Growth of an RNA virus in single cells reveals a broad fitness distribution

Abstract

Genetic and environmental factors will influence the growth of an RNA virus, but their relative contributions are challenging to resolve because standard culture methods mask how virus particles interact with individual host cells. Here, single particles of vesicular stomatitis virus, a prototype RNA virus, were used to infect individual BHK cells. Infected cells produced 50 to 8000 progeny virus particles, but these differences were lost upon subsequent culture, suggesting the diversity of yields reflected cell-to-cell differences rather than viral genetic variation. Cells infected at different phases of their cell cycle produced from 1400 (early S) to 8700 (G(2)M) infectious virus particles, coinciding with the middle-to-upper range of the observed distribution. Fluctuations in virus and cell compositions and noisy gene expression may also contribute to the broad distribution of virus yields. These findings take a step toward quantifying how environmental variation can impact the fitness distribution of an RNA virus.

Fig. 1

Detection and quantification of GFP by FACS. (a) Pseudo-color plots of PI intensity and GFP intensity at 2, 3, 4 and 5 h post-infection (HPI) for BHK cells infected with VSV-GFP at MOI 0.01. Events range from high density (red) to low density (blue). Region Q4 at the lower right defines the population of live (PI-negative) and infected (GFP-positive) cells. (b) Change in percentage of GFP-positive cells and mean GFP intensity (arbitrary units) with time post-infection (hours).

Fig. 2

One-step growth curves of BHK cells with and without sorting. Cells infected at MOI 5 were either sorted (open circles) or not sorted (solid diamonds) and production of viral progeny was determined.

Fig. 3

Fitness distribution from single-cell yields of virus production. (Upper panel) Cells infected at low MOI were sorted and yields of virus progeny were determined. (Lower panel) Persistence of different growth phenotypes was tested by measuring growth yields from viruses descended from high- and low-yield infections.

Fig. 4

Virus yield dependence on cell size. (a) Infected cells divided into three groups based on their FSC intensity and pulse width. High-FSC, medium-FSC and low-FSC cell populations accounted for 25%, 50% and 25% of the cells respectively, and the mean value of FSC was determined for each group. (b) Cell size relationship to FSC. One set of collected cells was imaged by an inverted epifluorescent microscope with a CCD camera. For each group, approximately 100 cells were manually measured from phase-contrast images and their sizes were averaged. A parallel set of collected cells was cultured until 24 HPI and the supernatants were titered by plaque assay. (c) Relationship of virus yield to cell size.

Fig. 5

Effects of cell cycle on virus yield. (a) Change in DNA content of cells following release from aphidicolin treatment. (b) Distribution of cells in different phases of the cell cycle as determined by ModFit. Cells are in G₂M (black), late S (hatched), early S (gray), and G₀G₁ (white) phases. (c) Virus yields from BHK cells infected at different times following release from aphidicolin treatment. Measured virus yields (black squares) and super-position model (black line) are shown. Four replicate measurements were performed for each time point.