Enhanced Identifications and Quantification Through Retention Time Down-Sampling in Fast-Cycling Diagonal-PASEF Methods
Bibliographic record
Abstract
Data-independent acquisition (DIA) mass spectrometry is essential for comprehensive quantification of proteomes, enabling deeper insights into cellular processes and disease mechanisms. On the timsTOF platform, diagonal-PASEF acquisition methods have recently been introduced to directly and continuously cover the observed diagonal shape of the peptide precursor ion distributions. Diagonal-PASEF has already shown great promise, and its adaptation as a routine workflow can be further pushed with improved method development as well as enhanced algorithmic solutions. Here, we conducted a systematic and comprehensive optimization of diagonal-PASEF for 17-min gradients on the timsTOF HT in conjunction with Spectronaut. We demonstrate that Spectronaut fully supports all tested diagonal-PASEF methods independent of the number of slices or overlaps and with minimal user intervention required. We derive an optimized analysis strategy where we coupled diagonal-PASEF acquisitions to retention time down-sampling by summation (RTsum) and thereby exploit the fast-cycling nature of diagonal-PASEF methods. Through the combination of RTsum with diagonal-PASEF, we demonstrate that this strategy yields higher signal-to-noise ratios while retaining the peak shape for analytes of interest ultimately improving overall number of peptide and protein identifications of diagonal-PASEF. Importantly, combining RTsum with diagonal-PASEF improved overall identifications and quantitative precision when compared to dia-PASEF with variable quadrupole isolation widths and across different input amounts for cell line injections. We also tested the performance of diagonal-PASEF in controlled quantitative experiments where diagonal-PASEF outperformed dia-PASEF in the overall number of retained candidates below 1% or 5% error-rate, quantitative precision, and identifications on peptide level and protein level. These data indicate that RTsum demonstrates a positive use case of the high sampling rate of diagonal-PASEF and might therefore be a valuable addition to existing analysis pipelines. Collectively, our findings imply that diagonal-PASEF is developing into a competitive alternative to dia-PASEF and that the data analysis options are making fast progress.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".