Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0

Subsets of visceral adipose tissue nuclei with discrete levels of 5-hydroxymethylcytosine


ABSTRACT: We explored the hypothesis that adipose tissue contains epigenetically distinct subpopulations of adipocytes that are differentially potentiated to record cellular memories of their environment. Adipocytes are large, fragile, and technically difficult to efficiently isolate and fractionate. We developed fluorescence nuclear cytometry (FNC) and fluorescence activated nuclear sorting (FANS) of cellular nuclei from Sus scrofa visceral adipose tissue (SsVAT) using the levels of the pan-adipocyte protein, peroxisome proliferator-activated receptor gamma-2 (PPARg2) to distinguish PPARg2-Positive nuclei from PPARg2-Neg (negative) leukocyte, endothelial, and adipocyte progenitor cell nuclei. PPARg2-Postive VAT nuclei showed 2- to 50-fold higher levels of transcripts encoding most of the chromatin-remodeling factors assayed regulating the methylation of histones and DNA cytosine (e.g., DNMT1, DNMT3A, TET2, TET3, KMT2C, SETDB1, PAXP1, ARID1A, KMT2C, JMJD6, CARM1/PRMT4, PRMT5). PPARg2-Positive nuclei have a large decondensed chromatin structure. TAB-seq demonstrated 5´-hydroxymethylcytosine (5hmC) levels were remarkably dynamic in the gene body of PPARg2-Positive nuclei, dropping 3.8-fold from the highest quintile of expressed genes to the lowest. Sus scrofa VAT (SsVAT) nuclei were isolated from SsVAT. SsVAT nuclei were stained with PPARg2 and sorted with fluorescence activated nuclear sorting (FANS) into PPARg2-High, PPARg2-Med (Medium), PPARg2-Low, and PPARg2-Neg (Negative) four populations.TAB-seq data on 5-hydroxymehtylcytosine (Yu, M. et al. 2012. Cell 149, 1368-1380) was collected from genomic DNA isolated from PPARg2-High, PPARg2-Med+Low (pooled PPARg2-Med and PPARg2-Low), and PPARg2-Neg SsVAT nuclei.

ORGANISM(S): Sus scrofa

SUBMITTER: Robert Schmitz 

PROVIDER: E-GEOD-73684 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

Similar Datasets

2016-07-20 | GSE73684 | GEO
2015-12-06 | E-GEOD-75493 | biostudies-arrayexpress
2014-09-01 | E-GEOD-45204 | biostudies-arrayexpress
2012-05-04 | GSE37533 | GEO
2012-05-03 | E-GEOD-37533 | biostudies-arrayexpress
2015-12-06 | E-GEOD-75707 | biostudies-arrayexpress
2019-06-25 | GSE107729 | GEO
2017-06-08 | GSE97255 | GEO
2023-09-21 | GSE243448 | GEO
2021-02-01 | GSE151768 | GEO