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      An integrated quantification method to increase the precision, robustness, and resolution of protein measurement in human plasma samples

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          Abstract

          Background

          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.

          Results

          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.

          Conclusions

          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/1559-0275-12-3) contains supplementary material, which is available to authorized users.

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          Most cited references31

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          Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS.

          A need exists for technologies that permit the direct quantification of differences in protein and posttranslationally modified protein expression levels. Here we present a strategy for the absolute quantification (termed AQUA) of proteins and their modification states. Peptides are synthesized with incorporated stable isotopes as ideal internal standards to mimic native peptides formed by proteolysis. These synthetic peptides can also be prepared with covalent modifications (e.g., phosphorylation, methylation, acetylation, etc.) that are chemically identical to naturally occurring posttranslational modifications. Such AQUA internal standard peptides are then used to precisely and quantitatively measure the absolute levels of proteins and posttranslationally modified proteins after proteolysis by using a selected reaction monitoring analysis in a tandem mass spectrometer. In the present work, the AQUA strategy was used to (i) quantify low abundance yeast proteins involved in gene silencing, (ii) quantitatively determine the cell cycle-dependent phosphorylation of Ser-1126 of human separase protein, and (iii) identify kinases capable of phosphorylating Ser-1501 of separase in an in vitro kinase assay. The methods described here represent focused, alternative approaches for studying the dynamically changing proteome.
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            Removing Batch Effects in Analysis of Expression Microarray Data: An Evaluation of Six Batch Adjustment Methods

            The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.
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              Quantitative mass spectrometric multiple reaction monitoring assays for major plasma proteins.

              Quantitative LC-MS/MS assays were designed for tryptic peptides representing 53 high and medium abundance proteins in human plasma using a multiplexed multiple reaction monitoring (MRM) approach. Of these, 47 produced acceptable quantitative data, demonstrating within-run coefficients of variation (CVs) (n = 10) of 2-22% (78% of assays had CV <10%). A number of peptides gave CVs in the range 2-7% in five experiments (10 replicate runs each) continuously measuring 137 MRMs, demonstrating the precision achievable in complex digests. Depletion of six high abundance proteins by immunosubtraction significantly improved CVs compared with whole plasma, but analytes could be detected in both sample types. Replicate digest and depletion/digest runs yielded correlation coefficients (R(2)) of 0.995 and 0.989, respectively. Absolute analyte specificity for each peptide was demonstrated using MRM-triggered MS/MS scans. Reliable detection of L-selectin (measured at 0.67 microg/ml) indicates that proteins down to the microg/ml level can be quantitated in plasma with minimal sample preparation, yielding a dynamic range of 4.5 orders of magnitude in a single experiment. Peptide MRM measurements in plasma digests thus provide a rapid and specific assay platform for biomarker validation, one that can be extended to lower abundance proteins by enrichment of specific target peptides (stable isotope standards and capture by anti-peptide antibodies (SISCAPA)).
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                Author and article information

                Contributors
                xli@indidx.com
                llee@indidx.com
                chayward@indidx.com
                mbrusnia@fhcrc.org
                pfong@indidx.com
                matt.mclean@dupont.com
                jmulligan@indidx.com
                dspicer@indidx.com
                kfang@indidx.com
                shunsucker@indidx.com
                pkearney@indidx.com
                Journal
                Clin Proteomics
                Clin Proteomics
                Clinical Proteomics
                BioMed Central (London )
                1542-6416
                1559-0275
                29 January 2015
                2015
                : 12
                : 1
                : 3
                Affiliations
                [ ]Integrated Diagnostics, 219 Terry Avenue North, Suite 100, 98109 Seattle, WA USA
                [ ]Fred Hutchinson Cancer Research Center, 1100 Fairview Ave N., M4-A830, 98109 Seattle, WA USA
                [ ]DuPont Industrial Biosciences, 925 Page Mill Road, Palo, 94304 Alto, CA USA
                Article
                91
                10.1186/1559-0275-12-3
                4363461
                25838814
                61ff1f0c-41bb-432a-b7f9-866752d40ca4
                © Li et al.; licensee BioMed Central. 2015

                This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 2 October 2014
                : 26 December 2014
                Categories
                Research
                Custom metadata
                © The author(s) 2015

                Molecular medicine
                multiple reaction monitoring,plasma or serum analysis,quantitative proteomics,clinical proteomics,mass spectrometry,immunoaffinity depletion,bioinformatics

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