Skip to content

Latest commit

 

History

History
29 lines (21 loc) · 2.8 KB

README.md

File metadata and controls

29 lines (21 loc) · 2.8 KB

Unveiling Adipose Populations Linked to Metabolic Health in Obesity

Isabel Reinisch, Adhideb Ghosh, Falko Noé, Wenfei Sun, Hua Dong, Peter Leary, Arne Dietrich, Anne Hoffmann, Matthias Blüher, Christian Wolfrum

This respository contains code and files related to our study: Unveiling adipose populations linked to metabolic health in obesity. Cell Metabolism.

Abstract

Precision medicine is still not considered as a standard of care in obesity treatment, despite a large heterogeneity in the metabolic phenotype of individuals with obesity. One of the strongest factors influencing the variability in metabolic disease risk is adipose tissue (AT) dysfunction; however, there is little understanding of the link between distinct cell populations, cell-type-specific transcriptional programs, and disease severity. Here, we generated a comprehensive cellular map of subcutaneous and visceral AT of individuals with metabolically healthy and unhealthy obesity. By combining single-nucleus RNA-sequencing data with bulk transcriptomics and clinical parameters, we identified that mesothelial cells, adipocytes, and adipocyte-progenitor cells exhibit the strongest correlation with metabolic disease. Furthermore, we uncovered cell-specific transcriptional programs, such as the transitioning of mesothelial cells to a mesenchymal phenotype, that are involved in uncoupling obesity from metabolic disease. Together, these findings provide valuable insights by revealing biological drivers of clinical endpoints.

Graphical Abstract

Interactive web apps to explore data

 App to explore correlations between bulk gene expression and clinical parameters

 App to explore snRNAseq data from subcutaneous AT

 App to explore snRNAseq data from visceral AT

Content

  • DEGs: Contains tissue and cell type specific DEGs for subcutaneous / visceral AT
  • MarkerGenes: Contains cell type and subpopulation specific top marker genes for subcutaneous / visceral AT
  • SNPdemux: Contains code to run SNP demultiplexing using cellSNP and vireo
  • Rscripts: Contains scripts used to analyze data
    • bulkRNA: DE analysis, Bisque deconvolution, Clinical correlations
    • snRNA: Pre-processing, Sample integration, Multi-cellular factor analysis, Cell type re-clustering

Contact

For questions regarding data analysis, please write to adhideb.ghosh[AT]hest.ethz.ch.

For questions regarding web applications, please write to falnoe[AT]ethz.ch.

Corresponding authors: Matthias Blüher (matthias.blueher[AT]medizin.uni-leipzig.de) and Christian Wolfrum (christian-wolfrum[AT]ethz.ch).