Relative and absolute quantitative shotgun proteomics: targeting low-abundance proteins in Arabidopsis thaliana
Bibliographic record
Abstract
The plant system is a highly dynamic structure on all molecular levels, transcripts, proteins, and metabolites. Thus, protein analysis has to cope with a highly dynamic range of concentrations. A severe problem is the detection of low-abundance proteins in the presence of housekeeping proteins. Basically three approaches are facilitated to measure protein abundance in a comprehensive manner: 2DE and one- or multi-dimensional shotgun proteomics, with or without stable-isotope labelling. These comparative techniques allow for the identification of altered protein levels compared with a reference state. However, they are limited to the analysis of medium/high-abundance proteins. Using stable-isotope dilution techniques it is possible to target the quantitative analysis to low-abundance proteins and to measure absolute concentrations of proteins. Based on multi-dimensional non-gel shotgun proteomics in Arabidopis thaliana, a list of tryptic peptides comprising >1000 proteins was generated. A strategy for quantitative plant proteomics is proposed using this master-list for selecting signature peptides of proteins. To prove the concept, a liquid chromatography-high-resolution triple quadrupole multiple reaction monitoring-mass spectrometry technique is described to determine the absolute amount of a low-abundance sucrose synthase isoform out of an ultra-complex A. thaliana protein extract.
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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".