Dataset Information

Revealing the angiopathy of lacrimal gland lesion in type 2 diabetes

ABSTRACT: Purpose: The goals of this study are to compare type 2 diabetes lacrimal gland transcriptome profiling (RNA-seq) with the counterparts. Methods: Lacrimal gland mRNA profiles of 24 weeks T2DM mice (C57 mice with high fat diet + STZ intraperitoneal injection) and counterparts were enriched by NEBNext® Poly(A) mRNA Magnetic Isolation Module (New England Biolabs), and the produced RNA was used for construction library, via KAPA Stranded RNA-Seq Library Prep Kit (Illumina). The prepared RNA-seq libraries were qualified using Agilent 2100 Bioanalyzer and quantified by qPCR absolute quantification method. The sequencing was then performed using Illumina NovaSeq 6000. After quality control, the fragments were 5’, 3’-adaptor trimmed and filtered <=16 bp reads with cutadapt software. The trimmed reads were aligned to a reference genome with Hisat2 software. The transcript abundance for each sample was estimated with StringTie (v1.2.3), and the FPKM values for gene level were calculated with R package Ballgown (v2.6.0). The differentially expressed genes (DEGs) analysis was also performed with Ballgown. Fold change (cutoff 1.5), p-value (cutoff 0.05) and FPKM (≥0.5 mean in one group) were used for filtering differentially expressed genes and transcripts. Conclusions: Our study represents the first detailed analysis of T2DM lacrimal gland 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 showed that the vascular related genes were altered in the T2DM.

ORGANISM(S): Mus musculus

PROVIDER: GSE179231 | GEO | 2021/08/08

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description: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.

Dataset Information

Revealing the angiopathy of lacrimal gland lesion in type 2 diabetes

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure