Functional microvascular networks in engineered tissues depend on coordinated endothelial–stromal interactions and evolving extracellular matrix (ECM) mechanics, yet fluorescent staining precludes longitudinal studies and is incompatible with repeated particle-based microrheology and traction force measurements. We develop a deep-learning virtual labeling approach that recovers nuclear, cytoskeletal, and endothelial fluorescence from label-free images acquired in fibrin scaffolds. Human umbilical vein endothelial cells and lung fibroblasts were co-cultured in three-dimensional (3D) fibrin hydrogels containing 2 μ m silica microbeads that can be used to probe local matrix mechanics. Paired transmission and confocal fluorescence z-stacks (DAPI, phalloidin, UEA I) were used to train a 3D U-Net to generate virtual nuclei, actin, and vascular channels directly from bead-containing label-free volumes. To match channel-specific morphology, edge- and structure-preserving losses were assigned to phalloidin and UEA I, while a sparsity-aware loss was applied to DAPI, improving reconstruction quality across mean squared error, structural similarity, peak signal-to-noise ratio, and correlation. Virtual phalloidin preserved fibrillar density and orientation, virtual UEA I reproduced vessel continuity and density without false matrix labeling, and virtual DAPI enabled nuclei segmentation with Dice scores and total cell counts indistinguishable from ground truth (GT). Microbeads did not generate spurious signal in any channel, demonstrating robustness to scattering particles. Together, these results show that virtual fluorescent labeling can replace destructive staining for quantifying fibrillar architecture, vessel density, and cell number in bead-laden fibrin constructs, and establish a practical route toward longitudinal studies of coupled microvascular morphogenesis and ECM mechanics. Statement of Significance Fluorescent staining is widely used to study vascular growth dynamics in biomaterials but requires destructive fixation, preventing repeated measurements in the same sample. We introduce a label-free deep learning approach that predicts multiple fluorescence markers in fully three-dimensional fibrin hydrogels undergoing capillary morphogenesis. Unlike prior virtual staining methods focused on 2D samples, our model operates in thick, optically complex 3D co-cultures and is robust to highly scattering micron-scale tracer beads used for particle-based micromechanical measurements. A channel-specific loss design preserves sharp boundaries and filament continuity, enabling accurate vascular morphometrics and cell-type identifications that are directly interchangeable with measurements from destructive staining. This approach supports non-destructive, quantitative analysis in tissue-engineering experiments and is extensible to other 3D extracellular matrices.
Eldeen et al. (Fri,) studied this question.