nSolver and Advanced Analysis

Results from each panel were merged into one data file for comparative analysis, normalized in nSolver following best practices, and analyzed using nSolver and Advanced Analysis software according to previously published protocols (Danaher et al., 2017). nSolver-generated heat maps were created using normalized (merged) data and agglomerative clustering, a bottom-up form of hierarchical clustering (NanoString User Manual C0019-08). For Advanced Analysis, normalized merged data was used (NanoString User Manual 10030-03). Differential expression (DE) analysis was performed to identify specific targets that exhibit significantly increased or decreased expression in response to naïve control values or, in the case of BALB/c C57BL/6 comparison, to C57BL/6 control values at each time point. Gene set analysis was run to determine the change in direction of regulation within each pre-defined gene set relative to naïve controls. Global significance scores, a summary T-statistic used to measure change (NanoString User Manual 10030-03) were calculated, and the directed global significance scores were expressed via heatmap. Cell type profiling module analysis was conducted to determine the relative abundance in classically activated cell types during infection using genes assigned to each cell type: T cells (CD3e, CD6, CD3g, TRAT1, and CD3d), CD8+ T cells (CD8a), macrophages (CD68 and CD84), and microglia (GPR84, LRRC25, IRF8, NCF1, TNF, TLR2, and TNF.1). NanoString cell type profiling is a validated method to measure relative abundance of up to 24 different immune cell types, with high concordance to flow cytometry (Danaher et al., 2017). Pathway analysis was also conducted to determine overall changes in pathways based on the first principal component of the targets within a pathway as annotated by NanoString (NanoString User Manual 10030-03). Direction of pathway change (up- or downregulated) was determined by cross referencing the pathway score with the corresponding volcano plot for that pathway. Summary pathway score plot colors are based on calculated scores and are represented as downregulation (blue) to upregulation (orange). Statistical significance was determined using R software. All data are available on Gene Expression Omnibus (GEO).