Identifying Modulators of Cellular Responses by Heterogeneity-sequencing

Abstract

The response of individual cells to drug treatment, virus infections or other molecular stimuli is highly heterogeneous and depends on the cells initial state. Library preparation for single-cell transcriptomics is destructive, precluding a direct comparison between the initial state and the stimulus outcome. Consequently, current methods are restricted to identifying correlative associations rather than resolving causal drivers of heterogeneous outcomes. We developed Heterogeneity-seq, which combines single-cell RNA-seq with metabolic RNA labeling (scSLAM-seq) and double machine learning to overcome this limitation. By leveraging simultaneous measurements of unlabeled and labeled RNA in individual cells, Heterogeneity-seq uncovers the transition from pre-stimulated cell states to distinct stimulation outcomes across thousands of cells. These links enable the identification of factors that causally govern heterogeneous cellular responses. We used Heterogeneity-seq to identify both known and novel genes that drive responses to drug treatment, as well as pro- and antiviral host factors governing cytomegalovirus infection.