Strong correlation of gene counts and differentially expressed genes between a 3′ RNA-Seq and an RNA hybridization platform in transcriptome analyses from canine archival tissues

TL;DR

This study compares two RNA analysis methods for archival canine tissues and finds strong agreement in gene counts but moderate overlap in differentially expressed genes.

Contribution

The study provides a direct comparison of 3′ RNA-Seq and RNA hybridization for FFPE tissues, highlighting their strengths and limitations.

Findings

3′ RNA-Seq and RNA hybridization showed strong overall gene count correlations (Pearson/Spearman 0.66–0.87).

Expression directions correlated very strongly (0.88–0.91), but DEG overlap was moderate (Jaccard index 0.53).

Both methods are suitable for FFPE tissues, with gene-wise correlations influenced by expression strength.

Abstract

Analyses of nucleic acids from archival tissues offer invaluable prospects for numerous fields of veterinary medicine, such as the study of differential gene expression in rare or historic diseases. The establishment of modern methodologies, however, raises questions regarding the comparability and reproducibility of data obtained from unlike tools. 3′ RNA-Seq and direct RNA hybridization are such conceptually different approaches for high-throughput transcriptome analysis. Since both are applicable to short, partially degraded mRNA fragments, they in principle allow investigations of formalin-fixed, paraffin-embedded (FFPE) tissues that are abundantly available in pathology archives. Here, we compared the two methods in several relevant details using the RNA from the same set of 35 FFPE canine tumors as input, including sample- and gene-wise count levels, gene expression strengths and directions, as well as the overlaps of differentially expressed genes (DEGs). Both methods proved suitable for their use on archival tissues with moderately to very strong overall count correlations, as indicated by a range of Pearson and Spearman means between 0.66 and 0.87. Of note, the gene-wise count correlations depended on gene expression strength. In an entity-contrasting comparison, expression directions correlated very strongly ranging from 0.88 to 0.91, but DEGs overlapped only moderately with a Jaccard index of 0.53. Finally, we contrasted the different practically relevant aspects of the two technologies with their distinct advantages that depend on the objectives and design of the study. This comparison will guide and help to select the appropriate method and to validate and interpret the data obtained.

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