An automated analysis of highly complex flow cytometry-based proteomic data.

Area of Science:

• Biochemistry
• Proteomics
• Analytical Chemistry

Background:

• Multiplexed measurement of biomolecules is crucial for high-content analysis.
• Flow cytometry offers a powerful platform for detecting cellular and molecular targets.
• Existing methods for analyzing complex microsphere data can be labor-intensive.

Purpose of the Study:

• To develop a software tool for automated analysis of color-coded microspheres in flow cytometry.
• To enable high-content cellular proteomics by harnessing flow cytometry data.
• To validate the approach using size exclusion chromatography-resolved microsphere-based affinity proteomics (Size-MAP).

Main Methods:

• Development of a software tool for automated gating, statistics extraction, and cross-sample analysis of microsphere data.
• Utilizing size exclusion chromatography-resolved microsphere-based affinity proteomics (Size-MAP).
• Employing multicolor flow cytometry for detection of captured, labeled proteins.

Main Results:

• The software successfully automated the analysis of over 1,000 microsphere subsets.
• Biological information was extracted from large-scale flow cytometry data, compressing 24 data points per antibody to 1-4 integrated values.
• Demonstrated the method's utility by analyzing protein changes in leukemia cells treated with imatinib mesylate.

Conclusions:

• The developed software enables efficient processing of large-scale flow cytometry data for high-content proteomics.
• This approach significantly enhances the capability of flow cytometry as a proteomics tool.
• The Size-MAP method combined with automated analysis provides a powerful platform for biomolecular discovery.