中国科学院北京生命科学研究院赵方庆等研究人员合作发现，SEVtras通过单细胞转录组以液滴分辨率划分细胞外小囊泡。这一研究成果于2023年12月4日在线发表在国际学术期刊《自然—方法学》上。

研究人员利用基于液滴的单细胞RNA 测序（scRNA-seq）技术，并引入一种名为SEVtras的算法来识别含有小型细胞外囊泡（sEV）的液滴，并估算单个细胞的sEV分泌活性（ESAI）。通过在模拟和真实数据集上的广泛验证，研究人员证明了SEVtras在捕获含sEV的液滴和描述特定细胞类型的分泌活性方面的功效。通过将SEVtras应用于四个肿瘤scRNA-seq数据集，研究人员进一步说明了ESAI可以作为肿瘤进展的有效指标，尤其是在早期阶段。随着scRNA-seq数据集的重要性和可用性不断提高，SEVtras有望为细胞异质性提供有价值的细胞外洞察。

据介绍，sEV正在成为各种生理和病理过程中的关键角色。然而，一个紧迫的挑战是缺乏高通量技术来揭示小细胞外囊泡错综复杂的异质性，并解码支配sEV分泌的潜在细胞行为。

附：英文原文

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

Author: He, Ruiqiao, Zhu, Junjie, Ji, Peifeng, Zhao, Fangqing

Issue&Volume: 2023-12-04

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.

DOI: 10.1038/s41592-023-02117-1

Source: https://www.nature.com/articles/s41592-023-02117-1