Bray, M. A. et al. Cell Painting, a high-content image-based assay for morphological profiling using multiplexed fluorescent dyes. Nat. Protoc. 11, 1757–1774 https://doi.org/10.1038/nprot.2016.105 (2016). This work describes the foundational protocol for the Cell Painting assay.Article CAS PubMed PubMed Central Google Scholar Kraus, O. et al., Masked autoencoders for microscopy are scalable learners of cellular biology. In 2024 IEEE/CVF Conf. Computer Vision and Pattern Recognition (CVPR) 11757–11768 https://doi.org/10.1109/CVPR52733.2024.01117 (2024). A scalable foundation model using masked autoencoders on large-scale proprietary Cell Painting data.Kim, V. et al. Self-supervision advances morphological profiling by unlocking powerful image representations. Sci. Rep. 15, 4876 (2025). A comparative study on different SSL methods using a small JUMP-CP dataset for both model development and validation.Article CAS PubMed PubMed Central Google Scholar Chandrasekaran, S. N. et al. JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations. Preprint at bioRxiv https://doi.org/10.1101/2023.03.23.534023 (2023). This preprint describes the JUMP-CP consortium dataset on which our foundation models were pretrained.Dapello, J. et al. scGeneScope: a treatment-matched single cell imaging and transcriptomics dataset and benchmark for treatment response modeling. Adv. Neural Inf. Process. Syst. https://doi.org/10.52202/085713-4706 (2026). This multimodal resource pairs Cell Painting images with single-cell transcriptomics, exemplifying an integrative framework that could be supported by our phenomic representations.Download references