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

Nature Machine Intelligence volume 4, pages 583–595 (2022)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

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.

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Data availability

All image data are freely available at https://osf.io/xntd658 and https://doi.org/10.17605/OSF.IO/XNTD6.

Code availability

The full source code and fully trained models are available at https://github.com/keiserlab/trans-channel-paper59 and https://doi.org/10.5281/zenodo.6336183.

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Acknowledgements

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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Author notes

  1. These authors contributed equally: Jay Conrad, Noah R. Johnson.

Authors and Affiliations

  1. Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, USA

    Daniel R. Wong, Jay Conrad, Noah R. Johnson, Jacob Ayers, Joanne C. Lee, Jisoo Lee, Stanley B. Prusiner, Nick A. Paras & Michael J. Keiser

  2. Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, USA

    Daniel R. Wong, Atul J. Butte & Michael J. Keiser

  3. Department of Pharmaceutical Chemistry, University of California, San Francisco, CA, USA

    Daniel R. Wong & Michael J. Keiser

  4. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA

    Daniel R. Wong, Annelies Laeremans, Sourav Bandyopadhyay & Michael J. Keiser

  5. Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, CA, USA

    Daniel R. Wong & Michael J. Keiser

  6. Department of Pediatrics, University of California, San Francisco, CA, USA

    Daniel R. Wong & Atul J. Butte

  7. Center for Data-Driven Insights and Innovation, University of California, Office of the President, Oakland, CA, USA

    Daniel R. Wong & Atul J. Butte

  8. University of Colorado Alzheimer’s and Cognition Center, Linda Crnic Institute for Down Syndrome, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA

    Noah R. Johnson

  9. Department of Neurology, University of Colorado, Anschutz Medical Campus, Aurora, CO, USA

    Noah R. Johnson

  10. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA

    Annelies Laeremans & Sourav Bandyopadhyay

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Contributions

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.

Corresponding author

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). https://doi.org/10.1038/s42256-022-00490-8

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