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Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor

Overview of attention for article published in Cell, March 2012
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

news
1 news outlet
blogs
2 blogs
twitter
16 tweeters
patent
4 patents
facebook
1 Facebook page
wikipedia
1 Wikipedia page

Citations

dimensions_citation
403 Dimensions

Readers on

mendeley
686 Mendeley
citeulike
10 CiteULike
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Title
Single-Cell Exome Sequencing Reveals Single-Nucleotide Mutation Characteristics of a Kidney Tumor
Published in
Cell, March 2012
DOI 10.1016/j.cell.2012.02.025
Pubmed ID
Authors

Xun Xu, Yong Hou, Xuyang Yin, Li Bao, Aifa Tang, Luting Song, Fuqiang Li, Shirley Tsang, Kui Wu, Hanjie Wu, Weiming He, Liang Zeng, Manjie Xing, Renhua Wu, Hui Jiang, Xiao Liu, Dandan Cao, Guangwu Guo, Xueda Hu, Yaoting Gui, Zesong Li, Wenyue Xie, Xiaojuan Sun, Min Shi, Zhiming Cai, Bin Wang, Meiming Zhong, Jingxiang Li, Zuhong Lu, Ning Gu, Xiuqing Zhang, Laurie Goodman, Lars Bolund, Jian Wang, Huanming Yang, Karsten Kristiansen, Michael Dean, Yingrui Li, Jun Wang

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer and has very few mutations that are shared between different patients. To better understand the intratumoral genetics underlying mutations of ccRCC, we carried out single-cell exome sequencing on a ccRCC tumor and its adjacent kidney tissue. Our data indicate that this tumor was unlikely to have resulted from mutations in VHL and PBRM1. Quantitative population genetic analysis indicates that the tumor did not contain any significant clonal subpopulations and also showed that mutations that had different allele frequencies within the population also had different mutation spectrums. Analyses of these data allowed us to delineate a detailed intratumoral genetic landscape at a single-cell level. Our pilot study demonstrates that ccRCC may be more genetically complex than previously thought and provides information that can lead to new ways to investigate individual tumors, with the aim of developing more effective cellular targeted therapies.

Twitter Demographics

The data shown below were collected from the profiles of 16 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 686 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 26 4%
Netherlands 6 <1%
United Kingdom 6 <1%
Switzerland 4 <1%
China 4 <1%
Germany 3 <1%
Korea, Republic of 3 <1%
Denmark 3 <1%
Brazil 2 <1%
Other 13 2%
Unknown 616 90%

Demographic breakdown

Readers by professional status Count As %
Researcher 210 31%
Student > Ph. D. Student 180 26%
Student > Master 63 9%
Student > Bachelor 50 7%
Other 37 5%
Other 146 21%
Readers by discipline Count As %
Agricultural and Biological Sciences 374 55%
Biochemistry, Genetics and Molecular Biology 114 17%
Medicine and Dentistry 84 12%
Unspecified 41 6%
Computer Science 21 3%
Other 52 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 42. 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 05 March 2016.
All research outputs
#334,060
of 12,247,570 outputs
Outputs from Cell
#1,703
of 14,427 outputs
Outputs of similar age
#2,295
of 113,621 outputs
Outputs of similar age from Cell
#16
of 142 outputs
Altmetric has tracked 12,247,570 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 14,427 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. This one has done well, scoring higher than 88% 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 113,621 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 142 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.