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Cited by 294

The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.

Identification of cell populations necessary for leaf-to-leaf electrical signaling in a wounded plant

Open access
Nguyen, Chi Tam, Kurenda, Andrzej, Stolz, Stéphanie, Chételat, Aurore, Farmer, Edward E.·2018·Proceedings of the National Academy of Sciences

Abstract

Significance: Numerous modes of long-distance electrical signaling exist in nature. The best known of these, axonal conduction, requires one primary cell population, i.e., neurons. In contrast, the cell types that mediate leaf-to-leaf electrical signaling in wounded plants have not been defined rigorously. Using genetic approaches, we find that two distinct populations of cells in the vasculature matrix are needed to perform this function. Surprisingly, these cells do not contact each other directly. As we further defined the plant wound response, we found that wound-induced membrane depolarizations preceded large intravasculature calcium fluxes. We reveal a two-cell-type mode of electrical signaling in leaves and discuss parallels and differences in electrical signaling outside the plant kingdom.

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Crossref · 10.1073/pnas.1807049115

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Metadata

Authors
Nguyen, C. T., Kurenda, A., Stolz, S., Chételat, A., & Farmer, E. E.
Venue
Proceedings of the National Academy of Sciences
Published
2018-09-18
Volume
115
Issue
40
Pages
10178-10183
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2890710617
Cited by
294
References
34
FWCI
23.67
Type
journal article
Language
en
Open access
hybrid

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CoChat. (2026). Citation check: Identification of cell populations necessary for leaf-to-leaf electrical signaling in a wounded plant. https://app.cochat.ai/papers/10.1073/pnas.1807049115
The paper
Nguyen, C. T., Kurenda, A., Stolz, S., Chételat, A., & Farmer, E. E. (2018). Identification of cell populations necessary for leaf-to-leaf electrical signaling in a wounded plant. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1807049115

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

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