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Advanced Cell Mapping Visualizations for Single Cell Functional Proteomics Enabling Patient Stratification
Author(s) -
Bowman Nick,
Liu Dong,
Paczkowski Patrick,
Chen Jon,
Rossi John,
Mackay Sean,
Bot Adrian,
Zhou Jing
Publication year - 2020
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201900270
Subject(s) - computational biology , single cell analysis , proteomics , computer science , chimeric antigen receptor , biology , cd19 , cell , t cell , bioinformatics , immune system , immunology , gene , genetics , biochemistry
Highly multiplexed single‐cell functional proteomics has emerged as one of the next‐generation toolkits for a deeper understanding of functional heterogeneity in cell. Different from the conventional population‐based bulk and single‐cell RNA‐Seq assays, the microchip‐based proteomics at the single‐cell resolution enables a unique identification of highly polyfunctional cell subsets that co‐secrete many proteins from live single cells and most importantly correlate with patient response to a therapy. The 32‐plex IsoCode chip technology has defined a polyfunctional strength index (PSI) of pre‐infusion anti‐CD19 chimeric antigen receptor (CAR)‐T products, that is significantly associated with patient response to the CAR‐T cell therapy. To complement the clinical relevance of the PSI, a comprehensive visualization toolkit of 3D uniform manifold approximation and projection (UMAP) and t‐distributed stochastic neighbor embedding (t‐SNE) in a proteomic analysis pipeline is developed, providing more advanced analytical algorithms for more intuitive data visualizations. The UMAP and t‐SNE of anti‐CD19 CAR‐T products reveal distinct cytokine profiles between nonresponders and responders and demonstrate a marked upregulation of antitumor‐associated cytokine signatures in CAR‐T cells from responding patients. Using this powerful while user‐friendly analytical tool, the multi‐dimensional single‐cell data can be dissected from complex immune responses and uncover underlying mechanisms, which can promote correlative biomarker discovery, improved bioprocessing, and personalized treatment development.