AN ARTIFICIAL INTELLIGENCE FOR RAPID IN-LINE LABEL-FREE HUMAN PLURIPOTENT STEM CELL COUNTING AND QUALITY ASSESSMENT

Ragunton, B. L.; Van Buskirk, S.; Wakefield, D.; Randive, N.; Pipathsouk, A.; Pei, B.; Zhou, H.; Yamawaki, T. M.; Berke, M.; Li, C.-M. K.; Hale, C.; Wang, S.; Chambers, S.

doi:10.1101/2023.03.20.533543

bioRxiv · 10.1101/2023.03.20.533543

AN ARTIFICIAL INTELLIGENCE FOR RAPID IN-LINE LABEL-FREE HUMAN PLURIPOTENT STEM CELL COUNTING AND QUALITY ASSESSMENT

Ragunton, B. L.·Van Buskirk, S.·Wakefield, D.·Randive, N.·Pipathsouk, A.·Pei, B.·Zhou, H.·Yamawaki, T. M.·Berke, M.·Li, C.-M. K.·Hale, C.·Wang, S.·Chambers, S.

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Abstract

The current state-of-the-art in hPSC culture is a bespoke and user-dependent process limiting the scale and complexity of the experiments performed and introducing operator-to-operator and day-to-day variation. Artificial intelligence (AI) offers the speed and flexibility to bridge the gap between a human-dependent process and industrial-scale automation. We evaluated an AI approach for counting exact cell numbers of undifferentiated human induced pluripotent stem cells in brightfield images for automating hPSC culture. The neural network generates a topological density map for accurate cell counts. We found that the image-based AI algorithm can determine a precise number of hPSCs and is superior to fluorescence-labeled object detection; the algorithm can ignore well edges, meniscus effects, and dust, achieving an average error of 5.6%. We have built a prototype capable of making a go/no go decision for stem cell passaging to perform 26,400 individual well-level counts from 422,400 images in 12 hours at low cost.

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Ragunton, B. L., Van Buskirk, S., Wakefield, D., Randive, N., Pipathsouk, A., Pei, B., Zhou, H., Yamawaki, T. M., Berke, M., Li, C.-M. K., Hale, C., Wang, S., Chambers, S.. 2023-03-21. AN ARTIFICIAL INTELLIGENCE FOR RAPID IN-LINE LABEL-FREE HUMAN PLURIPOTENT STEM CELL COUNTING AND QUALITY ASSESSMENT. https://doi.org/10.1101/2023.03.20.533543

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