Honors & Awards

  • Future of Science Fund Scholarship, Keystone Symposia (2020)
  • Travel Award, Stanford Bio-X (2020)
  • Travel Grant, Stanford Biosciences Office of Graduate Education (2020)
  • Best Graduate Student Oral Presentation, Stanford Immunology Annual Scientific Conference (2019)
  • 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 (2021)
  • BS, University of Texas at Austin, Cell and Molecular Biology (2015)
  • BMus, Texas State University, Sound Recording Techology (2007)

Stanford Advisors

Research & Scholarship

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

  • An Integrated Multi-omic Single-Cell Atlas of Human B Cell Identity. Immunity Glass, D. R., Tsai, A. G., Oliveria, J. P., Hartmann, F. J., Kimmey, S. C., Calderon, A. A., Borges, L., Glass, M. C., Wagar, L. E., Davis, M. M., Bendall, S. C. 2020; 53 (1): 217?32.e5


    B cells are capable of a wide range of effector functions including antibody secretion, antigen presentation, cytokine production, and generation of immunological memory. A consistent strategy for classifying human B cells by using surface molecules is essential to harness this functional diversity for clinical translation. We developed a highly multiplexed screen to quantify the co-expression of 351 surface molecules on millions of human B cells. We identified differentially expressed molecules and aligned their variance with isotype usage, VDJ sequence, metabolic profile, biosynthesis activity, and signaling response. Based on these analyses, we propose a classification scheme to segregate B cells from four lymphoid tissues into twelve unique subsets, including a CD45RB+CD27- early memory population, a class-switched CD39+ tonsil-resident population, and a CD19hiCD11c+ memory population that potently responds to immune activation. This classification framework and underlying datasets provide a resource for further investigations of human B cell identity and function.

    View details for DOI 10.1016/j.immuni.2020.06.013

    View details for PubMedID 32668225

  • Single-cell metabolic profiling of human cytotoxic T cells. Nature biotechnology Hartmann, F. J., Mrdjen, D., McCaffrey, E., Glass, D. R., Greenwald, N. F., Bharadwaj, A., Khair, Z., Verberk, S. G., Baranski, A., Baskar, R., Graf, W., Van Valen, D., Van den Bossche, J., Angelo, M., Bendall, S. C. 2020


    Cellular metabolism regulates immune cell activation, differentiation and effector functions, but current metabolic approaches lack single-cell resolution and simultaneous characterization of cellular phenotype. In this study, we developed an approach to characterize the metabolic regulome of single cells together with their phenotypic identity. The method, termed single-cell metabolic regulome profiling (scMEP), quantifies proteins that regulate metabolic pathway activity using high-dimensional antibody-based technologies. We employed mass cytometry (cytometry by time of flight, CyTOF) to benchmark scMEP against bulk metabolic assays by reconstructing the metabolic remodeling of in vitro-activated naive and memory CD8+ T cells. We applied the approach to clinical samples and identified tissue-restricted, metabolically repressed cytotoxic T cells in human colorectal carcinoma. Combining our method with multiplexed ion beam imaging by time of flight (MIBI-TOF), we uncovered the spatial organization of metabolic programs in human tissues, which indicated exclusion of metabolically repressed immune cells from the tumor-immune boundary. Overall, our approach enables robust approximation of metabolic and functional states in individual cells.

    View details for DOI 10.1038/s41587-020-0651-8

    View details for PubMedID 32868913

  • 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


    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

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