Protein quantification in dried blood spots by MRM mass spectrometry (981.8)
Bibliographic record
Abstract
Dried blood spot (DBS) sampling offers proven advantages over intravenous blood collection for clinical diagnostics targeting a wide array of biomarkers. The simplicity of this approach enables minimally‐trained staff to collect less than 100 microliters of blood from patients. Furthermore, many analytes are stable in the DBS format at room temperature reducing the challenges of sample storage and transportation. The most common clinical application of DBS sampling is the screening newborns for metabolism disorders by targeting small molecules by multiple reaction monitoring mass spectrometry (MRM‐MS). In addition, DBS‐MRM is increasingly employed for pre‐clinical toxicology and pharmacokinetics studies supporting small molecule drug development. The goal of our work is to integrate DBS methodology with multiplexed MRM assays for the quantification of endogenous proteins in human blood. Highly reproducible methods were developed for extracting dried proteins from collection cards (coefficient of variation <15% for full process technical replicates). These samples were then digested with trypsin and spiked with stable isotope‐labeled standard peptides to improve the precision of the assay. Finally, peptides were separated by reversed‐phase liquid chromatography and detected by an Agilent 6490 triple quadrupole mass spectrometer. Robust MRM assays were generated for over 30 proteins and most were stable in DBS samples over a wide range of storage temperatures. This work demonstrates considerable promise for clinical MRM assays targeting endogenous proteins in DBS samples. Grant Funding Source : Supported by Genome Canada, Genome BC, and the Western Economic Diversification of Canada
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".