Evidence map›Paper›PMID 36338333›Full record

ArticleFrontiers in neural circuits2022

Reconstructing neural circuits using multiresolution correlated light and electron microscopy.

Karl Friedrichsen, Pratyush Ramakrishna, Jen-Chun Hsiang, Katia Valkova, Daniel Kerschensteiner, Josh L Morgan

Open access · goldAbstract read
In one paragraph

Article in Frontiers in neural circuits, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
4.2field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 16 citations in OpenAlex.

  1. Article
  2. Metalens for passive generation of a droplet Bessel beam.Microsystems & nanoengineering · 2025
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  5. Evaluating the Quality of Serial EM Sections with Deep Learning.Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada · 2024
    Article
  6. Indirect Correlative Light and Electron Microscopy (iCLEM): A Novel Pipeline for Multiscale Quantification of Structure From Molecules to Organs.Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada · 2024
    Article
  7. Article
  8. Array tomography: trails to discovery.Methods in microscopy · 2024
    Article
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Karl FriedrichsenDepartment of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.
Pratyush RamakrishnaDepartment of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.
Jen-Chun HsiangDepartment of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.
Katia ValkovaDepartment of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.
Daniel KerschensteinerDepartment of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.
Josh L MorganDepartment of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, United States.
Washington University in St. Louis · US

Funding

SYNAPTIC ORGANIZATION AND VISUAL PROCESSING IN INTERNEURON CIRCUITS OF THE RETINAR01EY026978 · NEI · WASHINGTON UNIVERSITY · PI Daniel Kerschensteiner · 2016 to 2026
$3.8M
Visual pathway cooperation to align viewing strategies and processing specializations for predationR01EY034001 · NEI · WASHINGTON UNIVERSITY · PI Daniel Kerschensteiner · 2022 to 2026
$2.0M
Development and function of glomerular microcircuits in the visual thalamusR01EY029313 · NEI · WASHINGTON UNIVERSITY · PI MORGAN, JOSHUA L · 2020 to 2024
$1.9M
Tools and approaches for functional connectomics of dense neuropilsR21EY030623 · NEI · WASHINGTON UNIVERSITY · PI KERSCHENSTEINER, DANIEL, MORGAN, JOSHUA L · 2019 to 2020
$432k
NEI NIH HHS R01 EY026978NEI NIH HHS R01 EY029313NEI NIH HHS R01 EY034001NEI NIH HHS R21 EY030623
6 · The paper itself

Abstract

Correlated light and electron microscopy (CLEM) can be used to combine functional and molecular characterizations of neurons with detailed anatomical maps of their synaptic organization. Here we describe a multiresolution approach to CLEM (mrCLEM) that efficiently targets electron microscopy (EM) imaging to optically characterized cells while maintaining optimal tissue preparation for high-throughput EM reconstruction. This approach hinges on the ease with which arrays of sections collected on a solid substrate can be repeatedly imaged at different scales using scanning electron microscopy. We match this multiresolution EM imaging with multiresolution confocal mapping of the aldehyde-fixed tissue. Features visible in lower resolution EM correspond well to features visible in densely labeled optical maps of fixed tissue. Iterative feature matching, starting with gross anatomical correspondences and ending with subcellular structure, can then be used to target high-resolution EM image acquisition and annotation to cells of interest. To demonstrate this technique and range of images used to link live optical imaging to EM reconstructions, we provide a walkthrough of a mouse retinal light to EM experiment as well as some examples from mouse brain slices.

Indexed as

NeuronsAnimalsMiceMicroscopy, Electron, ScanningMicroscopy, Fluorescenceconfocal 3D microscopyconnectomicscorrelated light and electron microscopy (CLEM)electron microscopyneural circuitsynapsetissue mapping

Identifiers

PMID36338333
PMCPMC9635852
OpenAlexW4306971019

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.