RP expression profiles are reproducible across models and largely stable to stress vulnerability. The figure analyzes a data set from mice classified by stress vulnerability following a forced interaction test. (A) Scatter plot comparing log2(FoldChange) values from pairwise neuronal subclass comparisons between the stress-vulnerability data set (y-axis) and a reference data set from the same brain regions generated using the same single-cell technology (x-axis; Yao et al. 2021), including all detected genes. (B) Same analysis as in A, restricted to ribosomal protein (RP) genes only. Positive Spearman correlations observed for both all genes (ρ = 0.73) and RP genes alone (ρ = 0.56) indicate that baseline subclass-specific expression differences are reproducible across experimental models. (C) Volcano plot summarizing the results from direct differential expression comparisons between high- and low-stress vulnerability mice within each subclass. Dashed lines indicate the significance thresholds (Padj < 0.05 and |log2(FoldChange)| >0.585). Few RP genes show significant changes, with the most prominent being the overexpression of Rpl15 in Pvalb neurons of high-vulnerability mice. (D) DESeq2 normalized counts per biological replicate for the four RP genes showing significant changes between high- and low-stress vulnerability mice (Rpl15 in Pvalb, Uba52 in L5 NP CTX, and Rps2 and Rps27 in Vip neurons). Black dots represent pseudobulk values per replicate, and bars indicate group means. Statistical significance was assessed by pseudobulk DESeq2 analysis: (*) Padj < 0.05, (**) Padj < 0.01, (***) Padj < 0.001, (****) Padj < 0.0001. (E) Heat map of Z-score normalized expression for all 84 RP genes. Hierarchical clustering groups neuronal subclasses together regardless of the animal's stress vulnerability level (e.g., “Pvalb_high” clusters with “Pvalb_low” and “Pvalb_medium”), demonstrating that the characteristic RP profile of each subclass is largely stable.
