Label-Free Comparative Analysis of Proteomics Mixtures Using Chromatographic Alignment of High-Resolution μLC−MS Data

Finney, Gregory L.; Blackler, Adele; Hoopmann, Michael R.; Canterbury, Jesse D.; Wu, Christine C.; MacCoss, Michael J.

doi:10.1021/ac701649e

Cited by 59 publications

(69 citation statements)

References 62 publications

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“…Our LF quantitation approach is analogous to commonly used methods for MS1 signal profiling, in which a standardized LC-MS analysis and peak areas derived from peptide ions are used as the basis for sample comparisons (33,36,37,44). Our method differs from such MS1 profiling in that peak areas are extracted from MRM transitions, which are summed to generate peptide or protein peak areas.…”

Section: Discussionmentioning

confidence: 99%

“…The integrated peak areas for transitions from each peptide analyte are summed and reported as "peak area." The LF method is analogous to MS1 profiling approaches to peptide quantitation (32)(33)(34)(35)(36)(37), except that we have extended the concept from analysis of the MS1 signal to analysis of MRM transitions.…”

Section: Overview Of Analytical Methods and Approach To Comparisons-tmentioning

confidence: 99%

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Methods for Peptide and Protein Quantitation by Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry

Zhang

Liu

Zimmerman

et al. 2011

Molecular & Cellular Proteomics

108

139

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Liquid chromatography-multiple reaction monitoring mass spectrometry of peptides using stable isotope dilution (SID) provides a powerful tool for targeted protein quantitation. However, the high cost of labeled peptide standards for SID poses an obstacle to multiple reaction monitoring studies. We compared SID to a labeled reference peptide (LRP) method, which uses a single labeled peptide as a reference standard for all measured peptides, and a label-free (LF) approach, in which quantitation is based on analysis of un-normalized peak areas for detected MRM transitions. We analyzed peptides from the Escherichia coli proteins alkaline phosphatase and ␤-galactosidase spiked into lysates from human colon adenocarcinoma RKO cells. We also analyzed liquid chromatography-multiple reaction monitoring mass spectrometry data from a recently published interlaboratory study by the National A rapidly evolving approach to protein quantitation is the targeted analysis of representative peptides by liquid chromatography-tandem mass spectrometry by multiple reaction monitoring (LC-MRM-MS) 1 analysis (1-3). In this approach, peptides are quantified by monitoring several MRM transitions for each peptide with either a triple quadrupole or a quadrupole-ion trap instrument. Stable isotope dilution (SID), in which labeled peptides are used as internal standards is considered the gold standard for rigorous quantitation by LC-MRM-MS (1, 4, 5). In contrast to antibody-based quantitation, where antibody availability and specificity are often limiting, LC-MRM-MS enables configuration of an assay for essentially any protein. In practice, this approach has proven sensitive enough to apply to challenging protein quantitation problems. For example, proteins can be quantified at singledigit copy numbers in cells (6) and in plasma at levels approaching ng/ml (7,8). With antibody-based enrichment, LC-MRM-MS can achieve even greater sensitivity (9 -12).

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Section: Discussionmentioning

confidence: 99%

Section: Overview Of Analytical Methods and Approach To Comparisons-tmentioning

confidence: 99%

Methods for Peptide and Protein Quantitation by Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry

Zhang

Liu

Zimmerman

et al. 2011

Molecular & Cellular Proteomics

108

139

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“…These deviating peptides with questionable identities can drastically worsen the variances of protein abundances in the whole dataset and thus reduce the statistical power of the experiment (31). In contrast, in a quantification-centered approach, peptide abundance is the central factor to be investigated (9,15,(32)(33)(34). When peptide abundance is considered together with other parameters, such as RT difference and mass error, for the overall assessment of peptide reliability, deviating abundance behavior may result in exclusion of a given peptide from consideration (35).…”

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confidence: 99%

DeMix-Q: Quantification-Centered Data Processing Workflow

Zhang

Käll

Zubarev

2016

Molecular & Cellular Proteomics

120

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Label-free quantification (LFQ) is one of the most efficient approaches for quantifying proteome differences between multiple states of a biological system. LFQ aims to reproducibly identify and quantify peptides through multiple liquid-chromatography-coupled tandem mass spectrometry (LC-MS/MS) experiments. In the popular data-dependent acquisition (DDA) approach named Top-N DDA, the appearance of a peptide-like signal in a "survey" mass spectrum triggers a tandem mass spectrometry (MS/MS) event, targeting the (N) most-abundant precursor ions. Previous studies have shown that, due to the limited speed of a mass spectrometer, the majority of peptide ions detected in MS 1 are not targeted in MS/MS, especially when a nonfractionated complex sample is analyzed (1, 2). This low sampling efficiency (Ͻ50%), combined with the stochastic nature of precursor selection and a limited efficiency of MS/MS identification (Ͻ70%) (3), frequently causes the absence of MS/MS identification for an individual peptide in some LC-MS/MS experiments ("runs") within a larger dataset, even when replicate measurements are made (4). This deficiency is known as the missing value problem in LFQ. The problem significantly limits the size of the DDA-acquired proteomics dataset across which reliable quantification can be made for each protein (5, 6).One of the causes of the missing value problem is the traditional focus on the process of identifying a peptide as opposed to its quantification. For historical reasons, peptide sequence identification has been considered the focal point and the most important step in the whole proteomics procedure, while quantification came as almost an afterthought (7,8). This dominant proteomics paradigm can be characterized as the identification-centered approach, also known as a spectrum-centric approach (9). Only gradually the missing value problem has been identified as one of the biggest drawbacks of the DDA approach (4, 5). To address the reproducibility issue in MS/MS identification, several alternative data acquisition strategies had been suggested, including targeted (10) and semi-targeted (11, 12) approaches. However, none of the improved DDA strategies has solved the missing value problem anywhere close to the data-independent acquisition (DIA) (13,14). The latter approach, however, typically provides somewhat lower depth and breadth of the proteome coverage than the DDA methods.In our opinion, the DDA-associated missing value problem is caused by the sequential execution of two independent processes: peptide identification by MS/MS and its quantification by MS 1 . At first glance, performing MS 1 -based quantification simultaneously with MS/MS identification should provide an obvious solution to the missing value problem. Since MS 1 spectra contain many more peptide ions than are selected for MS/MS in DDA (or identified in DIA), the peptide's mass information is practically always present when an iden-

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“…To address the limitations of image-based methods, more recent approaches have relied on complex algorithms that process individual spectra one retention time point at a time and combine those results (14)(15)(16). We skip this individual scanprocessing step, representing the data in two dimensions: retention time and m/z.…”

Section: Background Lc-ms/ms Datamentioning

confidence: 99%

Protein quantification across hundreds of experimental conditions

Khan

Bloom

Garcia³

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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Quantitative studies of protein abundance rarely span more than a small number of experimental conditions and replicates. In contrast, quantitative studies of transcript abundance often span hundreds of experimental conditions and replicates. This situation exists, in part, because extracting quantitative data from large proteomics datasets is significantly more difficult than reading quantitative data from a gene expression microarray. To address this problem, we introduce two algorithmic advances in the processing of quantitative proteomics data. First, we use spacepartitioning data structures to handle the large size of these datasets. Second, we introduce techniques that combine graphtheoretic algorithms with space-partitioning data structures to collect relative protein abundance data across hundreds of experimental conditions and replicates. We validate these algorithmic techniques by analyzing several datasets and computing both internal and external measures of quantification accuracy. We demonstrate the scalability of these techniques by applying them to a large dataset that comprises a total of 472 experimental conditions and replicates.kd-tree ͉ orthogonal range query ͉ quantitative proteomics ͉ space partitioning data structures ͉ tandem mass spectrometry

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Label-Free Comparative Analysis of Proteomics Mixtures Using Chromatographic Alignment of High-Resolution μLC−MS Data

Cited by 59 publications

References 62 publications

Methods for Peptide and Protein Quantitation by Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry

Methods for Peptide and Protein Quantitation by Liquid Chromatography-Multiple Reaction Monitoring Mass Spectrometry

DeMix-Q: Quantification-Centered Data Processing Workflow

Protein quantification across hundreds of experimental conditions

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