Transcriptomic Features in a Single Extracellular Vesicle via Single‐Cell RNA Sequencing. Issue 11 (6th September 2022)

• Record Type: Journal Article
• Authors: Luo, Tao; Chen, Si‐Yi; Qiu, Zhi‐Xin; Miao, Ya‐Ru; Ding, Yue; Pan, Xiang‐Yu; Li, Yirong; Lei, Qian; Guo, An‐Yuan
• Is Part Of: Small methods. Volume 6:Issue 11(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-06
• Subjects: extracellular vesicles -- heterogeneity -- marker genes -- single‐extracellular vesicle analysis -- transcriptome; Nanotechnology -- Methodology -- Periodicals; Nanotechnology -- Periodicals; Periodicals; 620.5028
• DOI: 10.1002/smtd.202200881
• Languages: English
• ISSNs: 2366-9608

Abstract

Abstract: 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. Abstract : This is the first study to explore the transcriptomic features of extracellular vesicles (EVs) at the single‐EV level. The high‐throughput analysis of gene expression in single EVs is reported using the 10x Genomics platform with modified experimental and bioinformatics analytical protocols. The number of expressed genes in a single EV and the transcriptional heterogeneity at the single‐EV level are revealed.