Publications (2)10.97 Total impact
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Article: An assessment of peptide enrichment methods employing mTRAQ quantification approaches.
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ABSTRACT: The human plasma peptidome has potential in biomarker discovery not least because the plasma proteome is a challenging matrix due to its complexity and dynamic range. However, methods to significantly reduce the amount of protein present in plasma while retaining the less abundant peptides present in plasma samples has been a major issue. Here, we present a novel strategy which has been employed to assess the effectiveness of removing interfering proteins while retaining peptides of interest. To monitor peptide retention, a spiked in digested protein, in this case a synthetic QconCAT protein, was employed. This enabled a variety of target analytes (peptides) to be monitored for their retention in liquid phase, providing a broader picture of peptide loss from each method assessed. The incorporation of mTRAQ labeling allowed the presence of each peptide to be monitored, and accurate peptide losses to be determined in a Selected Reaction Monitoring (SRM) assay, thus, enabling an objective semiquantitative conclusion to be drawn regarding the suitability of each method for protein removal and peptide retention. We also assessed a range of methods for retaining nontryptic peptides in a plasma peptidomics workflow. From these data, we determined an optimal workflow for removing intact protein, while retaining peptides for MS-based analyses.Analytical Chemistry 07/2012; 84(13):5604-10. · 5.86 Impact Factor -
Article: Statistical Considerations of Optimal Study Design for Human Plasma Proteomics and Biomarker Discovery
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ABSTRACT: A mass spectrometry-based plasma biomarker discovery workflow was developed to facilitate biomarker discovery. Plasma from either healthy volunteers or patients with pancreatic cancer was 8-plex iTRAQ labeled, fractionated by 2-dimensional reversed phase chromatography and subjected to MALDI ToF/ToF mass spectrometry. Data were processed using a q-value based statistical approach to maximize protein quantification and identification. Technical (between duplicate samples) and biological variance (between and within individuals) were calculated and power analysis was thereby enabled. An a priori power analysis was carried out using samples from healthy volunteers to define sample sizes required for robust biomarker identification. The result was subsequently validated with a post hoc power analysis using a real clinical setting involving pancreatic cancer patients. This demonstrated that six samples per group (e.g., pre- vs post-treatment) may provide sufficient statistical power for most proteins with changes >2 fold. A reference standard allowed direct comparison of protein expression changes between multiple experiments. Analysis of patient plasma prior to treatment identified 29 proteins with significant changes within individual patient. Changes in Peroxiredoxin II levels were confirmed by Western blot. This q-value based statistical approach in combination with reference standard samples can be applied with confidence in the design and execution of clinical studies for predictive, prognostic, and/or pharmacodynamic biomarker discovery. The power analysis provides information required prior to study initiation.Keywords: mass spectrometry; plasma proteomics; biomarkers; power analysisJournal of Proteome Research 04/2012; 11(4). · 5.11 Impact Factor
