Computational image analysis of colony and nuclear morphology to evaluate human induced pluripotent stem cells.

Non-invasive evaluation of cell reprogramming by advanced image analysis is required to maintain the quality of cells intended for regenerative medicine. Here, we constructed living and unlabelled colony image libraries of various human induced pluripotent stem cell (iPSC) lines for supervised machine learning pattern recognition to accurately distinguish bona fide iPSCs from improperly reprogrammed cells. Furthermore, we found that image features for efficient discrimination reside in cellular components. In fact, extensive analysis of nuclear morphologies revealed dynamic and characteristic signatures, including the linear form of the promyelocytic leukaemia (PML)-defined structure in iPSCs, which was reversed to a regular sphere upon differentiation. Our data revealed that iPSCs have a markedly different overall nuclear architecture that may contribute to highly accurate discrimination based on the cell reprogramming status.