Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts

Lauren Schiff, Bianca Migliori, Ye Chen, Deidre Carter, Caitlyn Bonilla, Jenna Hall, Minjie Fan, Edmund Tam, Sara Ahadi, Brodie Fischbacher, Anton Geraschenko, Christopher J. Hunter, Subhashini Venugopalan, Sean DesMarteau, Arunachalam Narayanaswamy, Selwyn Jacob, Zan Armstrong, Peter Ferrarotto, Brian Williams, Geoff Buckley-Herd, Jon Hazard, Jordan Goldberg, Marc Coram, Reid Otto, Edward A. Baltz, Laura Andres-Martin, Orion Pritchard, Alyssa Duren-Lubanski, Ameya Daigavane, Kathryn Reggio, Phillip C. Nelson, Michael Frumkin, Susan L. Solomon, Lauren Bauer, Raeka S. Aiyar, Elizabeth Schwarzbach, Scott A. Noggle, Frederick J. Monsma, Daniel Paull, Marc Berndl (), Samuel J. Yang () and Bjarki Johannesson ()
Additional contact information
Lauren Schiff: Google Research
Bianca Migliori: The New York Stem Cell Foundation Research Institute
Ye Chen: Google Research
Deidre Carter: The New York Stem Cell Foundation Research Institute
Caitlyn Bonilla: Google Research
Jenna Hall: The New York Stem Cell Foundation Research Institute
Minjie Fan: Google Research
Edmund Tam: The New York Stem Cell Foundation Research Institute
Sara Ahadi: Google Research
Brodie Fischbacher: The New York Stem Cell Foundation Research Institute
Anton Geraschenko: Google Research
Christopher J. Hunter: The New York Stem Cell Foundation Research Institute
Subhashini Venugopalan: Google Research
Sean DesMarteau: The New York Stem Cell Foundation Research Institute
Arunachalam Narayanaswamy: Google Research
Selwyn Jacob: The New York Stem Cell Foundation Research Institute
Zan Armstrong: Google Research
Peter Ferrarotto: The New York Stem Cell Foundation Research Institute
Brian Williams: Google Research
Geoff Buckley-Herd: The New York Stem Cell Foundation Research Institute
Jon Hazard: Google Research
Jordan Goldberg: The New York Stem Cell Foundation Research Institute
Marc Coram: Google Research
Reid Otto: The New York Stem Cell Foundation Research Institute
Edward A. Baltz: Google Research
Laura Andres-Martin: The New York Stem Cell Foundation Research Institute
Orion Pritchard: Google Research
Alyssa Duren-Lubanski: The New York Stem Cell Foundation Research Institute
Ameya Daigavane: Google Research
Kathryn Reggio: The New York Stem Cell Foundation Research Institute
Phillip C. Nelson: Google Research
Michael Frumkin: Google Research
Susan L. Solomon: The New York Stem Cell Foundation Research Institute
Lauren Bauer: The New York Stem Cell Foundation Research Institute
Raeka S. Aiyar: The New York Stem Cell Foundation Research Institute
Elizabeth Schwarzbach: The New York Stem Cell Foundation Research Institute
Scott A. Noggle: The New York Stem Cell Foundation Research Institute
Frederick J. Monsma: The New York Stem Cell Foundation Research Institute
Daniel Paull: The New York Stem Cell Foundation Research Institute
Marc Berndl: Google Research
Samuel J. Yang: Google Research
Bjarki Johannesson: The New York Stem Cell Foundation Research Institute

Nature Communications, 2022, vol. 13, issue 1, 1-13

Abstract: Abstract Drug discovery for diseases such as Parkinson’s disease are impeded by the lack of screenable cellular phenotypes. We present an unbiased phenotypic profiling platform that combines automated cell culture, high-content imaging, Cell Painting, and deep learning. We applied this platform to primary fibroblasts from 91 Parkinson’s disease patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. We use fixed weights from a convolutional deep neural network trained on ImageNet to generate deep embeddings from each image and train machine learning models to detect morphological disease phenotypes. Our platform’s robustness and sensitivity allow the detection of individual-specific variation with high fidelity across batches and plate layouts. Lastly, our models confidently separate LRRK2 and sporadic Parkinson’s disease lines from healthy controls (receiver operating characteristic area under curve 0.79 (0.08 standard deviation)), supporting the capacity of this platform for complex disease modeling and drug screening applications.

Date: 2022
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Citations: View citations in EconPapers (2)

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DOI: 10.1038/s41467-022-28423-4

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