Abstract
In correlative light and electron microscopy (CLEM), the fluorescent images must be registered to the EM images with high precision. Due to the different contrast of EM and fluorescence images, automated correlation-based alignment is not directly possible, and registration is often done by hand using a fluorescent chromatin stain, or semi-automatically with fiducial markers. We introduce “DeepCLEM”, a fully automated CLEM registration workflow. A convolutional neural network predicts the fluorescent signal from the EM images, which is then automatically registered to the experimentally measured chromatin signal from the sample using correlation-based alignment. The complete workflow is available as a FIJI macro and could in principle be adapted for other imaging modalities as well as for 3D stacks.
Highlights
Correlative Light and Electron Microscopy (CLEM) combines the high resolution of electron microscopy (EM) with the molecular specificity of fluorescence microscopy
To visualize and interpret the results of CLEM, the fluorescent images must be registered to the EM images with high accuracy and precision
Further improvement and automation of the registration process is of great interest to make CLEM scalable to larger datasets
Summary
Correlative Light and Electron Microscopy (CLEM) combines the high resolution of electron microscopy (EM) with the molecular specificity of fluorescence microscopy. In superresolution array tomography (srAT) for example, serial sections are imaged first under the fluorescence microscope using super-resolution techniques such as structured illumination microscopy (SIM), and in the electron microscope[1]. With this technique, it is possible to identify and assign molecular identities to subcellular structures such as electrical synapses[1,2] or microdomains in bacterial membranes[3] that cannot be resolved by EM due to insufficient contrast. Due to the different contrasts of EM and fluorescence images, automated correlation-based image alignment, as used e.g. for aligning EM serial sections[4], is not directly possible. Further improvement and automation of the registration process is of great interest to make CLEM scalable to larger datasets
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