Redefining De Novo Gammaherpesvirus Infection Through High-Dimensional, Single-Cell Analysis of Virus and Host

Abstract

Virus infection is frequently characterized using bulk cell populations. How these findings correspond to infection in individual cells remains unclear. Here, we integrate high-dimensional single-cell approaches to quantify viral and host RNA and protein expression signatures using de novo infection with a well-characterized model gammaherpesvirus. While infected cells demonstrated genome-wide transcription, individual cells revealed pronounced variation in gene expression, with only 9 of 80 annotated viral open reading frames uniformly expressed in all cells, and a 1000-fold variation in viral RNA expression between cells. Single-cell analysis further revealed positive and negative gene correlations, many uniquely present in a subset of cells. Beyond variation in viral gene expression, individual cells demonstrated a pronounced, dichotomous signature in host gene expression, revealed by measuring host RNA abundance and post-translational protein modifications. These studies provide a resource for the high-dimensional analysis of virus infection, and a conceptual framework to define virus infection as the sum of virus and host responses at the single-cell level. HIGHLIGHTSO_LICyTOF and scRNA-seq identify wide variation in gene expression between infected cells. C_LIO_LIHost RNA expression and post-translational modifications stratify virus infection. C_LIO_LISingle cell RNA analysis reveals new relationships in viral gene expression. C_LIO_LISimultaneous measurement of virus and host defines distinct infection states. C_LI