Viruses in connectomics: Viral transneuronal tracers and genetically modified recombinants as neuroscience research tools
Résumé
Connectomic studies have become 'viral', as viral pathogens have been turned into irreplaceable neuroscience research tools. Highly sensitive viral transneuronal tracing technologies are available, based on the use of alpha-herpesviruses and a rhabdovirus (rabies virus), which function as self-amplifying markers by replicating in recipient neurons. These viruses highly differ with regard to host range, cellular receptors, peripheral uptake, replication, transport direction and specificity. Their characteristics, that make them useful for different purposes, will be highlighted and contrasted. Only transneuronal tracing with rabies virus is entirely specific. The neuroscientist toolbox currently include wild-type alpha-herpesviruses and rabies virus strains enabling polysynaptic tracing of neuronal networks across multiple synapses, as well as genetically modified viral tracers for dual transneuronal tracing, and complementary viral tools including defective and chimeric recombinants that function as single step or monosynaptically restricted tracers, or serve for monitoring and manipulating neuronal activity and gene expression. Methodological issues that are crucial for appropriate use of these technologies will be summarized. Among wild-type and genetically engineered viral tools, rabies virus and chimeric recombinants based on rabies virus as virus backbone are the most powerful, because of the ability of rabies virus to propagate exclusively among connected neurons unidirectionally (retrogradely), without affecting neuronal function. Understanding in depth viral properties is essential for neuroscientists who intend to exploit alpha-herpesviruses, rhabdoviruses or derived recombinants as research tools. Key knowledge will be summarized regarding their cellular receptors, intracellular trafficking and strategies to contrast host defense that explain their different pathophysiology and properties as research tools.
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Ugolini 2020 _10.1016j.jneumeth.2020.108917_acceptéPreProof.pdf (6.8 Mo)
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