MASS SPECTROMETRY OUTGROWS SIMPLE BIOCHEMISTRY: NEW APPROACHES TO ORGANELLE PROTEOMICS
Bibliographic record
Abstract
Organelles are subcellular compartments or structures that typically carry out a defined set of functions within the cell. The functions of many organelles are known or predicted, but without knowing all the components of any recognized organelle it is difficult to fully understand them. Mass spectrometry-based proteomics now allows for routine identification of several hundreds or thousands of proteins in very complex samples; for cell biologists, organelles represent perhaps the most interesting class of cellular components to apply this new technology to. However, in order to analyze the proteome of an organelle it first must be purified, and the limitations in purifying any biological sample to homogeneity quickly become apparent to the vigilant mass spectrometrist. At the end of an organelle proteomic investigation, investigators are left with a long list of proteins whose location needs to be verified by an orthogonal method, a daunting prospect; or, they must accept an unknown and possibly very high level of incorrect localizations. Some of these caveats can be partially overcome by incorporating quantitative aspects into organelle proteomic studies. This review discusses some alternative approaches to organelle proteomics where questions of specificity and/or functional relevance are addressed by incorporating a quantitative dimension into the experiment.
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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) | 0.000 | 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 | 0.000 | 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".