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Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer

Overview of attention for article published in Giga Science, August 2012
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (93rd percentile)

Mentioned by

blogs
1 blog
twitter
13 tweeters
peer_reviews
1 peer review site
googleplus
1 Google+ user

Citations

dimensions_citation
67 Dimensions

Readers on

mendeley
107 Mendeley
citeulike
2 CiteULike
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Title
Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer
Published in
Giga Science, August 2012
DOI 10.1186/2047-217x-1-12
Pubmed ID
Authors

Li, Yingrui, Yingrui Li, Xun Xu, Luting Song, Yong Hou, Zesong Li, Shirley Tsang, Fuqiang Li, Kate MG Im, Kui Wu, Hanjie Wu, Xiaofei Ye, Guibo Li, Linlin Wang, Bo Zhang, Jie Liang, Wei Xie, Renhua Wu, Hui Jiang, Xiao Liu, Chang Yu, Hancheng Zheng, Min Jian, Liping Nie, Lei Wan, Min Shi, Xiaojuan Sun, Aifa Tang, Guangwu Guo, Yaoting Gui, Zhiming Cai, Jingxiang Li, Wen Wang, Zuhong Lu, Xiuqing Zhang, Lars Bolund, Karsten Kristiansen, Jian Wang, Huanming Yang, Michael Dean, Jun Wang, Xu, Xun, Song, Luting, Hou, Yong, Li, Zesong, Tsang, Shirley, Li, Fuqiang, Im, Kate McGee, Wu, Kui, Wu, Hanjie, Ye, Xiaofei, Li, Guibo, Wang, Linlin, Zhang, Bo, Liang, Jie, Xie, Wei, Wu, Renhua, Jiang, Hui, Liu, Xiao, Yu, Chang, Zheng, Hancheng, Jian, Min, Nie, Liping, Wan, Lei, Shi, Min, Sun, Xiaojuan, Tang, Aifa, Guo, Guangwu, Gui, Yaoting, Cai, Zhiming, Li, Jingxiang, Wang, Wen, Lu, Zuhong, Zhang, Xiuqing, Bolund, Lars, Kristiansen, Karsten, Wang, Jian, Yang, Huanming, Dean, Michael, Wang, Jun

Abstract

Cancers arise through an evolutionary process in which cell populations are subjected to selection; however, to date, the process of bladder cancer, which is one of the most common cancers in the world, remains unknown at a single-cell level. We carried out single-cell exome sequencing of 66 individual tumor cells from a muscle-invasive bladder transitional cell carcinoma (TCC). Analyses of the somatic mutant allele frequency spectrum and clonal structure revealed that the tumor cells were derived from a single ancestral cell, but that subsequent evolution occurred, leading to two distinct tumor cell subpopulations. By analyzing recurrently mutant genes in an additional cohort of 99 TCC tumors, we identified genes that might play roles in the maintenance of the ancestral clone and in the muscle-invasive capability of subclones of this bladder cancer, respectively. This work provides a new approach of investigating the genetic details of bladder tumoral changes at the single-cell level and a new method for assessing bladder cancer evolution at a cell-population level.

Twitter Demographics

The data shown below were collected from the profiles of 13 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 107 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 3%
Netherlands 2 2%
Hong Kong 2 2%
Denmark 1 <1%
China 1 <1%
Canada 1 <1%
Unknown 97 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 30%
Researcher 24 22%
Student > Master 11 10%
Student > Bachelor 8 7%
Student > Postgraduate 7 7%
Other 25 23%
Readers by discipline Count As %
Agricultural and Biological Sciences 45 42%
Biochemistry, Genetics and Molecular Biology 24 22%
Medicine and Dentistry 16 15%
Unspecified 7 7%
Computer Science 6 6%
Other 9 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 16. 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 14 July 2015.
All research outputs
#850,149
of 12,438,597 outputs
Outputs from Giga Science
#217
of 507 outputs
Outputs of similar age
#8,309
of 122,401 outputs
Outputs of similar age from Giga Science
#1
of 1 outputs
Altmetric has tracked 12,438,597 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 507 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.6. This one has gotten more attention than average, scoring higher than 57% 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 122,401 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 93% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them