Biology subjects

Maughon, T. S.

Publications and source records attributed to Maughon, T. S..

2 recordsLinked to original sources

bioRxiv2023

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency

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.

bioengineering↗

bioRxiv2021

Multi-omics characterization of mesenchymal stem/stromal cells for the identification of putative critical quality attributes

BackgroundMesenchymal stromal cells (MSCs) have shown great promise in the field of regenerative medicine as many studies have shown that MSCs possess immunomodulatory function. Despite this promise, no MSC therapies have been granted licensure from the FDA. This lack of successful clinical translation is due in part to MSC heterogeneity and a lack of critical quality attributes (CQAs). While MSC Indoleamine 2,3-dioxygnease (IDO) activity has been shown to correlate with MSC function, multiple CQAs may be needed to better predict MSC function. MethodsThree MSC lines (two bone marrow, one iPSC) were expanded to three passages. At the time of harvest for each passage, cell pellets were collected for nuclear magnetic resonance (NMR) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS), and media was collected for cytokine profiling. Harvested cells were also cryopreserved for assessing function using T cell proliferation and IDO activity assays. Linear regression was performed on functional and multiomics data to reduce the number of important features, and partial least squares regression (PLSR) was used to obtain putative CQAs based on variable importance in projection (VIP) scores. ResultsSignificant functional heterogeneity (in terms of T cell suppression and IDO activity) was observed between the three MSC lines, as well as donor-dependent differences based on passage. Omics characterization revealed distinct differences between cell lines using principal component analysis (PCA). Cell lines separated along principal component 1 based on tissue source (bone marrow vs. iPSC-derived) for NMR, MS, and cytokine profiles. PLSR modeling of important features predicts MSC functional capacity with NMR (R2=0.86), MS (R2=0.83), cytokines (R2=0.70), and a combination of all features (R2=0.88). DiscussionThe work described here provides a platform for identifying putative CQAs for predicting MSC functional capacity using PLSR modeling that could be used as release criteria and guide future manufacturing strategies for MSCs and other cell therapies.

bioengineering↗