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This paper presents advancements in automated early-stage prediction of thesuccess of reprogramming human induced pluripotent stem cells (iPSCs) as apotential source for regenerative cell therapies.The minuscule success rate ofiPSC-reprogramming of around $ 0.01% $ to $ 0.1% $ makes it labor-intensive,time-consuming, and exorbitantly expensive to generate a stable iPSC line.Since that requires culturing of millions of cells and intense biologicalscrutiny of multiple clones to identify a single optimal clone. The ability toreliably predict which cells are likely to establish as an optimal iPSC line atan early stage of pluripotency would therefore be ground-breaking in renderingthis a practical and cost-effective approach to personalized medicine. Temporalinformation about changes in cellular appearance over time is crucial forpredicting its future growth outcomes. In order to generate this data, we firstperformed continuous time-lapse imaging of iPSCs in culture using an ultra-highresolution microscope. We then annotated the locations and identities of cellsin late-stage images where reliable manual identification is possible. Next, wepropagated these labels backwards in time using a semi-automated trackingsystem to obtain labels for early stages of growth. Finally, we used this datato train deep neural networks to perform automatic cell segmentation andclassification. Our code and data are available athttps://github.com/abhineet123/ipsc_prediction.

Towards Early Prediction of Human iPSC Reprogramming Success