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Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture

Overview of attention for article published in PLoS Computational Biology, February 2010
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Citations

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Title
Molecular Predictors of 3D Morphogenesis by Breast Cancer Cell Lines in 3D Culture
Published in
PLoS Computational Biology, February 2010
DOI 10.1371/journal.pcbi.1000684
Pubmed ID
Authors

Ju Han, Hang Chang, Orsi Giricz, Genee Y. Lee, Frederick L. Baehner, Joe W. Gray, Mina J. Bissell, Paraic A. Kenny, Bahram Parvin

Abstract

Correlative analysis of molecular markers with phenotypic signatures is the simplest model for hypothesis generation. In this paper, a panel of 24 breast cell lines was grown in 3D culture, their morphology was imaged through phase contrast microscopy, and computational methods were developed to segment and represent each colony at multiple dimensions. Subsequently, subpopulations from these morphological responses were identified through consensus clustering to reveal three clusters of round, grape-like, and stellate phenotypes. In some cases, cell lines with particular pathobiological phenotypes clustered together (e.g., ERBB2 amplified cell lines sharing the same morphometric properties as the grape-like phenotype). Next, associations with molecular features were realized through (i) differential analysis within each morphological cluster, and (ii) regression analysis across the entire panel of cell lines. In both cases, the dominant genes that are predictive of the morphological signatures were identified. Specifically, PPARgamma has been associated with the invasive stellate morphological phenotype, which corresponds to triple-negative pathobiology. PPARgamma has been validated through two supporting biological assays.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 3%
Portugal 1 <1%
South Africa 1 <1%
France 1 <1%
United Kingdom 1 <1%
India 1 <1%
Unknown 130 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 24%
Researcher 32 23%
Student > Master 13 9%
Student > Bachelor 11 8%
Student > Doctoral Student 9 6%
Other 27 19%
Unknown 14 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 54 39%
Biochemistry, Genetics and Molecular Biology 32 23%
Medicine and Dentistry 9 6%
Engineering 9 6%
Chemistry 4 3%
Other 13 9%
Unknown 18 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 13 February 2012.
All research outputs
#20,656,820
of 25,374,647 outputs
Outputs from PLoS Computational Biology
#8,208
of 8,960 outputs
Outputs of similar age
#93,250
of 102,464 outputs
Outputs of similar age from PLoS Computational Biology
#48
of 52 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 8,960 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.4. This one is in the 4th percentile – i.e., 4% of its peers scored the same or lower than it.
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We're also able to compare this research output to 52 others from the same source and published within six weeks on either side of this one. This one is in the 3rd percentile – i.e., 3% of its contemporaries scored the same or lower than it.