Next Generation Sequencing Facilitates Quantitative Analysis of Control siRNA, RAB11A siRNA1 and RAB11A siRNA2 suppressed EBC-1 cell's Transcriptomes
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ABSTRACT: Next Generation Sequencing Facilitates Quantitative Analysis of Control siRNA, RAB11A siRNA1 and RAB11A siRNA2 suppressed EBC-1 cell's Transcriptomes
Project description:This study aims to find cellular pathway changes in RAB11A suppressed in lung squamous cell carcinoma cell line EBC-1 compared to control and to evaluate downregulating genes by RAB11A suppression by siRNA. Total RNA was extracted from EBC-1 transfected with control siRNA and RAB11A specific siRNAs using the RNeasy mini kit (Qiagen, Hilden, Germany). CAGE library preparation, sequencing, mapping, and gene expression and motif discovery analysis were performed by DNAFORM (Yokohama, Japan). In brief, RNA quality was assessed by Bioanalyzer (Agilent) to ensure that RIN (RNA integrity number) is over 7.0 and A260/280 and 260/230 ratios are over 1.7. First-strand cDNAs were transcribed to the 5’end of capped RNAs, attached to CAGE “bar code” tags, and the sequenced CAGE tags were mapped to human hg19 genomes using BWA software (v0.5.9) after discarding ribosomal or non-A/C/G/T base-containing RNAs. For tag clustering, CAGE-tag 5’ coordinates were input for CAGEr clustering using Paraclu algorithm with default parameters. Using CAGE analysis, we could validate the downregulation of FGF/FGFR signal-related genes in RAB11A-suppressed EBC-1 cells compared to the control cells. Finally, this study showed that RAB11 was related to tumor aggressiveness and growth in vitro and in vivo via activation of FGFR signals.
Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to analysis the differiational genes and pathways in Ctrl-siRNA and c-Myc-siRNA lymphoma cells by using NGS-derived lymphoma transcriptome profiling (RNA-seq). Methods: Ctrl-siRNA and c-Myc-siRNA cells' mRNA profiles were generated by deep sequencing, in triplicate, using Illumina HiSeq 4000. The sequence reads that passed quality filters were analyzed at the transcript isoform level with following methods: Alignment by using HISAT2 v2.1, IGV was used to to view the mapping result by the Heatmap, histogram, scatter plot or other stytle, FPKM was then calculated to estimate the expression level of genes in each sample, DEGseq v1.18.0 was used for differential gene expression analysis between two samples with non biological replicates and Function Enrichment Analysis including GO enrichment analysis and KEGG . Conclusions: Our study represents the first detailed analysis of Ctrl-siRNA and c-Myc-siRNA cells' 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:Purpose: The goal of this study is to demonstrate the gene profiles of ASGR1 knock down mediated by siRNA compared with siControl in Huh7 cells. Methods: The Huh7 cells were transfected with small interference RNA (siRNA) targeting scramble (WT) or ASGR1 (AS2) for 8 hours, then cells were refreshed with Dulbecco's Modified Eagle Medium (DMEM) supplemented with 100 units ml-1 penicillin, 100 μg ml-1 streptomycin sulfate, and 10% fetal bovine serum (FBS). After 72 hours, the cells were harvested for RNA isolation and followed by high-throughput sequencing, in triplicate, using Illumina GAIIx. The sequence reads that passed quality filters were analyzed at the transcriptome with Hisat2(v2.0.1). qRT–PCR validation was performed using SYBR Green assays. Results: Using an optimized data analysis workflow, we mapped about 45 million sequence reads per sample to the human genome in the WT and AS2 cell lines Hisat2 (v2.0.1). RNA-seq data confirmed stable expression of the known housekeeping genes, and these genes were validated with qRT–PCR. RNA-seq data had a linear relationship with qRT–PCR for more than a goodness of fit (R2) of 0.9. Approximately 4.2% of the transcripts showed significantly differential expression between the WT and AS2 cell lines, with a log2 fold change ≥1.0 and p value <0.05. Altered expression of 5 genes was confirmed with qRT–PCR, demonstrating the high degree of sensitivity of the RNA-seq method. Conclusions: Our study represents the first detailed transcriptomes analysis of ASGR1 knock down in Huh7 cells, 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 the WT and AS2 cell. We conclude that RNA-seq based transcriptome characterization would expedite genetic network analyses and permit the dissection of complex biologic functions.
Project description:Purpose: The purpose of this study is to detect activated or silenced genes during bone marrow derived macrophages (BMDMs) transfected with control siRNA or Acly-E14 siRNA. Gene expression differences between two samples could be found using transcriptome profiling (RNA-seq) analysis. Methods: Mouse BMDMs were generated from bone marrow cells in RPMI-1640 medium with recombinant mouse M-CSF (20ng/ml). BMDMs were stained to confirm the surface expression of CD11b and F4/80. Cells with purity >97.5% were used for subsequent experiments. BMDMs were transfected with control siRNA or Acly-E14 siRNA. 48 hour later, they were stimulated with LPS (100ng/ml) for 4 hours, of which RNA profiles were generated by deep sequencing, using Illumina. Results: We mapped about 10 million sequence reads per sample to the mouse genome, identified hundreds of genes with significant mRNA variation between BMDMs transfected with the indicated siRNAs.
Project description:PurposeNext-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.MethodsRetinal 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: Burrows-Wheeler Aligner (BWA) followed by ANOVA (ANOVA) and TopHat followed by Cufflinks. qRT-PCR validation was performed using TaqMan and SYBR Green assays.ResultsUsing 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 qRT-PCR. RNA-seq data had a linear relationship with qRT-PCR for more than four orders of magnitude and a goodness of fit (R(2)) 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 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 retinal function. Data analysis with BWA and TopHat workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling.ConclusionsOur 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.