Single-cell analysis and stochastic modelling unveil large cell-to-cell variability in influenza A virus infection

• Published in: Nature communications Vol. 6; no. 1; p. 8938
• Main Authors: Heldt, Frank S., Kupke, Sascha Y., Dorl, Sebastian, Reichl, Udo, Frensing, Timo
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20.11.2015; Nature Publishing Group; Nature Pub. Group
• Subjects: 631/326/596/1578; 631/553/2706; 631/553/2709; 692/699/255/2514; Animals; Cell Line; Cell Survival; Heterogeneity; Humanities and Social Sciences; Humans; Influenza A virus - chemistry; Influenza A virus - genetics; Influenza A virus - physiology; Influenza, Human - physiopathology; Influenza, Human - virology; Kinetics; Models, Theoretical; multidisciplinary; Science; Science (multidisciplinary); Single-Cell Analysis; Stochastic models
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/ncomms9938

Biochemical reactions are subject to stochastic fluctuations that can give rise to cell-to-cell variability. Yet, how this variability affects viral infections, which themselves involve noisy reactions, remains largely elusive. Here we present single-cell experiments and stochastic simulations that reveal a large heterogeneity between influenza A virus (IAV)-infected cells. In particular, experimental data show that progeny virus titres range from 1 to 970 plaque-forming units and intracellular viral RNA (vRNA) levels span three orders of magnitude. Moreover, the segmentation of IAV genomes seems to increase the susceptibility of their replication to noise, since the level of different genome segments can vary substantially within a cell. In addition, simulations suggest that the abortion of virus entry and random degradation of vRNAs can result in a large fraction of non-productive cells after single-hit infection. These results challenge current beliefs that cell population measurements and deterministic simulations are an accurate representation of viral infections. Cell-to-cell variability in viral infection means that cell population measurements may not be an accurate representation of the process. Using both experimental and modelling approaches the authors confirm this notion showing that influenza virus infections are variable processes affected by intrinsic and extrinsic noise.