Evidence map›Paper›PMID 37429995›Full record

ArticleNature methods2023

Dense 4D nanoscale reconstruction of living brain tissue.

Philipp Velicky, Eder Miguel, Julia M Michalska, Julia Lyudchik, Donglai Wei, Zudi Lin, Jake F Watson, Jakob Troidl, Johanna Beyer, Yoav Ben-Simon and 10 more

Open access · hybridAbstract read
In one paragraph

Article in Nature methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
16.1field-weighted citation impact, top 1% 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

22 citing papers in PubMed, 46 citations in OpenAlex.

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  20. Year in review 2023.Nature methods · 2024
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

20 authors at 6 institutions in 4 countries.

Philipp VelickyInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.ORCID http://orcid.org/0000-0002-2340-7431
Eder MiguelInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.ORCID http://orcid.org/0000-0001-5665-0430
Julia M MichalskaInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Julia LyudchikInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Donglai WeiSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Zudi LinSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0003-4176-8035
Jake F WatsonInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.ORCID http://orcid.org/0000-0002-8698-3823
Jakob TroidlSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Johanna BeyerSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
Yoav Ben-SimonInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Christoph SommerInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Wiebke JahrInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.ORCID http://orcid.org/0000-0003-0201-2315
Alban CenameriInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Johannes BroichhagenLeibniz-Forschungsinstitut für Molekulare Pharmakologie, Berlin, Germany.ORCID http://orcid.org/0000-0003-3084-6595
Seth G N GrantGenes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK.ORCID http://orcid.org/0000-0001-8732-8735
Peter JonasInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.ORCID http://orcid.org/0000-0001-5001-4804
Gaia NovarinoInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Hanspeter PfisterSchool of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.ORCID http://orcid.org/0000-0002-3620-2582
Bernd BickelInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
Johann G DanzlInstitute of Science and Technology Austria (ISTA), Klosterneuburg, Austria. johann.danzl@ist.ac.at.ORCID http://orcid.org/0000-0001-8559-3973
Institute of Science and Technology Austria · ATHarvard University · USAllen Institute for Brain Science · USBoston College · USLeibniz-Forschungsinstitut für Molekulare Pharmakologie · DEUniversity of Edinburgh · GB

Funding

Austrian Science Fund FWF I 3600Austrian Science Fund FWF W 1232Wellcome TrustWellcome Trust 202932
6 · The paper itself

Abstract

Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain's complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.

Indexed as

BrainSynapsesImage Processing, Computer-AssistedMicroscopy, Fluorescence

Identifiers

PMID37429995
PMCPMC10406607
OpenAlexW4383814759

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.