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Cellular organization and molecular differentiation model of breast cancer-associated fibroblasts

Overview of attention for article published in Molecular Cancer, April 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (61st percentile)
  • Good Attention Score compared to outputs of the same age and source (76th percentile)

Mentioned by

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1 X user
wikipedia
2 Wikipedia pages

Citations

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44 Dimensions

Readers on

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78 Mendeley
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Title
Cellular organization and molecular differentiation model of breast cancer-associated fibroblasts
Published in
Molecular Cancer, April 2017
DOI 10.1186/s12943-017-0642-7
Pubmed ID
Authors

Susann Busch, Daniel Andersson, Eva Bom, Claire Walsh, Anders Ståhlberg, Göran Landberg

Abstract

The role of cancer-associated fibroblasts (CAFs) during tumour progression is obscured by the inherently complex, heterotypic nature of fibroblast cells and behaviours in various subtypes of malignancies. Therefore, we sought to identify distinct fibroblast subpopulations at the single-cell level. Using single-cell quantitative PCR as a powerful tool to study heterogeneity and rare cell events, in a high-throughput manner a panel of gene targets are run simultaneously on transcripts isolated from single cells obtained by fluorescence-activated cell sort. Assessment of cells with stem-like characteristics was attained by anchorage-independent, anoikis-resistant culture. Single-cell analysis of fibroblasts and their tumour-activated counterparts demonstrated molecularly distinct cell types defined by differential expression of characteristic mesenchymal and fibroblast activation markers. Identified subpopulations presented overlapping gene expression patterns indicating transitional molecular states during fibroblast differentiation. Using single-cell resolution data we generated a molecular differentiation model which enabled the classification of patient-derived fibroblasts, validating our modelling approach. Remarkably, a subset of fibroblasts displayed expression of pluripotency markers, which was enriched for in non-adherent conditions. Yet the ability to form single-cell derived spheres was generally reduced in CAFs and upon fibroblast activation through TGFβ1 ligand and cancer cell-secreted factors. Hence, our data imply the existence of putative stem/progenitor cells as a physiological feature of undifferentiated fibroblasts. Within this comprehensive study we have identified distinct and intersecting molecular profiles defining fibroblast activation states and propose that underlying cellular heterogeneity, fibroblasts are hierarchically organized. Understanding the molecular make-up of cellular organization and differentiation routes will facilitate the discovery of more specific markers for stromal subtypes and targets for anti-stromal therapies.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 78 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 26%
Researcher 15 19%
Student > Bachelor 8 10%
Student > Doctoral Student 7 9%
Student > Master 6 8%
Other 6 8%
Unknown 16 21%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 20 26%
Agricultural and Biological Sciences 14 18%
Medicine and Dentistry 13 17%
Engineering 4 5%
Immunology and Microbiology 4 5%
Other 5 6%
Unknown 18 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 November 2017.
All research outputs
#7,277,723
of 22,962,258 outputs
Outputs from Molecular Cancer
#525
of 1,728 outputs
Outputs of similar age
#116,395
of 308,921 outputs
Outputs of similar age from Molecular Cancer
#7
of 34 outputs
Altmetric has tracked 22,962,258 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 1,728 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.7. This one has gotten more attention than average, scoring higher than 68% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 308,921 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 34 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 76% of its contemporaries.