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Learn MorePurpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare NGS-derived retinal transcriptome profiling (RNA-seq) to microarray and quantitative reverse transcription polymerase chain reaction (qRTPCR) methods and to evaluate protocols for optimal high-throughput data analysis; Methods: Retinal mRNA profiles of 21-day-old wild-type (WT) and neural retina leucine zipper knockout (Nrl/) mice were generated by deep sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: BurrowsWheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRTPCR validation was performed using TaqMan and SYBR Green assays; Results: Using an optimized data analysis workflow, we mapped about 30 million sequence reads per sample to the mouse genome (build mm9) and identified 16,014 transcripts in the retinas of WT and Nrl/ mice with BWA workflow and 34,115 transcripts with TopHat workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 12 of these were validated with qRTPCR. RNA-seq data had a linear relationship with qRTPCR for more than four orders of magnitude and a goodness of fit (R2) of 0.8798. Approximately 10% of the transcripts showed differential expression between the WT and Nrl/ retina, with a fold change 1.5 and p value <0.05. Altered expression of 25 genes was confirmed with qRTPCR, demonstrating the high degree of sensitivity of the RNA-seq method. Hierarchical clustering of differentially expressed genes uncovered several as yet uncharacterized genes that may contribute to retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling.; Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with biologic replicates, generated by RNA-seq technology. The optimized data analysis workflows reported here should provide a framework for comparative investigations of expression profiles. Our results show that NGS offers a comprehensive and more accurate quantitative and qualitative evaluation of mRNA content within a cell or tissue. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.; Related datasets : Human datasets are present in the dbGaP study id phs001158.v3.p1 . Use the sample attribute "Project id" to determine which samples In that were used for thie project "Pharmacological target focused transcriptomic analysis of native versus cultured human and mouse dorsal root ganglia" SOURCE: Theodore,John,Price (theodore.price@utdallas.edu) - Pain Neurobiology Research Group The University of Texas at Dallas
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