Label-Free Prediction of Fluorescently Labeled Fibrin Networks.

While fluorescent labeling has been the standard for visualizing fibers within fibrillar scaffold models of the extracellular matrix (ECM), the use of fluorescent dyes can compromise cell viability and photobleach prematurely. The intricate fibrillar composition of ECM is crucial for its viscoelastic properties, which regulate intracellular signaling and provide structural support for cells. Naturally derived biomaterials such as fibrin and collagen replicate these fibrillar structures, but longitudinal confocal imaging of fibers using fluorescent dyes may impact cell function and photobleach the sample long before termination of the experiment. An alternative technique is reflection confocal microscopy (RCM) that provides high-resolution images of fibers. However, RCM is sensitive to fiber orientation relative to the optical axis, and consequently, many fibers are not detected. We aim to recover these fibers. Here, we propose a deep learning tool for predicting fluorescently labeled optical sections from unlabeled image stacks. Specifically, our model is conditioned to reproduce fluorescent labeling using RCM images at 3 laser wavelengths and a single laser transmission image. The model is implemented using a fully convolutional image-to-image mapping architecture with a hybrid loss function that includes both low-dimensional statistical and high-dimensional structural components. Upon convergence, the proposed method accurately recovers 3-dimensional fibrous architecture without substantial differences in fiber length or fiber count. However, the predicted fibers were slightly wider than original fluorescent labels (0.213 ± 0.009 μm). The model can be implemented on any commercial laser scanning microscope, providing wide use in the study of ECM biology.