SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes

Quick Facts

  • Publication title: SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes
  • Journal: Nat Methods
  • Year: 2023
  • DOI: 10.1038/s41592-023-02117-1
  • PMID/PMCID: 38049696; PMC10864178

Research overview

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.

Key findings

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.

Here we 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, we demonstrate SEVtras' efficacy in capturing sEV-containing droplets and characterizing the secretion activity of specific cell types.

Echo Biotech Role

Echo Biotech contributed EV isolation and purification, EV material/reference-material supply; the study also used or cited Exosupur®, ContrExo®.

Related platforms: Exoomics®, EV Materials / Reference Materials, Research Reagents & Tools

Related services and capabilities: Tissue EV Isolation & Purification, EV Material / Reference Material Supply

Related products or reagents: Exosupur® EV Isolation/Purification Kit, ContrExo® EV Reference Material

References

Original publication: SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes Nat Methods. 2023. DOI: 10.1038/s41592-023-02117-1. PMID/PMCID: 38049696; PMC10864178.