Revealing therapeutic single-cell transcriptomic signatures using a simple classifier.

Abstract

Single-cell RNA sequencing (scRNA-seq) has become a routine tool for characterizing heterogeneous populations. Here, we present Cell-Sign, a detection algorithm that can classify cells according to drug treatments at the single-cell level. The method allows the identification of individual cells that remain hidden in conventional dimensionality reduction approaches and reveals both the drug a cell was exposed to and the duration of exposure. We show how our approach can be used to identify drug targets by comparing single-cell Perturb-seq data with drug signatures from the Library of Integrated Network-based Cellular Signatures (LINCS). Cell-Sign can contribute to highly multiplexed single-cell drug discovery and the identification of novel drug targets.