JUMP Cell Painting dataset: morphological impact of 136,000 chemical and genetic perturbations
Chandrasekaran, S. N.; Ackerman, J.; Alix, E.; Ando, D. M.; Arevalo, J.; Bennion, M.; Boisseau, N.; Borowa, A.; Boyd, J. D.; Brino, L.; Byrne, P. J.; Ceulemans, H.; Ch'ng, C.; Cimini, B. A.; Clevert, D.-A.; Deflaux, N.; Doench, J. G.; Dorval, T.; Doyonnas, R.; Dragone, V.; Engkvist, O.; Faloon, P. W.; Fritchman, B.; Fuchs, F.; Garg, S.; Gilbert, T. J.; Glazer, D.; Gnutt, D.; Goodale, A.; Grignard, J.; Guenther, J.; Han, Y.; Hanifehlou, Z.; Hariharan, S.; Hernandez, D.; Horman, S. R.; Hormel, G.; Huntley, M.; Icke, I.; Iida, M.; Jacob, C. B.; Jaensch, S.; Khetan, J.; Kost-Alimova, M.; Krawiec,
Show abstract
Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery: https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available at https://phenaid.ardigen.com/jumpcpexplorer/.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Optimizing the Cell Painting assay for image-based profiling 95%
- OCTAD: an open workplace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features 94%
- Z-REX: Shepherding Reactive Electrophiles to Specific Proteins Expressed either Tissue-Specifically or Ubiquitously, and Recording the Resultant Functional Electrophile-Induced Redox Responses in Larval Fish 93%
Similar papers in this journal
- Comparative evaluation of cell-based assay technologies for scoring drug-induced condensation of SARS-CoV-2 nucleocapsid protein 93%
- Alternate dyes for image-based profiling assays 93%
- High Content Phenotypic Profiling in Oesophageal Adenocarcinoma Identifies Selectively Active Pharmacological Classes of Drugs for Repurposing and Chemical Starting Points for Novel Drug Discovery 92%
Similar papers in this journal
Similar papers in this journal
- Mass Cytometric and Transcriptomic Profiling of Epithelial-Mesenchymal Transitions in Human Mammary Cell Lines 91%
- A Multi-center Cross-platform Single-cell RNA Sequencing Reference Dataset 91%
- Integrated cancer cell-specific single-cell RNA-seq datasets of immune checkpoint blockade-treated patients 90%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.