Mapping transcriptional responses to cellular perturbations with RNA fingerprinting.

Abstract

Single-cell perturbation dictionaries 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 perturbation representations, or "fingerprints," from single-cell data, then probabilistically assigns query cells to one or more candidate perturbations. We benchmark on ground-truth datasets, demonstrating accurate assignments at single-cell resolution, scalability to genome-wide screens, and the ability to resolve combinatorial perturbations. We demonstrate broad utility across diverse biological settings: identifying context-specific regulators of p53 under ribosomal stress, characterizing dose-dependent drug mechanisms of action, 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 their underlying perturbations.