Progress in single-cell technologies encouraged the growth of single-virus genomics, trying to characterize yet unknown viral species but also the genetic mutations of variants and their relationships to the virus’ infectivity. But single-cell technologies can also look at host cells to determine their proclivity to be infected by a virion. Prof. Urs Greber and Dr. Maarit Suomalainen from the Department of Molecular Life Sciences at the University of Zurich recently compiled and analyzed the latest single-cell data on virus infection variability and accepted to comment on their findings.
Single-cell variability: a pandora’s box.
“Virus variability, even for RNA viruses, most likely does not explain the cell-to-cell variability in the infection phenotype,” explains Dr. Suomalainen. “It is something that papers on the influenza virus in particular suggest at the moment.”
This implies other factors are at play from the host side, influencing its vulnerability to infection. The idea is not new and has been studied since the 1950s, but the recent progress in single-cell technologies enabled a cell-to-cell comparison of those factors at a larger scale.
However, the challenge is a daunting one: there is no universal mechanism of viral infection that could be systematically compared from cell to cell.
“Every virus has found its own proprietary solution to deal with the cells,” clarifies Prof. Greber. “Their interactions with cells are distinct, using different entry routes, different egress routes, different ways to survive and persist in cells”.
And even breaking down those interactions for a single viral species is no small task despite the wealth of data generated by single-cell studies.
“It is a big challenge to measure all these steps, to conceptualize virus infections into discrete units with their own mechanisms and efficiencies,” continues Prof. Greber. “And these steps are variable from cell to cell, and some steps might be more variable than others. And like many -omics studies, the next challenge is to understand how those steps work from the big landscape picture.”
To evaluate the impact of single-cell technologies on our understanding of the virus-cell interactions, the researchers selected seven viruses: human adenovirus, coxsackie virus, influenza A virus, dengue virus, zika virus, herpes simplex virus and sendai virus.
What kind of data does single-virus genomics produces? It is a great experiment in parallel with classic metagenomics studies and they complement each other, explains Prof. Manuel Martinez-Garcia in his interview.
A plethora of single-cell methods to track viral infection
In the review, the researchers highlight the complementarity and limitations of existing single-cell techniques.
ScRNA-seq might provide a lot of information about the gene expression from both the host cell and the virus, but the setup and bioinformatic analysis are complex. RNA-FISH on the other hand looks at the actual number of viral or host RNA transcripts in a cell, although the number of transcripts that can be marked is limited. Tracking methods, such as live cell fluorescence microscopy and single virion tracking, are also essential to elucidate the variability behind each infection step.
Diving deep into the mechanism of infection for each of the seven viruses, the researchers underlined the variability, or lack thereof, of many of those steps.
“If you look at adenoviruses for example, we measured all the steps such as cell binding, endocytosis, penetration, transport to the nucleus, viral delivery of the genome into the nucleus, transcriptional activity of the viral genome, etc.,” adds Prof. Greber. “And of all these steps, one of the most variable ones was viral gene expression.”
The single-cell resolution studies of an adenovirus infection of standard laboratory cell lines revealed huge discrepancies from cell to cell.
“You see that there are cells that only have a few incoming virus genomes delivered in the nucleus, but they can have many, many different discrete copies of the immediate early viral protein transcript. Whereas other cells that have up to 20 or so viral genomes in the nucleus may only have a few viral transcripts. There’s a big disconnect between the numbers of genomes in the nucleus and the viral gene expression.”
Four principles underlying single-cell variability
Pooling the studies from the seven viruses selected in their review, the researchers highlighted four cellular mechanisms from viral studies impacting cell-to-cell variability in a single population.
- Hereditary mutations in the cell genome
- Dynamic changes in gene expression, such as epigenetics
- Cell state, such as its position in the cell cycle or a history of previous infections
- Phenotypic changes, such as the presence and organization of organelles and macromolecules
Although it is not an exhaustive list of factors for cell-to-cell variability, the combination of all this data makes any interpretation already extremely complex.
“It is a bit difficult to address which of those principles of survivability might have a larger impact than the others,” comments Dr. Suomalainen. “It is still an emerging field. We have known for a long time about cell states affecting infection rates, but other factors such as cellular memory or single-cell metabolomics we do not know very much about yet.”
However, the parallel development of multi-omics pipelines, live cell and virus tracking methods, and computational modeling will hopefully help integrating cell-to-cell variability into already complex infection models.
A word of advice to start studying cell-to-cell variability
Prof. Greber and Dr. Suomalainen also shared tips for setting up cell-to-cell variability experiments and interpreting the data.
“The best thing would be to have as many essays at hand as possible because a single essay doesn’t tell you much, ‘advises Dr. Suomalainen. “You can have all the -omics data you want, but if you have no way to look at them, to dive deeper, you will never find what is really happening beyond correlations”.
“And I’d simply talk about this with the experts that know more before even starting to your experiments”, concludes Prof. Greber.