Multi-laboratory assessment of reproducibility, qualitative and quantitative performance of SWATH-mass spectrometry
Bibliographic record
Abstract
Abstract Quantitative proteomics employing mass spectrometry has become an indispensable tool in basic and applied life science research. Methods based on data-dependent acquisition have proved extremely valuable for qualitative proteome analysis but historically have struggled to achieve reproducible quantitative data if large sample cohorts are comparatively analyzed. Targeted proteomics, most commonly implemented as selected reaction monitoring, has emerged as a powerful alternative and succeeded in providing a data independent approach for reproducible quantitative proteomics data but is limited in the number of proteins quantified. SWATH-MS is a recently introduced technique consisting of a data-independent acquisition and a targeted data analysis strategy that aims to maintain the favorable quantitative characteristics (accuracy, sensitivity, specificity) achieved in targeted proteomics but on the scale of thousands of proteins. While previous SWATH-MS studies have shown high intra-lab reproducibility, this has not been evaluated on an inter-lab basis. In this multi-laboratory evaluation study using data from 11 sites worldwide, we have demonstrated that using SWATH-MS we can consistently detect and quantify more than 4,000 proteins from HEK293 cells and that the quantitative protein data generated across laboratories is reproducible. Using synthetic peptide dilution series, we have shown that the sensitivity, dynamic range and reproducibility established with SWATH-MS methods are also uniformly achieved across labs. This study demonstrates that SWATH-MS is a reproducible and accurate technique that can be confidently deployed for large-scale protein quantification in life science research.
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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 | 0.038 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".