Cell states beyond transcriptomics: Integrating structural organization and gene expression in hiPSC-derived...

Area of Science:

• Cell Biology
• Biophysics
• Stem Cell Biology

Background:

• Defining cell states rigorously remains a challenge in biology.
• Current methods often rely on anatomical or physiological functions, lacking quantitative precision.
• A systematic approach to classifying subcellular organization is needed.

Purpose of the Study:

• To develop and apply a quantitative, imaging-based platform for automated cell state classification.
• To systematically analyze subcellular organization and gene expression in human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs).
• To create a publicly available dataset for studying cell state heterogeneity.

Main Methods:

• Development of a quantitative, imaging-based platform for high-throughput cell classification.
• Systematic quantification of subcellular organization (sarcomere organization) and mRNA abundance in >30,000 hiPSC-CMs.
• Analysis of correlations between subcellular organization and gene expression patterns.

Main Results:

• Generated a comprehensive dataset of subcellular organization and gene expression in hiPSC-CMs.
• Observed significant heterogeneity in both subcellular organization and mRNA abundance within the hiPSC-CM population.
• Found that while some genes (e.g., MYH7) showed correlation, gene expression alone was insufficient to define cell states due to frequent uncorrelation with organization.

Conclusions:

• Cell state is not solely determined by gene expression; it is a multidimensional property.
• A comprehensive definition of cell state requires integrating quantitative, multidimensional traits, including subcellular organization, across space, time, and function.
• The developed platform and dataset provide a foundation for future cell state investigations.