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

ABSTRACT: 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.

SUBMITTER: Becht E

PROVIDER: S-EPMC8457665 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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

Becht Etienne E Tolstrup Daniel D Dutertre Charles-Antoine CA Morawski Peter A PA Campbell Daniel J DJ Ginhoux Florent F Newell Evan W EW Gottardo Raphael R Headley Mark B MB

Science advances 20210922 39

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 ...[more]

PMID: 34550730

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Project description:Mitochondria are highly dynamic organelles whose fusion and fission play an increasingly important role in a number of both normal and pathological cellular functions. Despite the increased interest in mitochondrial dynamics, robust, and quantitative methods to analyze mitochondrial fusion and fission activity in intact cells have not been developed. The current state-of-the art method to measure mitochondrial fusion activity is the polyethylene glycol (PEG) fusion assay in which cells expressing distinct mitochondrially-targeted fluorescent proteins (FPs) are fused together and mitochondrial fusion activity is determined by the rate at which color mixing occurs. Although this assay is useful, cell-cell fusion events are rare, and finding the number of fused cells required to generate statistically rigorous data is both tedious and time-consuming. Furthermore, the data-collection methods available for fluorescence microscopy lead to inherent selection biases that are difficult to control for. To that end, we have developed an unbiased and high-throughput method to detect, image, and analyze fused cells using the Amnis ImagestreamX™ MKII. With IDEAS™ software, we developed algorithms for identifying the fused cells (two nuclei within a single cell), distinguishing them from cell aggregates. Additionally, using the fluorescence localization of the mitochondrially-targeted fluorescent proteins (YFP and DsRed), we applied a modified co-localization algorithm to identify those cells that had a high co-localization score indicating mitochondrial fusion activity. These algorithms were tested using negative controls (FPs associated with fusion deficient mitochondria) and positive controls (cells expressing both FPs in the same mitochondria). Once validated these algorithms could be applied to test samples to evaluate the degree of mitochondrial fusion in cells with various genetic mutations. Ultimately, this new method is the first robust, high-throughput way to directly measure mitochondrial fusion in intact cells. Given how many cellular processes are being linked mitochondrial dynamics, this technique will provide a powerful new tool in the study of this important organelle. © 2016 International Society for Advancement of Cytometry.

Dataset Information

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

Publications

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

Similar Datasets

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