Quantitative Protein Profiling by Mass Spectrometry Using Label-Free Proteomics
Bibliographic record
Abstract
"Gel-free," or mass spectrometry (MS)-based, proteomics techniques are emerging as the methods of choice for quantitatively comparing proteins levels among biological proteomes, since they are more sensitive and reproducible than two dimensional gel (2-DE)-based methods. Currently, the MS-based methods utilize mainly stable isotope labels (e.g., ICAT, iTRAQ) that enable easy identification of differentially expressed proteins in two or more samples. "Label-free" MS-based methods would alleviate several limitations of the labeling methods, provided that relative quantitative profiling of proteins among multiple MS runs is achievable. However, comparisons of multiple MS runs of highly complex biological samples are very challenging and time consuming. To alleviate this problem, several laboratories and MS vendors have developed software for computer-assisted comparisons of multiple label-free MS runs to allow profiling of differentially expressed proteins. In this chapter, we describe the use of custom-developed MatchRx software in quantitative comparison of multiple label-free MS runs. We also describe details of sample preparation, fractionation, statistical analysis, and protein database searching for label-free comparative quantitative proteomics, as well as the application of a "targeted" MS approach, which includes quantification of the samples using MS followed by selective identification of only the differentially expressed peptides using tandem MS (MS/MS).
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".