Analysis of transcriptional modules during human fibroblast ageing

Sci Rep. 2020 Nov 5;10(1):19086. doi: 10.1038/s41598-020-76117-y.


For systematic identification of transcription signatures of human cell aging, we carried out Weighted Gene Co-expression Network Analysis (WGCNA) with the RNA-sequencing data generated with young to old human dermal fibroblasts. By relating the modules to the donor's traits, we uncovered the natural aging- and premature aging disease-associated modules. The STRING functional association networks built with the core module memberships provided a systematic overview of genome-wide transcriptional changes upon aging. We validated the selected candidates via quantitative reverse transcription PCR (RT-qPCR) assay with young and aged human fibroblasts, and uncovered several genes involved in ECM, cell, and nuclear mechanics as a potential aging biomarker. Collectively, our study not only provides a snapshot of functional changes during human fibroblast aging but also presents potential aging markers that are relevant to cell mechanics.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Aging / genetics
  • Aging / metabolism
  • Aging / pathology
  • Biomarkers / metabolism
  • Cell Line
  • Cell Nucleus / genetics
  • Cell Nucleus / metabolism
  • Cellular Senescence / genetics*
  • Child
  • Child, Preschool
  • Extracellular Matrix / genetics
  • Extracellular Matrix / metabolism
  • Female
  • Fibroblasts / cytology*
  • Fibroblasts / metabolism*
  • Gene Expression Profiling
  • Gene Regulatory Networks*
  • Genome, Human
  • Humans
  • Infant
  • Male
  • Middle Aged
  • Progeria / genetics
  • Progeria / metabolism
  • Progeria / pathology
  • RNA-Seq
  • Skin / cytology
  • Skin / metabolism
  • Young Adult


  • Biomarkers