scSTATseq sequencing of single-cell cell lines and primary cells. Mus musculus

scRNAseq has become a highly useful tool for interrogating cellular heterogeneity and differentiation processes. However, libraries constructed using current state-of-the-art methods are occluded by technical dropout, wherein genes with real expression are not detected/incompletely captured due to technical deficiencies. Dropout may introduce artifacts in analysis of scRNAseq, including in trajectory inference of cellular differentiation. To overcome this challenge, we present scSTATseq, a novel library construction method leveraging early tagmentation prior to large-scale amplification. Compared with existing methodologies, scSTATseq is uniquely insulated against technical dropout in use cases with cells of low RNA content, and efficiently captures genes with low levels of expression when benchmarked across both cell lines and primary cells. Investigation of osteoclast differentiation using scSTATseq helped identify novel pathways, including a vesicular-transport process governed by Rab15, that contribute critically towards trajectory progression. scSTATseq can also be easily modified to incorporate additional steps, such as metabolic labeling of mRNA.