Whole cell, label free protein quantitation with data independent acquisition: Quantitation at the MS2 level
Bibliographic record
Abstract
Label free quantitation by measurement of peptide fragment signal intensity (MS2 quantitation) is a technique that has seen limited use due to the stochastic nature of data dependent acquisition (DDA). However, data independent acquisition has the potential to make large scale MS2 quantitation a more viable technique. In this study we used an implementation of data independent acquisition--SWATH--to perform label free protein quantitation in a model bacterium Clostridium stercorarium. Four tryptic digests analyzed by SWATH were probed by an ion library containing information on peptide mass and retention time obtained from DDA experiments. Application of this ion library to SWATH data quantified 1030 proteins with at least two peptides quantified (∼ 40% of predicted proteins in the C. stercorarium genome) in each replicate. Quantitative results obtained were very consistent between biological replicates (R(2) ∼ 0.960). Protein quantitation by summation of peptide fragment signal intensities was also highly consistent between biological replicates (R(2) ∼ 0.930), indicating that this approach may have increased viability compared to recent applications in label free protein quantitation. SWATH based quantitation was able to consistently detect differences in relative protein quantity and it provided coverage for a number of proteins that were missed in some samples by DDA analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".