Evaluating Cell Processes, Quality, and Biomarkers in Pluripotent Stem Cells Using Video Bioinformatics

Area of Science:

• Stem Cell Biology
• Biotechnology
• Cellular Imaging

Background:

• Automated quality control is crucial for stem cell research, regenerative medicine, and toxicology.
• Current methods for evaluating stem cell quality are often manual, time-consuming, and prone to bias.
• There is a need for non-invasive, high-content tools to monitor stem cell health and processes during in vitro culture.

Purpose of the Study:

• To present StemCellQC, an automated, unbiased toolkit for assessing stem cell quality and cellular processes.
• To identify morphological and dynamic features indicative of stem cell health, unhealth, and death.
• To develop a predictive model for stem cell quality using video analysis.

Main Methods:

• Utilized time-lapse phase-contrast video microscopy to capture cell behavior.
• Developed an automated high-content profiling toolkit (StemCellQC) to extract 24 morphological and dynamic features.
• Analyzed features in healthy, unhealthy, and dying human embryonic stem cell (hESC) colonies.

Main Results:

• Identified specific morphological and dynamic features that differ between healthy and unhealthy/dying hESC colonies.
• Discovered biomarkers that predict changes in cellular processes before manual detection.
• Achieved 96% accuracy in distinguishing healthy from unhealthy/dying hESC colonies using StemCellQC over 48 hours.

Conclusions:

• StemCellQC provides an accurate and efficient method for non-invasive stem cell quality control.
• The toolkit reduces manual labor, handling errors, and human bias in stem cell assessment.
• StemCellQC has the potential to enhance stem cell research, regenerative therapies, and toxicological studies by enabling reliable monitoring of cell quality and processes.