Super-resolution imaging of biological organisms: seeing below the classical limits

超分辨率成像技术允许研究远小于传统限制的结构，传统限制约为光波长的一半。2014年诺贝尔化学奖强调了其对生物科学的重要性。已经开发了两大类用于超分辨率成像的技术，基于扫描的（例如STED，GSD）和定位显微镜（例如PALM，STORM，PAINT）。两者都提供了对生物样品内小于30 nm的结构成像的能力;然而，对于成功的成像仍然存在许多挑战，例如光学不均匀性对成像过程的影响，对由局部化成像产生的大数据集的有效处理，用合适的染料对感兴趣的结构的有效标记，以及进一步提高采集速度，视场，该项目的重点是开发关键的使能技术，用于在活体组织中的大光学深度处执行超分辨率成像。学生将有机会发展广泛的技能，这些技能对于下一代显微镜的研究至关重要，例如开发纳米金刚石作为活脑组织中神经活动的标记，并使用自适应光学增强超分辨率显微镜。学生将获得广泛的技能经验，如：光学硬件设计和校准，开发硬件控制和数据处理软件，标记成像样品，并与生物科学领域的同事合作，以确保我们的研究也能够实现新的生物科学。

Super-resolution imaging techniques allow studying of structures much smaller than the traditional limit of approximately half the wavelength of the light. The 2014 Nobel Prize in Chemistry has emphasised its fundamental importance to the biological sciences. Two broad classes of techniques for super-resolution imaging have been developed, scanning-based (e.g. STED, GSD) and localisation microscopy (e.g. PALM, STORM, PAINT). Both offer the ability to image structures smaller than 30 nm within biological samples; however there are still numerous challenges to successful imaging such as the effects of optical inhomogeneities on the imaging process, the efficient processing of the large data sets generated by localisation imaging, effective labelling of structures of interest with suitable dyes and the desire to further increase speed of acquisition, field of view, resolution and perform multi-colour imaging in 3D.This project focusses on developing key enabling technologies for performing super-resolution imaging at large optical depths in living tissue. The student will have the opportunity to develop a wide range of skills that are essential for performing research into next-generation microscopy, e.g. developing nanodiamond as a marker for neural activity in living brain tissue and working with adaptive-optics enhanced super-resolution microscopes. The student will gain a wide range of experience in skills such as: Optical hardware design and alignment, developing software for hardware control and data processing, labelling samples for imaging and collaboration with colleagues in the biological sciences to ensure our research also enables new biological science.