Adipose-Derived Stem Cells Differentiate Into Insulin-Producing Cells in 2D Culture With Photobiomodulation: A Comparative Analysis of Wavelength and Fluence Parameters

Abstract

Diabetes mellitus is a chronic metabolic disorder characterized by the loss or dysfunction of insulin-producing beta (β) cells. Adipose-derived stem cells (ADSCs) represent a promising source for generating functional insulin-producing β cells due to their accessibility and differentiation potential. Photobiomodulation (PBM), a non-invasive light-based therapy, has emerged as an innovative strategy to enhance stem cell differentiation efficiency. Evidence suggests that green (525 nm) and near-infrared (825 nm) wavelengths, applied individually or in combination, can modulate cellular metabolism, ATP production, and differentiation-related signaling pathways, thereby influencing ADSC commitment toward insulin-producing β-cell-like phenotypes. This in vitro study evaluated the effects of PBM at 525 nm and 825 nm, delivered individually and in combination at energy fluences of 5 J/cm² and 10 J/cm², on the differentiation of ADSCs cultured in β-cell induction medium into insulin-producing β-cell-like cells under two-dimensional (2D) culture conditions at 24 h, 5 days, and 10 days. Cellular responses were evaluated using adenosine triphosphate (ATP) luminescence assays, lactate dehydrogenase (LDH) activity assays, Giemsa staining, Live/Dead viability assays, and dithizone (DTZ) staining. ATP levels varied significantly among the experimental groups, reflecting changes in cellular metabolic activity associated with β-cell induction and PBM exposure, and reduced LDH activity, suggesting decreased cytotoxicity. Giemsa staining revealed morphological changes consistent with β-cell differentiation, while Live/Dead assays demonstrated the maintenance of cell viability across all experimental groups. Dithizone staining identified the presence of zinc-rich insulin-producing clusters. These findings highlight the importance of PBM wavelength and fluence optimization in regenerative stem cell applications.