Optimizing cell therapy by sorting cells with high extracellular vesicle secretion.

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.

Associating growth factor secretions and transcriptomes of single cells in nanovials using SEC-seq.

Cells secrete numerous bioactive molecules that are essential for the function of healthy organisms. However, scalable methods are needed to link individual cell secretions to their transcriptional state over time. Here, by developing and using secretion-encoded single-cell sequencing (SEC-seq), which exploits hydrogel particles with subnanolitre cavities (nanovials) to capture individual cells and their secretions, we simultaneously measured the secretion of vascular endothelial growth factor A (VEGF-A) and the transcriptome for thousands of individual mesenchymal stromal cells. Our data indicate that VEGF-A secretion is heterogeneous across the cell population and is poorly correlated with the VEGFA transcript level. The highest VEGF-A secretion occurs in a subpopulation of mesenchymal stromal cells characterized by a unique gene expression signature comprising a surface marker, interleukin-13 receptor subunit alpha 2 (IL13RA2), which allowed the enrichment of this subpopulation. SEC-seq enables the identification of gene signatures linked to specific secretory states, facilitating mechanistic studies, the isolation of secretory subpopulations and the development of means to modulate cellular secretion.