Ultra-Fast Multi-Organ Proteomics Unveils Tissue-Specific Mechanisms of Drug Efficacy and Toxicity
Bibliographic record
Abstract
Rapid and comprehensive analysis of complex proteomes across large sample sets is vital for unlocking the potential of systems biology. We present UFP-MS, an ultra-fast mass spectrometry (MS) proteomics method that integrates narrow-window data-independent acquisition (nDIA) with short-gradient micro-flow chromatography, enabling profiling of >240 samples per day. This optimized MS approach identifies 6,201 and 7,466 human proteins with 1- and 2-min gradients, respectively. Our streamlined sample preparation workflow features high-throughput homogenization, adaptive focused acoustics (AFA)-assisted proteolysis, and Evotip-accelerated desalting, allowing for the processing of up to 96 tissue samples in 5 h. As a practical application, we analyzed 507 samples from 13 mouse tissues treated with the enzyme-drug L-asparaginase (ASNase) or its glutaminase-free Q59L mutant, generating a quantitative profile of 11,472 proteins following drug treatment. The MS results confirmed the impact of ASNase on amino acid metabolism in solid tissues. Further analysis revealed broad suppression of anticoagulants and cholesterol metabolism and uncovered numerous tissue-specific dysregulated pathways. In summary, the UFP-MS method greatly accelerates the generation of biological insights and clinically actionable hypotheses into tissue-specific vulnerabilities targeted by ASNase.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".