DeadEasy Mito-Glia: Automatic Counting of Mitotic Cells and Glial Cells in Drosophila

被引:11
|
作者
Forero, Manuel Guillermo [1 ]
Learte, Anabel R. [1 ]
Cartwright, Stephanie [1 ]
Hidalgo, Alicia [1 ]
机构
[1] Univ Birmingham, Sch Biosci, Neurodev Grp, Birmingham, W Midlands, England
来源
PLOS ONE | 2010年 / 5卷 / 05期
基金
英国生物技术与生命科学研究理事会; 英国惠康基金;
关键词
D O I
10.1371/journal.pone.0010557
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Cell number changes during normal development, and in disease (e.g., neurodegeneration, cancer). Many genes affect cell number, thus functional genetic analysis frequently requires analysis of cell number alterations upon loss of function mutations or in gain of function experiments. Drosophila is a most powerful model organism to investigate the function of genes involved in development or disease in vivo. Image processing and pattern recognition techniques can be used to extract information from microscopy images to quantify automatically distinct cellular features, but these methods are still not very extended in this model organism. Thus cellular quantification is often carried out manually, which is laborious, tedious, error prone or humanly unfeasible. Here, we present DeadEasy Mito-Glia, an image processing method to count automatically the number of mitotic cells labelled with anti-phospho-histone H3 and of glial cells labelled with anti-Repo in Drosophila embryos. This programme belongs to the DeadEasy suite of which we have previously developed versions to count apoptotic cells and neuronal nuclei. Having separate programmes is paramount for accuracy. DeadEasy Mito-Glia is very easy to use, fast, objective and very accurate when counting dividing cells and glial cells labelled with a nuclear marker. Although this method has been validated for Drosophila embryos, we provide an interactive window for biologists to easily extend its application to other nuclear markers and other sample types. DeadEasy MitoGlia is freely available as an ImageJ plug-in, it increases the repertoire of tools for in vivo genetic analysis, and it will be of interest to a broad community of developmental, cancer and neuro-biologists.
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页数:7
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