ArticleeLife2018
FluoEM, virtual labeling of axons in three-dimensional electron microscopy data for long-range connectomics.
Article in eLife, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Synaptic-resolution connectomics: towards large brains and connectomic screening.Nature reviews. Neuroscience · 2026Review
- Diazinon residues levels in farm-gate Brassica oleracea var. acephala of Kimira-Oluch smallholder farm improvement project, Kenya.PloS one · 2025Article
- Cognitive synaptopathy: synaptic and dendritic spine dysfunction in age-related cognitive disorders.Frontiers in aging neuroscience · 2024Review
- Dense 4D nanoscale reconstruction of living brain tissue.Nature methods · 2023Article
- A new form of axonal pathology in a spinal model of neuromyelitis optica.Brain : a journal of neurology · 2022Article
- Reconstructing neural circuits using multiresolution correlated light and electron microscopy.Frontiers in neural circuits · 2022Article
- Permeabilization-freeeLife · 2021Article
- Targeting Functionally Characterized Synaptic Architecture Using Inherent Fiducials and 3D Correlative Microscopy.Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada · 2021Article
- Niwaki Instead of Random Forests: Targeted Serial Sectioning Scanning Electron Microscopy With Reimaging Capabilities for Exploring Central Nervous System Cell Biology and Pathology.Frontiers in neuroanatomy · 2021Review
- Myelin replacement triggered by single-cell demyelination in mouse cortex.Nature communications · 2020Article
- Article
- DeepBouton: Automated Identification of Single-Neuron Axonal Boutons at the Brain-Wide Scale.Frontiers in neuroinformatics · 2019Article
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Authors and funding
4 authors.
Funding
Abstract
The labeling and identification of long-range axonal inputs from multiple sources within densely reconstructed electron microscopy (EM) datasets from mammalian brains has been notoriously difficult because of the limited color label space of EM. Here, we report FluoEM for the identification of multi-color fluorescently labeled axons in dense EM data without the need for artificial fiducial marks or chemical label conversion. The approach is based on correlated tissue imaging and computational matching of neurite reconstructions, amounting to a virtual color labeling of axons in dense EM circuit data. We show that the identification of fluorescent light- microscopically (LM) imaged axons in 3D EM data from mouse cortex is faithfully possible as soon as the EM dataset is about 40-50 µm in extent, relying on the unique trajectories of axons in dense mammalian neuropil. The method is exemplified for the identification of long-distance axonal input into layer 1 of the mouse cerebral cortex.
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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 OpenQuestion graph.