SARS-CoV-2 is an RNA enveloped virus responsible for the COVID-19 pandemic 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 100 nm diameter viral particles. Based on productive assays, we propose an optimal transfected plasmid ratio mimicking the viral RNA ratio in infected cells. This allows SARS-CoV-2 Virus-Like Particle (VLPs) formation composed of the viral structural proteins M, N, E and mature S. Furthermore, fluorescent or photoconvertible VLPs were generated by adding a fluorescent protein tag on N or M mixing with unlabeled viral proteins and characterized by western blots, atomic force microscopy coupled to fluorescence and immuno-spotting. Thanks to live fluorescence and super-resolution microscopies, we quantified VLPs size and concentration. SARS-CoV-2 VLPs present a diameter of 110 and 140 nm respectively for MNE-VLPs and MNES-VLPs with a concentration of 10e12 VLP/ml. In this condition, we were able to establish the incorporation of the Spike in the fluorescent VLPs. Finally, the Spike functionality was assessed by monitoring fluorescent MNES-VLPs docking and internalization in human pulmonary cells expressing or not the receptor hACE2. Results show a preferential maturation of S on N(GFP) labeled VLPs and an hACE2-dependent VLP internalization and a potential fusion in host cells. 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.
The increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the 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 fluorescently labeled intact particles of different viruses. Our assay achieves labeling, imaging, and virus identification in less than 5 min 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. We were also able to differentiate closely related strains of influenza, as well as SARS-CoV-2 variants. Additional and novel pathogens can easily be incorporated into the test through software updates, offering the potential to rapidly utilize the technology in future infectious disease outbreaks or pandemics. 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.
We present a polarimetric two-photon microscopy technique to quantitatively image the local static molecular orientational behavior in lipid and cell membranes. This approach, based on a tunable excitation polarization state complemented by a polarized readout, is easily implementable and does not require hypotheses on the molecular angular distribution such as its mean orientation, which is a main limitation in traditional fluorescence anisotropy measurements. The method is applied to the investigation of the molecular angular distribution in giant unilamellar vesicles formed by liquid-ordered and liquid-disordered micro-domains, and in COS-7 cell membranes. The highest order contrast between ordered and disordered domains is obtained for dyes locating within the membrane acyl chains.
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