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Estimating Cell Depth from Somatic Mutations

Overview of attention for article published in PLoS Computational Biology, May 2008
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Title
Estimating Cell Depth from Somatic Mutations
Published in
PLoS Computational Biology, May 2008
DOI 10.1371/journal.pcbi.1000058
Pubmed ID
Authors

Adam Wasserstrom, Dan Frumkin, Rivka Adar, Shalev Itzkovitz, Tomer Stern, Shai Kaplan, Gabi Shefer, Irena Shur, Lior Zangi, Yitzhak Reizel, Alon Harmelin, Yuval Dor, Nava Dekel, Yair Reisner, Dafna Benayahu, Eldad Tzahor, Eran Segal, Ehud Shapiro

Abstract

The depth of a cell of a multicellular organism is the number of cell divisions it underwent since the zygote, and knowing this basic cell property would help address fundamental problems in several areas of biology. At present, the depths of the vast majority of human and mouse cell types are unknown. Here, we show a method for estimating the depth of a cell by analyzing somatic mutations in its microsatellites, and provide to our knowledge for the first time reliable depth estimates for several cells types in mice. According to our estimates, the average depth of oocytes is 29, consistent with previous estimates. The average depth of B cells ranges from 34 to 79, linearly related to the mouse age, suggesting a rate of one cell division per day. In contrast, various types of adult stem cells underwent on average fewer cell divisions, supporting the notion that adult stem cells are relatively quiescent. Our method for depth estimation opens a window for revealing tissue turnover rates in animals, including humans, which has important implications for our knowledge of the body under physiological and pathological conditions.

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Geographical breakdown

Country Count As %
United States 5 6%
Israel 2 2%
France 1 1%
Norway 1 1%
Portugal 1 1%
Sweden 1 1%
Denmark 1 1%
United Kingdom 1 1%
Unknown 71 85%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 27%
Student > Ph. D. Student 22 26%
Professor > Associate Professor 9 11%
Student > Master 9 11%
Professor 5 6%
Other 13 15%
Unknown 3 4%
Readers by discipline Count As %
Agricultural and Biological Sciences 48 57%
Biochemistry, Genetics and Molecular Biology 9 11%
Medicine and Dentistry 6 7%
Computer Science 5 6%
Mathematics 2 2%
Other 9 11%
Unknown 5 6%