Dense 4D nanoscale reconstruction of living brain tissue.
Journal
Nature methods
ISSN: 1548-7105
Titre abrégé: Nat Methods
Pays: United States
ID NLM: 101215604
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
received:
03
08
2022
accepted:
22
05
2023
medline:
9
8
2023
pubmed:
11
7
2023
entrez:
10
7
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37429995
doi: 10.1038/s41592-023-01936-6
pii: 10.1038/s41592-023-01936-6
pmc: PMC10406607
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Langues
eng
Sous-ensembles de citation
IM
Pagination
1256-1265Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 202932
Pays : United Kingdom
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
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