Dataset Information

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

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.

SUBMITTER: Schiff L

PROVIDER: S-EPMC8956598 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Publications

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

Schiff Lauren L Migliori Bianca B Chen Ye Y Carter Deidre D Bonilla Caitlyn C Hall Jenna J Fan Minjie M Tam Edmund E Ahadi Sara S Fischbacher Brodie B Geraschenko Anton A Hunter Christopher J CJ Venugopalan Subhashini S DesMarteau Sean S Narayanaswamy Arunachalam A Jacob Selwyn S Armstrong Zan Z Ferrarotto Peter P Williams Brian B Buckley-Herd Geoff G Hazard Jon J Goldberg Jordan J Coram Marc M Otto Reid R Baltz Edward A EA Andres-Martin Laura L Pritchard Orion O Duren-Lubanski Alyssa A Daigavane Ameya A Reggio Kathryn K Nelson Phillip C PC Frumkin Michael M Solomon Susan L SL Bauer Lauren L Aiyar Raeka S RS Schwarzbach Elizabeth E Noggle Scott A SA Monsma Frederick J FJ Paull Daniel D Berndl Marc M Yang Samuel J SJ Johannesson Bjarki B

Nature communications 20220325 1

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 co ...[more]

PMID: 35338121

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Project description:Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR). Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009-2010 ("baseline images") and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading. Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving. Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.

Dataset Information

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

Publications

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

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