Advanced optical techniques for tissue identification and visualisation

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The goal of neuroscience is to understand how the nervous system organizes itself at molecular, cellular and network levels to process molecular and electrical signals into information and actions. However, this endeavour is greatly impeded by our limited ability to observe the structure and manipulate the function of the intact nervous system at the appropriate level (molecular, cellular or systemic). We work in close collaboration with neurobiologists to create tools that address these unmet needs with photonics-based technology. This is a lengthy process that has its origin in demonstrating how judiciously chosen physical phenomena (such as coherent Raman imaging or polarization control of the illumination) can be used to image previously unobtainable information, or how the integration of several notions from beam shaping and optical design can lead to novel devices with highly desirable properties to study the live nervous system. The purpose of this NSERC Discovery grant is to feed the proof-of-concept program that makes possible the development of such tools with students from physical sciences and engineering. The overall vision of this research program is to develop novel molecular imaging methods based on endogenous contrast mechanisms in tissue and develop the necessary analysis tools for very large 3D imaging datasets. To do so, we will target two specific problems (lipid membrane order and 3D analysis) and address them with projects that require an intimate knowledge of light-matter interactions and optical design. First, we propose to use an imaging strategy based on polarization-sensitive coherent Raman imaging to image the molecular order of lipids within membranes. Briefly, the coherent Raman process has been used for years to image the lipids of myelin, by us and others, and can yield images of the white matter in the nervous system. Here we will turn to our advantage the polarization-sensitivity of the coherent Raman process to extract the molecular order of lipids within myelin with a set of images at different polarizations. Second, we will develop intelligent compression strategies that will vectorize large datasets to make them easier to manipulate without losing information. This will be validated in two tests systems to quantify connectivity maps and regional distributions. At the end of our program, we will have delivered proofs of concept and validation of innovative imaging devices and image analysis that will be deployed to other researchers.