2. Academic Validation
  3. SEVtras delineates small extracellular vesicles at droplet resolution from single-cell transcriptomes

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

  • Nat Methods. 2023 Dec 4. doi: 10.1038/s41592-023-02117-1.
Ruiqiao He # 1,  Junjie Zhu # 2,  Peifeng Ji 3,  Fangqing Zhao 4 5 6
Affiliations

Affiliations

  • 1 Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China.
  • 2 Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.
  • 3 Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China. jipeifeng@biols.ac.cn.
  • 4 Beijing Institutes of Life Science, Chinese Academy of Sciences, Beijing, China. zhfq@biols.ac.cn.
  • 5 Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China. zhfq@biols.ac.cn.
  • 6 University of Chinese Academy of Sciences, Beijing, China. zhfq@biols.ac.cn.
  • # Contributed equally.
PMID: 38049696 DOI: 10.1038/s41592-023-02117-1
Abstract

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. By applying SEVtras to four tumor scRNA-seq datasets, we 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.

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