Complementary methods to assist subcellular fractionation in organellar proteomics
Bibliographic record
Abstract
Organellar proteomics aims to describe the full complement of proteins of subcellular structures and organelles. When compared with whole-cell or whole-tissue proteomes, the more focused results from subcellular proteomic studies have yielded relatively simpler datasets from which biologically relevant information can be more easily extracted. In every proteomic study, the quality and purity of the biological sample to be investigated is of the utmost importance for a successful analysis. In organellar proteomics, one of the most crucial steps in sample preparation is the initial subcellular fractionation procedure by which the enriched preparation of the sought-after organelle is obtained. In nearly all available organellar proteomic studies, the method of choice relies on one or several rounds of density-based gradient centrifugation. Although this method has been recognized for decades as yielding relatively pure preparations of organelles, recent technological advances in protein separation and identification can now reveal even minute amounts of contamination, which in turn can greatly complicate data interpretation. The scope of this review focuses on recently published innovative complementary or alternative methods to perform subcellular fractionation, which can further refine the way in which sample preparation is accomplished in organellar proteomics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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".