Diversity of dynamic voltage patterns in neuronal dendrites revealed by nanopipette electrophysiology.


Journal

Nanoscale
ISSN: 2040-3372
Titre abrégé: Nanoscale
Pays: England
ID NLM: 101525249

Informations de publication

Date de publication:
27 Jul 2023
Historique:
medline: 28 7 2023
pubmed: 17 7 2023
entrez: 17 7 2023
Statut: epublish

Résumé

Dendrites and dendritic spines are the essential cellular compartments in neuronal communication, conveying information through transient voltage signals. Our understanding of these compartmentalized voltage dynamics in fine, distal neuronal dendrites remains poor due to the difficulties inherent to accessing and stably recording from such small, nanoscale cellular compartments for a sustained time. To overcome these challenges, we use nanopipettes that permit long and stable recordings directly from fine neuronal dendrites. We reveal a diversity of voltage dynamics present locally in dendrites, such as spontaneous voltage transients, bursting events and oscillating periods of silence and firing activity, all of which we characterized using segmentation analysis. Remarkably, we find that neuronal dendrites can display spontaneous hyperpolarisation events, and sustain transient hyperpolarised states. The voltage patterns were activity-dependent, with a stronger dependency on synaptic activity than on action potentials. Long-time recordings of fine dendritic protrusions show complex voltage dynamics that may represent a previously unexplored contribution to dendritic computations.

Identifiants

pubmed: 37455621
doi: 10.1039/d2nr03475a
pmc: PMC10373629
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

12245-12254

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Auteurs

Jeffrey Mc Hugh (J)

Centre for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Université PSL, Labex Memolife, Paris, France. jeffrey.mc-hugh@college-de-france.fr.
Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK.

Stanislaw Makarchuk (S)

Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.

Daria Mozheiko (D)

Centre for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Université PSL, Labex Memolife, Paris, France. jeffrey.mc-hugh@college-de-france.fr.
Doctoral School No 158, Sorbonne Université, Paris, France.

Ana Fernandez-Villegas (A)

Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.

Gabriele S Kaminski Schierle (GS)

Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.

Clemens F Kaminski (CF)

Department of Chemical Engineering and Biotechnology, University of Cambridge, Philippa Fawcett Drive, Cambridge CB3 0AS, UK.

Ulrich F Keyser (UF)

Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, UK.

David Holcman (D)

Group Data Modelling, Computational Biology and Predictive Medicine, Institut de Biologie de l'Ecole Normale Supérieure (IBENS), CNRS, INSERM, Université PSL, Labex Memolife, Paris, France.
Churchill College, University of Cambridge, Cambridge CB3 0DS, UK.

Nathalie Rouach (N)

Centre for Interdisciplinary Research in Biology, Collège de France, CNRS, INSERM, Université PSL, Labex Memolife, Paris, France. jeffrey.mc-hugh@college-de-france.fr.
Churchill College, University of Cambridge, Cambridge CB3 0DS, UK.

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Classifications MeSH