PT  - JOURNAL ARTICLE
AU  - Lauren Schiff
AU  - Bianca Migliori
AU  - Ye Chen
AU  - Deidre Carter
AU  - Caitlyn Bonilla
AU  - Jenna Hall
AU  - Minjie Fan
AU  - Edmund Tam
AU  - Sara Ahadi
AU  - Brodie Fischbacher
AU  - Anton Geraschenko
AU  - Christopher J. Hunter
AU  - Subhashini Venugopalan
AU  - Sean DesMarteau
AU  - Arunachalam Narayanaswamy
AU  - Selwyn Jacob
AU  - Zan Armstrong
AU  - Peter Ferrarotto
AU  - Brian Williams
AU  - Geoff Buckley-Herd
AU  - Jon Hazard
AU  - Jordan Goldberg
AU  - Marc Coram
AU  - Reid Otto
AU  - Edward A. Baltz
AU  - Laura Andres-Martin
AU  - Orion Pritchard
AU  - Alyssa Duren-Lubanski
AU  - Ameya Daigavane
AU  - Kathryn Reggio
AU  - NYSCF Global Stem Cell Array Team
AU  - Lauren Bauer
AU  - Raeka S. Aiyar
AU  - Elizabeth Schwarzbach
AU  - Daniel Paull
AU  - Scott A. Noggle
AU  - Frederick J. Monsma, Jr.
AU  - Marc Berndl
AU  - Samuel J. Yang
AU  - Bjarki Johannesson
TI  - Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts
AID  - 10.1101/2020.11.13.380576
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2020.11.13.380576
4099  - http://biorxiv.org/content/early/2021/10/20/2020.11.13.380576.short
4100  - http://biorxiv.org/content/early/2021/10/20/2020.11.13.380576.full
AB  - Drug discovery for diseases such as Parkinson’s disease (PD) are impeded by the lack of screenable cellular phenotypes. We present a novel, 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 PD patients and matched healthy controls, creating the largest publicly available Cell Painting image dataset to date at 48 terabytes. Using fixed weights from a convolutional deep neural network trained on ImageNet, we generated deep embeddings from each image and trained machine learning models to detect morphological disease phenotypes. Our platform’s robustness and sensitivity allowed the detection of individual-specific variation with high fidelity, across batches and plate layouts. Lastly, our models confidently separated LRRK2 and sporadic PD lines from healthy controls (ROC AUC 0.79 (0.08 standard deviation (SD))) supporting the capacity of this platform for complex disease modeling and drug screening applications.Competing Interest StatementY.C., M.F., S.A., A.G., S.V., A.N., Z.A., B.W., J.K., M.C., E.A.B., O.P., A.D., M.B., and S.J.Y. were employed by Google. M.F., A.G., S.V., A.N., Z.A., B.W., J.K., M.C., E.A.B., O.P., M.B., and S.J.Y. own Alphabet stock.