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Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.
Van Grouw, Alexandria; Colonna, Maxwell B; Maughon, Ty S; Shen, Xunan; Larey, Andrew M; Moore, Samuel G; Yeago, Carolyn; Fernández, Facundo M; Edison, Arthur S; Stice, Steven L; Bowles-Welch, Annie C; Marklein, Ross A.
Afiliación
  • Van Grouw A; School of Chemistry and Biochemistry and Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
  • Colonna MB; Department of Biochemistry & Molecular Biology, Complex Carbohydrate Research Center and Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Maughon TS; School of Chemical, Materials, and Biomedical Engineering, Regenerative Bioscience Center, University of Georgia, Athens, GA, USA.
  • Shen X; Regenerative Bioscience Center, Department of Animal and Dairy Sciences, University of Georgia, Athens, GA, USA.
  • Larey AM; Department of Biochemistry & Molecular Biology, Complex Carbohydrate Research Center and Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Moore SG; School of Chemical, Materials, and Biomedical Engineering, Regenerative Bioscience Center, University of Georgia, Athens, GA, USA.
  • Yeago C; Systems Mass Spectrometry Core, Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
  • Fernández FM; Marcus Center for Therapeutic Cell Characterization and Manufacturing, Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
  • Edison AS; School of Chemistry and Biochemistry and Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
  • Stice SL; Department of Biochemistry & Molecular Biology, Complex Carbohydrate Research Center and Institute of Bioinformatics, University of Georgia, Athens, GA, USA.
  • Bowles-Welch AC; Regenerative Bioscience Center, Department of Animal and Dairy Sciences, University of Georgia, Athens, GA, USA.
  • Marklein RA; Marcus Center for Therapeutic Cell Characterization and Manufacturing, Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA, USA.
Stem Cells ; 41(8): 792-808, 2023 08 16.
Article en En | MEDLINE | ID: mdl-37279550
ABSTRACT
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.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Células Madre Mesenquimatosas Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Stem Cells Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Células Madre Mesenquimatosas Tipo de estudio: Guideline / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Stem Cells Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos