RNAseq profiling of blood from patients with coronary artery disease: Signature of a T cell imbalance

Figures

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  Fig. 1. Schematic of study design. Patients presenting to their cardiologist with stable chest pain can have several underlying disorders, including CAD. The patients referred for invasive coronary angiography (ICA) were candidates for the current study. On the basis of ICA results, patients were categorized into LOW (stenosis <20 %, N = 79), or MID + CAD (stenosis >20 %, N = 98) groups (left panel). Prior to ICA, a blood sample (3 ml) was drawn into Tempus blood RNA stabilizer, and frozen at −80 °C until it was analyzed by Illumina Total RNAseq (amplified). A subset of 82 patients was also analyzed by SeqLL true single-molecule (tSMS, unamplified) RNAseq. Differentially expressed transcripts were identified and analyzed as described (right panel).

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  Fig. 2. Scatterplot of RNA expression in LOW vs MID + CAD Patients. Patients presenting for elective ICA were divided into LOW CAD (<20 % stenosis, n = 38) and MID + CAD (>20 % stenosis, n = 44). Preserved whole blood RNA was analyzed by RNAseq on the Illumina Total RNA amplified platform, aligned, counted, and averaged within groups to calculate the average expression per transcript (RPKM, colored squares) in the LOW CAD group (X axis) versus the MID + CAD group (Y axis, both log2 scale). Differentially expressed gene (DEG) transcripts were identified by a triple filter of a minimum RPKM, >1.5-fold change, and p < 0.05 uncorrected for multiple testing (open black squares).

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  Fig. 3. Comparison of CAD patients analyzed by SeqLL and Illumina RNAseq platforms. The patients in the Discovery cohort (n = 82) were analyzed on both SeqLL and Illumina platforms. The overall average correlation of transcript levels was 0.87 (Spearman R) for the raw transcript counts and 0.83 for TPM normalized counts, with the distributions shown by box and whiskers (Panel A). The DEGs identified by each platform had 58 common transcripts, with 57 changing in the same direction (∼9 % overlap, Panel B). Those 57 transcripts showed highly correlated quantification on the 2 platforms (Pearson r = 0.96, Panel C), and the 10 most increased and decreased transcripts are shown in Panel D.

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  Fig. 4. Cell type-specific RNA transcript expression in CAD groups. A pre-curated set of transcripts with relative enrichment in particular blood cell types was derived from single cell RNAseq data from the Human Blood Atlas portal [26]. RNAseq levels were computed for each cell-type set (∼10–20 transcripts per cell type) in LOW (n = 38), MID (n = 19), and CAD (n = 25) groups of the Discovery cohort (n = 82). Asterisks indicate average transcript levels compared by a paired t-test between LOW and MID, or LOW and CAD groups has a p-value <0.05 (*) or 0.01 (**) uncorrected for multiple testing. More detailed subsets of cells (e.g. Treg, or B memory) can be found in Supplementary Data 4. Cell type-specific RNA transcript expression in CAD groups. A pre-curated set of transcripts with relative enrichment in particular blood cell types was derived from single cell RNAseq data from the Human Blood Atlas portal [26]. RNAseq levels were computed for each cell-type set (∼10–20 transcripts per cell type) in LOW (n = 38), MID (n = 19), and CAD (n = 25) groups of the Discovery cohort (n = 82). Asterisks indicate average transcript levels compared by a paired t-test between LOW and MID, or LOW and…

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  Fig. 5. Weighted Gene Co-Expression Analysis (WGCNA) of CAD-related transcripts. The raw expression data of 28 K transcripts from the combined cohort of 177 patients was analyzed for covariant modules of transcripts across patients without regarding to any clinical grouping. A total of 98 covariant modules were identified, and then their association with 16 clinical traits was analyzed to yield a module-by-trait association, which was then filtered to show the most strongly associated modules (Y axis) with each of clinical traits (X axis) color-coded by a positive (red) or negative (green) correlation, with the p-value of the association in parentheses. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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  Fig. 6. Schematic representation of Treg-related TRACs identified by RNAseq. The differentially expressed genes (DEGs) identified by RNAseq were curated by automated and manual analysis to identify the molecular pathways involved. The resulting pattern points to changes in the ‘immune synapse’, which involves both endocytic pathways of T cell receptor (TCR)-containing vesicles, as well as ciliary protrusions that couple to intracellular signaling pathways.