Metabolites Report on Early Stem Cell Osteodifferentiation: New Markers for Effective Bone Regeneration

abstract

Mesenchymal stem cells (MSC) are essential for tissue regeneration, although their biological variability (e.g. interdonor differences) limits their therapeutic potential. New strategies to accurately monitor/predict MSC osteogenic capacity are needed for selecting the most suitable donors/cells. Here, intra- and extracellular metabolic variations accompanying human adipose-derived MSC (hAMSC) osteodifferentiation are characterized for 3 donors over 21 days using untargeted nuclear magnetic resonance (NMR) metabolomics. A donor-independent 9-endometabolite signature (choline, ethanolamine, UDP-GlcNAc, UDP-GalNAc, methylguanidine, phosphocreatine, phosphocholine, ADP and one unknown) predicts osteodifferentiation from day 7, with > 95% sensitivity, specificity, and classification rates, achieving 100% predictive scores by day 14. In addition, osteogenic-specific donor-independent patterns are identified in 17 exometabolites: i) reduced uptake of glutamine, branched-chain amino acids, 2-hydroxyisobutyrate, pyruvate and glucose; ii) increased secretion of 3-hydroxybutyrate, ornithine, pyroglutamate and lactate; iii) decreased secretion of 3-hydroxyisobutyrate, _-ketoglutarate and citrate; and iv) consistent final levels of alanine, choline and histidine (regardless of their variations). This 17-exometabolite signature enables non-invasive detection of osteodifferentiation with 100% sensitivity, specificity, and classification after day 4. These findings show that differentiating cells efficiently manage metabolic resources by reconfiguring key pathways, e.g. partially shifting from glycolysis/OxPhos to the PCr-Cr axis for ATP and phosphate production, activating antioxidative mechanisms, and adapting protein glycosylation and membrane metabolism. These donor-independent signatures constitute a robust tool to accurately assess/predict osteogenic potential, enabling effective and early MSC donor selection.

authors

Daniela S.C. Bispo, João A. Rodrigues, Inês Graça, Catarina S.H. Jesus, Marlene Correia, Iola F. Duarte, Brian J. Goodfellow, Mariana B. Oliveira, João F. Mano, Ana M. Gil

our authors

acknowledgements

BetterBone (2022.04286.PTDC, doi: 10.54499/2022.04286.PTDC) & BIOIMPLANT (PTDC/BTM-ORG/28835/2017), through COMPETE2020/FEDER (POCI-01-0145-FEDER-028835). CICECO-Aveiro Institute of Materials UIDB/50011/2020 (doi: 10.54499/UIDB/50011/2020), UIDP/50011/2020 (doi: 10.54499/UIDP/50011/2020) & LA/P/0006/2020 (doi: 10.54499/LA/P/0006/2020), financed by national funds through the FCT/MCTES (PIDDAC). SFRH/BD/150655/2020 (doi: 10.54499/SFRH/BD/150655/2020, DSB PhD grant). Portuguese National NMR Network (Project Nº022161) through FEDER (COMPETE 2020/POCI/PORL/FCT (PIDDAC).

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