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MotivationHigh-parameter single-cell technologies can reveal novel cell populations of interest, but studying or validating these populations using lower-parameter methods remains challenging.ResultsHere, we present GateFinder, an algorithm that enriches high-dimensional cell types with simple, stepwise polygon gates requiring only two markers at a time. A series of case studies of complex cell types illustrates how simplified enrichment strategies can enable more efficient assays, reveal novel biomarkers and clarify underlying biology.Availability and implementationThe GateFinder algorithm is implemented as a free and open-source package for BioConductor: https://nalab.stanford.edu/gatefinder.Supplementary informationSupplementary data are available at Bioinformatics online.

Advances in single-cell mass cytometry have increasingly improved highly multidimensional characterization of immune cell heterogeneity. The immunoassay multiplexing capacity relies on monoclonal antibodies labeled with stable heavy-metal isotopes. To date, a variety of rare-earth elements and noble and post-transition metal isotopes have been used in mass cytometry; nevertheless, the methods used for antibody conjugation differ because of the individual metal coordination chemistries and distinct stabilities of various metal cations. Herein, we provide three optimized protocols for conjugating monoclonal IgG antibodies with 48 high-purity heavy-metal isotopes: (i) 38 isotopes of lanthanides, 2 isotopes of indium, and 1 isotope of yttrium; (ii) 6 isotopes of palladium; and (iii) 1 isotope of bismuth. Bifunctional chelating agents containing coordinative ligands of monomeric DOTA (1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid) or polymeric pentetic acid (DTPA) were used to stably sequester isotopic cations in aqueous solutions and were subsequently coupled to IgG antibodies using site-specific biorthogonal reactions. Furthermore, quantification methods based on antibody inherent absorption at 280 nm and on extrinsic absorption at 562 nm after staining with bicinchoninic acid (BCA) are reported to determine metal-isotope-tagged antibodies. In addition, a freeze-drying procedure to prepare palladium isotopic mass tags is described. To demonstrate the utility, experiments using six palladium-tagged CD45 antibodies for barcoding assays of live immune cells in cytometry by time-of-flight (CyTOF) are described. Conjugation of pure isotopes of lanthanides, indium, or yttrium takes ~3.5 h. Conjugation of bismuth takes ~4 h. Preparation of palladium mass tags takes ~8 h. Conjugation of pure isotopes of palladium takes ~2.5 h. Antibody titration takes ~4 h.

We have performed an in-depth single-cell phenotypic characterization of high-grade serous ovarian cancer (HGSOC) by multiparametric mass cytometry (CyTOF). Using a CyTOF antibody panel to interrogate features of HGSOC biology, combined with unsupervised computational analysis, we identified noteworthy cell types co-occurring across the tumors. In addition to a dominant cell subset, each tumor harbored rarer cell phenotypes. One such group co-expressed E-cadherin and vimentin (EV), suggesting their potential role in epithelial mesenchymal transition, which was substantiated by pairwise correlation analyses. Furthermore, tumors from patients with poorer outcome had an increased frequency of another rare cell type that co-expressed vimentin, HE4, and cMyc. These poorer-outcome tumors also populated more cell phenotypes, as quantified by Simpson's diversity index. Thus, despite the recognized genomic complexity of the disease, the specific cell phenotypes uncovered here offer a focus for therapeutic intervention and disease monitoring.

Immune cells function in an interacting hierarchy that coordinates the activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology.

Immune cells function in an interacting hierarchy that coordinates the activities of various cell types according to genetic and environmental contexts. We developed graphical approaches to construct an extensible immune reference map from mass cytometry data of cells from different organs, incorporating landmark cell populations as flags on the map to compare cells from distinct samples. The maps recapitulated canonical cellular phenotypes and revealed reproducible, tissue-specific deviations. The approach revealed influences of genetic variation and circadian rhythms on immune system structure, enabled direct comparisons of murine and human blood cell phenotypes, and even enabled archival fluorescence-based flow cytometry data to be mapped onto the reference framework. This foundational reference map provides a working definition of systemic immune organization to which new data can be integrated to reveal deviations driven by genetics, environment, or pathology.

Insight into the cancer cell populations that are responsible for relapsed disease is needed to improve outcomes. Here we report a single-cell-based study of B cell precursor acute lymphoblastic leukemia at diagnosis that reveals hidden developmentally dependent cell signaling states that are uniquely associated with relapse. By using mass cytometry we simultaneously quantified 35 proteins involved in B cell development in 60 primary diagnostic samples. Each leukemia cell was then matched to its nearest healthy B cell population by a developmental classifier that operated at the single-cell level. Machine learning identified six features of expanded leukemic populations that were sufficient to predict patient relapse at diagnosis. These features implicated the pro-BII subpopulation of B cells with activated mTOR signaling, and the pre-BI subpopulation of B cells with activated and unresponsive pre-B cell receptor signaling, to be associated with relapse. This model, termed 'developmentally dependent predictor of relapse' (DDPR), significantly improves currently established risk stratification methods. DDPR features exist at diagnosis and persist at relapse. By leveraging a data-driven approach, we demonstrate the predictive value of single-cell 'omics' for patient stratification in a translational setting and provide a framework for its application to human cancer., Author(s): Zinaida Good [1, 2, 3, 4]; Jolanda Sarno [5, 6]; Astraea Jager [1, 2, 5]; Nikolay Samusik [1, 2]; Nima Aghaeepour [1, 2]; Erin F Simonds [1, 2]; Leah [...]