Abstract

Cell images contain a vast amount of quantifiable information about the status of the cell: for example, whether it is diseased, whether it is responding to a drug treatment, or whether a pathway has been disrupted by a genetic mutation. We aim to go beyond measuring individual cell phenotypes that biologists already know are relevant to a particular disease. Instead, in a strategy called image-based profiling, hundreds of features of cells (or other biological samples, such as tissues or whole organisms) are extracted from images using advanced computer vision techniques, including deep learning, using whatever stains are present in the experiment, even label-free imaging. Just like transcriptional profiling, the similarities and differences in the patterns of extracted features reveal connections among diseases, drugs, and genes.

We are harvesting similarities in image-based profiles to identify, at a single-cell level, how diseases, drugs, and genes affect cells, which can uncover small molecules’ mechanism of action, discover gene functions, predict assay outcomes, discover disease-associated phenotypes, identify the functional impact of disease-associated alleles, and find novel therapeutic candidates. As part of the JUMP -Cell Painting Consortium we are aiming to produce the world’s largest public Cell Painting gene/compound image resource, with 140,000 perturbations in five replicates. Pooled image-based profiling is also in development. With these new data, we hope to implement drug discovery-accelerating applications at scale.