A regression model approach to enable cell morphology correction in high-throughput flow cytometry.

Area of Science:

• Cellular and Molecular Biology
• Quantitative Biology
• Systems Biology

Background:

• Single-cell analysis using flow cytometry often relies on gating, which can exclude cells with diverse morphologies.
• Morphological variations across experimental conditions hinder quantitative comparisons of cellular responses.
• Existing methods struggle to capture the full spectrum of phenotypic variability in cell populations.

Purpose of the Study:

• To develop a novel regression-based approach to correct for cell size and granularity variations in flow cytometry data.
• To enable quantitative analysis of cellular heterogeneity and transcriptional noise in high-throughput experiments.
• To identify genes involved in regulating population-level responses to stimuli.

Main Methods:

• Developed a regression method to normalize fluorescence intensity data, accounting for cell size and granularity.
• Applied the method to analyze a library of yeast knockout strains.
• Investigated population responses to oleic acid induction.

Main Results:

• The regression approach successfully corrected for morphological variability without discarding cells.
• Identified genes essential for establishing a bimodal response to oleic acid in yeast populations.
• Discovered that epigenetic regulators and nucleoporins maintain an 'unresponsive' cell subpopulation.

Conclusions:

• The developed regression method enhances quantitative analysis of single-cell heterogeneity in high-throughput studies.
• The findings highlight the role of specific genes in enabling population-level bet-hedging strategies.
• Understanding cellular heterogeneity is key to deciphering complex biological responses and evolutionary advantages.