A guide to analysis and reconstruction of serial block face scanning electron microscopy data

Area of Science:

• Microscopy and Imaging Technologies
• Computational Biology and Bioinformatics
• Cell Biology and Ultrastructure

Background:

• Serial block face scanning electron microscopy (SBF-SEM) generates large 3D ultrastructural datasets.
• Analyzing these complex datasets for 3D reconstruction presents significant computational challenges for researchers.
• Existing protocols for SBF-SEM data analysis are often insufficient or difficult to follow.

Purpose of the Study:

• To provide a detailed, step-by-step protocol for analyzing SBF-SEM data.
• To guide researchers in selecting appropriate software and segmentation techniques for 3D reconstruction.
• To demonstrate the application of these methods across different biological tissues.

Main Methods:

• Utilized serial block face scanning electron microscopy (SBF-SEM) for data acquisition.
• Processed tissue samples using a heavy metal protocol optimized for SBF-SEM.
• Analyzed resulting image stacks using Fiji, Amira, and MIB software with various segmentation tools.

Main Results:

• Demonstrated 3D reconstruction of ultrastructural features from SBF-SEM data.
• Compared the effectiveness of different segmentation methods (e.g., interpolation, thresholding) for various structures.
• Showcased successful application on skeletal muscle, cardiac tissue, and locust brain datasets.

Conclusions:

• The choice of analysis and segmentation method is dependent on the specific tissue and structures being studied.
• Understanding tissue characteristics is crucial for maximizing informative output from SBF-SEM data.
• This protocol and accompanying tutorials aim to simplify SBF-SEM data analysis and 3D reconstruction for researchers.