GSE195438
Next Generation Sequencing Facilitates Quantitative Analysis of Wild Type and Sirt3K223R Bone-Marrow derived Macrophages Transcriptomes
Purpose: Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compare Transcriptomes of Sirt3 WT and KR macrophages with high-throughput data analysis Methods: Macrophages' mRNA profiles of 8 weeks-old wild-type (WT) and Sirt3 K223R mice were generated by deep sequencing, in quadruplicate, using Total RNA from each sample was quantified using a NanoDrop ND-1000 instrument. 1µg total RNA  was used to prepare the sequencing library in the following steps: 1. Total RNA is enriched by oligo (dT) magnetic beads (rRNA removed); 2. RNA-seq library preparation using KAPA Stranded RNA-Seq Library Prep Kit (Illumina), which incorporates dUTP into the second cDNA strand and renders the RNA-seq library strand-specific. The completed libraries were qualified with Agilent 2100 Bioanalyzer and quantified by absolute quantification qPCR method. To sequence the libraries on the Illumina HiSeq 4000 instrument, the barcoded  libraries were mixed, denatured to single stranded DNA in NaOH, captured on Illumina flow cell, amplified in situ, and subsequently sequenced for 150 cycles for both ends on Illumina HiSeq instrument. Results: Image analysis and base calling were performed using Solexa pipeline v1.8 (Off-Line Base Caller software, v1.8). Sequence quality was examined using the FastQC  software. The trimmed reads (trimmed 5', 3'-adaptor bases using cutadapt) were aligned to reference genome using Hisat2 software. The transcript abundances for each sample was estimated with StringTie , and the FPKM value for gene and transcript level were calculated with R package Ballgown. The differentially expressed genes and transcripts were filtered using R package Ballgown. The novel genes and transcripts were predicted from assembled results by comparing to the reference annotation using StringTie and Ballgown, then use CPAT to assess the coding potential of those sequences. Then use rMATS  to detecting alternative splicing events and plots. Principle Component Analysis (PCA) and correlation analysis were based on gene expression level, Hierarchical Clustering, Gene  Ontology, Pathway analysis, Gene Ontology, Pathway analysis, scatter plots and volcano plots were performed with the differentially expressed genes in R, Python or shell environment for statistical computing and graphics. Conclusions: Our study represents the first detailed analysis of retinal transcriptomes, with 4 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 offer 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: Macrophages mRNA profiles of 8-week-old wild type (WT) and Sirt3K223R(KR) mice