Optimizing cell therapy by sorting cells with high extracellular vesicle secretion

Doyeon Koo, Xiao Cheng, Shreya Udani, Sevana Baghdasarian, Dashuai Zhu, Junlang Li, Brian Hall, Natalie Tsubamoto, Shiqi Hu, Jina Ko, Ke Cheng () and Dino Di Carlo ()
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Doyeon Koo: University of California, Los Angeles
Xiao Cheng: University of North Carolina at Chapel Hill and North Carolina State University
Shreya Udani: University of California, Los Angeles
Sevana Baghdasarian: University of California, Los Angeles
Dashuai Zhu: Columbia University
Junlang Li: Xsome Biotech
Brian Hall: Cytek Biosciences
Natalie Tsubamoto: University of California, Los Angeles
Shiqi Hu: Columbia University
Jina Ko: University of Pennsylvania
Ke Cheng: Columbia University
Dino Di Carlo: University of California, Los Angeles

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Critical challenges remain in clinical translation of extracellular vesicle (EV)-based therapeutics due to the absence of methods to enrich cells with high EV secretion. Current cell sorting methods are limited to surface markers that are uncorrelated to EV secretion or therapeutic potential. Here, we utilize a nanovial technology for enrichment of millions of single cells based on EV secretion. This approach is applied to select mesenchymal stem cells (MSCs) with high EV secretion as therapeutic cells for improving treatment. The selected MSCs exhibit distinct transcriptional profiles associated with EV biogenesis and vascular regeneration and maintain high levels of EV secretion after sorting and regrowth. In a mouse model of myocardial infarction, treatment with high-secreting MSCs improves heart functions compared to treatment with low-secreting MSCs. These findings highlight the therapeutic importance of EV secretion in regenerative cell therapies and suggest that selecting cells based on EV secretion could enhance therapeutic efficacy.

Date: 2024
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DOI: 10.1038/s41467-024-49123-1

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