Sorting high extracellular vesicle secretors for optimal cell therapy

Columbia Univ New York Morningside, New York NY

Investigators

Abstract

PROJECT SUMMARY Stem cell therapies are gaining traction in the treatment of ischemic diseases such as myocardial infarction. Among these, mesenchymal stem cells (MSCs) have emerged as a promising treatment avenue. Extracellular vesicles (EVs) released from MSCs play pivotal roles in driving regenerative cell therapies. However, notable discrepancies exist in the EV secretion capabilities among MSCs, leading to inconsistencies in therapeutic outcomes. To optimize cell therapies, it is critical to sort out and enrich cells based on EV secretion amount and type. However, current cell sorting techniques, focus solely on cell surface markers and do not address secretions like EVs. Consequently, there is an urgent need to develop a technology to enrich millions of single cells based on their EV-secreting capacity, thus enhancing the efficacy of stem cell therapy. Here, we introduce a technology called "nanovials," designed to sort millions of cells based on their EV secretion levels using tetraspanin molecular markers. We hypothesize that this technology can enrich populations of MSC with inheritable high EV-secreting ability that leads to higher therapeutic efficacy. This platform also helps to uncover crucial insights into EV biogenesis and secretion pathways through integration with single-cell transcriptomic analysis. The overarching goal of this R01 is to optimize the methods to enrich EV-secreting MSCs and test the efficacy of subpopulations of high- and low-EV secreting cells in small and large animal models of myocardial infarction. AIM 1: Fabricate Nanovials and optimize EV-secreting cell sorting methods. AIM 2: Comprehensive characterization of the high EV-secreting MSCs. AIM 3: Determine therapeutic effects of high EV-secreting MSCs in a clinically relevant porcine model of myocardial infarction. We have developed a platform to isolate cells with enhanced EV secretion capabilities. This innovation has the potential to mitigate the issues linked to EV secretion variability and batch discrepancies present in existing cell therapies. Moreover, leveraging transcriptomic analysis enables us to categorize cells based on their propensity for EV secretion. This refined categorization aids in discerning which subset of stem cells yield superior therapeutic results, thereby guiding our efforts to engineer quantitatively repeatable cell therapies.