1. In silico labeling enables kinetic myelination assay in brightfield
- Author
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Jian Fang, Eun Yeong Bergsdorf, Vincent Unterreiner, Agustina La Greca, Oleksandr Dergai, Isabelle Claerr, Ngoc-Hong Luong-Nguyen, Inga Galuba, Ioannis Moutsatsos, Shinji Hatakeyama, Paul Groot-Kormelink, Fanning Zeng, and Xian Zhang
- Abstract
Recent advances with deep neural networks have shown the feasibility of acquiring brightfield images with transmitted light and applying in-silico labeling to predict fluorescent images. We have developed a novel in-silico labeling method based on a generative adversarial network and outperforms the state-of-the-art Unet method in generating realistic fluorescent images and quantitatively recapitulating real staining signals, as demonstrated in a complex co-culture myelination assay. Furthermore, we have performed the assay in live mode with multiple kinetic points, applied in-silico labeling to predict fluorescent images from brightfield and quantified the kinetic phenotypic changes. Thus, the proposed approach provides a potential tool to study the kinetics of cellular phenotypic changes with brightfield imaging.
- Published
- 2022
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