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Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment

Overview of attention for article published in Genome Biology, April 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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16 X users
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

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66 Dimensions

Readers on

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129 Mendeley
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1 CiteULike
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Title
Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment
Published in
Genome Biology, April 2015
DOI 10.1186/s13059-015-0636-y
Pubmed ID
Authors

Ayako Suzuki, Koutatsu Matsushima, Hideki Makinoshima, Sumio Sugano, Takashi Kohno, Katsuya Tsuchihara, Yutaka Suzuki

Abstract

To understand the heterogeneous behaviors of individual cancer cells, it is essential to investigate gene expression levels as well as their divergence between different individual cells. Recent advances in next-generation sequencing-related technologies have enabled us to conduct a single-cell RNA-Seq analysis of a series of lung adenocarcinoma cell lines. We analyze a total of 336 single-cell RNA-Seq libraries from seven cell lines. The results are highly robust regarding both average expression levels and the relative gene expression differences between individual cells. Gene expression diversity is characteristic depending on genes and pathways. Analyses of individual cells treated with the multi-tyrosine kinase inhibitor vandetanib reveal that, while the ribosomal genes and many other so-called house-keeping genes reduce their relative expression diversity during the drug treatment, the genes that are directly targeted by vandetanib, the EGFR and RET genes, remain constant. Rigid transcriptional control of these genes may not allow plastic changes of their expression with the drug treatment or during the cellular acquisition of drug resistance. Additionally, we find that the gene expression patterns of cancer-related genes are sometimes more diverse than expected based on the founder cells. Furthermore, we find that this diversity is occasionally latent in a normal state and initially becomes apparent after the drug treatment. Characteristic patterns in gene expression divergence, which would not be revealed by transcriptome analysis of bulk cells, may also play important roles when cells acquire drug resistance, perhaps by providing a cellular reservoir for gene expression programs.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users 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 129 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 <1%
Korea, Republic of 1 <1%
United States 1 <1%
Unknown 126 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 30 23%
Researcher 28 22%
Student > Master 12 9%
Student > Doctoral Student 11 9%
Student > Bachelor 9 7%
Other 17 13%
Unknown 22 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 37 29%
Biochemistry, Genetics and Molecular Biology 36 28%
Medicine and Dentistry 16 12%
Computer Science 9 7%
Mathematics 2 2%
Other 5 4%
Unknown 24 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 30 April 2017.
All research outputs
#3,561,374
of 25,374,647 outputs
Outputs from Genome Biology
#2,465
of 4,467 outputs
Outputs of similar age
#44,521
of 279,377 outputs
Outputs of similar age from Genome Biology
#43
of 62 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one is in the 44th percentile – i.e., 44% of its peers scored the same or lower than it.
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 279,377 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 62 others from the same source and published within six weeks on either side of this one. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.