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Although considerable evidence exists supporting the use of mesenchymal stromal cells (MSCs) for treating immune diseases, successful clinical translation has been challenging and has led researchers to investigate cell-free alternatives. MSC-derived extracellular vesicles (MSC-EVs) have been shown to mediate a significant portion of the observed therapeutic effect, including immunosuppression. MSCs have been shown to respond to different aspects of the injury microenvironment such as inflammatory cytokines and hypoxia, although acidosis has not been investigated and different conditions have not been assessed in terms of their effects on MSC-EV function. This study investigated the effects of acidosis, hypoxia, and inflammatory cytokine priming on MSCs and MSC-EVs.We cultured MSCs in the presence of acidosis, hypoxia, or inflammatory cytokines (Interferon-gamma and Tumor Necrosis Factor-alpha) and compared the characteristics of their EVs as well as their uptake by and suppression of different T cell subsets. MSCs showed a greater effect on suppressing activated CD4 + and CD8 + T cells than MSC-EVs.However, MSC-EVs from MSCs primed with acidosis increased CD4 + and CD8 + regulatory T cell frequency in vitro. This functional response was reflected by MSC-EV uptake. MSC-EVs from acidosis-primed MSCs were taken up by CD4 + and CD8 + regulatory T cells at a significantly higher level than MSC-EVs from control, hypoxic, and inflammatory cytokine groups. These data suggest that a simple low-cost alteration in MSC culture conditions, acidosis, can generate extracelluar vesicles that have a desirable influence on anti inflammatory T cell subtypes.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high potency MSC lines and metabolic engineering.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells, such as T cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of MSC immunomodulatory function (T cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high potency MSC lines and metabolic engineering.