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Updated: Oct 19, 2025
Determination of the Relative Cell Surface and Total Expression of Recombinant Ion Channels Using Flow Cytometry
Published on: September 28, 2016
High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine
Etienne Becht1, Daniel Tolstrup2, Charles-Antoine Dutertre3,4,5
1Vaccine and Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Insights
Infinity Flow uses machine learning to analyze hundreds of proteins on millions of cells, revealing new insights into lung tissue and cancer. This cost-effective method enhances single-cell proteomics for complex biological systems.
Area of Science:
- Immunology
- Proteomics
- Computational Biology
Background:
- High-dimensional analysis is crucial for understanding complex disease microenvironments.
- Current methods face limitations in analyzing vast cellular and protein data.
Purpose of the Study:
- To develop a scalable and cost-effective method for high-dimensional single-cell proteomics.
- To comprehensively analyze cellular heterogeneity in complex tissues like the lung.
Main Methods:
- Developed Infinity Flow, integrating hundreds of flow cytometry panels with machine learning.
- Applied supervised machine learning to enhance clustering and dimensionality reduction algorithms.
- Analyzed millions of individual cells for coexpression patterns of hundreds of surface proteins.
Main Results:
- Enabled comprehensive analysis of steady-state murine lung cellular composition.
- Identified novel cellular heterogeneity in lungs of mice with melanoma metastasis.
- Demonstrated enhanced accuracy and depth in data analysis using Infinity Flow.
Conclusions:
- Infinity Flow provides a scalable, low-cost solution for single-cell proteomics in complex tissues.
- The approach significantly advances the ability to dissect cellular heterogeneity in disease.
- Facilitates deeper understanding of tissue microenvironments in immunology research.
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.
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