Evaluation of Human Adipose Tissue Stromal Heterogeneity in Metabolic Disease Using Single Cell RNA-Seq

Abstract

We have developed a robust protocol to generate single cell transcriptional profiles from subcutaneous adipose tissue samples of human subjects using Drop-seq, a newly developed, cost-efficient method of highly parallel genome-wide expression profiling using nanoliter droplets. We have collected subcutaneous adipose tissue samples from multiple individuals and generated single-cell profiles for ~6000 cells. Preliminary analysis demonstrates expression profiles can be used to cluster individual cells into distinct cell types in an unbiased fashioned. We recover expected cell types known to be contained within adipose tissue and suggest new cell types and subtypes that had not previously been described. We can confirm many previously defined transcriptional markers for known cell types and discover many specific novel ones as well. These data provide a comprehensive transcriptional atlas of subcutaneous adipose tissue cell types that will provide molecular handles to understanding and manipulating each cell types function. These results are hypothesis-generating and provide the foundation for future studies that will 1) validate the role for newly identified mediators of obesity and insulin resistance in animal models and 2) examine novel targets against which we can design therapies to combat obesity and its related complications.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2016
Accession Number
AD1034068

Entities

People

  • Linus T. Tsai

Organizations

  • Beth Israel Deaconess Medical Center

Tags

DTIC Thesaurus Topics

  • Adipose Tissue
  • Biomedical Research
  • Blood
  • Brain
  • Cells
  • Cellular Networks
  • Diabetes
  • Diabetes Mellitus
  • Diseases And Disorders
  • Electronic Mail
  • Glucose Metabolism Disorders
  • Heterogeneity
  • Medical Personnel
  • Metabolic Diseases
  • Resistance
  • Test And Evaluation
  • Tissues

Fields of Study

  • Biology

Readers

  • Cardiovascular Physiology
  • Molecular and genetic basis of cancer.
  • Oncology and Biomarker-Based Cancer Detection.