Luo Tao, Chen Si-Yi, Qiu Zhi-Xin, Miao Ya-Ru, Ding Yue, Pan Xiang-Yu, Li Yirong, Lei Qian, Guo An-Yuan
Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
Center for Artificial Intelligence Biology, Hubei Bioinformatics and Molecular Imaging Key Laboratory, Key Laboratory of Molecular Biophysics of the Ministry of Education, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, 430074, China.
Small Methods. 2022 Nov;6(11):e2200881. doi: 10.1002/smtd.202200881. Epub 2022 Sep 6.
Although many studies have investigated functional molecules in extracellular vesicles (EVs), the exact number of ribonucleic acid molecules in a single-EV is unknown. Therefore, it is critical to explore the transcriptomic features and heterogeneity at the level of a single-EV. Here, using the 10x Genomics platform, the RNA cargos are profiled in single EVs derived from human K562 and mesenchymal stem cells. The key steps are labeling intact EVs using calcein-AM, detecting the EV concentration via flow cytometry, and using the CB2 algorithm with adaptive thresholds to effectively distinguish real EVs from background. The gene number in a single-EV varied from 6 to 148, with a mean of 52. Ribosomal genes, mitochondrial genes, and eukaryotic translation elongation factor 1 alpha has a high EV percentage in all EV samples. Hemoglobin genes are uniquely highly expressed in K562-EVs, and cytoskeleton genes are enriched in MSC-EVs. Ten or more clusters with different marker genes in each single-EV dataset demonstrated EV heterogeneity. Moreover, integrating EVs and their parental cells reveal both EVs and cells in each cluster, indicating different cell origins of various EVs. To the best of the author's knowledge, this study provides the first high-throughput transcriptome at the single-EV level and improves the understanding of EVs.
尽管许多研究已经对细胞外囊泡（EVs）中的功能分子进行了调查，但单个EV中核糖核酸分子的确切数量尚不清楚。因此，在单个EV水平上探索转录组特征和异质性至关重要。在此，使用10x基因组学平台，对源自人K562和间充质干细胞的单个EV中的RNA货物进行了分析。关键步骤包括使用钙黄绿素-AM标记完整的EV，通过流式细胞术检测EV浓度，以及使用具有自适应阈值的CB2算法有效区分真实的EV与背景。单个EV中的基因数量从6到148不等，平均为52。核糖体基因、线粒体基因和真核翻译延伸因子1α在所有EV样本中的EV百分比都很高。血红蛋白基因在K562-EV中独特地高表达，而细胞骨架基因在MSC-EV中富集。每个单个EV数据集中具有不同标记基因的十个或更多簇显示出EV的异质性。此外，整合EV及其亲本细胞揭示了每个簇中的EV和细胞，表明各种EV具有不同的细胞起源。据作者所知，本研究提供了首个单个EV水平的高通量转录组，并增进了对EV的理解。
