Honors & Awards

  • Parker Scholar, Parker Institute for Cancer Immunotherapy (2023-2025)
  • Irvington Postdoctoral Fellowship, Cancer Research Institute (2022-2025)
  • Ruth L. Kirschstein NRSA for Individual Predoctoral Fellows (F31) Award, National Cancer Institute (2020-2021)
  • ARCS Foundation Scholar, ARCS (2019-2020)

Professional Education

  • Doctor of Philosophy, University of California San Francisco (2021)
  • PhD, University of California, San Francisco, Biochemistry and Molecular Biology (2021)
  • BA, Wesleyan University, Biology; Science in Society; Molecular Biology and Biochemistry (2014)

Stanford Advisors


  • Zev Gartner, David Patterson, Eric Chow, Christopher McGinnis, Robert Weber. "United States Patent PCT/US2019/040898 Lipid-modified oligonucleotides and methods of using the same", The Regents of the University of California, Sep 1, 2020

Lab Affiliations

All Publications

  • MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices NATURE METHODS McGinnistm, C. S., Patterson, D. M., Winkler, J., Conrad, D. N., Hein, M. Y., Srivastava, V., Hu, J. L., Murrow, L. M., Weissman, J. S., Werb, Z., Chow, E. D., Gartner, Z. J. 2019; 16 (7): 619-+


    Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such as cell doublets. However, universal and scalable sample barcoding strategies have not been described. We therefore developed MULTI-seq: multiplexing using lipid-tagged indices for single-cell and single-nucleus RNA sequencing. MULTI-seq reagents can barcode any cell type or nucleus from any species with an accessible plasma membrane. The method involves minimal sample processing, thereby preserving cell viability and endogenous gene expression patterns. When cells are classified into sample groups using MULTI-seq barcode abundances, data quality is improved through doublet identification and recovery of cells with low RNA content that would otherwise be discarded by standard quality-control workflows. We use MULTI-seq to track the dynamics of T-cell activation, perform a 96-plex perturbation experiment with primary human mammary epithelial cells and multiplex cryopreserved tumors and metastatic sites isolated from a patient-derived xenograft mouse model of triple-negative breast cancer.

    View details for DOI 10.1038/s41592-019-0433-8

    View details for Web of Science ID 000473098700025

    View details for PubMedID 31209384

    View details for PubMedCentralID PMC6837808

  • DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors CELL SYSTEMS McGinnis, C. S., Murrow, L. M., Gartner, Z. J. 2019; 8 (4): 329-+


    Single-cell RNA sequencing (scRNA-seq) data are commonly affected by technical artifacts known as "doublets," which limit cell throughput and lead to spurious biological conclusions. Here, we present a computational doublet detection tool-DoubletFinder-that identifies doublets using only gene expression data. DoubletFinder predicts doublets according to each real cell's proximity in gene expression space to artificial doublets created by averaging the transcriptional profile of randomly chosen cell pairs. We first use scRNA-seq datasets where the identity of doublets is known to show that DoubletFinder identifies doublets formed from transcriptionally distinct cells. When these doublets are removed, the identification of differentially expressed genes is enhanced. Second, we provide a method for estimating DoubletFinder input parameters, allowing its application across scRNA-seq datasets with diverse distributions of cell types. Lastly, we present "best practices" for DoubletFinder applications and illustrate that DoubletFinder is insensitive to an experimentally validated kidney cell type with "hybrid" expression features.

    View details for DOI 10.1016/j.cels.2019.03.003

    View details for Web of Science ID 000465541100006

    View details for PubMedID 30954475

    View details for PubMedCentralID PMC6853612

  • Mapping hormone-regulated cell-cell interaction networks in the human breast at single-cell resolution. Cell systems Murrow, L. M., Weber, R. J., Caruso, J. A., McGinnis, C. S., Phong, K., Gascard, P., Rabadam, G., Borowsky, A. D., Desai, T. A., Thomson, M., Tlsty, T., Gartner, Z. J. 2022; 13 (8): 644-664.e8


    The rise and fall of estrogen and progesterone across menstrual cycles and during pregnancy regulates breast development and modifies cancer risk. How these hormones impact each cell type in the breast remains poorly understood because they act indirectly through paracrine networks. Using single-cell analysis of premenopausal breast tissue, we reveal a network of coordinated transcriptional programs representing the tissue-level response to changing hormone levels. Our computational approach, DECIPHER-seq, leverages person-to-person variability in breast composition and cell state to uncover programs that co-vary across individuals. We use differences in cell-type proportions to infer a subset of programs that arise from direct cell-cell interactions regulated by hormones. Further, we demonstrate that prior pregnancy and obesity modify hormone responsiveness through distinct mechanisms: obesity reduces the proportion of hormone-responsive cells, whereas pregnancy dampens the direct response of these cells to hormones. Together, these results provide a comprehensive map of the cycling human breast.

    View details for DOI 10.1016/j.cels.2022.06.005

    View details for PubMedID 35863345

    View details for PubMedCentralID PMC9590200

  • Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution. Cell Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., Horns, F., Qiu, X., Chen, M. Z., Freed-Pastor, W. A., McGinnis, C. S., Patterson, D. M., Gartner, Z. J., Chow, E. D., Bivona, T. G., Chan, M. M., Yosef, N., Jacks, T., Weissman, J. S. 2022; 185 (11): 1905-1923.e25


    Tumor evolution is driven by the progressive acquisition of genetic and epigenetic alterations that enable uncontrolled growth and expansion to neighboring and distal tissues. The study of phylogenetic relationships between cancer cells provides key insights into these processes. Here, we introduced an evolving lineage-tracing system with a single-cell RNA-seq readout into a mouse model of Kras;Trp53(KP)-driven lung adenocarcinoma and tracked tumor evolution from single-transformed cells to metastatic tumors at unprecedented resolution. We found that the loss of the initial, stable alveolar-type2-like state was accompanied by a transient increase in plasticity. This was followed by the adoption of distinct transcriptional programs that enable rapid expansion and, ultimately, clonal sweep of stable subclones capable of metastasizing. Finally, tumors develop through stereotypical evolutionary trajectories, and perturbing additional tumor suppressors accelerates progression by creating novel trajectories. Our study elucidates the hierarchical nature of tumor evolution and, more broadly, enables in-depth studies of tumor progression.

    View details for DOI 10.1016/j.cell.2022.04.015

    View details for PubMedID 35523183

  • Single cell enhancer activity distinguishes GABAergic and cholinergic lineages in embryonic mouse basal ganglia. Proceedings of the National Academy of Sciences of the United States of America Su-Feher, L., Rubin, A. N., Silberberg, S. N., Catta-Preta, R., Lim, K. J., Ypsilanti, A. R., Zdilar, I., McGinnis, C. S., McKinsey, G. L., Rubino, T. E., Hawrylycz, M. J., Thompson, C., Gartner, Z. J., Puelles, L., Zeng, H., Rubenstein, J. L., Nord, A. S. 2022; 119 (15): e2108760119


    SignificanceDuring brain development, neurons are generated by spatially and temporally distinct processes that remain to be fully characterized. The ganglionic eminences (GEs) in the embryonic subpallium give rise to GABAergic and cholinergic neuron lineages that form the basal ganglia or migrate to the cerebral cortex. Beyond a limited set of canonical RNA markers, the transcriptional states of GE progenitors and immature neurons cells remain poorly defined. We combine enhancer labeling, single-cell transcriptomics using transcription factor-anchored clustering, and integration with in situ hybridization data to distinguish emerging neuronal populations in embryonic mouse basal ganglia. Our results demonstrate the specificity of enhancer-based labeling at single-cell resolution and reveal developmental origins and specification processes of critical neuronal lineages.

    View details for DOI 10.1073/pnas.2108760119

    View details for PubMedID 35377797

  • AMULET: a novel read count-based method for effective multiplet detection from single nucleus ATAC-seq data. Genome biology Thibodeau, A., Eroglu, A., McGinnis, C. S., Lawlor, N., Nehar-Belaid, D., Kursawe, R., Marches, R., Conrad, D. N., Kuchel, G. A., Gartner, Z. J., Banchereau, J., Stitzel, M. L., Cicek, A. E., Ucar, D. 2021; 22 (1): 252


    Detecting multiplets in single nucleus (sn)ATAC-seq data is challenging due to data sparsity and limited dynamic range. AMULET (ATAC-seq MULtiplet Estimation Tool) enumerates regions with greater than two uniquely aligned reads across the genome to effectively detect multiplets. We evaluate the method by generating snATAC-seq data in the human blood and pancreatic islet samples. AMULET has high precision, estimated via donor-based multiplexing, and high recall, estimated via simulated multiplets, compared to alternatives and identifies multiplets most effectively when a certain read depth of 25K median valid reads per nucleus is achieved.

    View details for DOI 10.1186/s13059-021-02469-x

    View details for PubMedID 34465366

    View details for PubMedCentralID PMC8408950

  • No detectable alloreactive transcriptional responses under standard sample preparation conditions during donor-multiplexed single-cell RNA sequencing of peripheral blood mononuclear cells BMC BIOLOGY McGinnis, C. S., Siegel, D. A., Xie, G., Hartoularos, G., Stone, M., Ye, C. J., Gartner, Z. J., Roan, N. R., Lee, S. A. 2021; 19 (1): 10


    Single-cell RNA sequencing (scRNA-seq) provides high-dimensional measurements of transcript counts in individual cells. However, high assay costs and artifacts associated with analyzing samples across multiple sequencing runs limit the study of large numbers of samples. Sample multiplexing technologies such as MULTI-seq and antibody hashing using single-cell multiplexing kit (SCMK) reagents (BD Biosciences) use sample-specific sequence tags to enable individual samples to be sequenced in a pooled format, markedly lowering per-sample processing and sequencing costs while minimizing technical artifacts. Critically, however, pooling samples could introduce new artifacts, partially negating the benefits of sample multiplexing. In particular, no study to date has evaluated whether pooling peripheral blood mononuclear cells (PBMCs) from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) results in significant changes in gene expression resulting from alloreactivity (i.e., response to non-self). The ability to demonstrate minimal to no alloreactivity is crucial to avoid confounded data analyses, particularly for cross-sectional studies evaluating changes in immunologic gene signatures.Here, we applied the 10x Genomics scRNA-seq platform to MULTI-seq and/or SCMK-labeled PBMCs from a single donor with and without pooling with PBMCs from unrelated donors for 30 min at 4 °C. We did not detect any alloreactivity signal between mixed and unmixed PBMCs across a variety of metrics, including alloreactivity marker gene expression in CD4+ T cells, cell type proportion shifts, and global gene expression profile comparisons using Gene Set Enrichment Analysis and Jensen-Shannon Divergence. These results were additionally mirrored in publicly-available scRNA-seq data generated using a similar experimental design. Moreover, we identified confounding gene expression signatures linked to PBMC preparation method (e.g., Trima apheresis), as well as SCMK sample classification biases against activated CD4+ T cells which were recapitulated in two other SCMK-incorporating scRNA-seq datasets.We demonstrate that (i) mixing PBMCs from unrelated donors under standard scRNA-seq sample preparation conditions (e.g., 30 min co-incubation at 4 °C) does not cause an allogeneic response, and (ii) that Trima apheresis and PBMC sample multiplexing using SCMK reagents can introduce undesirable technical artifacts into scRNA-seq data. Collectively, these observations establish important benchmarks for future cross-sectional immunological scRNA-seq experiments.

    View details for DOI 10.1186/s12915-020-00941-x

    View details for Web of Science ID 000609168700001

    View details for PubMedID 33472616

    View details for PubMedCentralID PMC7816397

  • Human microglia states are conserved across experimental models and regulate neural stem cell responses in chimeric organoids. Cell stem cell Popova, G., Soliman, S. S., Kim, C. N., Keefe, M. G., Hennick, K. M., Jain, S., Li, T., Tejera, D., Shin, D., Chhun, B. B., McGinnis, C. S., Speir, M., Gartner, Z. J., Mehta, S. B., Haeussler, M., Hengen, K. B., Ransohoff, R. R., Piao, X., Nowakowski, T. J. 2021


    Microglia are resident macrophages in the brain that emerge in early development and respond to the local environment by altering their molecular and phenotypic states. Fundamental questions about microglia diversity and function during development remain unanswered because we lack experimental strategies to interrogate their interactions with other cell types and responses to perturbations ex vivo. We compared human microglia states across culture models, including cultured primary and pluripotent stem cell-derived microglia. We developed a "report card" of gene expression signatures across these distinct models to facilitate characterization of their responses across experimental models, perturbations, and disease conditions. Xenotransplantation of human microglia into cerebral organoids allowed us to characterize key transcriptional programs of developing microglia in vitro and reveal that microglia induce transcriptional changes in neural stem cells and decrease interferon signaling response genes. Microglia additionally accelerate the emergence of synchronized oscillatory network activity in brain organoids by modulating synaptic density.

    View details for DOI 10.1016/j.stem.2021.08.015

    View details for PubMedID 34536354

  • ZipSeq: barcoding for real-time mapping of single cell transcriptomes NATURE METHODS Hu, K. H., Eichorst, J. P., McGinnis, C. S., Patterson, D. M., Chow, E. D., Kersten, K., Jameson, S. C., Gartner, Z. J., Rao, A. A., Krummel, M. F. 2020; 17 (8): 833-+


    Spatial transcriptomics seeks to integrate single cell transcriptomic data within the three-dimensional space of multicellular biology. Current methods to correlate a cell's position with its transcriptome in living tissues have various limitations. We developed an approach, called 'ZipSeq', that uses patterned illumination and photocaged oligonucleotides to serially print barcodes ('zipcodes') onto live cells in intact tissues, in real time and with an on-the-fly selection of patterns. Using ZipSeq, we mapped gene expression in three settings: in vitro wound healing, live lymph node sections and a live tumor microenvironment. In all cases, we discovered new gene expression patterns associated with histological structures. In the tumor microenvironment, this demonstrated a trajectory of myeloid and T cell differentiation from the periphery inward. A combinatorial variation of ZipSeq efficiently scales in the number of regions defined, providing a pathway for complete mapping of live tissues, subsequent to real-time imaging or perturbation.

    View details for DOI 10.1038/s41592-020-0880-2

    View details for Web of Science ID 000545908800002

    View details for PubMedID 32632238

    View details for PubMedCentralID PMC7891292

  • Cell population structure prior to bifurcation predicts efficiency of directed differentiation in human induced pluripotent cells PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA Bargaje, R., Trachanaa, K., Shelton, M. N., McGinnis, C. S., Zhou, J. X., Chadick, C., Cook, S., Cavanaugh, C., Huang, S., Hood, L. 2017; 114 (9): 2271-2276


    Steering the differentiation of induced pluripotent stem cells (iPSCs) toward specific cell types is crucial for patient-specific disease modeling and drug testing. This effort requires the capacity to predict and control when and how multipotent progenitor cells commit to the desired cell fate. Cell fate commitment represents a critical state transition or "tipping point" at which complex systems undergo a sudden qualitative shift. To characterize such transitions during iPSC to cardiomyocyte differentiation, we analyzed the gene expression patterns of 96 developmental genes at single-cell resolution. We identified a bifurcation event early in the trajectory when a primitive streak-like cell population segregated into the mesodermal and endodermal lineages. Before this branching point, we could detect the signature of an imminent critical transition: increase in cell heterogeneity and coordination of gene expression. Correlation analysis of gene expression profiles at the tipping point indicates transcription factors that drive the state transition toward each alternative cell fate and their relationships with specific phenotypic readouts. The latter helps us to facilitate small molecule screening for differentiation efficiency. To this end, we set up an analysis of cell population structure at the tipping point after systematic variation of the protocol to bias the differentiation toward mesodermal or endodermal cell lineage. We were able to predict the proportion of cardiomyocytes many days before cells manifest the differentiated phenotype. The analysis of cell populations undergoing a critical state transition thus affords a tool to forecast cell fate outcomes and can be used to optimize differentiation protocols to obtain desired cell populations.

    View details for DOI 10.1073/pnas.1621412114

    View details for Web of Science ID 000395101200057

    View details for PubMedID 28167799

    View details for PubMedCentralID PMC5338498