Lygeri Sakellaridi

Abstract 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.

SLAM-seq preprocessing All SLAM-seq data used here were processed using the GRAND-SLAM pipeline17. Fastq files were downloaded from the SRA database. The accession number were: GSE99970 for the pulse-chase 4sU labeling data set in mESCs from ref. 7, GSE99974 (only wt cells) and GSE99972 for the progressive labeling in mESCs7, GSE139151 for the NXF1 knockdown data set from ref. 18, GSE162323 for the SARS-CoV-2 data set from ref. 22, and GSE155604 for the BANP depletion data set from ref. 16.