Machine Learning and Metabolomics Predict Mesenchymal Stem Cell Osteogenic Differentiation in 2D and 3D Cultures.

ABSTRACT: Stem cells have been widely used to produce artificial bone grafts. Nonetheless, the variability in the degree of stem cell differentiation is an inherent drawback of artificial graft development and requires robust evaluation tools that can certify the quality of stem cell-based products and avoid source-tissue-related and patient-specific variability in outcomes. Omics analyses have been utilised for the evaluation of stem cell attributes in all stages of stem cell biomanufacturing. Herein, metabolomics in combination with machine learning was utilised for the benchmarking of osteogenic differentiation quality in 2D and 3D cultures. Metabolomics analysis was performed with the use of gas chromatography-mass spectrometry (GC-MS). A set of 11 metabolites was used to train an XGboost model

SUBMITTER: Klontzas ME 

PROVIDER: S-EPMC11680063 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature