Photochromic reversion enables long-term tracking of single molecules in living plants.

Abstract

Single-molecule imaging enables the observation of individual molecules in living cells (DEste et al., 2024; Kusumi et al., 2014; Lelek et al., 2021; Nguyen et al., 2023). In plants, however, the tracking of single molecules is typically limited to a few hundred milliseconds (Bayle et al., 2021; Gronnier et al., 2017; Hosy et al., 2015), precluding the observation of dynamic cellular processes at molecular resolution. Here, we describe photochromic reversion, an imaging modality that enables long-term single-molecule tracking of genetically encoded translational fusions. Using this approach, we achieve minute-long tracking of individual cell-surface receptors and reveal previously inaccessible dynamic spatial arrest events of single plasma membrane proteins. We further developed and benchmarked computational analysis of spatial arrests (CASTA), a machine learning-based tool that automatically detects and analyses spatial, temporal, and diffusional properties of these events, thereby enabling precise nanoscale kinetic measurements. Together, these advances provide a powerful framework for deciphering the principles governing membrane dynamics and function.

Plant phenotyping relevance

植物細胞表面受容体の動態を長時間単一分子イメージングで取得し、空間停止イベントを解析するCASTAも開発・ベンチマークしており、植物状態の測定法が中心である。

Code and data availability

The paper describes CASTA, a Python package for spatial arrest analysis, and plant single-molecule tracking data, but no supplied block contains any public deposit, repository, or availability URL for data, code, or models. CASTA is only described as 'Available as an easy to setup and use Python package' with no public

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