An optimised tissue disaggregation and data processing pipeline for characterising fibroblast phenotypes using single-cell RNA sequencing

Sci Rep. 2019 Jul 3;9(1):9580. doi: 10.1038/s41598-019-45842-4.


Single-cell RNA sequencing (scRNA-Seq) provides a valuable platform for characterising multicellular ecosystems. Fibroblasts are a heterogeneous cell type involved in many physiological and pathological processes, but remain poorly-characterised. Analysis of fibroblasts is challenging: these cells are difficult to isolate from tissues, and are therefore commonly under-represented in scRNA-seq datasets. Here, we describe an optimised approach for fibroblast isolation from human lung tissues. We demonstrate the potential for this procedure in characterising stromal cell phenotypes using scRNA-Seq, analyse the effect of tissue disaggregation on gene expression, and optimise data processing to improve clustering quality. We also assess the impact of in vitro culture conditions on stromal cell gene expression and proliferation, showing that altering these conditions can skew phenotypes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Cells, Cultured
  • Cluster Analysis
  • Collagenases / metabolism
  • Epithelial Cell Adhesion Molecule / metabolism
  • Fibroblasts / cytology
  • Fibroblasts / metabolism*
  • Humans
  • Leukocyte Common Antigens / metabolism
  • Lung / cytology
  • Phenotype
  • Sequence Analysis, RNA / methods*
  • Single-Cell Analysis
  • Stromal Cells / cytology
  • Stromal Cells / metabolism
  • Transcriptome


  • Epithelial Cell Adhesion Molecule
  • Leukocyte Common Antigens
  • Collagenases