Identifying Modulators of Cellular Responses by Heterogeneity-sequencing

The destructive nature of single-cell transcriptomics has hindered predicting and interpreting heterogeneous outcomes of molecular challenges. By exploiting information on the pre-perturbation state and fate of thousands of individual cells using droplet-based single cell RNA-seq with metabolic RNA labeling, we developed Heterogeneity-seq for predicting causal factors that impact on molecular outcomes. Heterogeneity-seq uncovered genes with an effect on drug treatment and novel pro- and antiviral host factors of cytomegalovirus infection.