Vandromme Camille, Kasprowicz Angelina, Courseaux Adeline, Trinel Dave, Facon Maud, Putaux Jean-Luc, D'Hulst Christophe, Wattebled Fabrice, Spriet Corentin
Univ. Lille, CNRS, UMR8576 - UGSF - Unité de Glycobiologie Structurale et Fonctionnelle, Lille, France.
Univ. Grenoble Alpes, CNRS, CERMAV, Grenoble, France.
Front Plant Sci. 2019 Sep 9;10:1075. doi: 10.3389/fpls.2019.01075. eCollection 2019.
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 starch granule imaging and its morphological analysis. NegFluo was applied to leaves of wild-type and 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 analysis of starch granules.
植物质体中积累的淀粉粒，根据其植物来源，在大小、形状、磷酸盐或蛋白质含量上有所不同。根据其大小，在食品和非食品工业中的应用也有所不同。在不改变其他特性（磷酸盐含量、蛋白质含量等）的情况下，掌握特定植物的淀粉粒大小具有挑战性。因此，开发一种简单有效的筛选方法来确定植物群体中淀粉粒的大小和形状备受关注。在本研究中，我们提出了一种新方法NegFluo，它结合了叶片中的负共聚焦自发荧光成像和基于机器学习（ML）的图像分析。它为淀粉粒成像及其形态分析提供了一个快速、自动化且易于使用的流程。NegFluo应用于野生型和突变型植物的叶片。我们通过比较使用NegFluo的形态量化结果与依赖淀粉粒纯化或结合手动图像分析的复杂电子显微镜的现有方法，验证了其准确性。因此，NegFluo为淀粉粒的快速分析开辟了道路。
