{"database":"bioimages","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"submitter":[null],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-BIAD1319"],"repository":["bioimages"],"figure_sub":["Specimen","Image analysis","Funding","Study Component","organisation","Biosample","Associations","Image acquisition"],"pubmed_authors":["Alex K. Shalek","Nuo Liu"],"additional_accession":[]},"is_claimable":false,"name":"Compressed phenotypic screening empowers scalable biological discovery","description":"High-throughput phenotypic screens leveraging biochemical perturbations and high-content readouts are\npoised to advance therapeutic discovery, yet they remain constrained by limitations of scale. To address\n2\nthis, we establish a method of pooling exogenous perturbations followed by computational deconvolution\nto compress a screen’s required sample, labor, and financial input. We benchmark the approach with a\nbioactive small molecule library and a high-content imaging readout, demonstrating the feasibility and\nincreased efficiency of compressed experimental designs compared to conventional approaches. To prove\ngeneralizability, we apply compressed screening in two different biological discovery campaigns. In the\nfirst, we use early-passage pancreatic cancer organoids to map transcriptional","dates":{"release":"2024-08-15T00:00:00Z","modification":"2024-08-15T18:00:14.347Z","creation":"2024-08-15T18:00:14.347Z"},"accession":"S-BIAD1319","cross_references":{}}