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

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.