Towards Early Prediction of Human iPSC Reprogramming Success

Abhineet Singh10000-0002-5624-8377, Ila Jasra20000-0002-2842-8565, Omar Mouhammed20000-0003-3537-9420, Nidheesh Dadheech20000-0002-7155-0486, Nilanjan Ray10000-0002-7588-5400, James Shapiro20000-0002-6215-0990
1: Department of Computing Science, University of Alberta, 2: Alberta Diabetes Institute, University of Alberta
Publication date: 2023/11/10
PDF · Code · arXiv


This paper presents advancements in automated early-stage prediction of the success of reprogramming human induced pluripotent stem cells (iPSCs) as a potential source for regenerative cell therapies. The minuscule success rate of iPSC-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 biological scrutiny of multiple clones to identify a single optimal clone. The ability to reliably predict which cells are likely to establish as an optimal iPSC line at an early stage of pluripotency would therefore be ground-breaking in rendering this a practical and cost-effective approach to personalized medicine.
Temporal information about changes in cellular appearance over time is crucial for predicting its future growth outcomes. In order to generate this data, we first performed continuous time-lapse imaging of iPSCs in culture using an ultra-high resolution microscope. We then annotated the locations and identities of cells in late-stage images where reliable manual identification is possible. Next, we propagated these labels backwards in time using a semi-automated tracking system to obtain labels for early stages of growth. Finally, we used this data to train deep neural networks to perform automatic cell segmentation and classification.
Our code and data are available at


iPSC · microscopy imaging · time-lapse imaging · deep learning · transformers · classification · segmentation · tracking · retrospective labeling

Bibtex @article{melba:2023:014:singh, title = "Towards Early Prediction of Human iPSC Reprogramming Success", author = "Singh, Abhineet and Jasra, Ila and Mouhammed, Omar and Dadheech, Nidheesh and Ray, Nilanjan and Shapiro, James", journal = "Machine Learning for Biomedical Imaging", volume = "2", issue = "October 2023 issue", year = "2023", pages = "390--407", issn = "2766-905X", doi = "", url = "" }
RISTY - JOUR AU - Singh, Abhineet AU - Jasra, Ila AU - Mouhammed, Omar AU - Dadheech, Nidheesh AU - Ray, Nilanjan AU - Shapiro, James PY - 2023 TI - Towards Early Prediction of Human iPSC Reprogramming Success T2 - Machine Learning for Biomedical Imaging VL - 2 IS - October 2023 issue SP - 390 EP - 407 SN - 2766-905X DO - UR - ER -

2023:014 cover