Regularized Single-cell Imaging Enables Generalizable AI models for Stain-free Cell Viability Screening

Abstract Cell viability assays are essential tools in biomedical research and drug development. Artificial intelligence (AI) offers the potential to simplify these assays by predicting cell viability directly from brightfield microscopy images, but current models lack generalizability across diverse cell types and treatments. Here, we introduce a strategy called “regularized imaging”, where single cells are isolated in nanowells to generate standardized image patches that simplify segmentation and improve training data quality. We trained our model using example images of live and dead cells from a single cell line exposed to four cytotoxic conditions (ethanol, andrographolide, daunorubicin, and serum starvation). Despite this narrow training dataset, the resulting model accurately identified live and dead cells after treatment by previously unseen compounds, successfully replicating dose-response curves comparable to fluorescence live/dead assays. Importantly, this model effectively generalized across diverse cell types, including both adherent and suspension cells. Additionally, microscopy-based cell viability analysis is non-destructive, enabling repeated measurements to perform kinetic studies to distinguish between fast- and slow-acting compounds. Our findings highlight how regularized single cell imaging enables the training of broadly generalizable AI models to recognize biologically relevant cell features for label-free cell screening workflows. One-sentence Summary Regularized single-cell imaging in nanowells enables training of generalizable AI models for accurate, stain-free viability screening across previously unseen cell types and contexts. Competing Interest Statement S.G.B. and H.M. have financial interest in ImageCyte Technologies, which is commercializing the nanowell-in-microwell plates. Some of the authors are inventors on patent applications own by the University of British Columbia. Footnotes Code and Data availability All source code, dataset, and trained models have been deposited at https://github.com/Pan-De/stain-free-viability-screening-using-nanowells Funding Statement This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada (2020-05412, 2020-00530, 590749-24). P.D. acknowledges funding from the China Scholarship Council and the Tai Hung Fai Charitable Foundation. S.G.B. acknowledges funding from the Society for Laboratory Automation and Screening Graduate Education Fellowship Grant. Conflict of Interest Disclosure S.G.B. and H.M. have financial interest in ImageCyte Technologies, which is commercializing the nanowell-in-microwell plates. Some of the authors are inventors on patent applications own by the University of British Columbia. https://github.com/Pan-De/stain-free-viability-screening-using-nanowells