Eosin whole-brain mount staining to analyze neurodegeneration in a fly model of Alzheimer’s disease
In the fly brain, neurodegeneration is detected by the presence of vacuoles using conventional hematoxylin and eosin (H&E) or phalloidin staining, which are lengthy and expensive processes. Here, we present a faster and cost-effective 2-day protocol to visualize vacuoles in a fly model of Alzhei...
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Veröffentlicht in: | STAR protocols 2022-06, Vol.3 (2), p.101377, Article 101377 |
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Sprache: | eng |
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Zusammenfassung: | In the fly brain, neurodegeneration is detected by the presence of vacuoles using conventional hematoxylin and eosin (H&E) or phalloidin staining, which are lengthy and expensive processes. Here, we present a faster and cost-effective 2-day protocol to visualize vacuoles in a fly model of Alzheimer disease. We describe eosin staining in the whole-brain mount, followed by confocal microscopy and image analysis with an open source Fiji plugin. This protocol can be applied to visualize different modules in the fly brain.
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•Efficient and cost-effective protocol to analyze neurodegeneration in Drosophila•Details of eosin whole-brain mount staining to assess vacuoles in the fly brain•Eosin whole-brain mount can also be used to assess the morphology of brain modules•Development of an open source Fiji plugin to assist image analysis
Publisher's note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
In the fly brain, neurodegeneration is detected by the presence of vacuoles using conventional hematoxylin and eosin (H&E) or phalloidin staining, which are lengthy and expensive processes. Here, we present a faster and cost-effective 2-day protocol to visualize vacuoles in a fly model of Alzheimer disease. We describe eosin staining in the whole-brain mount, followed by confocal microscopy and image analysis with an open source Fiji plugin. This protocol can be applied to visualize different modules in the fly brain. |
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ISSN: | 2666-1667 2666-1667 |
DOI: | 10.1016/j.xpro.2022.101377 |