Optimization of bottom-up proteomic sample preparation by design of experiments for absolute quantification of 257 proteins in human plasma using UPLC-MRM-MS
Bibliographic record
Abstract
INTRODUCTION: Targeted protein absolute quantification using mass spectrometry holds promise for identifying biomarkers for diagnosis, prognosis, and personalized medicine. However, complex and time-consuming workflows, particularly during sample preparation, present significant bottlenecks. Addressing these challenges is critical for the applicability of absolute quantification of proteins in clinical research settings. AREAS COVERED: We explore optimization strategies for protein digestion in bottom-up proteomics sample preparation. Design of experiments (DoE), a statistical approach for systematically evaluating multiple experimental factors, was used for simultaneous optimization of digestion time, temperature, enzyme-to-protein substrate ratio, and denaturing agent. Furthermore, the lower limit of quantification (LLOQ) for our platform was improved by using the Waters Xevo TQ-XS UPLC-MRM-MS. The integration of automated sample preparation into the workflow enabled reproducible absolute quantification of 257 proteins in human plasma. EXPERT OPINION: We successfully reduced protein digestion time from 18 hours (overnight) to 4 hours while maintaining relative digestion efficiency. We improved the sensitivity of the assay via the optimized workflow and were able to quantify proteins that previously fell below the LLOQ. These advancements, combined with automation, provide a practical, efficient, and reproducible workflow suitable for clinical research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".