Qin Fu1, Eric Grote2, Jie Zhu2, Christine Jelinek2, Anna Köttgen3, Josef Coresh4, Jennifer E Van Eyk5. 1. Advanced Clinical Biosystems Research Institute, The Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA; qin.fu@cshs.org. 2. Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD; 3. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; Renal Division, Medical Center, University of Freiburg, Freiburg, Germany. 4. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; 5. Advanced Clinical Biosystems Research Institute, The Heart Institute, Cedars Sinai Medical Center, Los Angeles, CA; Department of Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD;
Abstract
BACKGROUND: Many avenues have been proposed for a seamless transition between biomarker discovery data and selected reaction monitoring (SRM) assays for biomarker validation. Unfortunately, studies with the abundant urinary protein uromodulin have shown that these methods do not converge on a consistent set of surrogate peptides for targeted mass spectrometry. As an alternative, we present an empirical peptide selection work flow for robust protein quantification. METHODS: We compared the relative SRM signal intensity of 12 uromodulin-derived peptides between tryptic digests of 9 urine samples. Pairwise CVs between the 12 peptides were 0.19-0.99. We used a correlation matrix to identify peptides that reproducibly tracked the amount of uromodulin protein and selected 4 peptides with robust and highly correlated SRM signals. Absolute quantification was performed with stable isotope-labeled versions of these peptides as internal standards and a standard curve prepared from a tryptic digest of purified uromodulin. RESULTS: Absolute quantification of uromodulin in 40 clinical urine samples yielded interpeptide correlations of ≥0.984 and correlations of ≥0.912 with ELISA data. The SRM assays were linear over >3 orders of magnitude and had typical interdigest CVs of <10%, interinjection CVs of <7%, and intertransition CVs of <7%. CONCLUSIONS: Comparing the apparent abundance of a plurality of peptides derived from the same target protein makes it possible to select signature peptides that are unaffected by the unpredictable confounding factors inevitably present in biological samples.
BACKGROUND: Many avenues have been proposed for a seamless transition between biomarker discovery data and selected reaction monitoring (SRM) assays for biomarker validation. Unfortunately, studies with the abundant urinary protein uromodulin have shown that these methods do not converge on a consistent set of surrogate peptides for targeted mass spectrometry. As an alternative, we present an empirical peptide selection work flow for robust protein quantification. METHODS: We compared the relative SRM signal intensity of 12 uromodulin-derived peptides between tryptic digests of 9 urine samples. Pairwise CVs between the 12 peptides were 0.19-0.99. We used a correlation matrix to identify peptides that reproducibly tracked the amount of uromodulin protein and selected 4 peptides with robust and highly correlated SRM signals. Absolute quantification was performed with stable isotope-labeled versions of these peptides as internal standards and a standard curve prepared from a tryptic digest of purified uromodulin. RESULTS: Absolute quantification of uromodulin in 40 clinical urine samples yielded interpeptide correlations of ≥0.984 and correlations of ≥0.912 with ELISA data. The SRM assays were linear over >3 orders of magnitude and had typical interdigest CVs of <10%, interinjection CVs of <7%, and intertransition CVs of <7%. CONCLUSIONS: Comparing the apparent abundance of a plurality of peptides derived from the same target protein makes it possible to select signature peptides that are unaffected by the unpredictable confounding factors inevitably present in biological samples.
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