Honors & Awards


  • CRI Irvington Postdoctoral Fellowship, Cancer Research Institute (2022)
  • Mahan Postdoctoral Fellowship, Fred Hutchinson Cancer Center (2021)
  • Bio-X SIGF Fellowship, Stanford University (2018)
  • CERSI Scholar, UCSF-Stanford CERSI (2017)
  • Stanford Graduate Fellowship, Stanford University (2015)

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)

All Publications


  • Supervised dimensionality reduction for exploration of single-cell data by HSS-LDA. Patterns (New York, N.Y.) Amouzgar, M., Glass, D. R., Baskar, R., Averbukh, I., Kimmey, S. C., Tsai, A. G., Hartmann, F. J., Bendall, S. C. 2022; 3 (8): 100536

    Abstract

    Single-cell technologies generate large, high-dimensional datasets encompassing a diversity of omics. Dimensionality reduction captures the structure and heterogeneity of the original dataset, creating low-dimensional visualizations that contribute to the human understanding of data. Existing algorithms are typically unsupervised, using measured features to generate manifolds, disregarding known biological labels such as cell type or experimental time point. We repurpose the classification algorithm, linear discriminant analysis (LDA), for supervised dimensionality reduction of single-cell data. LDA identifies linear combinations of predictors that optimally separate a priori classes, enabling the study of specific aspects of cellular heterogeneity. We implement feature selection by hybrid subset selection (HSS) and demonstrate that this computationally efficient approach generates non-stochastic, interpretable axesamenable to diverse biological processes such as differentiation over time and cell cycle. We benchmark HSS-LDA against several popular dimensionality-reduction algorithms and illustrate its utility and versatility for the exploration of single-cell mass cytometry, transcriptomics, and chromatin accessibility data.

    View details for DOI 10.1016/j.patter.2022.100536

    View details for PubMedID 36033591

  • Data science through the lens of systems immunology. Patterns (New York, N.Y.) Glass, D. R., Amouzgar, M. 2022; 3 (8): 100574

    Abstract

    Glass, a post-doctoral researcher, and Amouzgar, a PhD student, in Bendall lab proposed a supervised dimensionality reduction method to explore and analyze single-cell data. Their Patterns paper highlights the advantages of supervised learning in single-cell datasets with class labels. They talk about the essential role of data science in this project and in their lives.

    View details for DOI 10.1016/j.patter.2022.100574

    View details for PubMedID 36033601

  • Human IL-10-producing B cells have diverse states that are induced from multiple B cell subsets. Cell reports Glass, M. C., Glass, D. R., Oliveria, J. P., Mbiribindi, B., Esquivel, C. O., Krams, S. M., Bendall, S. C., Martinez, O. M. 2022; 39 (3): 110728

    Abstract

    Regulatory B cells (Bregs) suppress immune responses through the secretion of interleukin-10 (IL-10). This immunomodulatory capacity holds therapeutic potential, yet a definitional immunophenotype for enumeration and prospective isolation of B cells capable of IL-10 production remains elusive. Here, we simultaneously quantify cytokine production and immunophenotype in human peripheral B cells across a range of stimulatory conditions and time points using mass cytometry. Our analysis shows that multiple functional B cell subsets produce IL-10 and that no phenotype uniquely identifies IL-10+ B cells. Further, a significant portion of IL-10+ B cells co-express the pro-inflammatory cytokines IL-6 and tumor necrosis factor alpha (TNFα). Despite this heterogeneity, operationally tolerant liver transplant recipients have a unique enrichment of IL-10+, but not TNFα+ or IL-6+, B cells compared with transplant recipients receiving immunosuppression. Thus, human IL-10-producing B cells constitute an induced, transient state arising from a diversity of B cell subsets that may contribute to maintenance of immune homeostasis.

    View details for DOI 10.1016/j.celrep.2022.110728

    View details for PubMedID 35443184

  • Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell Risom, T., Glass, D. R., Averbukh, I., Liu, C. C., Baranski, A., Kagel, A., McCaffrey, E. F., Greenwald, N. F., Rivero-Gutiérrez, B., Strand, S. H., Varma, S., Kong, A., Keren, L., Srivastava, S., Zhu, C., Khair, Z., Veis, D. J., Deschryver, K., Vennam, S., Maley, C., Hwang, E. S., Marks, J. R., Bendall, S. C., Colditz, G. A., West, R. B., Angelo, M. 2022; 185 (2): 299-310.e18

    Abstract

    Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.

    View details for DOI 10.1016/j.cell.2021.12.023

    View details for PubMedID 35063072

  • Multiplexed single-cell morphometry for hematopathology diagnostics. Nature medicine Tsai, A. G., Glass, D. R., Juntilla, M. n., Hartmann, F. J., Oak, J. S., Fernandez-Pol, S. n., 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

  • 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. n., Glass, M. C., Wagar, L. E., Davis, M. M., Bendall, S. C. 2020; 53 (1): 217–32.e5

    Abstract

    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. n., McCaffrey, E. n., Glass, D. R., Greenwald, N. F., Bharadwaj, A. n., Khair, Z. n., Verberk, S. G., Baranski, A. n., Baskar, R. n., Graf, W. n., Van Valen, D. n., Van den Bossche, J. n., Angelo, M. n., Bendall, S. C. 2020

    Abstract

    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