DeadEasy Mito-Glia: Automatic Counting of Mitotic Cells and Glial Cells in Drosophila
| Autor: | Stephanie Cartwright, Anabel R. Learte, Alicia Hidalgo, Manuel G. Forero |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2010 |
| Předmět: |
Embryo
Nonmammalian ved/biology.organism_classification_rank.species Cell Biology/Cell Growth and Division Cell Count 02 engineering and technology Automation 0202 electrical engineering electronic engineering information engineering Image Processing Computer-Assisted Genetics and Genomics/Genetics of Disease Developmental Biology/Organogenesis Genetics and Genomics/Medical Genetics 0303 health sciences Multidisciplinary biology Developmental Biology/Morphogenesis and Cell Biology Neuroscience/Neuronal and Glial Cell Biology Neurodegeneration Neuroscience/Neurodevelopment 3. Good health Cell biology Drosophila melanogaster Medicine 020201 artificial intelligence & image processing Neuroglia Algorithms Research Article Science Mitosis Computer Science/Applications Mathematics/Algorithms 03 medical and health sciences In vivo medicine Animals Model organism Gene Genetics and Genomics/Cancer Genetics Loss function 030304 developmental biology ved/biology Computational Biology Reproducibility of Results Genetics and Genomics biology.organism_classification medicine.disease Cell Biology/Neuronal and Glial Cell Biology Function (biology) |
| Zdroj: | PLoS ONE PLoS ONE, Vol 5, Iss 5, p e10557 (2010) |
| ISSN: | 1932-6203 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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