B-153 Improving Accuracy of IgG subclasses Quantitation in Serum Using Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) on SCIEX 7500
Bibliographic record
Abstract
Abstract Background Serum IgG subclasses (IgGSC) are most often measured for identification of selective immunodeficiency disease and IgG4-related disease (IgG4RD). Traditional nephelometric (IN) assays suffer from errors related to antigen excess and cross-reactivity that can impact the accuracy of results. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been the routine testing methodology for serum IgG subclasses at St. Paul’s Hospital since 2015 due to the simplicity and freedom from inter-subclass cross-reactivity problems. In brief, 20 μL of serum sample is denatured, reduced, alkylated and digested, processed semi-automatically on Hamilton Starlet liquid handler. This is followed by analysis on a SCIEX 5500 QTRAP LC-MS/MS. However, when the method was migrated to the SCIEX 7500, it suffered from calibration curve splitting and poor between-device comparability. It was hypothesized that this could be caused by detuning of some high concentration analytes, suboptimal internal standard (IS) concentrations and, suboptimal IS multiple reaction monitoring (MRM) transition choices, and matrix effects from artificial calibrators. Methods Several modifications were made to improve accuracy and robustness: 1) new in-house calibrators were prepared using patient sample pool instead of using Binding Site IgG Subclass Kit calibrators. 2) More IS MRM transitions were added to the method with each analyte MRM quantitated using its own IS MRM using the same fragment ion. The IS concentration was also increased 5-fold for IgG1 and Total IgG. 3) To avoid problems arising from selectively ion detuning, the injection volume was reduced from 10 μL to 1 μL which permitted collision energies (CEs) to be normalized for each analyte/IS MRM pair. Results The new SCIEX 7500 method compares well with the original method (N=168) with Passing Bablok regression slopes of 0.94, 1.1, 1.0, 0.98, and 0.98 for IgG1-4 and IgG total respectively with corresponding intercepts of 0.3, -0.1, 0.0, 0.0, and 0.41 g/L and R^2 of 0.96, 0.95, 0.99, 0.98, and 0.95. Median differences were -1.76%, 1.07%, 1.06%, 6.13% and 1.91%. In a typical batch of 90 patient samples and four sets of calibrators, the calibration curves overlap well. Use of patient-based calibrators allowed calibration values to be centered in the range of typical patient sample values, particularly for IgG4, where the calibration curve was extended to 3.46 g/L compared with the previous high calibrator value of 0.48 g/L. Conclusions The new IgG subclass quantitation method on SCEIX 7500 LC-MS/MS system is robust with excellent reproducibility and between-device comparability.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".