ArticleNature communications2022
Integrating deep learning and unbiased automated high-content screening to identify complex disease signatures in human fibroblasts.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Artificial Intelligence-Powered High-Content Analysis: Methodologies and Applications in Bioactive Compound Discovery from Natural Sources.Molecules (Basel, Switzerland) · 2026Review
- Induced pluripotent stem cells from discovery to translation.Nature medicine · 2026Review
- A host-centric morphological profiling approach to identify repurposed antiviral drugs.iScience · 2026Article
- From past to future: Digital approaches to success of clinical drug trials for Parkinson's disease.Journal of Parkinson's disease · 2026Review
- Technologies to measure and modulate protein subcellular localization.Nature reviews. Molecular cell biology · 2026Review
- Retrieval-Based Evaluation of Cell Painting Feature Spaces Reveals Differences in the Preservation of Biologically Meaningful Phenotypic Similarity.International journal of molecular sciences · 2026Article
- Impact of regularization methods and outlier removal on unsupervised sample classification.BMC bioinformatics · 2026Article
- Identifying and targeting abnormal mitochondrial localization associated with psychosis.bioRxiv : the preprint server for biology · 2026Article
- A deterministic method for quantifying spindle-shaped cells in noisy bright-field microscopy.Scientific reports · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- A Flexible End-to-End Automated Sample Preparation Workflow Enables Standardized and Scalable Bottom-up Proteomics.Analytical chemistry · 2025Article
- Morphological profiling reveals neuroprotection via mitochondrial uncoupling in human dopaminergic neurons.Scientific reports · 2025Article
- Article
- Combining phenomics with transcriptomics reveals cell-type-specific morphological and molecular signatures of the 22q11.2 deletion.Nature communications · 2025Article
- Changes in iPSC-astrocyte morphology reflect Alzheimer's disease patient clinical markers.Stem cells (Dayton, Ohio) · 2025Article
- Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.Communications biology · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Article
- An induced pluripotent stem cell-based chemical genetic approach for studying spinal muscular atrophy.Frontiers in neuroscience · 2025Article
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43 authors.
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No grant is acknowledged in the PubMed record.
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.
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