Abstract: The process of reprogramming patient samples to human induced pluripotent stem cells (iPSCs) is stochastic, asynchronous, and inefficient leading to a heterogeneous population of cells. Here, we track the reprogramming status of single patient-derived cells during reprogramming with label-free live-cell imaging of cellular metabolism and nuclear morphometry to identify high-quality iPSCs. Erythroid progenitor cells (EPCs) isolated from human peripheral blood showed distinct patterns of autofluorescence lifetime for the reduced form of nicotinamide adenine dinucleotide (phosphate) [NAD(P)H] and flavin adenine dinucleotide (FAD) during reprogramming. Random forest models classified starting EPCs, partially-reprogrammed intermediate cells, and iPSCs with ∼95% accuracy. Reprogramming trajectories resolved at the single cell level indicated significant reprogramming heterogeneity along different branches of cell state. This combination of micropatterning, autofluorescence imaging, and machine learning provides a unique non-destructive method to assess the quality of iPSCs in real-time for various applications in regenerative medicine, cell therapy biomanufacturing, and disease modeling.
Journal Link: 10.1101/2021.12.08.471827 Journal Link: Publisher Website Journal Link: Download PDF Journal Link: Google Scholar