Label-Free Prediction of Fluorescently Labeled Fibrin Networks.

Area of Science:

• Biomaterials Science
• Microscopy
• Computational Biology

Background:

• Fluorescent labeling is standard for visualizing extracellular matrix (ECM) fibers but can harm cells and photobleach.
• ECM's fibrillar structure is vital for cell signaling and structural support.
• Reflection confocal microscopy (RCM) offers high-resolution imaging but misses fibers due to orientation sensitivity.

Purpose of the Study:

• To develop a deep learning tool to predict fluorescently labeled optical sections from unlabeled image stacks.
• To recover ECM fibers missed by reflection confocal microscopy (RCM).
• To enable accurate 3D reconstruction of fibrous architecture without compromising cell viability.

Main Methods:

• A deep learning model using a fully convolutional image-to-image mapping architecture was developed.
• The model was trained to predict fluorescent labels from RCM images at 3 laser wavelengths and one transmission image.
• A hybrid loss function incorporating statistical and structural components was utilized.

Main Results:

• The deep learning tool accurately recovered 3D fibrous architecture from unlabeled RCM data.
• No substantial differences were observed in predicted fiber length or count compared to fluorescent labeling.
• Predicted fibers showed a slight increase in width (0.213 ± 0.009 μm) compared to fluorescent labels.

Conclusions:

• The proposed deep learning method enables accurate 3D reconstruction of ECM fibrous architecture using RCM.
• This approach overcomes limitations of fluorescent labeling and RCM, preserving cell viability.
• The tool is compatible with standard laser scanning microscopes, offering broad applications in ECM biology research.