NegFluo, a Fast and Efficient Method to ...
Document type :
Article dans une revue scientifique
DOI :
Permalink :
Title :
NegFluo, a Fast and Efficient Method to Determine Starch Granule Size and Morphology In Situ in Plant Chloroplasts
Author(s) :
Vandromme, Camille [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Kasprowicz, Angelina [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Courseaux, Adeline [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Trinel, Dave [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Facon, Maud [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Putaux, Jean-Luc [Auteur]
Centre de Recherches sur les Macromolécules Végétales [CERMAV]
D'hulst, Christophe [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Wattebled, Fabrice [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Spriet, Corentin [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Kasprowicz, Angelina [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Courseaux, Adeline [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Trinel, Dave [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Facon, Maud [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Putaux, Jean-Luc [Auteur]
Centre de Recherches sur les Macromolécules Végétales [CERMAV]
D'hulst, Christophe [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Wattebled, Fabrice [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Unité de Glycobiologie Structurale et Fonctionnelle (UGSF) - UMR 8576
Spriet, Corentin [Auteur]
Unité de Glycobiologie Structurale et Fonctionnelle - UMR 8576 [UGSF]
Journal title :
Frontiers in Plant Science
Abbreviated title :
Front. Plant Sci.
Volume number :
10
Publisher :
Frontiers Media SA
Publication date :
2019-09-09
ISSN :
1664-462X
English keyword(s) :
starch
confocal fluorescence imaging
machine learning
Arabidopsis
starch granule morphology
autofluorescence
confocal fluorescence imaging
machine learning
Arabidopsis
starch granule morphology
autofluorescence
HAL domain(s) :
Sciences du Vivant [q-bio]/Biologie végétale
English abstract : [en]
Starch granules that accumulate in the plastids of plants vary in size, shape, phosphate, or protein content according to their botanical origin. Depending on their size, the applications in food and nonfood industries ...
Show more >Starch granules that accumulate in the plastids of plants vary in size, shape, phosphate, or protein content according to their botanical origin. Depending on their size, the applications in food and nonfood industries differ. Being able to master starch granule size for a specific plant, without alteration of other characteristics (phosphate content, protein content, etc.), is challenging. The development of a simple and effective screening method to determine the size and shape of starch granules in a plant population is therefore of prime interest. In this study, we propose a new method, NegFluo, that combines negative confocal autofluorescence imaging in leaf and machine learning (ML)-based image analysis. It provides a fast, automated, and easy-to-use pipeline for both in situ starch granule imaging and its morphological analysis. NegFluo was applied to Arabidopsis leaves of wild-type and ss4 mutant plants. We validated its accuracy by comparing morphological quantifications using NegFluo and state-of-the-art methods relying either on starch granule purification or on preparation-intensive electron microscopy combined with manual image analysis. NegFluo thus opens the way to fast in situ analysis of starch granules.Show less >
Show more >Starch granules that accumulate in the plastids of plants vary in size, shape, phosphate, or protein content according to their botanical origin. Depending on their size, the applications in food and nonfood industries differ. Being able to master starch granule size for a specific plant, without alteration of other characteristics (phosphate content, protein content, etc.), is challenging. The development of a simple and effective screening method to determine the size and shape of starch granules in a plant population is therefore of prime interest. In this study, we propose a new method, NegFluo, that combines negative confocal autofluorescence imaging in leaf and machine learning (ML)-based image analysis. It provides a fast, automated, and easy-to-use pipeline for both in situ starch granule imaging and its morphological analysis. NegFluo was applied to Arabidopsis leaves of wild-type and ss4 mutant plants. We validated its accuracy by comparing morphological quantifications using NegFluo and state-of-the-art methods relying either on starch granule purification or on preparation-intensive electron microscopy combined with manual image analysis. NegFluo thus opens the way to fast in situ analysis of starch granules.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Non spécifiée
ANR Project :
Administrative institution(s) :
Université de Lille
CNRS
CNRS
Research team(s) :
Plant Storage Polysaccharides
Research platform(s) :
Traitement de l'image et du signal pour la biologie (TISBio)
Submission date :
2020-07-07T13:08:28Z
2020-09-28T10:44:32Z
2020-09-28T10:44:32Z
Files
- P19.42 NegFluo.pdf
- Version éditeur
- Restricted access
- Access the document