Data-Driven Maturity Level Evaluation for Cardiomyocytes Derived from Human Pluripotent Stem Cells (Invited Paper).

Area of Science:

• Biomedical Engineering
• Stem Cell Biology
• Cardiovascular Research

Background:

• Cardiovascular diseases are a major global health concern.
• Human pluripotent stem cells (hPSCs) differentiate into cardiomyocytes (hPSC-CMs) for disease modeling and therapies.
• Immature hPSC-CMs limit their clinical and experimental utility, with a lack of standardized maturity metrics.

Purpose of the Study:

• To develop a data-driven pipeline for quantifying hPSC-CM maturity.
• To establish standardized metrics for assessing hPSC-CM maturation.
• To identify key cardiac genes correlating with maturation stages.

Main Methods:

• Utilized gene expression data across various cardiac development stages.
• Employed machine learning algorithms to identify predictive genes.
• Validated culture time as a proxy for maturity.

Main Results:

• Developed a pipeline that quantifies hPSC-CM maturity using gene expression.
• Achieved an average discrepancy of 4.461 days between predicted and observed culture time.
• Identified CASQ2 (Calsequestrin 2) as the most critical gene associated with culture duration.

Conclusions:

• Established a novel, data-driven framework for assessing hPSC-CM maturity.
• This quantitative approach surpasses traditional qualitative methods.
• Provides a foundation for real-time maturity monitoring devices and improved maturation strategies.