Dual Color tau-STED Super Resolution Microscopy in Arabidopsis Root Tip.

Abstract

Super-resolution microscopy has transformed our ability to visualize subcellular structures, but its application in plant biology remains challenging due to the optical complexity of plant tissues. Here, we present a detailed protocol for tau-STED microscopy (Leica Microsystems), which combines stimulated emission depletion (STED) with fluorescence lifetime imaging (FLIM) to achieve nanoscale resolution while minimizing phototoxicity. This method leverages time-correlated single-photon counting (TCSPC) to separate fluorescence signals based on their lifetimes, enhancing signal specificity and enabling the visualization of elusive subcellular compartments in Arabidopsis thaliana root tips. The protocol covers sample preparation, fluorophore selection, microscope configuration, image acquisition, and data analysis, providing a step-by-step guide to optimize tau-STED imaging for plant cell biology. By addressing the unique challenges of plant tissue imaging, such as autofluorescence, refractive index mismatches, and light scattering, this approach facilitates super-resolution imaging of intracellular structures, including the plant endoplasmic reticulum-Golgi intermediate compartment (ERGIC). This protocol is designed to be accessible to researchers with basic microscopy experience and offers a robust framework for exploring subcellular dynamics in plants with unprecedented detail. Key features • tau-STED integrates STED signals with fluorescence lifetime via phasor analysis at confocal speeds, enabling low-noise super-resolution imaging. • Morphometry analysis workflow at super resolution.

Plant phenotyping relevance

植物組織の細胞内構造を超解像で取得・解析する顕微鏡プロトコルであり、植物表現型の画像取得法が中心。超解像下の形態計測ワークフローも含む。

Code and data availability

This is a Bio-protocol methods article describing tau-STED imaging of Arabidopsis root tips and a FIJI morphometry workflow. The supplied blocks contain no public phenotype/trait datasets, no deposited image or sensor data, and no author analysis code, scripts, or trained models with an explicit public repository URL.

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