Small extracellular vesicles (sEVs) are emerging as pivotal players in a wide range of physiological and pathological processes. However, a pressing challenge has been the lack of high-throughput techniques capable of unraveling the intricate heterogeneity of sEVs and decoding the underlying cellular behaviors governing sEV secretion. Researchers at the Chinese Academy of Sciences leverage droplet-based single-cell RNA sequencing (scRNA-seq) and introduce an algorithm, SEVtras, to identify sEV-containing droplets and estimate the sEV secretion activity (ESAI) of individual cells. Through extensive validations on both simulated and real datasets, the researchers demonstrate SEVtras’ efficacy in capturing sEV-containing droplets and characterizing the secretion activity of specific cell types. By applying SEVtras to four tumor scRNA-seq datasets, they further illustrate that the ESAI can serve as a potent indicator of tumor progression, particularly in the early stages. With the increasing importance and availability of scRNA-seq datasets, SEVtras holds promise in offering valuable extracellular insights into the cell heterogeneity.
Overview of SEVtras
The left panel shows what is initialed by the SEV gene set, SEVtras uses expectation–maximization (EM) iterations to identify sEV-containing droplets from massive cell-free droplets in scRNA-seq data. On achieving convergence through these iterations, SEVtras assigns a classification score to each droplet. To ensure the comparability of SEVtras scores across different samples, the results from each sample are aggregated through a voting and unification process. The middle panel shows the functional analyses for these droplets identified by SEVtras. The right panel shows that SEVtras enables large-scale screening of cell-sEV pairwise signatures in the clinic.
Availability – SEVtras is available on GitHub at https://github.com/bioinfo-biols/SEVtras.