Open-source Single-particle Analysis for Super-resolution Microscopy with VirusMapper

Abstract

Super-resolution fluorescence microscopy is currently revolutionizing cell biology research. Its capacity to break the resolution limit of around 300 nm allows for the routine imaging of nanoscale biological complexes and processes. This increase in resolution also means that methods popular in electron microscopy, such as single-particle analysis, can readily be applied to super-resolution fluorescence microscopy. By combining this analytical approach with super-resolution optical imaging, it becomes possible to take advantage of the molecule-specific labeling capacity of fluorescence microscopy to generate structural maps of molecular elements within a metastable structure. To this end, we have developed a novel algorithm — VirusMapper — packaged as an easy-to-use, high-performance, and high-throughput ImageJ plugin. This article presents an in-depth guide to this software, showcasing its ability to uncover novel structural features in biological molecular complexes. Here, we present how to assemble compatible data and provide a step-by-step protocol on how to use this algorithm to apply single-particle analysis to super-resolution images.

NanoJ: a high-performance open-source super-resolution microscopy toolbox

“…As part of the NanoJ framework, we include a unique singleparticle analysis (SPA) tool called NanoJ-VirusMapper. It is the first open-source, freely available algorithm for unbiased, high-throughput SPA of fluorescence imaging and allows the structural modelling of viruses and other macromolecular complexes [12][13][14]. The principle of SPA is to image many identical copies of a structure, independently of its orientation, and align and combine them to build an averaged structural map of the underlying structure with high SNR [37][38][39][40][41].…”

“…NanoJ is available as a series of ImageJ-based plugins which can be used independently or concomitantly. NanoJ (figure 1) is comprised of the following modules: NanoJ-Core-general image correction tools including drift correction and channel registration, both based on crosscorrelation analysis; NanoJ-SRRF-an analytical approach capable of extracting super-resolution data from a short sequence of diffraction-limited images, which can be acquired using most microscopes [9,10]; NanoJ-SQUIRREL-an algorithm to evaluate resolution and the presence of artefacts in super-resolution images [11]; NanoJ-VirusMapper-a single particle analysis method to generate nanoscale models of biological structures such as viruses [12][13][14]; NanoJ-Fluidics-a hardware and software framework to control fluidics devices, enabling automation of multiplexed experiments [15]. Thus, the NanoJ framework is capable of solving common imaging problems with broad biological applications and is compatible with a multitude of fluorescence microscope setups and experimental protocols.…”

Poxviruses package viral redox proteins in lateral bodies and modulate the host oxidative response

“…While indicative of LB residence, to validate the MS results and confirm the localization of these proteins to LBs we turned to super-resolution microscopy. We have previously used structured illumination microscopy (SIM), stimulated emission depletion (STED), and stochastic optical reconstruction microscopy (STORM), in combination with single particle averaging to map viral proteins to distinct VACV substructures including LBs, cores and the viral membrane (68)(69)(70)(71)(72).…”

Towards a Quantitative Single Particle Characterization by Super Resolution Microscopy: From Virus Structures to Antivirals Design

“…Finally, the increased popularity of super-resolution studies on viruses has inspired researchers to develop tools to analyze super-resolution data like VirusMapper, an open ImageJ tool to resolve nanostructures and create 2-D models from SRM data on viruses ( Gray et al, 2016 , 2017 ) in combination with several optimized protocols to study single viral particles with SRM ( Gray and Albrecht, 2019 ; Pereira et al, 2019 ).…”