Optimized production and fluorescent labelling of SARS-CoV-2 Virus-Like-Particles to study virus assembly and entry.

SARS-CoV-2 is an RNA enveloped virus responsible for the COVID-19 pandemia that conducted in 6 million deaths worldwide so far. SARS-CoV-2 particles are mainly composed of the 4 main structural proteins M, N, E and S to form 100nm diameter viral particles. Based on productive assays, we propose an optimal transfected plasmid ratio mimicking the virus RNA ratio allowing SARS-CoV-2 Virus-Like Particle (VLPs) formation composed of the viral structural proteins M, N, E and S. Furthermore, monochrome, dual-color fluorescent or photoconvertible VLPs were produced. Thanks to live fluorescence and super-resolution microscopy, we quantified VLPs size and concentration. It shows a diameter of 110 and 140 nm respectively for MNE-VLPs and MNES-VLPs with a minimum concentration of 10e12 VLP/ml. SARS-CoV-2 VLPs could tolerate the integration of fluorescent N and M tagged proteins without impairing particle assembly. In this condition, we were able to establish incorporation of the mature Spike in fluorescent VLPs. The Spike functionality was then shown by monitoring fluorescent MNES-VLPs docking and endocytosis in human pulmonary cells expressing the receptor hACE2. This work provides new insights on the use of non-fluorescent and fluorescent VLPs to study and visualize the SARS-CoV-2 viral life cycle in a safe environment (BSL-2 instead of BSL-3). Moreover, optimized SARS-CoV-2 VLP production can be further adapted to vaccine design strategies.

Virus detection and identification in minutes using single-particle imaging and deep learning

The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses. Additionally, we were able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Single-particle imaging combined with deep learning therefore offers a promising alternative to traditional viral diagnostic and genomic sequencing methods, and has the potential for significant impact.