Evaluating gene expression in C57BL/6J and DBA/2J mouse striatum using RNA-Seq and microarrays

Abstract

C57BL/6J (B6) and DBA/2J (D2) are two of the most commonly used inbred mouse strains in neuroscience research. However, the only currently available mouse genome is based entirely on the B6 strain sequence. Subsequently, oligonucleotide microarray probes are based solely on this B6 reference sequence, making their application for gene expression profiling comparisons across mouse strains dubious due to their allelic sequence differences, including single nucleotide polymorphisms (SNPs). The emergence of next-generation sequencing (NGS) and the RNA-Seq application provides a clear alternative to oligonucleotide arrays for detecting differential gene expression without the problems inherent to hybridization-based technologies. Using RNA-Seq, an average of 22 million short sequencing reads were generated per sample for 21 samples (10 B6 and 11 D2), and these reads were aligned to the mouse reference genome, allowing 16,183 Ensembl genes to be queried in striatum for both strains. To determine differential expression, 'digital mRNA counting' is applied based on reads that map to exons. The current study compares RNA-Seq (Illumina GA IIx) with two microarray platforms (Illumina MouseRef-8 v2.0 and Affymetrix MOE 430 2.0) to detect differential striatal gene expression between the B6 and D2 inbred mouse strains. We show that by using stringent data processing requirements differential expression as determined by RNA-Seq is concordant with both the Affymetrix and Illumina platforms in more instances than it is concordant with only a single platform, and that instances of discordance with respect to direction of fold change were rare. Finally, we show that additional information is gained from RNA-Seq compared to hybridization-based techniques as RNA-Seq detects more genes than either microarray platform. The majority of genes differentially expressed in RNA-Seq were only detected as present in RNA-Seq, which is important for studies with smaller effect sizes where the sensitivity of hybridization-based techniques could bias interpretation.

Figure 1. Distribution of read counts per gene per lane/flowcell.

Shown in A) is the distribution of non-normalized counts per gene. The counts, however, were divided by a constant defined here as the mean of the total unique read counts in megabases (MB). This was done to facilitate comparison of the boxplots. For B), the count values were normalized by the upper quartile corrected total counts in MB. The upper quartile correction values were computed using edgeR . Additionally both plots are shown on the log2 scale. Also note that the X-axis is labeled in the form strain_lane_flowcell.

Figure 2. Platform overlap of detection of mouse genes expressed in striatum.

For RNA-Seq: the genes that pass filters for detection when RNA-Seq data are aligned to all genes annotated in Ensembl. For microarray: the genes detected as present with at least one probeset (Affymetrix) or probe (Illumina) after filtering.

Figure 3. Platform overlap of differential expression (DE) detected by RNA-Seq.

Of the 1524 genes DE by RNA-Seq data, 555 are also detected on Affymetrix MOE 430 2.0 and Illumina MouseRef-8 v2.0. When queried on all three platforms, 144 show DE on all three platforms (5 show DE on all three platforms, but do not agree in direction of fold change) indicating genes that are likely uniformly DE across all exons. 125 and 54 are DE on Affymetrix and RNA-Seq or Illumina and RNA-Seq, respectively (with 3 and 2 disagreeing in direction of fold change, respectively). While detected on all three platforms, 222 only show DE on RNA-Seq.