B-153 Improving Accuracy of IgG subclasses Quantitation in Serum Using Liquid Chromatography - Tandem Mass Spectrometry (LC-MS/MS) on SCIEX 7500
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».