Chapter title |
In vivo RNAi in the Drosophila Follicular Epithelium: Analysis of Stem Cell Maintenance, Proliferation, and Differentiation
|
---|---|
Chapter number | 14 |
Book title |
RNAi and Small Regulatory RNAs in Stem Cells
|
Published in |
Methods in molecular biology, July 2017
|
DOI | 10.1007/978-1-4939-7108-4_14 |
Pubmed ID | |
Book ISBNs |
978-1-4939-7106-0, 978-1-4939-7108-4
|
Authors |
Veit Riechmann, Riechmann, Veit |
Editors |
Baohong Zhang |
Abstract |
In vivo RNAi in Drosophila facilitates simple and rapid analysis of gene functions in a cell- or tissue-specific manner. The versatility of the UAS-GAL4 system allows to control exactly where and when during development the function of a gene is depleted. The epithelium of the ovary is a particularly good model to study in a living animal how stem cells are maintained and how their descendants proliferate and differentiate. Here I provide basic information about the publicly available reagents for in vivo RNAi, and I describe how the oogenesis system can be applied to analyze stem cells and epithelial development at a histological level. Moreover, I give helpful hints to optimize the use of the UAS-GAL4 system for RNAi induction in the follicular epithelium. Finally, I provide detailed step-by-step protocols for ovary dissection, antibody stainings, and ovary mounting for microscopic analysis. |
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Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 10 July 2017.
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#5,799,012
of 22,986,950 outputs
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#1,636
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#91,337
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Outputs of similar age from Methods in molecular biology
#17
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Altmetric has tracked 22,986,950 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 13,149 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 86% of its peers.
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