Studying fiber architectures in tumor tissues is of great importance as the cancer cells interact with fiber structures in the Extracellular Matrix (ECM). Computational Scattered Light Imaging (ComSLI) represents an innovative, non-destructive approach to whole-slide imaging with micrometer resolution, uniquely capable of accurately unraveling the complex, intertwined fiber structures in biological tissues. So far, it has been mainly used to study highly interwoven nerve fiber architectures in brain tissues. In this study, we extend the application of ComSLI to visualize fibers in diverse tumor tissues, including oral squamous cell carcinoma (OSCC) and colorectal cancer.
Computational Scattered Light Imaging (ComSLI) is a novel, non-destructive, whole-slide imaging method with the unique ability to precisely disentangle densely interwoven fiber structures in biological tissues. ComSLI can be performed on microscopy slides regularly prepared within the histopathological routine. Although it is a label-free method and does not require any staining, it also works on stained tissues for various stains. So far, ComSLI has been used to visualize nerve fibers in brain tissues [1-3]. In this study, we visualize muscle and collagen fibers in oral tissues for the first time.
REFERENCES:
[1] Menzel, M., et al. "Using light and X-ray scattering to untangle complex neuronal orientations and validate diffusion MRI." Elife 12 (2023).
[2] Menzel, M., et al. "Scattered Light Imaging: Resolving the substructure of nerve fiber crossings in whole brain sections with micrometer resolution." NeuroImage 233 (2021).
[3] Menzel, M., et al. "Toward a high-resolution reconstruction of 3D nerve fiber architectures and crossings in the brain using light scattering measurements and finite-difference time-domain simulations." Physical Review X 10.2 (2020).
We propose the application of Compressed Sensing to Computational Scattered Light Imaging to decrease measurement time and data storage. Computational Scattered Light Imaging (ComSLI) determines three-dimensional fiber orientations and crossings in biomedical tissues like brain tissue. Currently, conventional ComSLI is time-consuming and generates large data. Compressed Sensing reconstructs signals with fewer samples than required by the Shannon-Nyquist theorem with minimal perceptual loss, significantly reducing the number of measurements. We introduce an optimized illumination strategy for ComSLI based on the Discrete Cosine Transform and validate it by reconstructing characteristic scattering patterns in vervet brain tissue, thereby demonstrating the feasibility of Compressed Sensing in ComSLI.
In this work, we employ an integrated label-free dual approach that combines Polarized
Light Imaging with Two-Photon Fluorescence Microscopy to study fiber orientations of
myelinated axons in fixed brain slices from different species.
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