Award details

Seeing the virus with topological optical microscopy

Reference	BB/X003477/1
Principal Investigator / Supervisor	Professor Peter Smith
Co-Investigators / Co-Supervisors	Dr Nikitas Papasimakis, Dr Sandra Wilks, Professor Nikolay Zheludev
Institution	University of Southampton
Department	Sch of Biological Sciences
Funding type	Research
Value (£)	180,022
Status	Current
Type	Research Grant
Start date	01/10/2022
End date	31/03/2024
Duration	18 months

Abstract

The ability to image viruses and study their interactions with surfaces is of great importance for understanding viral transmission and controlling epidemics, such as the ongoing COVID-19 pandemic. Such studies require unlabelled optical imaging with resolution at the 10 nm scale. However, conventional optical microscopy is limited by the diffraction limit to a resolution of ~300 nm. On the other hand, higher resolution approaches are either invasive and slow (e.g. electron microscopy, near-field methods) or require fluorescent labels (STED/PALM/STORM). As such, currently available imaging modalities are not adequate for imaging viruses in vivo. Here, we will address this challenge by employing a newly introduced method termed Deeply Subwavelength Topological Microscopy (DSTM). DSTM employs illumination of the target with topologically structured light and object reconstruction based on artificial intelligence. It is an unlabelled method that allows us to overcome the resolution limit of optical microscopy by orders of magnitude and to date has allowed metrology at nm scales. The proposed project will address the non-trivial challenge of developing DSTM as an imaging platform to bridge the gap in biological imaging between conventional unlabelled optical imaging and electron microscopy. DSTM will enable a rapid and inexpensive assay of the interactions of viruses with different surfaces, a front-line defence against viral transmission. By performing non-trivial modifications, we will adapt a DSTM microscope (currently operational at Southampton) to an instrument suitable for high-resolution biological imaging. We will use DSTM to image viruses on different surfaces as well as in real-world conditions focusing on identifying antimicrobial properties of the substrate. Our approach will advance our understanding of viral structure and dynamics, potentially leading to novel screening approaches for antimicrobial surfaces.

Summary

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.

Committee	Not funded via Committee
Research Topics	X – not assigned to a current Research Topic
Research Priority	X – Research Priority information not available
Research Initiative	UKRI Basic Technologies [2022]
Funding Scheme	X – not Funded via a specific Funding Scheme

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