ArticleNature methods2025
CLEM-Reg: an automated point cloud-based registration algorithm for volume correlative light and electron microscopy.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Vision foundation model for 3D magnetic resonance imaging segmentation, classification, and registration.Medical image analysis · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- NucleoNet and DropNet: Generalist deep learning models for instance segmentation of nuclei and lipid droplets from electron microscopy images.bioRxiv : the preprint server for biology · 2026Article
- VASCilia is an open-source, deep learning-based tool for 3D analysis of cochlear hair cell stereocilia bundles.PLoS biology · 2026Article
- A workflow for semi-automated volume correlative light microscopy and transmission electron tomography.Journal of microscopy · 2025Article
- VASCilia (Vision Analysis StereoCilia): A Napari Plugin for Deep Learning-Based 3D Analysis of Cochlear Hair Cell Stereocilia Bundles.bioRxiv : the preprint server for biology · 2025Article
- Article
- Multimodal bioimaging across disciplines and scales: challenges, opportunities and breaking down barriers.Npj imaging · 2024Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Volume correlative light and electron microscopy (vCLEM) is a powerful imaging technique that enables the visualization of fluorescently labeled proteins within their ultrastructural context. Currently, vCLEM alignment relies on time-consuming and subjective manual methods. This paper presents CLEM-Reg, an algorithm that automates the three-dimensional alignment of vCLEM datasets by leveraging probabilistic point cloud registration techniques. Point clouds are derived from segmentations of common structures in each modality, created by state-of-the-art open-source methods. CLEM-Reg drastically reduces the registration time of vCLEM datasets to a few minutes and achieves correlation of fluorescent signal to submicron target structures in electron microscopy on three newly acquired vCLEM benchmark datasets. CLEM-Reg was then used to automatically obtain vCLEM overlays to unambiguously identify TGN46-positive transport carriers involved in protein trafficking between the trans-Golgi network and plasma membrane. Datasets are available on EMPIAR and BioStudies, and a napari plugin is provided to aid end-user adoption.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.