Evidence map›Paper›PMID 31270426›Full record

ArticleScientific reports2019

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

Sara Waise, Rachel Parker, Matthew J J Rose-Zerilli, David M Layfield, Oliver Wood, Jonathan West, Christian H Ottensmeier, Gareth J Thomas, Christopher J Hanley

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.

0numbers the graph read from it
0cells of the map it votes in
49citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

49 citing papers in PubMed, 68 citations in OpenAlex.

  1. Article
  2. Chemokine Landscapes of the Tumor Microenvironment.bioRxiv : the preprint server for biology · 2026
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  5. Modeling CAF-tumor interactions to overcome therapy resistance.Journal of experimental & clinical cancer research : CR · 2026
    Review
  6. Review
  7. Loss of lung mesenchymal identities in culture and a screening approach to identify restorative cues.American journal of physiology. Lung cellular and molecular physiology · 2025
    Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
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  17. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 2 institutions in 1 country.

Sara WaiseCancer Sciences Unit, University of Southampton, Southampton, UK.ORCID http://orcid.org/0000-0003-0929-3121
Rachel ParkerCancer Sciences Unit, University of Southampton, Southampton, UK.
Matthew J J Rose-ZerilliCancer Sciences Unit, University of Southampton, Southampton, UK.
David M LayfieldCancer Sciences Unit, University of Southampton, Southampton, UK.
Oliver WoodCancer Sciences Unit, University of Southampton, Southampton, UK.
Jonathan WestCancer Sciences Unit, University of Southampton, Southampton, UK.ORCID http://orcid.org/0000-0002-5709-6790
Christian H OttensmeierCancer Sciences Unit, University of Southampton, Southampton, UK.ORCID http://orcid.org/0000-0003-3619-1657
Gareth J ThomasCancer Sciences Unit, University of Southampton, Southampton, UK.
Christopher J HanleyCancer Sciences Unit, University of Southampton, Southampton, UK. C.J.Hanley@soton.ac.uk.
University of Southampton · GBCancer Research UK · GB

Funding

Medical Research Council MC_PC_15078Medical Research Council MR/R001286/1
6 · The paper itself

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.

Indexed as

Cells, CulturedCluster AnalysisCollagenasesEpithelial Cell Adhesion MoleculeFibroblastsHumansLeukocyte Common AntigensLungPhenotypeSequence Analysis, RNASingle-Cell AnalysisStromal CellsTranscriptomeCollagenasesEpithelial Cell Adhesion MoleculeLeukocyte Common Antigens

Identifiers

PMID31270426
PMCPMC6610623
OpenAlexW2955881766

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.