posted on 2015-12-17, 00:33authored byArnoud
J. Groen, Gloria Sancho-Andrés, Lisa M. Breckels, Laurent Gatto, Fernando Aniento, Kathryn S. Lilley
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.