ArticleScientific reports2022
Label-free prediction of cell painting from brightfield images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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Who cites it
35 citing papers in PubMed.
- Quantitative imaging of dynamic processes in intestinal organoids.Nature protocols · 2026Review
- Deep learning recognises antibiotic modes of action from brightfield images.Nature communications · 2026Article
- Mapping human microglial morphological diversity via handcrafted and deep learning-derived image features.iScience · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Deep Learning-Powered Scalable Cancer Organ Chip for Cancer Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- 2D Multimodal Image Collection for Fluorescence Prediction from Transmitted Light Microscopy.Scientific data · 2026Article
- Unlocking the potential of computational phenotypic drug discovery: methods, challenges, and future directions.NPJ systems biology and applications · 2026Review
- Counting cells can accurately predict small-molecule bioactivity benchmarks.Nature communications · 2026Article
- Regularized Single-Cell Imaging Enables Generalizable AI Models for Stain-Free Cell Viability Screening.Small methods · 2026Article
- Cell context-dependent in silico organelle localization in label-free microscopy images.Nature methods · 2026Article
- A self-supervised machine learning pipeline for extracting information from live cell images at multiple doses and timepoints.Scientific reports · 2026Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Confounder-aware foundation modeling for accurate phenotype profiling in cell imaging.Npj imaging · 2025Article
- High-Throughput Label-Free Continuous Quantification of Muscle Stem Cell Proliferation and Myogenic Differentiation.Stem cell reviews and reports · 2025Article
- On the utility of virtual staining for downstream applications as it relates to task network capacity.Biomedical optics express · 2025Article
- Alternate dyes for image-based profiling assays.SLAS discovery : advancing life sciences R & D · 2025Article
- A machine learning-based analysis method for small molecule high content screening of three-dimensional cancer spheroid morphology.Molecular pharmacology · 2025Article
- Article
- A novel flexible identity-net with diffusion models for painting-style generation.Scientific reports · 2025Article
- Extensible Immunofluorescence (ExIF) accessibly generates high-plexity datasets by integrating standard 4-plex imaging data.Nature communications · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
Cell Painting is a high-content image-based assay applied in drug discovery to predict bioactivity, assess toxicity and understand mechanisms of action of chemical and genetic perturbations. We investigate label-free Cell Painting by predicting the five fluorescent Cell Painting channels from brightfield input. We train and validate two deep learning models with a dataset representing 17 batches, and we evaluate on batches treated with compounds from a phenotypic set. The mean Pearson correlation coefficient of the predicted images across all channels is 0.84. Without incorporating features into the model training, we achieved a mean correlation of 0.45 with ground truth features extracted using a segmentation-based feature extraction pipeline. Additionally, we identified 30 features which correlated greater than 0.8 to the ground truth. Toxicity analysis on the label-free Cell Painting resulted a sensitivity of 62.5% and specificity of 99.3% on images from unseen batches. We provide a breakdown of the feature profiles by channel and feature type to understand the potential and limitations of label-free morphological profiling. We demonstrate that label-free Cell Painting has the potential to be used for downstream analyses and could allow for repurposing imaging channels for other non-generic fluorescent stains of more targeted biological interest.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.