Data is released publicly!

• Find the data and details at our JUMP Cell Painting Datasets landing page on Github.
• If you'd like to take a look at the data interactively, Ardigen provides the free, public JUMP-CP Data Explorer to search for similarities between various phenotypes and the corresponding perturbations.

Design of the experimental dataset

• We chose U2OS (osteosarcoma) cells for our major data production work.
• We tested a subset of the genome as CRISPR knockdowns and another subset (with some overlap) of overexpression (ORF) reagents.
• Partners exchanged ~120,000 compounds and ran ~5 replicates of each, performed as 1-2 replicates at 3-5 different sites around the world.

Control sets of reagents

The JUMP-Cell Painting Consortium worked to develop standards and optimize processes to facilitate the worldwide community using Cell Painting. Settling on these standards will allow scientists across institutions to align and compare their data.

• JUMP-Target: Lists and 384-well plate maps of 306 compounds and corresponding genetic perturbations, designed to assess connectivity in profiling assays.
  • JUMP-Target is described here: https://github.com/jump-cellpainting/JUMP-Target 
• JUMP-MOA: List and a 384-well plate map of 90 compounds in quadruplicate (corresponding to 47 mechanism-of-action classes), designed to assess connectivity in profiling assays.
  • JUMP-MOA is described here: https://github.com/jump-cellpainting/JUMP-MOA 
• Positive controls: We recommend a set of 8 compounds to go on each sample plate. 
  • The list is here: https://github.com/jump-cellpainting/JUMP-Target#positive-control-compounds

Optimized Cell Painting protocol

• We have settled assay parameters, with some modifications from that in Bray et al. Nature Protocols 2016.
• Details can be found on the public Cell Painting wiki.

Optimized computational pipelines for Cell Painting

• CellProfiler pipelines for segmentation and feature extraction as well as QC and illumination correction can be found on Github here.
• Pycytominer workflow for processing CellProfiler features (profile annotation, normalization and feature selection) can be found on GitHub.
• Deep learning based workflows are under construction.

Please also check the Cell Painting wiki for guidance on implementing Cell Painting.