Next Generation Sequencing Facilitates Quantitative Analysis of WT MRSA USA300 Infected HEKn and HaCaT Transcriptomes. Homo sapiens

Purpose: 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 (qRT–PCR) methods and to evaluate protocols for optimal high-throughput data analysis Method: HaCaTs and primary keratinocytes were infected with WT USA300 S. aureus (MOI 100:1) for 1 hour before cell lysates were prepared. Poly-A pull-down was used to enrich mRNAs from total RNA samples (200ng-1ug per sample, RIN>8 required) and to proceed to library preparation by using Illumina TruSeq RNA prep kit. Libraries were then sequenced using Illumina HiSeq2500 at Columbia Genome Center. The samples were multiplexed in each lane, which yields targeted number of singleend/paired-end 100bp reads for each sample, as a fraction of 180 million reads for the whole lane. The raw read files were not available for submission because they have been deleted by the core facility. Results: Using an optimized data analysis workflow, we identified 15,780 transcripts in the skin of both HEKn and HaCaT cells. Expression values were analyzed using Ingenuity Pathway Analysis (IPA). Conclusions: Our study represents the first detailed analysis of HEKn and HaCaT transcriptomes, with biologic replicates, generated by RNA-seq technology after WT MRSA USA300 infection. 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. Overall design: Skin mRNA profiles of HEKn and HaCaT cells were generated by deep sequencing, in triplicate, using Illumina HiSeq2500