Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

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Highlights d The cell wall component xyloglucan is required for differential growth d Xyloglucan deficiency perturbs cell wall mechanics and polar auxin transport d Auxin response factor ARF2 mediates in interplay between cell wall and auxin

In contrast with the wealth of recent reports about the function of m-adaptins and clathrin adaptor protein (AP) complexes, there is very little information about the motifs that determine the sorting of membrane proteins within clathrin-coated vesicles in plants. Here, we investigated putative sorting signals in the large cytosolic loop of the Arabidopsis (Arabidopsis thaliana) PIN-FORMED1 (PIN1) auxin transporter, which are involved in binding m-adaptins and thus in PIN1 trafficking and localization. We found that Phe-165 and Tyr-280, Tyr-328, and Tyr-394 are involved in the binding of different m-adaptins in vitro. However, only Phe-165, which binds mA(m2)-and mD(m3)-adaptin, was found to be essential for PIN1 trafficking and localization in vivo. The PIN1:GFP-F165A mutant showed reduced endocytosis but also localized to intracellular structures containing several layers of membranes and endoplasmic reticulum (ER) markers, suggesting that they correspond to ER or ER-derived membranes. While PIN1:GFP localized normally in a mA (m2)-adaptin mutant, it accumulated in big intracellular structures containing LysoTracker in a mD (m3)-adaptin mutant, consistent with previous results obtained with mutants of other subunits of the AP-3 complex. Our data suggest that Phe-165, through the binding of mA (m2)-and mD (m3)-adaptin, is important for PIN1 endocytosis and for PIN1 trafficking along the secretory pathway, respectively.

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