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Identifying protein-protein interactions (PPI) is crucial to understand any type of biological process. Many PPI tools are available, yet only some function within the context of a plant cell. Narrowing down even further, only few PPI tools allow visualizing higher order interactions. Here, we present a novel and conditional in vivo PPI tool for plant research. Knocksideways in plants (KSP) uses the ability of rapamycin to alter the localization of a bait protein and its interactors via the heterodimerization of FKBP and FRB domains. KSP is inherently free from many limitations, which other PPI systems hold. It is an in vivo tool, it is flexible concerning the orientation of protein tagging as long as this does not interfere with the interaction and it is compatible with a broad range of fluorophores. KSP is also a conditional tool and therefore does not require additional controls. The interactions can be quantified and in high throughput by the scripts that we provide. Finally, we demonstrate that KSP can visualize higher-order interactions. It is therefore a versatile tool, complementing the PPI methods field with unique characteristics and applications.

Cell division depends on the correct localization of the cyclin-dependent kinases that are regulated by phosphorylation, cyclin proteolysis, and protein-protein interactions. Although immunological assays can define cell cycle protein abundance and localization, they are not suitable for detecting the dynamic rearrangements of molecular components during cell division. Here, we applied an in vivo approach to trace the subcellular localization of 60 Arabidopsis (Arabidopsis thaliana) core cell cycle proteins fused to green fluorescent proteins during cell division in tobacco (Nicotiana tabacum) and Arabidopsis. Several cell cycle proteins showed a dynamic association with mitotic structures, such as condensed chromosomes and the preprophase band in both species, suggesting a strong conservation of targeting mechanisms. Furthermore, colocalized proteins were shown to bind in vivo, strengthening their localization-function connection. Thus, we identified unknown spatiotemporal territories where functional cell cycle protein interactions are most likely to occur.