Understanding how viral populations evolve and adapt is crucial. Just like animals or plants, viruses exist as networks of different genetic types, connected through mutations. Imagine a web of interconnected pathways where each point represents a different version, or genotype, of a virus. These connections are formed through mutations, which are changes in the genetic code of the virus. Mapping out these connections and understanding how viral populations move and change within this web is key to understanding how they adapt to their environment.
One area where these mutational networks are especially important is in viral populations that mutate rapidly. Viruses like these can quickly explore a wide range of genetic variations, helping them adapt to new challenges, such as a host’s immune response or antiviral drugs.
To study these complex networks, researchers at the Laboratory of Viral Diseases, NIAID, NIH have developed a method called SEARCHLIGHT (single-cell RNA-sequencing enabled acquisition of mRNA and consensus haplotypes linking individual genotypes and host transcriptomes). This technique allows scientists to capture and assemble viral genetic types, or haplotypes, from hundreds of individual infected cells. By doing so, they can see a detailed picture of the viral population structure.
Traditional single-cell sequencing and the development of SEARCHLIGHT
(A) Conventional method: cDNAs generated in each droplet are processed into short-read libraries, typically capturing 90 to 200 nucleotides (nt) of viral sequence in each read, primarily from the 3′ end of a polyadenylated transcript. (B) The gel bead captured along with individual cells barcodes newly synthesized cDNA via the template-switch sequence encoded in the oligonucleotides conjugated to each bead, marking the cDNA with the cell barcode and unique molecular identifier (UMI). (C) SEARCHLIGHT method: Barcoded cDNAs from tiled, virus-specific primers are used to generate long-read sequencing libraries that cover the entire viral genome, yielding ~2 kb of viral sequence per read. (D) Histogram of the proportion of viral genome captured in each cell during SEARCHLIGHT sequencing of EV-D68–infected RD cells. Fill color represents the average depth of coverage across the viral genome in each cell. The inset shows the cells from which consensus haplotypes were recovered for subsequent analysis. (E) The bulk view of population diversity (left) and the phased haplotype alignment (right). In the haplotype alignment, each haplotype derived from an individual cell is shown as a single row, with differences from the modal genotype shown as points. Color indicates non-synonymous or synonymous mutations. Viral proteins are labeled and denoted by alternate shading.
But SEARCHLIGHT does more than just map out viral genotypes. It also captures information about the host cell’s transcriptome, which is the full set of mRNA molecules expressed by the cell. By linking the viral genotypes to the host cell’s transcriptome, researchers can understand how different viral genetic types affect the host cell’s behavior and how the host cell’s responses influence viral adaptation.
Using SEARCHLIGHT, these researchers have uncovered the complex and dynamic structures of enterovirus populations. Enteroviruses are a group of viruses that include many important pathogens, such as poliovirus and coxsackievirus. The study reveals how these viral populations use “mutational tunnels” to navigate through their evolutionary landscapes. These tunnels allow viruses to maintain connections with multiple adaptive genotypes simultaneously, giving them a greater ability to survive and thrive in changing environments.
The SEARCHLIGHT technique provides a powerful tool for studying the complex world of viral populations. By linking viral genetic structures with host cell responses, researchers can gain new insights into how viruses adapt and survive. This knowledge is essential for developing new strategies to combat viral infections and improve public health.
Dábilla N, Dolan PT. (2024) Structure and dynamics of enterovirus genotype networks. Sci Adv 10(25):eado1693. [article]