Gene expression analysis from splenocytes treated with mPVAT-conditioned media
Data files
Jul 27, 2024 version files 59.77 MB
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fpkm_sample.xls.xlsx
3.04 MB
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gene.fa
54.85 MB
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HFvsCtrl_DEG_all_PVATCM.xls.xlsx
152.99 KB
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MapStat_summary.xls.xlsx
10.85 KB
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QC_Summary.xlsx
10.81 KB
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readcount.xls.xlsx
1.70 MB
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README.md
5.31 KB
Abstract
The overall purpose of this study was to understand the impact of a high-fat diet on the perivascular microenvironment in Dahl S rats, a spontaneously hypertensive rat strain. Specifically, the study was designed to understand whether a high-fat diet promotes inflammation in the perivascular adipose tissue microenvironment. To address this, we collected mesenteric PVAT from Dahl S rats on either a control or a high-fat diet and used the PVAT to make conditioned media. We then cultured rat splenocytes in either mPVAT-conditioned media or in standard tissue culture media and activated them with a T cell-specific activator (anti-CD3/anti-CD28). We then isolated RNA from the splenocytes which was used for gene expression analysis by RNA-sequencing.
Primary splenocytes were activated with a T cell-specific activator in the presence and absence of perivascular adipose tissue (PVAT)-conditioned media. The PVAT was collected from Dahl S rats on either a control or high-fat diet for 10 weeks. RNA from the cells was extracted using the RNeasy Mini Kit (Qiagen, Germantown, MD). Transcriptome analysis of RNA sequencing (30M raw reads/sample) was performed via Illumina platforms by Novogene (Sacramento, CA). After quality control, HISAT2 was used to map the filtered sequenced reads to the reference genome. Gene expression level is estimated by the FPKM (Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced) method. The coexpression Venn diagram was used to visualize the number of genes that are uniquely expressed within each group/sample. Differentially expressed genes (DEGs) analysis of HF condition and CTL condition was performed using the DESeq2 R package. Volcano plots were used to infer the overall distribution of DEGs. Hierarchical clustering analysis was carried out of log2(FPKM+1) of union DEGs, within all comparison groups.