GEOapplication/xmlftp://ftp.ncbi.nlm.nih.gov/geo/series/GSE339nnn/GSE339457/primaryOK200TranscriptomicsHomo sapiensExpression profiling by high throughput sequencinghttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE339457GEOGSEfalseMapping transcriptional responses to cellular perturbation dictionaries with RNA fingerprinting [scRNA-Seq]Single-cell perturbation dictionaries systematically measure how cells respond to genetic and chemical perturbations, creating the opportunity to assign causal interpretations to observational data. We introduce RNA fingerprinting, a statistical framework that maps transcriptional responses from new experiments onto reference perturbation dictionaries. RNA fingerprinting learns representations of perturbations, or ``fingerprints," from single-cell data, then probabilistically assigns query cells to one or more candidate perturbations. We benchmark our method across ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate its broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing drug mechanisms of action and dose-dependent off-target effects, and uncovering cytokine-driven B cell heterogeneity during secondary influenza infection in vivo. Together, these results establish RNA fingerprinting as a versatile framework for interpreting single-cell datasets by linking cellular states to the underlying perturbations which generated them.2026/07/27GSE339457GSM9895960GSM9895959GSM989596118573339457Homo sapiens