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

View ORCID ProfileDaniel R. Wong, Jay Conrad, Noah Johnson, View ORCID ProfileJacob Ayers, Annelies Laeremans, Joanne C. Lee, Jisoo Lee, View ORCID ProfileStanley B. Prusiner, View ORCID ProfileSourav Bandyopadhyay, View ORCID ProfileAtul J. Butte, View ORCID ProfileNick Paras, View ORCID ProfileMichael J. Keiser
doi: https://doi.org/10.1101/2021.01.08.425973
Daniel R. Wong
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
2Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, 94158, USA
3Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA
4Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94158, USA
5Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, 94158, USA
6Department of Pediatrics, University of California, San Francisco, CA, 94158, USA
7Center for Data-Driven Insights and Innovation, University of California, Office of the President, Oakland, CA, 94607, USA
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  • ORCID record for Daniel R. Wong
Jay Conrad
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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Noah Johnson
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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Jacob Ayers
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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Annelies Laeremans
4Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94158, USA
8Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94158, USA
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Joanne C. Lee
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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Jisoo Lee
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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Stanley B. Prusiner
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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  • ORCID record for Stanley B. Prusiner
Sourav Bandyopadhyay
4Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94158, USA
8Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, San Francisco, CA, 94158, USA
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Atul J. Butte
2Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, 94158, USA
6Department of Pediatrics, University of California, San Francisco, CA, 94158, USA
7Center for Data-Driven Insights and Innovation, University of California, Office of the President, Oakland, CA, 94607, USA
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Nick Paras
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
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Michael J. Keiser
1Institute for Neurodegenerative Diseases, University of California, San Francisco, CA, 94158, USA
2Bakar Computational Health Sciences Institute, University of California, San Francisco, CA, 94158, USA
3Department of Pharmaceutical Chemistry, University of California, San Francisco, San Francisco, CA, 94158, USA
4Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, 94158, USA
5Kavli Institute for Fundamental Neuroscience, University of California, San Francisco, San Francisco, CA, 94158, USA
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  • ORCID record for Michael J. Keiser
  • For correspondence: keiser@keiserlab.org
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Abstract

In microscopy-based drug screens, fluorescent markers carry critical information on how compounds affect different biological processes. However, practical considerations may hinder the use of certain fluorescent markers. Here, we present a deep learning method for overcoming this limitation. 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 (AD) from high-content high-throughput screening (HCS). The ML method identified novel compounds that effectively blocked tau aggregation, which would have 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.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://github.com/keiserlab/trans-channel-paper

  • https://osf.io/xntd6/

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 05, 2021.
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Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics
Daniel R. Wong, Jay Conrad, Noah Johnson, Jacob Ayers, Annelies Laeremans, Joanne C. Lee, Jisoo Lee, Stanley B. Prusiner, Sourav Bandyopadhyay, Atul J. Butte, Nick Paras, Michael J. Keiser
bioRxiv 2021.01.08.425973; doi: https://doi.org/10.1101/2021.01.08.425973
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Trans-channel fluorescence learning improves high-content screening for Alzheimer’s disease therapeutics
Daniel R. Wong, Jay Conrad, Noah Johnson, Jacob Ayers, Annelies Laeremans, Joanne C. Lee, Jisoo Lee, Stanley B. Prusiner, Sourav Bandyopadhyay, Atul J. Butte, Nick Paras, Michael J. Keiser
bioRxiv 2021.01.08.425973; doi: https://doi.org/10.1101/2021.01.08.425973

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