Erin Weisbart

Abstract Cell Painting images offer valuable insights into a cell’s state and enable many biological applications, but publicly available arrayed datasets only include hundreds of genes perturbed. The JUMP Cell Painting Consortium perturbed roughly 75% of the protein-coding genome in human U-2 OS cells, generating a rich resource of single-cell images and extracted features.

Abstract Advances in high-throughput microscopy have enabled the rapid acquisition of large numbers of high-content microscopy images. Next, whether by deep learning or classical algorithms, image analysis pipelines commonly produce single-cell features. To process these single cells for downstream applications, we present Pycytominer, a user-friendly, open-source Python package that implements the bioinformatics steps key to image-based profiling.

Abstract A key challenge of the modern genomics era is developing empirical data-driven representations of gene function. Here we present the first unbiased morphology-based genome-wide perturbation atlas in human cells, containing three genome-wide genotype–phenotype maps comprising CRISPR–Cas9-based knockouts of >20,000 genes in >30 million cells.