Processing Embryo, Eggshell, and Fungal Culture for Scanning Electron Microscopy.

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ID: 63882
2019
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Abstract
Although scanning electron microscopy (SEM) is being widely used for the ultra-structural analysis of various biological and non-biological samples, methods involved in processing different biological samples involve unique practices. All conventional practices described in the literature for processing samples still find useful applications, but subtle changes in the sample preparation can alter image quality, as well as, introduce artifacts. Hence, using a unique sample preparation technique specific to the type of tissue analyzed is required to obtain a good quality image with ultrastructural resolution. The focus of this study is to provide the optimal sample preparation protocols for imaging embryos, rigid eggshells, and fungal cultures using SEM. The following optimizations were recommended to yield good results for the three different delicate biological samples studied. Use of milder fixatives like 4% paraformaldehyde or 3% glutaraldehyde followed by dehydration with ethanol series is mandatory. Fungal mycelium on agar blocks obtained by slide cultures yields a better ultrastructural integrity compared to cultures taken directly from agar plates. Chemical drying of embryos with HMDS provides drying without introducing surface tension artifacts compared to critical point drying. HMDS prevents cracking caused by shrinkage as samples are less brittle during drying. However, for fungal culture, critical point drying provides acceptable image quality compared to chemical drying. Eggshells can be imaged with no special preparation steps except for thorough washing and air drying prior to mounting. Preparation methodologies were standardized based on acceptable image quality obtained with each trial.
Reference Key
gibbons2019processingjournal Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Gibbons, Jessica;Geetha-Loganathan, Poongodi;
Journal Journal of visualized experiments : JoVE
Year 2019
DOI
10.3791/60018
URL
Keywords
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