hours earlier. This paradox in the average response illustrates how average cell responses can obscure the actual single cell response dynamics and support the role of cell-to-cell response variability in maintaining an efficient and controlled immune response. Results The population (average) levels of IFNB1 and DDX58 transcripts in human MDDCs were measured as a function of time following NDV infection at multiplicity of infection, MOI = 0.5, as shown in Figure 1 (see also Supplementary Figure S1). Our previous work [14] showed that at ten hours post infection both genes have reached approximately maximal expression levels. Half-maximal DDX58 expression occurred approximately 3 hours after infection, while half-maximal IFNB1 expression occurred 4–5 hours later. The time course for synthesis of the NDV L gene at 0, 3, 6, and 10 hours post infection (Supplementary Figure S1C) showed significant NDV genome replication well before the half-maximal expression level of IFNB1 was reached. MXA induction (Supplementary Figure S1D) mimicked that of DDX58 (Supplementary Figure S1B), and IFNA1 induction (Supplementary Figure S1E) mimicked that of IFNB1 (Supplementary Figure S1A), as expected from their common activation pathways. However, since DDX58 is an interferon-induced gene, we would expect its expression to increase only after IFNB1 expression increases. Paradoxically, the reverse temporal order is observed (Figure 1A and Supplementary Figure S1). However, close inspection of the data (Supplementary Figure S2A) shows evidence for an initial and small first phase of IFNB1 induction that begins as early as 1 hour after infection and never reaches levels above 1% of maximum until the sizeable induction observed after 6 hours. To evaluate the robustness of this low level IFNB1 induction, we performed similar experiments with MOI = 0.1, 0.5, 1.0, 2.0 and 5.0 (Supplementary Figure S2), and obtained similar results. These findings demonstrate that an early low level of induction of IFNB1 precedes - and therefore might be responsible for - the induction of DDX58. Figure 1. Time course of IFNB1 and DDX58 induction. Measure- ment of IFNB1 (solid line) and DDX58 (dashed line) expression in human DCs at 1, 2, 4, 6, 8, 10, 12, 14, 16, and 18 hours following NDV infection. Percent of maximal induction was measured by microarray, compared to non-infected control, and shows that half-maximal induction of DDX58 occurs hours prior to IFNB1 half-maximal induction. doi:10.1371/journal.pone.0016614.g001 IFNB1-DDX58 Feedback Loop Cell-to-Cell Variability The role of this early IFNb in the induction of DDX58 was tested by performing population and single cell assays measuring IFNB1 and DDX58 in the presence and absence of antibodies that attenuate extracellular interferon signaling. Cells were infected at an MOI = 0.5. As shown in Figure 2A, a mixture of antibodies binding interferons and the interferon receptor eliminated the induction of DDX58 and dramatically reduced induction of IFNB1, confirming the formulation that IFNb produced from the nearly undetectable levels of early IFNB1 is responsible for the apparently paradoxical induction of DDX58. As only half the cells were infected, in order to determine the distribution of responses observed in individual cells, we performed single cell mRNA assays (Figure 2B). Notably, in the absence of blocking antibodies about half the cells showed induction of IFNB1, while all cells showed induction of DDX58. In the presence of blocking antibodies, most cells showed control levels of both transcripts. Because IFNB1 is an intronless gene, the control levels of expression reflect the 4 DNA strands encoding the gene within each cell (the sense and the anti-sense DNA strands of the gene on both alleles). In the presence of blocking antibodies, a few cells were detected that showed a modest induction of INFB1 or of DDX58 greater than the levels measured in control cells. The results shown in Figure 2B showed significant cell-to-cell variability in the expression level of IFNB1, which ranged over three orders of magnitude. One factor that can differ among cells is their degree of differentiation, which is reflected in the level of the differentiation marker CD14. In order to test whether this variability contributes the large variation in gene induction with virus infection, we sorted cells into high and low CD14 subpopulations prior to infection with virus. In both groups, a similar and broad distribution of single-cell IFNB1 gene responses was observed (Supplementary Figure S3). These results suggest that the heterogeneous levels of IFNB1 in individual cells does not result from cell heterogeneity in differentiation and reflects both the absence of expression in uninfected cells and the noise in the transcriptional induction of this gene. What is the source of the low level of IFNB1 detected at early time points after virus infection that is necessary to initiate the positive feedback loop and generate a full antiviral response? Given the high intercellular variation in responses, the most parsimonious hypothesis is that a few infected cells are competent to induce IFNB1 before interferon activation of JAK-STAT signaling. We explored this hypothesis and its implications by developing a formal model of the system that could lead to testable predictions. The model was agent-based (ABM) stochastically simulating intercellular IFNb signaling and the temporal evolution of the immune response in individual cells. The constitutive RIG-I distribution across cells, and the parameters of IFNB1 induction were taken such that only the small number of cells with large amounts of RIG-I protein responded to infection. (see Materials and Methods and for additional details). The model can be accessed at http://tsb.mssm.edu/DCresponse2viralInfABM. Figure 3A shows the time courses of the average induction of IFNB1 and of DDX58 obtained in the simulation, which are consonant with the experimental results shown in Figure 1A. A more stringent test of the model is provided by the distributions of single cell results obtained by simulation (Figure 3B). The model was simulated using a constant parameter set both with and without blocking antibodies and the pattern of responses in single cells was determined. The antibody blockage was implemented in the model by introducing a degradation rate for extracellular interferon b protein, which allowed it time to bind to the cell that secreted it, but made it unlikely that it would reach a neighboring PLoS ONE | www.plosone.org 2 February 2011 | Volume 6 | Issue 2 | e16614