High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning.

Autor:	Becht E; Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Tolstrup D; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Dutertre CA; Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore.; Program in Emerging Infectious Disease, Duke-NUS Medical School, Singapore, Singapore.; Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore 169856, Singapore., Morawski PA; Center for Fundamental Immunology, Benaroya Research Institute, Seattle, WA, USA., Campbell DJ; Center for Fundamental Immunology, Benaroya Research Institute, Seattle, WA, USA.; Department of Immunology, University of Washington School of Medicine, Seattle, WA, USA., Ginhoux F; Singapore Immunology Network, Agency for Science, Technology and Research, Singapore, Singapore.; Translational Immunology Institute, SingHealth Duke-NUS Academic Medical Center, Singapore 169856, Singapore.; Shanghai Institute of Immunology, Shanghai JiaoTong University School of Medicine, 280 South Chongqing Road, Shanghai 200025, China., Newell EW; Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Gottardo R; Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA., Headley MB; Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.; Department of Immunology, University of Washington School of Medicine, Seattle, WA, USA.
Jazyk:	angličtina
Zdroj:	Science advances [Sci Adv] 2021 Sep 24; Vol. 7 (39), pp. eabg0505. Date of Electronic Publication: 2021 Sep 22.
DOI:	10.1126/sciadv.abg0505
Abstrakt:	Modern immunologic research increasingly requires high-dimensional analyses to understand the complex milieu of cell types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the coexpression patterns of hundreds of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and the identification of previously unknown cellular heterogeneity in the lungs of melanoma metastasis–bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost, and accessible solution to single-cell proteomics in complex tissues.
Databáze:	MEDLINE
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