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Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics

A preprint version of the article is available at bioRxiv.


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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Fig. 1: A cellular Tau(P301S)-YFP aggregation assay that models prion propagation in vitro with an additional AT8-pTau channel for ML training.
Fig. 2: The ML model generated a phosphorylated AT8-pTau channel, solely from DAPI and YFP-tau channels.
Fig. 3: The ML model outperformed all others.
Fig. 4: Transforming the archival HCS data with ML-predicted AT8-pTau images revealed previously unknown active compounds.
Fig. 5: ML triaged active compounds better than other methods.
Fig. 6: The trans-fluorescence learning method generated accurate cyclin-B1 signal from Hoechst signal, on an independent and biologically unrelated dataset.

Data availability

All image data are freely available at and

Code availability

The full source code and fully trained models are available at and


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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.).

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Authors and Affiliations



Conceptualization was provided by D.R.W., J.C., N.J., N.A.P. and M.J.K., methodology by D.R.W., J.C., N.J., N.A.P. and M.J.K., software by D.R.W., validation by D.R.W., A.L. and S.B.P., formal analysis by D.R.W., investigations by D.R.W., J.C., N.J., J.C.L., J.A., J.L. and A.L., resources by S.B.P., S.B., A.J.B., N.A.P. and M.J.K. and data curation by D.R.W. The original draft was written by D.R.W. Reviewing and editing was carried out by D.R.W. and M.J.K. Visualization was provided by D.R.W., supervision by S.B.P., S.B., A.J.B., N.A.P. and M.J.K., project administration by D.R.W. and M.J.K. and funding acquisition by S.B.P. and M.J.K.

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Correspondence to Michael J. Keiser.

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Competing interests

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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Nature Machine Intelligence thanks Florian Heigwer and Hao Zhu for their contribution to the peer review of this work.

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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).

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