Title |
An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples
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Published in |
Clinical Proteomics, January 2015
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DOI | 10.1186/1559-0275-12-3 |
Pubmed ID | |
Authors |
Xiao-jun Li, Lik Wee Lee, Clive Hayward, Mi-Youn Brusniak, Pui-Yee Fong, Matthew McLean, JoAnne Mulligan, Douglas Spicer, Kenneth C Fang, Stephen W Hunsucker, Paul Kearney |
Abstract |
Current quantification methods for mass spectrometry (MS)-based proteomics either do not provide sufficient control of variability or are difficult to implement for routine clinical testing. We present here an integrated quantification (InteQuan) method that better controls pre-analytical and analytical variability than the popular quantification method using stable isotope-labeled standard peptides (SISQuan). We quantified 16 lung cancer biomarker candidates in human plasma samples in three assessment studies, using immunoaffinity depletion coupled with multiple reaction monitoring (MRM) MS. InteQuan outperformed SISQuan in precision in all three studies and tolerated a two-fold difference in sample loading. The three studies lasted over six months and encountered major changes in experimental settings. Nevertheless, plasma proteins in low ng/ml to low μg/ml concentrations were measured with a median technical coefficient of variation (CV) of 11.9% using InteQuan. The corresponding median CV using SISQuan was 15.3% after linear fitting. Furthermore, InteQuan surpassed SISQuan in measuring biological difference among clinical samples and in distinguishing benign versus cancer plasma samples. We demonstrated that InteQuan is a simple yet robust quantification method for MS-based quantitative proteomics, especially for applications in biomarker research and in routine clinical testing. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Italy | 1 | 3% |
Unknown | 34 | 97% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 9 | 26% |
Student > Ph. D. Student | 8 | 23% |
Unspecified | 4 | 11% |
Student > Bachelor | 2 | 6% |
Other | 2 | 6% |
Other | 7 | 20% |
Unknown | 3 | 9% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 8 | 23% |
Medicine and Dentistry | 7 | 20% |
Biochemistry, Genetics and Molecular Biology | 6 | 17% |
Unspecified | 4 | 11% |
Chemistry | 3 | 9% |
Other | 4 | 11% |
Unknown | 3 | 9% |