HSV-1 single cell analysis reveals anti-viral and developmental programs activation in distinct sub-populations

Abstract
Data availability
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Abstract

Viral infection is usually studied at the population level by averaging over millions of cells. However, infection at the single-cell level is highly heterogeneous, where most infected cells give rise to none or few viral progeny while some cells produce thousands. Analysis of HSV-1 infection by population averaged measurements has taught us a lot about the course of viral infection, but has also produced contradictory results, such as the concurrent activation and inhibition of type I interferon signaling during infection. Here, we combine live-cell imaging and single-cell RNA sequencing to characterize viral and host transcriptional heterogeneity during HSV-1 infection of primary human cells. We find extreme variability in the level of viral gene expression among individually infected cells and show that they cluster into transcriptionally distinct sub-populations. We find that anti-viral signaling is initiated in a rare group of abortively infected cells, while highly infected cells undergo cellular reprogramming to an embryonic-like transcriptional state. This reprogramming involves the recruitment of b-catenin to the host nucleus and viral replication compartments and is required for late viral gene expression and progeny production. These findings uncover the transcriptional differences in cells with variable infection outcomes and shed new light on the manipulation of host pathways by HSV-1.

Data availability

All sequencing data has been deposited in the Gene Expression Omnibus (GEO) under accession number GSE126042. All the scripts used for data analysis and visualization are available through GitHub at: https://github.com/nirdrayman/single-cell-RNAseq-HSV1.git.

The following data sets were generated

1. Drayman N
2. Patel P
3. Vistain L
4. Tay S
(2019) Single cell analysis of HSV-1 infection reveals anti-viral and developmental programs are activated in distinct sub-populations with opposite outcomes
NCBI Gene Expression Omnibus, GSE126042.

https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE126042
1. Drayman N
(2019) single-cell-RNAseq-HSV1
GitHub, nirdrayman/single-cell-RNAseq-HSV1.

https://github.com/nirdrayman/single-cell-RNAseq-HSV1

Article and author information

Author details

Nir Drayman

Institute of Molecular Engineering, University of Chicago, Chicago, United States

For correspondence
nirdra@uchicago.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0003-4460-9558
Parthiv Patel

Institute of Molecular Engineering, University of Chicago, Chicago, United States

Competing interests
The authors declare that no competing interests exist.
Luke Vistain

Institute of Molecular Engineering, University of Chicago, Chicago, United States

Competing interests
The authors declare that no competing interests exist.
Savas Tay

Institute of Molecular Engineering, University of Chicago, Chicago, United States

For correspondence
tays@uchicago.edu

Competing interests
The authors declare that no competing interests exist.

Funding

Human Frontier Science Program (post-doctoral fellowship)

Nir Drayman

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.