancer diagnostics as well as basic research.

Lab Affiliations


All Publications


  • Multiplexed single-cell morphometry for hematopathology diagnostics. Nature medicine Tsai, A. G., Glass, D. R., Juntilla, M., Hartmann, F. J., Oak, J. S., Fernandez-Pol, S., Ohgami, R. S., Bendall, S. C. 2020; 26 (3): 408–17

    Abstract

    The diagnosis of lymphomas and leukemias requires hematopathologists to integrate microscopically visible cellular morphology with antibody-identified cell surface molecule expression. To merge these into one high-throughput, highly multiplexed, single-cell assay, we quantify cell morphological features by their underlying, antibody-measurable molecular components, which empowers mass cytometers to 'see' like pathologists. When applied to 71 diverse clinical samples, single-cell morphometric profiling reveals robust and distinct patterns of 'morphometric' markers for each major cell type. Individually, lamin B1 highlights acute leukemias, lamin A/C helps distinguish normal from neoplastic mature T cells, and VAMP-7 recapitulates light-cytometric side scatter. Combined with machine learning, morphometric markers form intuitive visualizations of normal and neoplastic cellular distribution and differentiation. When recalibrated for myelomonocytic blast enumeration, this approach is superior to flow cytometry and comparable to expert microscopy, bypassing years of specialized training. The contextualization of traditional surface markers on independent morphometric frameworks permits more sensitive and automated diagnosis of complex hematopoietic diseases.

    View details for DOI 10.1038/s41591-020-0783-x

    View details for PubMedID 32161403