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Machine Learning-Driven Automated Culture Reveals Similar Performance of mTeSR+ and StemFlex in Human iP11N Pluripotent Stem Cells

Abstract

Human induced pluripotent stem cells (hiPSCs) are powerful tools for disease modeling and therapeutic development, though their utility remains sensitive to operator-dependent variability and media formulation. Commercial stem cell media formulations, such as mTeSR+ and StemFlex, are designed to support robust pluripotent stem cell maintenance and improve reproducibility. While both are widely used, direct comparisons are limited. We evaluated mTeSR+ and StemFlex using the CellXpress.ai automated tissue culture platform in combination with IN Carta machine-learning-based image analysis to monitor and standardize feeding, passaging, and maintenance. Wild-type iP11N hiPSCs were cultured under standardized automated conditions with growth kinetics, immunocytochemical marker expression, and RT-qPCR assessment. Automated analysis revealed no significant differences in proliferation rates between the formulations; however, longitudinal image-based quantification identified statistically significant differences (<1%) in differentiated cell area fraction across the imaging period. Immunocytochemistry demonstrated comparable expression and localization of pluripotency-associated markers, and RT-qPCR analysis confirmed similar expression of stemness factors. These findings demonstrate that both media support comparable growth and maintenance of pluripotency under standardized automated conditions on iP11N wild-type stem cells in the short-term culture conditions analyzed. Additionally, these data highlight the value of automated imaging and machine-learning-based analysis in reducing operator-dependent variability and improving consistency in hiPSC culture, supporting its application in high-throughput stem cell research.

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