{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Mehta N"],"funding":["National Science Foundation"],"pagination":["2103955"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8680429"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["31(43)"],"pubmed_abstract":["Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping-based machine learning analysis is reported to distinguish differentiating human adipose-derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single-cell resolution. The label-free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton-based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix-assisted laser desorption/ionization-mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution."],"journal":["Advanced functional materials"],"pubmed_title":["Multimodal Label-Free Monitoring of Adipogenic Stem Cell Differentiation Using Endogenous Optical Biomarkers."],"pmcid":["PMC8680429"],"funding_grant_id":["2045640"],"pubmed_authors":["Mehta N","Shaik S","Fu X","Sheikh E","Chaichi A","Gartia MR","Liu Q","Devireddy R","Donnarumma F","Hasan SMA","Prasad A","Sahu SP","Murray KK"],"additional_accession":[]},"is_claimable":false,"name":"Multimodal Label-Free Monitoring of Adipogenic Stem Cell Differentiation Using Endogenous Optical Biomarkers.","description":"Stem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping-based machine learning analysis is reported to distinguish differentiating human adipose-derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single-cell resolution. The label-free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton-based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix-assisted laser desorption/ionization-mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label-free identification of stem cell differentiation with high spatial and chemical resolution.","dates":{"release":"2021-01-01T00:00:00Z","publication":"2021 Oct","modification":"2025-04-04T07:26:38.733Z","creation":"2025-04-04T07:26:38.733Z"},"accession":"S-EPMC8680429","cross_references":{"pubmed":["34924914"],"doi":["10.1002/adfm.202103955"]}}