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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".