Library-free BoxCarDIA solves the missing value problem in label-free quantitative proteomics
Bibliographic record
Abstract
Abstract The last decade has seen significant advances in the application of quantitative mass spectrometry-based proteomics technologies to tackle important questions in plant biology. The current standard for quantitative proteomics in plants is the use of data-dependent acquisition (DDA) analysis with or without the use of chemical labels. However, the DDA approach preferentially measures higher abundant proteins, and often requires data imputation due to quantification inconsistency between samples. In this study we systematically benchmarked a recently developed library-free data-independent acquisition (directDIA) method against a state-of-the-art DDA label-free quantitative proteomics workflow for plants. We next developed a novel acquisition approach combining MS 1 -level BoxCar acquisition with MS 2 -level directDIA analysis that we call BoxCarDIA. DirectDIA achieves a 33% increase in protein quantification over traditional DDA, and BoxCarDIA a further 8%, without any changes in instrumentation, offline fractionation, or increases in mass-spectrometer acquisition time. BoxCarDIA, especially, offers wholly reproducible quantification of proteins between replicate injections, thereby addressing the long-standing missing-value problem in label-free quantitative proteomics. Further, we find that the gains in dynamic range sampling by directDIA and BoxCarDIA translate to deeper quantification of key, low abundant, functional protein classes (e.g., protein kinases and transcription factors) that are underrepresented in data acquired using DDA. We applied these methods to perform a quantitative proteomic comparison of dark and light grown Arabidopsis cell cultures, providing a critical resource for future plant interactome studies. Our results establish BoxCarDIA as the new method of choice in quantitative proteomics using Orbitrap-type mass-spectrometers, particularly for proteomes with large dynamic range such as that of plants.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".