The technique we have developed is called Structured Illumination Light-sheet Microscopy with Axial Sweeping, or SILMAS. It is designed to image cleared tissue samples at a high, uniform, and isotropic resolution. In light-sheet 3D imaging, 2D planes are gather and stacked on top of each other to create a 3D volume. The trick with this technique lies in the structured illumination. By adding a structure to the light used to illuminate the brain, we can reduce blurring effects from imperfections in the tissue clearing. This is crucial to properly be able to separate the data in one image in the stack from the next. To further improve the imaging quality, axial sweeping allows us to image each plane in one large image, rather than stitching together multiple small images with varying resolution.
In one of our ongoing studies, we are developing a neural network to quantify Lewy pathology, which is key in the development of Parkinson’s disease. The goal of the study is to examine to what extent SILMAS data can help optimize the training of a neural network, and reduce the time needed to manually label image data. This would streamline the work flow for data processing in neuroscience significantly, contributing to more rapid progress in the field.