A data-driven bioinformatics platform for the design and analysis of multiplexed antibody-based cytometry experiments in cancer research

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ABSTRACT Tumor progression, resistance to therapy, and metastasis are closely related to the characteristics of the tumor cell ecosystem. Multiplexed antibody-based cytometry is the standard method for phenotypic characterization of tissue composition, pathogenesis, and immune infiltration with single-cell (and sometimes spatial) resolution. The identification of cell populations in these data is facilitated by algorithms that cluster cells according to their antigenic profile, as well as by predefined sets of markers that have historically evolved by trial and error. However, the annotation of these data is a manual, subjective, and laborious process that hinders the reproducibility and accuracy of the results. The design of antibody panels that include specific markers for all cell types and states present in a tissue is usually unfeasible, and the efficiency of commonly used markers is unknown. Consequently, cell clusters can differ little in their antigenic profile or contain a mixture of cell types. To overcome these limitations, this project will develop informatics technologies that leverage existing single-cell transcriptomic atlases to assist and automate the design and analyses of multiplexed antibody-based cytometry experiments. Our working hypothesis is that the vast amount of available single-cell transcriptomic data of tissues can inform the design, annotation, and analysis of cytometry experiments. We will develop and evaluate informatics technologies for establishing reference antigenic profiles and optimal antibody panels based on single-cell proteotranscriptomic data (Aim 1 ), and for automating the identification, annotation, and gating of cell populations in multiplexed antibody-based cytometry experiments (Aim 2). These new computational methods will enable any researcher to 1) automatically identify and annotate cell populations in a cytometry dataset based on reference single-cell data hosted in a repository, 2) define optimal gates for sorting cell populations, 3) transfer gates across experiments, 4) design optimal antibody panels for a given tissue or set of cell populations, and 5) infer the gene expression profile of cells. We will implement these methods in an open-source software and online portal for the transcriptome-guided annotation and analysis of cytometry data of tumors, and will closely work with end-users through several planned workshops and tutorials to maximize the utility and outreach of this platform (Aim 3). We will test our platform on leukemic and pancreatic cancer tissues profiled with spectral flow cytometry and multiplexed quantitative immunohistochemistry. The informatics technologies developed in this project will transform cancer research by boosting the phenotypic resolution, accuracy, and reproducibility of multiplexed antibody-based cytometry analyses of tumor tissues.