Advanced optical techniques for tissue identification and visualisation

神经科学的目标是了解神经系统如何在分子，细胞和网络水平上组织自己，将分子和电信号处理成信息和行动。然而，我们在适当水平（分子、细胞或系统）观察完整神经系统的结构和操纵其功能的能力有限，极大地阻碍了这一努力。我们与神经生物学家密切合作，开发工具，利用基于光子学的技术来满足这些未满足的需求。这是一个漫长的过程，其起源在于证明如何明智地选择物理现象（如相干拉曼成像或照明的偏振控制）可以用于成像以前无法获得的信息，或者如何整合光束成形和光学设计的几个概念可以导致具有高度理想特性的新设备来研究活体神经系统。这个NSERC发现补助金的目的是喂养的概念验证程序，使这些工具的开发与物理科学和工程的学生成为可能。该研究计划的总体愿景是开发基于组织中内源性对比机制的新型分子成像方法，并为非常大的3D成像数据集开发必要的分析工具。要做到这一点，我们将针对两个具体的问题（脂质膜秩序和3D分析），并解决他们的项目，需要光物质相互作用和光学设计的深入了解。首先，我们提出使用基于偏振敏感相干拉曼成像的成像策略来成像膜内脂质的分子顺序。简而言之，相干拉曼过程多年来一直被我们和其他人用来成像髓鞘的脂质，并且可以产生神经系统中白色物质的图像。在这里，我们将转向我们的优势，相干拉曼过程中的偏振敏感性，以提取髓鞘内的脂质分子顺序与一组图像在不同的偏振。其次，我们将开发智能压缩策略，将大型数据集矢量化，使其更容易操作而不会丢失信息。这将在两个测试系统中得到验证，以量化连通性地图和区域分布。在我们的计划结束时，我们将提供创新成像设备和图像分析的概念证明和验证，这些设备和分析将部署给其他研究人员。

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.