The binary mask of each region was applied to each fluorescence channel and 569 fluorescence mean values were extracted for the 9 newly-created images. A recapitulative 570 montage image was then created to quickly estimate segmentation quality. The imageJ macro 571 and sample images are available in the Zenodo repository, 572 http://doi.org/10.5281/zenodo.4809980.573 574 AI Segmentation 575 The Machine learning approach is based on the “Waikato Environment for Knowledge 576 Analysis” (WEKA) implemented in ImageJ (Witten et al., 2016). We first defined a 577 classification based on four categories: i) secondary cell wall, ii) cell corners, iii) compound 578 middle lamella and iv) backgro
Open resource ↗zenodo · 10.5281/zenodo.4809980 · pdf-raw-page:21 lines:1-63Unverified paper record
Mapping lignification dynamics with a combination of chemistry, data segmentation and ratiometric analysis
bioRxiv · 23 Jun 2021 · 10.1101/2021.06.21.449296
Abstract
This article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL” for REP orter R atiometrics I ntegrating S egmentation for A nalyzing L ignification. REPRISAL consists of the combination of three separate approaches. In the first approach, H*, G* and S* monolignol chemical reporters, corresponding to p -coumaryl alcohol, coniferyl alcohol and sinapyl alcohol, are used to label the growing lignin polymer in a fluorescent triple labelling strategy based on the sequential use of 3 main bioorthogonal chemical reactions. In the second step, an automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones corresponding to cell corners (CC), compound middle lamella (CML) and secondary cell walls (SCW). The last step corresponds to the exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution (ratiometric method 1) and proportions (ratiometric method 2) within the different cell wall zones. In order to demonstrate the potential of REPRISAL for investigating lignin formation we firstly describe its use to map developmentally-related changes in the lignification capacity of WT Arabidopsis interfascicular fiber cells. We then show how it can be used to reveal subtle phenotypical differences in lignification by analyzing the Arabidopsis prx64 peroxidase mutant and provide further evidence for the implication of the AtPRX64 protein in floral stem lignification. Finally, we demonstrate the general applicability of REPRISAL by using it to map lignification capacity in poplar, flax and maize.
Plant phenotyping relevance
REPRISALという蛍光画像・自動セグメンテーション・比率解析を統合した、細胞壁リグニン形成状態の植物フェノタイピング手法を開発し、複数種・変異体で適用している。
abstractThis article describes a new methodology for detailed mapping of the lignification capacity of plant cell walls that we have called “REPRISAL”
abstractan automatic parametric and/or artificial intelligence (AI) segmentation algorithm is developed that assigns fluorescent image pixels to 3 distinct cell wall zones
abstractthe exploitation of a ratiometric approach enabling statistical analyses of differences in monolignol reporter distribution
Code and data availability
The authors publicly deposited their Fiji/ImageJ segmentation plugin (GUI, parametric macro, WEKA classifier and training data) plus representative confocal sample images in a Zenodo repository, explicitly referenced in the methods and supplementary data as containing the paper's lignification ratiometric analysis tool
This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.