Figure 1.
Identification of differentially expressed genes.
(A) Detection threshold determination. False positive and negative rates for the detection of genes as a function of detection threshold used, demonstrating how a detection threshold of 0.04 FPKM was determined. A more conservative threshold 0.1 FPKM was chosen for downstream analysis (the probability that a transcript can be reliably detected is ~0.99). (B) Table and Venn diagrams show the distribution of genes that changed > 2 fold and were statistically significant (t-test p < 0.05) in 2D and 7D stages.
Figure 2.
The top canonical pathways enriched in the differentially expressed genes.
(A) Top 15 canonical pathways enriched in top 10% up/down-regulated genes of 2D/CTR and 7D/CTR are shown. The –log(P value) of the enrichment of each canonical pathway was plotted. (B) LXR/RXR Activation pathway up-regulated genes are colored pink. The color of a gene reflects its fold change. The higher the fold change the deeper the color.
Figure 3.
Alternative splicing analysis.
(A) The annotated isoform numbers per gene (x-axis) were plotted over number of genes (y-axis). (B) Splicing isoform expression of genes Spp1 and Morfl2 in acute and subacute phases. Error bars represent ±SEM. P values for transcript NM_009263, NM_00116829 and NM_001168230 were calculated by one-way ANOVA.
Figure 4.
Developing a systems based analysis framework to identify key determinants in the global gene network.
(A) Network constructed in 7D stage using top 10% up-regulated genes. Gene TNF (tumor necrosis factor) was highlighted with its connected edges (in blue). (B) The workflow of a systems based analysis framework in identifying potentially important genes.
Figure 5.
Relative expression fold change from qRT-PCR were calculated using 2-ΔΔCt method. Error bars represent ±SD (n=3). FPKM fold change were the ratio of average FPKM between samples.
Figure 6.
The expression of macrophage marker genes in both acute and subacute phases of SCI.
Expression profile of the common macrophage marker Itgam (A), M1 specific marker CD86 (B) and M2 specific marker Arg1(C) during the time-course of SCI. P values were calculated by one-way ANOVA. GSEA analysis: differential gene expression was ranked by fold change (x-axis: 2D vs control (D), 7D vs control (E), 7D vs 2D (F)). The most up-regulated genes are shown on the left side (red), while the most up-regulated genes were shown on the right side (blue). Black bars represent the positions of the M1 vs M2 up-regulated signature genes in the ranked list. Enrichment score (ES, Y-axis) reflects the degree the genes are overrepresented. When the distribution is at random, the enrichment score is zero. Enrichment of signature genes at the top of the ranked list results in a large positive deviation of the ES from zero. NES, normalized enrichment score; FDR, false discovery rate-adjusted q value.
Figure 7.
Expression dynamics of cell markers of various cell types.
Expression profile of OPC marker Cspg4, also known as NG2 (A), neural stem cell marker Nestin (B), endothelial marker Pecam-1, also known as CD31 (C), hematopoietic stem cell marker CD34 (D), reactive astrocyte markers GFAP(E), Lcn2 and Serpina3n (F,G), are shown. P values were calculated by one-way ANOVA.