ABSTRACTStem cell-based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective therapy requires precise non-destructive evaluation of the purity of stem cells with high sensitivity (< 0.001% of the number of cells). Here, we report a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping (SAM)-based machine learning analysis to distinguish differentiating human adipose derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enabled identification of differentiated cells at single cell resolution. The label-free HSI method was compared with the standard methods such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. Further, we performed Raman microscopy and multiphoton-based metabolic imaging to provide complimentary information for the functional imaging of the hASCs. Finally, the HSI method was validated using matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) 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. This could provide a powerful tool to assess the safety and efficacy of stem cell-based regenerative therapies.
Real-time label-free monitoring of adipose-derived stem cell differentiation with electric cell-substrate impedance sensing
