Electron beam and protease–induced modifications of ECM protein hydrogels: Collagen- and elastin-specific structural insights via fast green staining and fluorescence lifetime CLSM

Collagen–elastin hydrogels have gained increasing attention as versatile extracellular matrix-based biomaterials for tissue engineering, drug delivery, and regenerative medicine. Their tunable mechanical properties, inherent biocompatibility, and responsiveness to external cues make them ideal yet complex systems for biomedical applications. Recent studies have demonstrated that high-energy electron beam irradiation offers a reagent-free approach for crosslinking collagen-elastin hydrogels, enabling precise modulation of network density, mechanical behavior, and thermo-responsiveness under sterilizing conditions. To better understand and visualize the effects of electron beam treatment and enzymatic degradation on the microarchitecture of these hybrid materials we employed fluorescence lifetime-based confocal laser scanning microscopy combined with Fast Green staining. This technique enabled the simultaneous differentiation of collagen and elastin subnetworks at the fibrillar level and facilitated the investigation of the behavior of individual protein components during radiation-chemical processing, enzymatic degradation, and combinations thereof. Electron beam treated and untreated gels were exposed to selective enzymatic degradation using trypsin and elastase, providing insights into the proteolytic stability of the individual matrix components. Lifetime based imaging revealed significant differences in collagen and elastin susceptibility to enzymatic cleavage following electron beam treatment. To support the analysis, machine learning-based segmentation was applied, with a primary focus on voxel-wise classification using the 3D pixel classifier in AIVIA, allowing for a volumetric and spatially resolved characterization of the datasets. The findings highlight the potential of fluorescence lifetime-based confocal laser scanning microscopy and machine learning-based image analysis for characterization of extracellular matrix-mimetic biomaterials and their dynamic remodeling behavior, with implications for optimizing scaffold design in bioengineering applications.