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

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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/cm 2 and 10 J/cm 2 , 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.

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