Estimation of measurement uncertainty for the quantification of protein by ID-LC-MS/MS

The emergence of mass spectrometry (MS)-based methods to quantify proteins for clinical applications has led to the need for accurate and consistent measurements. To meet the clinical needs of MS-based protein results, it is important that the results are traceable to higher-order standards and methods and have defined uncertainty values. Therefore, we outline a comprehensive approach for the estimation of measurement uncertainty of a MS-based procedure for the quantification of a protein biomarker. Using a bottom-up approach, which is the model outlined in the Guide to the Expression of Uncertainty of Measurement (GUM), we evaluated the uncertainty components of a MS-based measurement procedure for a protein biomarker in a complex matrix. The cause-and-effect diagram of the procedure is used to identify each uncertainty component, and statistical equations are derived to determine the overall combined uncertainty. Evaluation of the uncertainty components not only enables the calculation of the measurement uncertainty but can also be used to determine if the procedure needs improvement. To demonstrate the use of the bottom-up approach, the overall combined uncertainty is estimated for the National Institute of Standards and Technology (NIST) candidate reference measurement procedure for albumin in human urine. The results of the uncertainty approach are applied to the determination of uncertainty for the certified value for albumin in candidate NIST Standard Reference Material (R) (SRM) 3666. This study provides a framework for measurement uncertainty estimation of a MS-based protein procedure by identifying the uncertainty components of the procedure to derive the overall combined uncertainty.

Automated summary

The emergence of mass spectrometry (MS)-based methods for protein quantification in clinical applications has necessitated the need for accurate and consistent measurements. A comprehensive approach for estimating the measurement uncertainty of a MS-based procedure for protein quantification is outlined using a bottom-up approach. The uncertainty components of the procedure are evaluated using a cause-and-effect diagram, and statistical equations are derived to determine the overall combined uncertainty.