Golgi and Related Vesicle Proteomics: Simplify to Identify
Bibliographic record
Abstract
Despite more than six decades of successful Golgi research, the fundamental question as to how biosynthetic material is transported through the secretory pathway remains unanswered.New technologies such as live cell imaging and correlative microscopy have highlighted the plastic nature of the Golgi, one that is sensitive to perturbation yet highly efficient in regaining both structure and function.Single molecule-microscopy and super resolution-microscopy further adds to this picture.Various models for protein transport have been put forward, each with its own merits and pitfalls but we are far from resolving whether one is more correct than the other.As such, our laboratory considers multiple mechanisms of Golgi transport until proven otherwise.This includes the two classical modes of transport, vesicular transport and cisternal progression/maturation as well as more recent models such as tubular inter-and intra-cisternal connections (long lasting or transient) and inter-Golgi stack transport.In this article, we focus on an emerging inductive technology, mass spectrometry-based proteomics that has already enabled insight into the relative composition of compartments and subcompartments of the secretory pathway including mechanistic aspects of protein transport.We note that proteomics, as with any other technology, is not a stand-alone technology but one that works best alongside complementary approaches. PROTEOMICS AS A TECHNOLOGYA s described and thoroughly discussed in a recent commentary (Nilsson et al. 2010), mass spectrometry (MS)-based proteomics as currently deployed is correlative despite its high mass accuracy.Yet it allows for a comprehensive and quantitative characterization of the protein composition of entire organelles, substructures, protein complexes, and biochemical fractions (e.g., detergent fractions upon phase separation).In a typical proteomic study, protein samples are separated according to their size using gel electrophoresis, i.e., Polyacrylamide Gel Electrophoresis or PAGE (e.g., SDS-PAGE) or by both size and isoelectric point, i.e., 2-D PAGE.Gel fragments containing separated proteins are then subjected to protease digestion in-gel, usually with trypsin, to generate peptides that are then subjected to liquid chromatography (LC).From the LC column, these peptides then enter the mass spectrometer where they are subjected to a first or parental MS scan.Using this parental scan,
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".