Seeing the virus with topological optical microscopy

用拓扑光学显微镜观察病毒

• 批准号: BB/X003477/1
• 负责人: Peter Smith
• 金额: $ 22.94万
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX003477%2F1
• 关键词: Seeing; virus; topological; optical; microscopy

The Problem: This project will develop a new optical approach to seeing small, unlabelled, living viruses and will deliver new ways of describing their interactions with surfaces. Surface treatments with various substances, notably copper, is a first line of defence against viral spread. Effective treated surfaces have been shown to cause structural damage when observed using an electron microscope (EM). However, assaying the effectiveness of coatings is difficult. Delivering a quick and generally usable assessment of viability through structural integrity would deliver a transformative and rapid assay. Current assays for capacity to infect form a bottleneck to designing suitable surfaces for placement in public spaces, hospital environments or on PPE, particularly when faced with rapid spread, as with SARS-CoV-2. Rapid PCR-based or immunological assays can provide information on viral presence/absence, but determination of viral viability still often relies on cell culture assays. EM is time consuming, expensive and can only be used on dead material. These are all laborious processes, only available to specialised labs and as indirect methods are at best semi-quantitative. An optical approach that can be fitted to a standard microscope platform to allow the study of viral responses and disruption on surfaces, will be a transformative step.The Solution: Here, we propose to develop a label-free, non-invasive optical imaging method with spatial resolution at one millionth of a millimetre (a nanometre) or a thousandth of the diameter of a human hair. Such microscopy is theoretically out of reach due to the Abbe diffraction limit. This would restrict resolution to approximately 190 nm for visible light. Viruses of interest are frequently only 10s of nanometres in diameter. However, our new approach will draw on recent developments in structuring light and artificial intelligence to address the challenge of single virus imaging while observing their interactions with surfaces. This proposal has two aims: 1. To develop an optical method for imaging biological systems that bridges the gap between conventional unlabelled optical imaging and electron microscopy.2. To study viruses with the method, pushing its development and resolution, while exploring its use as a method for directly observing viral structure and disruption on antimicrobial surfaces.Success will not only deliver new ways of studying viruses but will open a transformational pathway to unlabelled live cell imaging at the nanoscale.Our approach is based on the recently introduced concept of imaging by manipulating the physics of light. The target is illuminated with a structured light field and the corresponding scatter patterns are analysed with artificial intelligence. When these structured fields illuminate a small object, the scattered fields carry information about the geometry of the object at the subwavelength-scale. This has been shown to lead to orders of magnitude improvements in metrology. More recently, the applicants have implemented new imaging methods based on structured light. The step from metrology to imaging is, however, a non-trivial one, and to date only one-dimensional apertures have been imaged by this approach. However, the resolution improved conventional microscopy by an order of magnitude, and we anticipate that our new method will allow for another 10-fold enhancement. We propose to translate our method from nanoscale metrology to nanoscale biological imaging of 'living viruses'. A key challenge in extending to biological imaging involves the extraction of information from the complex scattered field patterns. To this end, we will use neural network-based deconvolution algorithms to perform image reconstruction, as demonstrated in the recent work of the Solution Providers. We are thus well placed to adapt the methodology to viral imaging, viability assays and further biological applications.

问题：该项目将开发一种新的光学方法，以查看小型，未标记的生活病毒，并将提供描述其与表面相互作用的新方法。用各种物质（尤其是铜）进行表面处理是针对病毒扩散的第一道防线。当使用电子显微镜（EM）观察时，已显示有效处理的表面已显示会造成结构损伤。但是，很难分析涂料的有效性。通过结构完整性对生存能力进行快速且一般可用的评估将提供一种变革性和快速的测定。目前的感染能力的测定法会形成瓶颈，以设计合适的表面，以在公共场所，医院环境或PPE上放置，尤其是在面对迅速传播时，就像SARS-COV-2一样。基于PCR的快速或免疫学测定可以提供有关病毒存在/不存在的信息，但是确定病毒活力的确定仍然通常取决于细胞培养分析。 EM耗时，昂贵，只能用于死材料上。这些都是费力的过程，仅适用于专业实验室，因为间接方法充其量是半定量的。可以安装在标准显微镜平台上的光学方法，以研究病毒反应和表面上的破坏，这将是一个变革性的步骤。解决方案：在这里，我们建议以无标记的非标签，非侵入性的光学成像方法，并以空间分辨率以一百万分（nanomer）或千千年的头发的一百万分（一百万分）的空间分辨率进行空间分辨率。由于ABBE衍射极限，从理论上讲，这种显微镜是无法触及的。对于可见光，这将限制分辨率约为190 nm。感兴趣的病毒通常只有10秒的直径。但是，我们的新方法将借鉴结构光和人工智能方面的最新发展，以应对单个病毒成像的挑战，同时观察它们与表面的相互作用。该提案有两个目的：1。开发一种光学方法来成像生物系统，以弥合常规未标记的光学成像和电子显微镜之间的差距。2。通过该方法研究病毒，推动其发展和解决方案，同时探索其作为直接观察病毒结构和抗菌表面上的破坏的方法。Success不仅会提供新的研究病毒方法，而且将开放一种基于MANIP的概念的概念的nansoscale the Patife the Prient of Maniip of Image of Image of Image of Imanip of Image的概念。目标用结构化的光场照亮，并用人工智能分析相应的散射模式。当这些结构化场照亮一个小物体时，散射的场携带有关子波长度上对象几何形状的信息。已显示这会导致计量学的数量级改善。最近，申请人基于结构化的光实施了新的成像方法。但是，从计量学到成像的步骤是一个非平凡的步骤，迄今为止，这种方法仅对一维孔径进行了成像。但是，该分辨率通过数量级改善了常规显微镜，我们预计我们的新方法将允许再增强10倍。我们建议将我们的方法从纳米级计量学转化为“活病毒”的纳米级生物学成像。扩展到生物成像的关键挑战涉及从复杂的散射场模式中提取信息。为此，我们将使用基于神经网络的反卷积算法来执行图像重建，如解决方案提供商的最新工作所示。因此，我们很适合将方法适应病毒成像，生存力测定和进一步的生物学应用。