Postmortem cardiac tissue maintains gene expression profile even after late harvesting

Abstract

Background: Gene expression studies can be used to help identify disease-associated genes by comparing the levels of expressed transcripts between cases and controls, and to identify functional genetic variants (expression quantitative loci or eQTLs) by comparing expression levels between individuals with different genotypes. While many of these studies are performed in blood or lymphoblastoid cell lines due to tissue accessibility, the relevance of expression differences in tissues that are not the primary site of disease is unclear. Further, many eQTLs are tissue specific. Thus, there is a clear and compelling need to conduct gene expression studies in tissues that are specifically relevant to the disease of interest. One major technical concern about using autopsy-derived tissue is how representative it is of physiologic conditions, given the effect of postmortem interval on tissue degradation.

Results: In this study, we monitored the gene expression of 13 tissue samples harvested from a rapid autopsy heart (non-failed heart) and 7 from a cardiac explant (failed heart) through 24 hours of autolysis. The 24 hour autopsy simulation was designed to reflect a typical autopsy scenario where a body may begin cooling to ambient temperature for ~12 hours, before transportation and storage in a refrigerated room in a morgue. In addition, we also simulated a scenario wherein the body was left at room temperature for up to 24 hours before being found. A small fraction (< 2.5%) of genes showed fluctuations in expression over the 24 hr period and largely belong to immune and signal response and energy metabolism-related processes. Global expression analysis suggests that RNA expression is reproducible over 24 hours of autolysis with 95% genes showing < 1.2 fold change. Comparing the rapid autopsy to the failed heart identified 480 differentially expressed genes, including several types of collagens, lumican (LUM), natriuretic peptide A (NPPA) and connective tissue growth factor (CTGF), which allows for the clear separation between failing and non-failing heart based on gene expression profiles.

Conclusions: Our results demonstrate that RNA from autopsy-derived tissue, even up to 24 hours of autolysis, can be used to identify biologically relevant expression pattern differences, thus serving as a practical source for gene expression experiments.

Figure 1

Simulated autopsy conditions. All heart samples were initially placed in a 37°C incubator that was then turned off and allowed to cool ~1°C/hr to reach room temperature ~12 hours later. Thereafter, to simulate cold and warm-24 hour autolysis tissue harvesting conditions, some hearts were cooled at 2-3°C/hr (to 4°C) (blue line) and others were left at room temperature (red line).

Figure 2

RNA quality and hybridization performance. The RIN number, a measure of RNA quality, ranged from 2.7-8.6 in the heart samples. The ROC AUC, a hybridization quality metric, ranged from 0.80 to 0.90, which is within the parameters suggested by Affymetrix. All samples showed robust hybridization irrespective of their RIN values (r² = 0.27).

Figure 3

Autolysis interval and RNA quality. The autolysis interval, which ranged from 0 to 24 hours, did not correlate with RNA integrity, measured as RIN numbers (r² = 0.002).

Figure 4

Principal component analysis (PCA) of heart and non-heart gene expression intensities. A scatterplot of the PC1 and PC2 from the PCA analysis of gene intensities of ~15,000 transcripts from the 20 autopsy and explant heart samples, along with reference triplicate HeLa controls and triplicate heart and liver data (obtained from Affymetrix), clearly separates the samples into heart and non-heart spaces.

Figure 5

Correlation between baseline and autolyzed gene expression. a and b. Scatterplots of expression intensity later time-points (x-axis) and the 0 hour time-point (y-axis) for (a) autopsy and (b) explant heart tissues. Red lines represent the linear regression line indicating relationship between expression profiles. Pearson's correlation (r), between the baseline and later timepoints, range from 0.92-0.98. c. Scatterplot of expression intensities between two technical replicates (HeLa Controls) with Pearson's correlation, (r) of 0.98.

Figure 6

Concordance in expression profiles in heart tissues. Pair-wise comparisons of all cardiac expressed genes (~8,400 based on the GNF anatomical system data: heart), expressed as the fold change between time-points. To establish a baseline level of expected concordance for technical replicates, we compared the expression profile of two HeLa controls, and observed 94.9% of genes with ≤1.25 fold change (red line). Comparing the baseline explant and autopsy to later timepoints, we saw no difference from technical replicates (95.9% and 93.4% ≤1.25 fold change, respectively) (purple and brown lines). However, comparing the autopsy to the explant, we saw a measurable difference (86.8% ≤1.25 fold change) (black line), and comparing either heart sample to the HeLa controls demonstrated a striking difference (48% ≤1.25 fold change) (green and blue lines).

Figure 7

Differential gene expression. Heatmap of transcripts with > 2 fold change in expression between the failing and non-failing heart; 374 genes are up-regulated and 108 down-regulated. (red = up-regulated, and green = down-regulated)

Figure 8

Differentially expressed and fluctuating genes. Expression profile of (a) NPPA, (b) NPPB and (c) POSTN genes in the failing and non-failing hearts. The genes are differentially expressed and show fluctuation in expression in the failed heart.