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Next Generation Sequencing Facilitates Quantitative Analysis of VSV-stimulated Wild Type and lncRNA-ISIR -/- spleen Transcriptomes

ABSTRACT: Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. Methods: spleen mRNA profiles of 8-weeks-old WT and lncRNA-ISIR, used to be called lnRNA-IRF3, knockout mice were generated by deep sequencing, using BGISEQ-500 system. The sequence reads that passed quality filters were analyzed at the transcriptional level with RSEM (RNA-seq by Expectation Maximization). Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, 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.

ORGANISM(S): Mus musculus

PROVIDER: GSE138700 | GEO | 2021/11/09

REPOSITORIES: GEO

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Project description:Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to access grafted human neural stem cells and host tissue transcriptomes Methods: Grafted mRNA profiles of 7 days transplanted hNSC 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 Burrows–Wheeler Aligner (BWA) followed by RNA-Seq by Expectation Maximization (RSEM). qRT–PCR validation was performed using TaqMan and SYBR Green assays Results: Using an optimized data analysis workflow, we mapped: 1) about 230 million sequence reads per sample to the human genome (build hg19) and identified 17,463 transcripts in the grafted hNSC; and 2) about 30 million sequence reads per sample to the rat genome (build rn5) and identified 13,4688 transcripts in the host tissues; using RSEM workflow. RNA-seq data confirmed stable expression of 25 known housekeeping genes, and 4 housekeeping genes were validated with qPCR. RNA-seq data had a linear relationship with qPCR for more than four orders of magnitude. Approximately 14% of the transcripts showed differential expression between the grafted hNSC in naive and stroke; and approximately 41% of the transcripts showed differential expression between the naive and stroke tissues, with p value <0.05. Altered expression of over 10 genes was confirmed with qPCR, 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 grafted hNSC and host function. Data analysis with TRAP and RSEM workflows revealed a significant overlap yet provided complementary insights in transcriptome profiling. Conclusions: Our study represents the first detailed analysis of grafted hNSC transcriptome, with biologic replicates, generated by TRAPseq technology. TRAPseq and RSEM could be applied to many xenograft cell transplantation paradigms. With this approach we can start to predict upstream regulators that signal between the host and graft, and to predict the downstream signaling pathways and biological processes these upstream regulators might affect.

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Next Generation Sequencing Facilitates Quantitative Analysis of VSV-stimulated Wild Type and lncRNA-ISIR -/- spleen Transcriptomes

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