Convex hull as diagnostic tool in single-molecule localization microscopy.

MOTIVATION: Single-molecule localization microscopy resolves individual fluorophores or fluorescence-labeled biomolecules. Data are 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 the 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 AND IMPLEMENTATION: https://github.com/super-resolution/Ebert-et-al-2022-supplement. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DeepCLEM: automated registration for correlative light and electron microscopy using deep learning.

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.