The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Quantification of Cardiovascular Biomarkers in Patient Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution
Open accessAbstract
Verification of candidate biomarkers requires specific assays to selectively detect and quantify target proteins in accessible biofluids. The primary objective of verification is to screen potential biomarkers to ensure that only the highest quality candidates from the discovery phase are taken forward into preclinical validation. Because antibody reagents for a clinical grade immunoassay often exist for a small number of candidates, alternative methodologies are required to credential new and unproven candidates in a statistically viable number of serum or plasma samples. Using multiple reaction monitoring coupled with stable isotope dilution MS, we developed quantitative, multiplexed assays in plasma for six proteins of clinical relevance to cardiac injury. The process described does not require antibodies for immunoaffinity enrichment of either proteins or peptides. Limits of detection and quantitation for each signature peptide used as surrogates for the target proteins were determined by the method of standard addition using synthetic peptides and plasma from a healthy donor. Limits of quantitation ranged from 2 to 15 ng/ml for most of the target proteins. Quantitative measurements were obtained for one to two signature peptides derived from each target protein, including low abundance protein markers of cardiac injury in the nanogram/milliliter range such as the cardiac troponins. Intra- and interassay coefficients of variation were predominantly <10 and 25%, respectively. The configured multiplex assay was then used to measure levels of these proteins across three time points in six patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy. These results are the first demonstration of a multiplexed, MS-based assay for detection and quantification of changes in concentration of proteins associated with cardiac injury in the low nanogram/milliliter range. Our results also demonstrate that these assays retain the necessary precision, reproducibility, and sensitivity to be applied to novel and uncharacterized candidate biomarkers for verification of proteins in blood.
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Cites
Modern Applied Statistics with S
Venables, W. N., & Ripley, B. D. (2002) · Statistics and Computing · doi:10.1007/978-0-387-21706-2 · Cited by 24,305
Practical Nonparametric Statistics
Groggel, D. J. (2000) · Technometrics · doi:10.1080/00401706.2000.10486067 · Cited by 11,041
Bootstrap Methods and Their Application
Loughin, T. M., Davison, A. C., & Hinkley, D. V. (1999) · Journal of the American Statistical Association · doi:10.2307/2669707 · Cited by 5,418
Limits for qualitative detection and quantitative determination. Application to radiochemistry
Currie, L. A. (1968) · Analytical Chemistry · doi:10.1021/ac60259a007 · Cited by 4,553
Modern Applied Statistics With S
(2003) · Technometrics · doi:10.1198/tech.2003.s33 · Cited by 2,762
Protein biomarker discovery and validation: the long and uncertain path to clinical utility
Rifai, N., Gillette, M. A., & Carr, S. A. (2006) · Nature Biotechnology · doi:10.1038/nbt1235 · Cited by 1,867
Self-citationKrämer, W. (2003) · Computational Statistics & Data Analysis · Cited by 1,858
Practical Nonparametric Statistics (2nd ed).
Groeneveld, R. A., & Conover, W. J. (1981) · Journal of the American Statistical Association · doi:10.2307/2287607 · Cited by 1,849
Passing, H., & Bablok, W. (1983) · cclm · doi:10.1515/cclm.1983.21.11.709 · Cited by 1,513
Anderson, N. L., Anderson, N. G., Haines, L. R., Hardie, D. B., Olafson, R. W., & Pearson, T. W. (2004) · Journal of Proteome Research · doi:10.1021/pr034086h · Cited by 804
Cited by
Naba, A., Clauser, K. R., Hoersch, S., Liu, H., Carr, S. A., & Hynes, R. O. (2012) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m111.014647 · Cited by 1,354
Self-citationSelected reaction monitoring–based proteomics: workflows, potential, pitfalls and future directions
Picotti, P., & Aebersold, R. (2012) · Nature Methods · doi:10.1038/nmeth.2015 · Cited by 1,302
Single-cell sequencing-based technologies will revolutionize whole-organism science
Shapiro, E., Biezuner, T., & Linnarsson, S. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3542 · Cited by 1,177
Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology
Cox, J., & Mann, M. (2011) · Annual Review of Biochemistry · doi:10.1146/annurev-biochem-061308-093216 · Cited by 685
Carr, S. A., Abbatiello, S. E., Ackermann, B. L., Borchers, C., Domon, B., Deutsch, E. W., Grant, R. P., Hoofnagle, A. N., Hüttenhain, R., Koomen, J. M., et al. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.036095 · Cited by 561
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Metadata
- Authors
- Keshishian, H., Addona, T., Burgess, M., Mani, D. R., Shi, X., Kuhn, E., Sabatine, M. S., Gerszten, R. E., & Carr, S. A.
- Venue
- Molecular & Cellular Proteomics
- Publisher
- Elsevier BV
- Published
- 2009-10
- Volume
- 8
- Issue
- 10
- Pages
- 2339-2349
- ISSN
- 1535-9476 (ISSN-L)
- OpenAlex
- W2104073709
- Cited by
- 286
- References
- 31
- FWCI
- 14.47
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Keshishian, H., Addona, T., Burgess, M., Mani, D. R., Shi, X., Kuhn, E., Sabatine, M. S., Gerszten, R. E., & Carr, S. A. (2009). Quantification of Cardiovascular Biomarkers in Patient Plasma by Targeted Mass Spectrometry and Stable Isotope Dilution. Molecular & Cellular Proteomics. https://doi.org/10.1074/mcp.m900140-mcp200
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