Perkins et al. Molecular Pain 2014, 10:7 http://www.molecularpain.com/content/10/1/7 MOLECULAR PAIN RESEARCH Open Access AcomparisonofRNA-seqandexonarraysfor whole genome transcription profiling of the L5 spinal nerve transection model of neuropathic pain in the rat James R Perkins1,2†, Ana Antunes-Martins3†, Margarita Calvo3, John Grist3, Werner Rust4, Ramona Schmid4, Tobias Hildebrandt4, Matthias Kohl5, Christine Orengo1, Stephen B McMahon3 and David LH Bennett3,6* Abstract Background: The past decade has seen an abundance of transcriptional profiling studies of preclinical models of persistent pain, predominantly employing microarray technology. In this study we directly compare exon microarrays to RNA-seq and investigate the ability of both platforms to detect differentially expressed genes following nerve injury using the L5 spinal nerve transection model of neuropathic pain. We also investigate the effects of increasing RNA-seq sequencing depth. Finally we take advantage of the “agnostic” approach of RNA-seq to discover areas of expression outside of annotated exons that show marked changes in expression following nerve injury. Results: RNA-seq and microarrays largely agree in terms of the genes called as differentially expressed. However, RNA-seq is able to interrogate a much larger proportion of the genome. It can also detect a greater number of differentially expressed genes than microarrays, across a wider range of fold changes and is able to assign a larger range of expression values to the genes it measures. The number of differentially expressed genes detected increases with sequencing depth. RNA-seq also allows the discovery of a number of genes displaying unusual and interesting patterns of non-exonic expression following nerve injury, an effect that cannot be detected using microarrays. Conclusion: We recommend the use of RNA-seq for future high-throughput transcriptomic experiments in pain studies. RNA-seq allowed the identification of a larger number of putative candidate pain genes than microarrays and can also detect a wider range of expression values in a neuropathic pain model. In addition, RNA-seq can interrogate the whole genome regardless of prior annotations, being able to detect transcription from areas of the genome not currently annotated as exons. Some of these areas are differentially expressed following nerve injury, and may represent novel genes or isoforms. We also recommend the use of a high sequencing depth in order to detect differential expression for genes with low levels of expression. Keywords: Whole-genome transcription profiling, Exon arrays, Microarrays, RNA-Sequencing, RNA-seq, Next generation sequencing, Spinal nerve transection, Nerve injury, Neuropathic pain, Differential gene expression * Correspondence: [email protected] †Equal contributors 3The Wolfson Centre for Age-Related Diseases, Wolfson Wing, Hodgkin Building, King’s College London, Guy's Campus, London Bridge, London SE1 1UL, UK 6Nuffield Department of Clinical Neurosciences, Level 6, West Wing, John Radcliffe Hospital, Oxford OX3 9DU, England Full list of author information is available at the end of the article © 2014 Perkins et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. Perkins et al. Molecular Pain 2014, 10:7 Page 2 of 26 http://www.molecularpain.com/content/10/1/7 Background Thus, analysis can be computationally expensive and Gene expression studies can be used to provide insights time consuming. A common challenge arises due to the into the molecular mechanisms underlying the onset presence of reads that cannot be mapped to the genome. and maintenance of pain [1-5]. Such approaches can be This can be due to genomic differences (such as poly- hypothesis-driven, assessing the expression of prese- morphisms) between the sample and the reference gen- lected candidate molecules, or hypothesis-independent, ome or erroneous base calling by the sequencing interrogating gene expression at the genome-wide level. technology [17]. Conversely, RNA-seq reads may map to Microarrays have been used extensively to investigate more than one genomic location. Such ambiguous reads transcriptional changes that occur in different parts of can lead to imprecise gene quantification. the central and peripheral nervous systems [4]. Such An important consideration when designing an RNA- studies have led to the discovery of novel pain-related seq experiment is sequencing depth, the number of genes, such as the Potassium voltage-gated channel sub- reads generated per sample. In general it is expected that family S member 1, KCNS1 [2], GTP cyclohydrolase 1, the higher the sequencing depth the more accurately the GCH1 [6] and the neuropeptide VGF nerve growth fac- transcriptome of the tissue of interest is quantified tor inducible [7]. In recent years RNA-sequencing [18,19]. This is particularly important for the accurate (RNA-seq) has emerged as an alternative platform for detection of lowly expressed genes, where problems re- high-throughput transcriptional profiling [8]. The plat- lated to sampling error can lead to an over or under- form has been used in a variety of studies, but so far estimation of transcript abundance. However, the cost only one report has described the use of RNA-seq to of the experiment will also increase with sampling measure gene expression changes in the peripheral ner- depth. vous system in an experimental model of pain [9]. It has In addition to the technical pros and cons of both also been used to perform microRNA profiling following platforms, price is an important factor. Although RNA- sciatic nerve injury [10] and to study gene expression seq costs are decreasing, microarrays remain more af- changes in the pre-frontal cortex following spared nerve fordable, and data analysis is more standardised and eas- injury [11]. ily implemented [16]. RNA-seq, on the other hand, Microarray chips measure the expression of thou- demands more computational power and bioinformatics sands of genes in a sample by quantifying the hybridisa- expertise; therefore it is important to determine to what tion of fragmented cDNA derived from gene-transcripts extent the additional knowledge generated by RNA-seq to a set of complementary probes specifically designed experiments outweighs the computational demands and to detect a set of genes or transcripts. They have been economic costs. used to study a variety of biological systems [12]. A large number of comparisons between RNA-seq and However, the use of probes leads to a number of gene expression microarrays have been reported, cover- drawbacks, including non-specific binding and signal ing a wide variety of different experimental designs, plat- saturation [13], which can negatively affect the meas- forms, organisms, tissues, cell lines and experimental urement of expression for both lowly and highly interventions. Table 1 shows an overview of these previ- expressed genes. Furthermore, microarray design is ous studies. So far, no direct comparison has been made based on prior knowledge of the transcriptome and using exon arrays for non-human, heterogeneous tissue therefore microarrays can only interrogate a subset of from different individuals. known (or predicted) transcripts. In this study we investigate whether RNA-seq RNA-seq represents an alternative to microarrays [8]. offers an advantage over microarrays for the study of It uses high throughput sequencing technology to inves- differential gene expression within dorsal root ganglia tigate RNA expression [14] and allows the quantification (DRG) following nerve injury using the L5 spinal of thousands of transcripts within a cell line or tissue nerve transection (SNT) model of neuropathic pain in without the need for a priori knowledge of the transcrip- the rat [33]. Expression changes induced by this injury tome. This “agnostic” approach represents a major ad- have been well characterised by microarrays [1,4,34], vantage over microarrays, allowing the discovery of new proteomics [35] and reverse transcription quantitative transcript variants, novel genes and the annotation of PCR (RT-qPCR) [1]. We have used technical replicates less well characterized genomes [15,16]. of the same biological samples and subjected them RNA-seq technology also suffers from drawbacks. The to exon expression array and RNA-seq analysis. This output of an RNA-seq experiment consists of millions of circumvents confounding effects brought about by reads, short sequences of cDNA derived from RNA mol- comparing distinct technologies using historical data ecules. These reads must be mapped to a reference gen- from previous/independent studies. We demonstrate ome in order to identify the genomic location of the the technical superiority of RNA-seq over microarrays originating transcript and thus quantify expression [17]. in terms of sensitivity (ability to measure transcripts) Perkins et al. Molecular Pain 2014, 10:7 Page 3 of 26 http://www.molecularpain.com/content/10/1/7 Table 1 Previous studies comparing microarray and sequencing platforms for the measurement of gene expression Study Array Sequencing Species Tissue/cell Replication Experimental platform platform line intervention/design Marioni Affymetrix HG- Illumina Human Liver/kidney Technical replications (3 per All RNA was