Unlocking the black box: multimodal imaging and quantitative analysis of plant vesicular trafficking

Abstract

How do plants, lacking a central nervous system, translate environmental stimuli into physiological actions within milliseconds? Vesicular trafficking acts as a cellular core signal and material transport hub that facilitates this rapid adaptation, yet its dynamic nature has long remained a "black box". Traditional imaging approaches have struggled not only with optical resolution (the "unseen"), but critically with a lack of quantitative precision (the "immeasurable") and the inability to track molecular history (the "unknown age"). This review synthesizes a new paradigm that unlocks this black box by integrating advanced chemical biology with deep learning computational analysis. We detail how multimodal strategies combining pH-sensitive probes (e.g., pHluorin), covalent tags (HaloTag), and fluorescent timers visualize molecular events with unprecedented fidelity. Furthermore, we explore how integrating next generation FRAP/FCS variants (DeepFRAP, FCSNet) with deep learning allows for the rigorous mathematical modeling of vesicle kinetics. By resolving long-standing controversies such as endocytic stoichiometry and secretory sorting logic, this quantitative framework maps nanoscale membrane dynamics to organismal phenotypes, ultimately refining our understanding of plant stress resilience and signal transduction.

Keywords

Vesicular trafficking / Live-cell imaging / Labeling techniques / Dynamics / Quantitative analysis

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Funding

National Natural Science Foundation of China(32370740)

Beijing Nova Program(20230484251)

Fundamental Research Funds for the Central Universities(QNTD202502)

Fundamental Research Funds of State Key Laboratory of Tree Genetics and Breeding(TGBFRF202414)

National Key Research and Development Program of China(2022YFF0712500)

5•5 Engineering Research & Innovation Team Project of Beijing Forestry University(BLRC2023C06)

Introducing Talents of Discipline to University(B13007)

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