Induced pluripotent stem cells (iPSCs) hold the potential for personalized regenerative medicine. Yet accurately gauging the stemness of each colony remains a challenge since existing methods are inconsistent or harmful to iPSCs. Addressing this, we introduce holotomography (HT), a non-invasive microscopic technique. HT, through three-dimensional refractive index distributions, revealed iPSC structures at various scales as well as properties like volume, mass density, and lipid ratio. We identified altered properties in iPSCs exposed to differentiation agents, and then employed a machine-learning algorithm to detect reduced stemness from images. Through these results, HT emerges as a potential tool for iPSC quality maintenance.
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