In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations, such as the labour and preparation formats needed to produce different image channels, hinder the use of certain fluorescent markers. Consequently, completed screens may lack biologically informative but experimentally impractical markers. Here we present a deep learning method for overcoming these limitations. We accurately generated predicted fluorescent signals from other related markers and validated this new machine learning (ML) method on two biologically distinct datasets. We used the ML method to improve the selection of biologically active compounds for Alzheimer’s disease from a completed high-content high-throughput screen (HCS) that only contained the original markers. The ML method identified novel compounds that effectively blocked tau aggregation, which had been missed by traditional screening approaches unguided by ML. The method improved triaging efficiency of compound rankings over conventional rankings by raw image channels. We reproduced this ML pipeline on a biologically independent cancer-based dataset, demonstrating its generalizability. The approach is disease-agnostic and applicable across diverse fluorescence microscopy datasets.
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This work was supported by grant no. 2018-191905 from the Chan Zuckerberg Initiative DAF, an advised fund of the Silicon Valley Community Foundation (M.J.K.), the National Institutes of Health (AG002132; S.B.P.), as well as by support from the Brockman Foundation (S.B.P.) and the Sherman Fairchild Foundation (S.B.P.).
The authors declare no competing interests. S.B.P. is a member of the Scientific Advisory Board of ViewPoint Therapeutics and a member of the Board of Directors of Trizell, Ltd, neither of which have contributed financial or any other support to the studies discussed here.
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Wong, D.R., Conrad, J., Johnson, N.R. et al. Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics. Nat Mach Intell 4, 583–595 (2022). https://doi.org/10.1038/s42256-022-00490-8
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