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Expression deconvolution: a reinterpretation of DNA microarray data reveals dynamic changes in cell populations.

Peng Lu¹, Aleksey Nakorchevskiy, Edward M Marcotte

¹Department of Chemistry and Biochemistry, Center for Computational Biology and Bioinformatics, 1 University Station, A4800, University of Texas, Austin, TX 78712-0159, USA.

Proceedings of the National Academy of Sciences of the United States of America

|August 23, 2003

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This study introduces expression deconvolution, a novel method to measure cell population dynamics using mRNA expression. This technique reveals cell cycle progression and responses to various conditions in yeast populations.

Area of Science:

Cell Biology
Genomics
Systems Biology

Background:

Cell populations evolve dynamically, a crucial aspect often unmeasured in experiments.
Understanding population dynamics is key to interpreting cellular responses and behaviors.

Purpose of the Study:

To develop and validate a method for measuring cell population dynamics based on mRNA expression patterns.
To apply this method to analyze yeast cell populations under diverse conditions.

Main Methods:

Developed 'expression deconvolution,' modeling population expression as a linear combination of pure cell type expression.
Reconstructed relative proportions of pure cell types within a mixed population.
Applied the method to yeast under varying conditions, including cell cycle, DNA damage arrest/release, sporulation, and environmental stress.

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Main Results:

Revealed detailed population dynamics of yeast cells during cell cycle progression, DNA damage response, sporulation, and stress.
Identified and temporally ordered cell cycle defects in 146 yeast deletion mutants, with six defects experimentally validated.
Enabled reinterpretation of cell cycle dynamics in previous microarray experiments.

Conclusions:

Expression deconvolution is a powerful tool for quantifying cell population dynamics.
The method offers a new perspective on cell cycle regulation and responses to perturbations.
Expression deconvolution has broad applicability for studying diverse cell population dynamics.