Absolute Quantitation of Proteins in Human Blood by Multiplexed Multiple Reaction Monitoring Mass Spectrometry
Bibliographic record
Abstract
Multiple reaction monitoring (MRM)-mass spectrometry (MS) with stable isotope-labeled standards (SIS) has proven adept in rapidly, precisely, and accurately quantifying proteins in complex biological samples. The impetus behind the early use of multiplexed MRM in proteomics was to expedite the verification and validation stages of the protein biomarker pipeline for clinical utility, which involves the analysis of hundreds or even thousands of samples. Moreover, once a multiplexed assay has been developed, however, it can be turned around and used for biomarker discovery, as has been demonstrated for cancer biomarkers by our laboratory and by others. Overall, these MRM-based methods compare favorably with antibody-based techniques, such as ELISAs or protein arrays, in that MRM-based methods are less expensive and can be developed more rapidly. There are two MRM-based platforms that are currently being developed: a standard-flow and a nano-flow LC/ESI-MRM-MS (liquid chromatography-electrospray ionization) platform. In this book chapter, we describe a recent study in which we evaluated these two platforms, both interfaced to the same mass spectrometer. This study demonstrated the enhanced performance metrics (in terms of sensitivity, dynamic range, and robustness) of the standard-flow ultra-high performance liquid chromatography (UHPLC) system compared to the nano-flow HPLC-Chip for the absolute quantitation of 48 plasma proteins. Using the standard-flow platform, we also developed two high-throughput assays for the analysis of a panel of 67 cardiovascular disease (CVD) biomarkers in non-depleted and non-enriched human plasma and a panel of 25 putative biomarkers in dried human blood spots (DBS). Since the nanoLC/MRM-MS platform has advantages under sample-limited conditions and for the analysis of certain specific peptides, the protocols for both systems are described here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".