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Cited by 70Single-cell and spatial transcriptomicsMolecular Biology Techniques and ApplicationsGene expression and cancer classification

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

Open access
Waise, Sara, Parker, Rachel, Rose-Zerilli, Matthew J. J., Layfield, David M., Wood, Oliver, West, Jonathan, Ottensmeier, Christian H., Thomas, Gareth J. +1 more·2019·Scientific Reports
DOI 10.1038/s41598-019-45842-4PMC PMC6610623
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Abstract

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.

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  • Detaching fibroblasts from tissue typically requires more aggressive methods than for leukocytes, and there is risk that fibroblasts are substantially under-represented in studies optimized for leukocyte biology.

    “Detaching fibroblasts from tissue typically requires more aggressive methods than for leukocytes, and there is risk that fibroblasts are substantially under-represented in studies optimized for leukocyte biology152.”

    From A framework for advancing our understanding of cancer-associated fibroblasts (2020) · cites this paper as [152]

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Cited by

Sorted by citations

Authors
Waise, S., Parker, R., Rose-Zerilli, M. J. J., Layfield, D. M., Wood, O., West, J., Ottensmeier, C. H., Thomas, G. J., & Hanley, C. J.
Affiliations
  • University of Southampton (GB)
  • Cancer Research UK (GB)
Corresponding author
Hanley, Christopher J.
ORCID
Venue
Scientific Reports
Publisher
Springer Science and Business Media LLC
Published
2019-07-03
Volume
9
Issue
1
Pages
9580-9580
ISSN
2045-2322 (ISSN-L)
DOI
10.1038/s41598-019-45842-4
PMC
PMC6610623
OpenAlex
W2955881766
Cited by
70
References
6
Citations by year
2026: 6 · 2025: 12 · 2024: 11 · 2023: 10 · 2022: 13 · 2021: 10 · 2020: 7 · 2019: 1
FWCI
2.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • RCUK | Medical Research Council (MR/R001286/1, MC_PC_15078)
  • Pathological Society of Great Britain and Ireland (3902032)
  • Cancer Research UK
  • Leuka (2016/JGF/0003)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Molecular Biology Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
fibroblast isolation, tissue disaggregation, single-cell RNA sequencing, lung fibroblasts, stromal cells, fibroblast heterogeneity, data pipelines, clustering quality, in vitro culture conditions, fibroblast proliferation, gene expression
MeSH terms
Epithelial Cell Adhesion Molecule, Cells, Cultured, Fibroblasts, Humans, Lung, Phenotype, Cluster Analysis, Stromal Cells, Collagenases, Sequence Analysis, RNA, Leukocyte Common Antigens, Single-Cell Analysis, Transcriptome

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