biostudies-literatureJiang LNICHD NIH HHSU.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)NHGRI NIH HHSNINDS NIH HHSU.S. Department of Health & Human Services | NIH | National Human Genome Research InstituteU.S. Department of Health & Human Services | NIH | NIH Office of the DirectorChan Zuckerberg InitiativeNIH HHSU.S. Department of Health & Human Services | NIH | NIH Office of the Director (OD)505-517https://www.ebi.ac.uk/biostudies/studies/S-EPMC12083445biostudies-literatureUnknown27(3)Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statistNature cell biologySystematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens.PMC12083445RM1 HG0110141OT2OD033760-01UG3 NS132139R01 HD096770RM1HG011014-02OT2 OD033760OT2 OD026673Jiang LPapalexi EIremadze NSatija RYun HLithwick-Yanai GMascio IWessels HHDalgarno CLipson DfalseSystematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens.Recent advancements in functional genomics have provided an unprecedented ability to measure diverse molecular modalities, but predicting causal regulatory relationships from observational data remains challenging. Here, we leverage pooled genetic screens and single-cell sequencing (Perturb-seq) to systematically identify the targets of signalling regulators in diverse biological contexts. We demonstrate how Perturb-seq is compatible with recent and commercially available advances in combinatorial indexing and next-generation sequencing, and perform more than 1,500 perturbations split across six cell lines and five biological signalling contexts. We introduce an improved computational framework (Mixscale) to address cellular variation in perturbation efficiency, alongside optimized statist2025-01-01T00:00:00Z2025 Mar2026-05-29T05:04:22.568Z2026-04-08T03:44:52.875ZS-EPMC120834454001156010.1038/s41556-025-01622-z