Abstract:
Hydrogels self-assembled from short peptides are garnering significant interest due to their remarkable mechanical and biological properties, making them promising for applications in tissue engineering, regenerative medicine, 3D cell culture, and drug delivery. One of the key challenges in developing hydrogels with different structures and chemical compositions is to carefully evaluate their biocompatibility with cells, including the ability of cells to survive, proliferate, and differentiate inside hydrogels. One method for assessing cell viability is fluorescence lifetime imaging microscopy (FLIM), which allows for the detection of changes in cellular metabolism via the signal of endogenous fluorophores at the single-cell level. In this study, the potential for assessing changes in cellular metabolism was investigated using human breast cancer cells (MCF-7) cultured on soft, self-organizing hydrogels with varying levels of biocompatibility made from Fmoc-FF peptide and chitosan. It was demonstrated that FLIM data, combined with advanced segmentation methods based on universal zero-shot Segment Anything neural network model, can assess the metabolic status of cells at the single-cell level in large sample sizes. The presented method enables the tracking of metabolic changes in cells adhered to soft hydrogels and the early detection of scaffold biocompatibility with tissue cells, which is promising for regenerative technology applications.
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