BoxCar and Library-Free Data-Independent Acquisition Substantially Improve the Depth, Range, and Completeness of Label-Free Quantitative Proteomics
Bibliographic record
Abstract
Data-dependent acquisition (DDA) methods are the current standard for quantitative proteomics in many biological systems. However, DDA preferentially measures highly abundant proteins and generates data that is plagued with missing values, requiring extensive imputation. Here, we demonstrate that library-free BoxCarDIA acquisition, combining MS1-level BoxCar acquisition with MS2-level data-independent acquisition (DIA) analysis, outperforms conventional DDA and other library-free DIA (directDIA) approaches. Using a combination of low- (HeLa cells) and high- ( Arabidopsis thaliana cell culture) dynamic range sample types, we demonstrate that BoxCarDIA can achieve a 40% increase in protein quantification over DDA without offline fractionation or an increase in mass-spectrometer acquisition time. Further, we provide empirical evidence for substantial gains in dynamic range sampling that translates to deeper quantification of low-abundance protein classes under-represented in DDA and directDIA data. Unlike both DDA and directDIA, our new BoxCarDIA method does not require full MS 1 scans while offering reproducible protein quantification between replicate injections and providing more robust biological inferences. Overall, our results advance the BoxCarDIA technique and establish it as the new method of choice for label-free quantitative proteomics across diverse sample types.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".