Unverified paper record
Targeting Ultrastructural Events at the Graft Interface of Arabidopsis thaliana by A Correlative Light Electron Microscopy Approach.
Bio-protocol · 20 Jan 2023 · 10.21769/bioprotoc.4590
Abstract
Combining two different plants together through grafting is one of the oldest horticultural techniques. In order to survive, both partners must communicate via the formation of de novo connections between the scion and the rootstock. Despite the importance of grafting, the ultrastructural processes occurring at the graft interface remain elusive due to the difficulty of locating the exact interface at the ultrastructural level. To date, only studies with interfamily grafts showing enough ultrastructural differences were able to reliably localize the grafting interface at the ultrastructural level under electron microscopy. Thanks to the implementation of correlative light electron microscopy (CLEM) approaches where the grafted partners were tagged with fluorescent proteins of different colors, the graft interface was successfully and reliably targeted. Here, we describe a protocol for CLEM for the model plant Arabidopsis thaliana , which unambiguously targets the graft interface at the ultrastructural level. Moreover, this protocol is compatible with immunolocalization and electron tomography acquisition to achieve a three-dimensional view of the ultrastructural events of interest in plant tissues. Graphical abstract.
Plant phenotyping relevance
植物組織のグラフト界面を超微細構造レベルで特定・可視化するCLEMプロトコルが研究の中心であり、植物形態状態の画像計測手法に該当する。
abstractMoreover, this protocol is compatible with immunolocalization and electron tomography acquisition to achieve a three-dimensional view of the ultrastructural events of interest in plant tissues.
Code and data availability
This is a CLEM protocol paper for Arabidopsis graft interface imaging. The only URLs mentioned are generic third-party software tools (Zeiss Zen, FEI TIA/Xplore3D, ImageJ, Bio-Formats, IMOD, Icy/ec-CLEM) used for acquisition and correlation — none are authors' paper-specific datasets, images, code, or models. No data,
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