Protein Quantification by Mass Spectrometry: Is It Ready for Prime Time?
Bibliographic record
Abstract
The successful interface of liquid chromatography with tandem mass spectrometry (LC-MS/MS)2 in the 1980s opened new avenues for measuring low and high molecular weight analytes with exceptional analytical specificity and sensitivity. As discussed in the previous Q & A article, mass spectrometry (MS) is now a routine tool for measuring steroid hormones, drugs, vitamins, amino acids, biogenic amines, and many other classes of small molecules. We are now entering an era of protein quantification by MS for diagnostic purposes. The challenges for measuring proteins vs small molecules with MS are well recognized. In this Q & A article, 4 leaders in the field have been asked to comment on current and future capabilities of MS to quantify proteins (single or multiple) without the need for antibodies or other labeling reagents. Why do you think MS-based methods for measuring proteins are not yet in widespread use in clinical laboratories? Samir Hanash3 : The instrumentation available in clinical laboratories generally has features particularly designed to meet the work flow and performance requirements applicable to a clinical laboratory, together with standard operating procedures. Proteomic analysis by MS is currently applied primarily for discovery and does not meet these requirements for routine clinical assays. At best, it would have to be considered a “specialized assay platform,” available at a limited number of laboratories. Mary Lopez4 : There exists the misconception that MS-based assays are difficult and require very experienced operators. The rapid evolution of this technology has made its operation no more complicated than the operation of clinical analyzers. There is also a perception that MS-based assays are expensive. With higher throughput, and the ability to multiplex assays, the cost per assay is not much different than for ELISAs or other routine assays. Lastly, there is a natural reluctance of users …
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".