A cost-efficient workflow for large-scale single-cell RNA-seq using combinatorial indexing and mostly-natural sequencing by synthesis

The ability to generate single-cell RNA-seq datasets at lower cost has been a key driver for large-scale studies such as cell atlases and wide Perturb-seq studies screens involving millions of analyzed cells. Here we benchmark the use of combinatorial cell indexing (Parse Biosciences) together with mostly-natural sequencing-by-synthesis (mnSBS, Ultima Genomics) to efficiently characterize a million cells as part of an on-going Perturb-seq study, in comparison with standard sequencing (Illumina) of the same single-cell libraries.