PT  - JOURNAL ARTICLE
AU  - Etienne Becht
AU  - Daniel Tolstrup
AU  - Charles-Antoine Dutertre
AU  - Florent Ginhoux
AU  - Evan W. Newell
AU  - Raphael Gottardo
AU  - Mark B. Headley
TI  - Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning
AID  - 10.1101/2020.06.17.152926
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.06.17.152926
4099  - http://biorxiv.org/content/early/2020/06/19/2020.06.17.152926.short
4100  - http://biorxiv.org/content/early/2020/06/19/2020.06.17.152926.full
AB  - Modern immunologic research increasingly requires high-dimensional analyses in order 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 co-expression patterns of 100s 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 to identify novel 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.Competing Interest StatementE.W.N. is a co-founder, advisor and shareholder of ImmunoScape Pte. Ltd. and an advisor for Neogene Therapeutics and Nanostring Technologies. R.G. declares ownership in CellSpace Biosciences.AUCArea under the receiver operating characteristic curveCDCluster of differentiationILCInnate lymphoid cellsMPCMassively-parallel cytometryPEPhycoerythrin