The internal organisation of the cell depends on tethers at destination organelles to selectively capture incoming transport vesicles to facilitate SNARE-mediated fusion. The golgin long coiled-coil proteins function as tethers that contributes to this specificity at the Golgi (1). Golgin-97, golgin-245 and GCC88 golgins of the trans-Golgi capture vesicles derived from endosomes, which serve to recycle the critical Golgi machinery required to deliver lysosomal hydrolases and to maintain exocytosis. Retrograde trafficking from endosomes to the trans-Golgi network (TGN) is a complex process that involves the sorting of transmembrane cargo proteins into distinct transport vesicles by adaptors from multiple pathways. The content of these distinct vesicles, which golgin they target and the factors that mediate this targeting are not well understood. The major challenges that have limited advances in these areas is the transient nature of vesicle tethering, and the redundancies in their mechanisms that confound experimental dissection. To gain better insight into these problems, we performed organelle proteomics using the Localisation of Organelle Proteins by Isotope Tagging after Differential ultraCentrifugation (LOPIT-DC) method on a system in which an ectopic golgin causes vesicles to accumulate in a tethered state (2). By incorporating Bayesian statistical modelling into our analysis (3), we determined that over 45 transmembrane proteins and 51 peripheral membrane proteins of the endosomal network are on vesicles captured by golgin-97, including known cargo and components of the clathrin/AP-1, retromer-dependent and -independent transport pathways. We also determined a distinct class of vesicles shared by golgin-97, golgin-245 and GCC88 that is enriched in TMEM87A, a multi-pass transmembrane protein of unknown function that has previously been implicated in endosome-to-Golgi retrograde transport (4). Finally, we categorically demonstrate that the vesicles that these golgins capture are retrograde transport vesicles based on the lack of enrichment of lysosomal hydrolases in our LOPIT-DC data, and from correlative light electron tomography images of spherical vesicles captured by golgin-97. Together, our study demonstrates the power of combining LOPIT-DC with Bayesian statistical analysis in interrogating the dynamic spatial movement of proteins in transport vesicles.
