Project description: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 Methods: mRNA profiles of si-Contol treated M010817 and siSMAD7 treated M010817 human melanoma cell line were generated by deep sequencing, in triplicate, using Illumina HiSeq 2500. The sequence reads that passed quality filters were analyzed at the transcript isoform level with two methods: Burrows–Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT–PCR validation was performed using TaqMan and SYBR Green assays Conclusions: Our study represents the first detailed analysis of si-Contol treated M010817 and siSMAD7 treated M010817 human melanoma cell line 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.
Project description:HUVECs transfected with siRNA targeting Neuropilin 1(NRP1) (si-NRP1) or a control non-targeting sequence (si-control) and exposed to unidirectional shear stress (20dyns/cm2), oscillatory flow (5dyn/cm2) or kept in absence of flow.
Project description:Transcriptional profiling in HACAT cells using a whole human genome array; HACAT cells treated with si RNA against Keap 1 or a scrambled si RNA sequence (Scram) vs HACAT cells mock transfected with lipofectamine (reference control) Experiment Overall Design: 2 biological replicates, 2 technical (dye swap) replicates per treatment.
Project description:To examine the role of WTAP in splicing regulation, we performed high-throughput mRNA sequencing (RNA-seq) on RNA isolated from control, WTAP or Virilizer siRNA-treated HUVECs, yielding 12 million uniquely mapped 75nt pair-end tags from each sample. MapSplice software was used for differential expression and differences in transcript splice junctions . mRNA profiles of control, WTAP or Virilizer siRNA-treated HUVECs were generated by deep sequencing using Illumina GAII.
Project description:To examine the role of WTAP in splicing regulation, we performed high-throughput mRNA sequencing (RNA-seq) on RNA isolated from control, WTAP or Virilizer siRNA-treated HUVECs, yielding 12 million uniquely mapped 75nt pair-end tags from each sample. MapSplice software was used for differential expression and differences in transcript splice junctions .
Project description:Purpose: The goals of this study are to compare VEGF-treated HUVECs with or without Verteporfin (VP) pretreatment 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 Methods: After serum-starving for 12 hours, HUVECs were divided into two group: VEGF and VEGF+VP. Cells from VEGF+VP group were pretreated with VP (4 μM) for 2 hours and then all cells were treated with VEGF (200 ng/mL) for another 24 hours. Subsequently, total RNA from HUVECs were prepared using Trizol reagent and mRNA library was constructed. RNA-sequencing: RNA-sequencing was carried out by BGI (Beijing Genomic Institute, ShenZhen, China). SOAPnuke software (v1.5.2) was used to filter the data for RNA-sequencing and then these data were mapped to the reference genome using HISAT2 software (v2.0.4). The clean reads were aligned to the gene set by Bowtie2 (v2.2.5). The expression level of genes was then measured by RSEM software (v1.2.12). The heatmap of top 40 different expression genes was drawn according to the gene expression with FPKM (fragment per kilobase of transcript per million). Reactome (https://www.reactome.org/) enrichment analysis of different expression genes was undertaken and the significant levels of terms and pathways were corrected by Q value. Results: The statistical results of significant DEGS confirmed that approximately 7204 genes of the transcripts showed differential expression between the VEGF group and VEGF+VP group, with a fold change ≥1.5 and p value <0.05. Altered expression of 20 genes was confirmed with qRT–PCR, 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 angiogenesis. Data analysis with GO analysis and GSEA analysis revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed transcriptomic analysis of VEGF treated HUVECs with or without VP treatment, 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.