In recent years new methodologies and workflow pipelines for acquiring correlated fluorescence microscopy and volume electron microscopy datasets have been extensively described and made accessible to users of different levels. Post-acquisition image processing, and particularly correlation of the optical and electron data in a single integrated three-dimensional framework can be key for extracting valuable information, especially when imaging large sample volumes such as whole cells or tissues. These tasks remain challenging and are often rate-limiting to most users. Here we provide a step-by-step guide to image processing and manual correlation using ImageJ and Amira software of a confocal microscopy stack and a focused ion beam/scanning electron microscopy (FIB/SEM) tomogram acquired using a correlative pipeline. These previously published datasets capture a highly transient invasion event by the bacterium Shigella flexneri infecting an epithelial cell grown in culture, and are made available here in their pre-processed form for readers who wish to gain hands-on experience in image processing and correlation using existing data. In this guide we describe a simple protocol for correlation based on internal sample features clearly visible by both fluorescence and electron microscopy, which is normally sufficient when correlating standard fluorescence microscopy stacks with FIB/SEM data. While the guide describes the treatment of specific datasets, it is applicable to a wide variety of samples and different microscopy approaches that require basic correlation and visualization of two or more datasets in a single integrated framework.
Keywords: Amira; CLEM; Correlative microscopy; FIB/SEM; Image processing; ImageJ; Invasion; Post-acquisition correlation; Shigella; Volume electron microscopy.
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