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.

Motivation Single-molecule localization microscopy resolves individual fluorophores or fluorescence-labeled biomolecules. Data is provided as a set of localizations that distribute normally around the true fluorophore position with a variance determined by the localization precision. Characterizing the spatial fluorophore distribution to differentiate between resolution-limited localization clusters, which resemble individual biomolecules, and extended structures, which represent aggregated molecular complexes, is a common challenge. Results We demonstrate use of the convex hull and related hull properties of localization clusters for diagnostic purposes, as a parameter for cluster selection, or as a tool to determine localization precision. Availability https://github.com/super-resolution/Ebert-et-al-2022-supplement. Supplementary information Supplementary data are available at Bioinformatics online.

These authors asked 86 reporters and their editors to fill out some measures of their knowledge of language mechanics and writing ability. The study found that editors judge reporters according to three major factors: writing mechanics, expressive skills, and journalistic abilities. Reporters judge themselves very similarly. On some tasks, college English majors scored better than did other types of college majors.

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 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 plugin and could in principle be adapted for other imaging modalities as well as for 3D stacks.