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Expression of MicroRNAs in the Stem Cell Niche of the Adult Mouse Incisor

Overview of attention for article published in PLOS ONE, September 2011
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Title
Expression of MicroRNAs in the Stem Cell Niche of the Adult Mouse Incisor
Published in
PLOS ONE, September 2011
DOI 10.1371/journal.pone.0024536
Pubmed ID
Authors

Andrew H. Jheon, Chun-Ying Li, Timothy Wen, Frederic Michon, Ophir D. Klein

Abstract

The mouse incisor is a valuable but under-utilized model organ for studying the behavior of adult stem cells. This remarkable tooth grows continuously throughout the animal's lifetime and houses two distinct epithelial stem cell niches called the labial and lingual cervical loop (laCL and liCL, respectively). These stem cells produce progeny that undergo a series of well-defined differentiation events en route to becoming enamel-producing ameloblasts. During this differentiation process, the progeny move out of the stem cell niche and migrate toward the distal tip of the tooth. Although the molecular pathways involved in tooth development are well documented, little is known about the roles of miRNAs in this process. We used microarray technology to compare the expression of miRNAs in three regions of the adult mouse incisor: the laCL, liCL, and ameloblasts. We identified 26 and 35 differentially expressed miRNAs from laCL/liCL and laCL/ameloblast comparisons, respectively. Out of 10 miRNAs selected for validation by qPCR, all transcripts were confirmed to be differentially expressed. In situ hybridization and target prediction analyses further supported the reliability of our microarray results. These studies point to miRNAs that likely play a role in the renewal and differentiation of adult stem cells during stem cell-fueled incisor growth.

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Geographical breakdown

Country Count As %
Australia 2 4%
Unknown 43 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 20%
Student > Ph. D. Student 7 16%
Professor 4 9%
Professor > Associate Professor 4 9%
Student > Master 4 9%
Other 9 20%
Unknown 8 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 42%
Medicine and Dentistry 11 24%
Biochemistry, Genetics and Molecular Biology 4 9%
Computer Science 1 2%
Materials Science 1 2%
Other 0 0%
Unknown 9 20%