{"database":"GEO","file_versions":[{"headers":{"Content-Type":["application/json"]},"body":{"files":{"Other":["ftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE345nnn/GSE345058/"]},"type":"primary"},"statusCode":"OK","statusCodeValue":200}],"scores":null,"additional":{"omics_type":["Other"],"species":["Homo sapiens"],"gds_type":["Other"],"full_dataset_link":["https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE345058"],"repository":["GEO"],"entry_type":["GSE"],"additional_accession":[]},"is_claimable":false,"name":"A multimodal perturbation atlas defines the phenotypic resolution of cellular morphology.","description":"Modeling cellular behavior requires measurements that capture how cells evolve across time, environments, and interventions. Microscopy is uniquely suited to this goal: it is non-destructive and can be applied to living cells in their native context. Yet its phenotypic resolving power remains incompletely characterized relative to molecular assays. Here, we present a multimodal perturbation atlas of 1,000 pooled CRISPR knockouts in A549 cells, profiled by fluorescence microscopy (42 live, 13 fixed markers), label-free quantitative phase imaging of the same live cells (at single timepoints), and single-cell RNA sequencing (scRNA-seq). We develop deep learning frameworks to interpret the rich cell-biological signatures in these ~65M single-cell profiles. At matched reagent cost, phase imaging exceeds the phenotypic resolution of both fluorescence imaging and scRNA-seq, and more reliably recovers higher-order pathway organization. These results establish intrinsic morphology as a high-precision readout of cellular state, and lay a foundation for live-cell profiling of phenotypic trajectories. An interactive data portal is available at https://biohub.ai/ops-explorer.","dates":{"publication":"2026/08/26"},"accession":"GSE345058","cross_references":{"GSM":["GSM9992859","GSM9992858","GSM9992879","GSM9992857","GSM9992863","GSM9992885","GSM9992862","GSM9992884","GSM9992883","GSM9992861","GSM9992882","GSM9992860","GSM9992867","GSM9992866","GSM9992865","GSM9992864","GSM9992881","GSM9992880","GSM9992869","GSM9992868","GSM9992874","GSM9992873","GSM9992872","GSM9992871","GSM9992856","GSM9992878","GSM9992877","GSM9992855","GSM9992876","GSM9992854","GSM9992875","GSM9992870"],"GPL":["34281"],"GSE":["345058"],"taxon":["Homo sapiens"]}}