High-throughput super-resolution analysis of influenza virus pleomorphism reveals insights into viral spatial organization

Many viruses form highly pleomorphic particles; in influenza, these particles range from spheres of ~ 100 nm in diameter to filaments of several microns in length. Virion structure is of interest, not only in the context of virus assembly, but also because pleomorphic variations may correlate with infectivity and pathogenicity. We have used fluorescence super-resolution microscopy combined with a rapid automated analysis pipeline to image many thousands of individual influenza virions, gaining information on their size, morphology and the distribution of membrane-embedded and internal proteins. We observed broad phenotypic variability in filament size, and Fourier transform analysis of super resolution images demonstrated no generalized common spatial frequency patterning of HA or NA on the virion surface, suggesting a model of virus particle assembly where the release of progeny filaments from cells occurs in a stochastic way. Finally, we showed that in long filaments, viral RNP complexes are located preferentially within Archetti bodies, suggesting that these structures may play a role in virus transmission. Our approach therefore offers exciting new insights into influenza virus morphology and represents a powerful technique that is easily extendable to the study of pleomorphism in other pathogenic viruses.

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.

Discrete SIRIR modelling using empirical infection data shows that SARS-CoV-2 infection provides short-term immunity

The novel coronavirus SARS-CoV-2, which causes the COVID-19 disease, is now a global pandemic. Since December 2019, it has infected millions of people, caused the deaths of hundreds of thousands, and resulted in incalculable social and economic damage. Understanding the infectivity and transmission dynamics of the virus is essential for understanding how best to reduce mortality whilst ensuring minimal social restrictions to the lives of the general population. Anecdotal evidence is available, but detailed studies have not yet revealed whether infection with the virus results in immunity. In this work, we have extended the generic SIR framework to analyse empirical infection and fatality data from different regions to investigate the reinfection frequency of the disease. Our model predicts that cases of reinfection should have been observed by now if primary SARS-CoV-2 infection did not protect from subsequent exposure in the short term, however, no such cases have been documented. This work, therefore, provides a useful insight for serological testing strategies, lockdown easing and vaccine design.