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dc.contributor.authorHembrow, J
dc.contributor.authorDeeks, MJ
dc.contributor.authorRichards, DM
dc.date.accessioned2023-09-06T15:58:47Z
dc.date.issued2023-08-30
dc.date.updated2023-09-06T15:46:07Z
dc.description.abstractThe actin cytoskeleton is essential in eukaryotes, not least in the plant kingdom where it plays key roles in cell expansion, cell division, environmental responses and pathogen defence. Yet, the precise structure-function relationships of properties of the actin network in plants are still to be unravelled, including details of how the network configuration depends upon cell type, tissue type and developmental stage. Part of the problem lies in the difficulty of extracting high-quality, quantitative measures of actin network features from microscopy data. To address this problem, we have developed DRAGoN, a novel image analysis algorithm that can automatically extract the actin network across a range of cell types, providing seventeen different quantitative measures that describe the network at a local level. Using this algorithm, we then studied a number of cases in Arabidopsis thaliana, including several different tissues, a variety of actin-affected mutants, and cells responding to powdery mildew. In many cases we found statistically-significant differences in actin network properties. In addition to these results, our algorithm is designed to be easily adaptable to other tissues, mutants and plants, and so will be a valuable asset for the study and future biological engineering of the actin cytoskeleton in globally-important crops.en_GB
dc.description.sponsorshipBiotechnology and Biological Sciences Research Council (BBSRC)en_GB
dc.description.sponsorshipWellcome Trusten_GB
dc.description.sponsorshipMedical Research Council (MRC)en_GB
dc.identifier.citationVol. 19, No. 8, article e1011407en_GB
dc.identifier.doihttps://doi.org/10.1371/journal.pcbi.1011407
dc.identifier.grantnumberBB/M009122/1en_GB
dc.identifier.grantnumberWT105618MAen_GB
dc.identifier.grantnumberMR/P022405/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/133939
dc.identifierORCID: 0000-0001-5487-5732 (Deeks, Michael J)
dc.identifierORCID: 0000-0001-6255-8761 (Richards, David M)
dc.language.isoenen_GB
dc.publisherPublic Library of Science (PLoS)en_GB
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pubmed/37647341en_GB
dc.relation.urlhttps://github.com/JordanHembrow5/DRAGoNen_GB
dc.rights© 2023 Hembrow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are crediteden_GB
dc.subjectActinen_GB
dc.subjectCytoskeletonen_GB
dc.subjectArabidopsisen_GB
dc.subjectImage Analysisen_GB
dc.subjectNetwork Extractionen_GB
dc.subjectDRAGoNen_GB
dc.titleAutomatic extraction of actin networks in plantsen_GB
dc.typeArticleen_GB
dc.date.available2023-09-06T15:58:47Z
dc.identifier.issn1553-734X
exeter.place-of-publicationUnited States
dc.descriptionThis is the author accepted manuscript. The final version is available on open access from Public Library of Science via the DOI in this recorden_GB
dc.descriptionData Availability: The authors confirm that all data underlying the findings are fully available without restriction. The complete code for this paper is available on a GitHub repository at https://github.com/JordanHembrow5/DRAGoN.en_GB
dc.identifier.eissn1553-7358
dc.identifier.journalPLoS Computational Biologyen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2023-08-01
rioxxterms.versionAMen_GB
rioxxterms.licenseref.startdate2023-08-30
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2023-09-06T15:55:06Z
refterms.versionFCDAM
refterms.dateFOA2023-09-06T15:58:51Z
refterms.panelAen_GB
refterms.dateFirstOnline2023-08-30


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© 2023 Hembrow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Except where otherwise noted, this item's licence is described as © 2023 Hembrow et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited