Intelligent Data Acquisition Blends Targeted and Discovery Methods

Bailey, Derek J.; McDevitt, Molly T.; Westphall, Michael S.; Pagliarini, David J.; Coon, Joshua J.

doi:10.1021/pr401278j

Cited by 50 publications

(71 citation statements)

References 58 publications

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“…neutral-loss triggered MS 3 ) (25,26) to generate the most informative fragmentation spectra according to the characteristics of the peptide under investigation. In addition, "Intelligent" data acquisition methods were designed to increase the MS/MS acquisition frequency of the peptides of interest in directed analyses (27), and to improve the acquisition efficiency (through a better scheduling (9,17,28)) and the measurement specificity of TDA (10,29). The IS-PRM method described in this study is taking advantage of dynamic data acquisition schemes to carry out targeted proteomics experiments at an unprecedented combination of scale and analytical performance.…”

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confidence: 99%

Large-Scale Targeted Proteomics Using Internal Standard Triggered-Parallel Reaction Monitoring (IS-PRM)*

Gallien

Kim

Domon

2015

Molecular & Cellular Proteomics

186

185

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Targeted high-resolution and accurate mass analyses performed on fast sequencing mass spectrometers have opened new avenues for quantitative proteomics. More specifically, parallel reaction monitoring (PRM) implemented on quadrupole-orbitrap instruments exhibits exquisite selectivity to discriminate interferences from analytes. Furthermore, the instrument trapping capability enhances the sensitivity of the measurements. The PRM technique, applied to the analysis of limited peptide sets (typically 50 peptides or less) in a complex matrix, resulted in an improved detection and quantification performance as compared with the reference method of selected reaction monitoring performed on triple quadrupole instruments. However, the implementation of PRM for the analysis of large peptide numbers requires the adjustment of mass spectrometry acquisition parameters, which affects dramatically the quality of the generated data, and thus the overall output of an experiment. A newly designed data acquisition scheme enabled the analysis of moderate-to-large peptide numbers while retaining a Liquid chromatography (LC) 1 coupled to tandem mass spectrometry (MS/MS) approaches have been widely acknowledged as one of the most effective methods to study complex proteomes. In particular, their preclinical, and also clinical applications, have contributed to advances in biomedical sciences. A bottom-up proteomics workflow relies on the enzymatic digestion of the proteins constituting a proteome to generate thousands of peptides, which are subsequently separated by liquid chromatography and analyzed by tandem mass spectrometry. Two main MS-based strategies have emerged from this generic process, which differ in their objectives and acquisition schemes and are commonly referred to as discovery and targeted strategies, respectively; both presenting advantages and drawbacks to study specific biological and clinical questions.Discovery proteomics, relying on nonsupervised data dependent acquisition (DDA), is routinely used to effectively profile, with broad coverage, the proteome under investigation (1, 2). This strategy is focused on protein identification but has limitations with respect to quantitative applications. The stochastic nature of DDA sampling results in "missing values" in replicated experiments, directly affecting quantitative studies, whereas low abundance components remain largely undetected (3). By contrast, targeted proteomics has emerged to more systematically quantify peptides/proteins present in a wide range of concentrations in complex samples (4). The hypothesis-driven nature of targeted data acquisition (TDA), where the peptides used as surrogates for a preselected set of proteins are consistently measured across a multitude of

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mentioning

confidence: 99%

Large-Scale Targeted Proteomics Using Internal Standard Triggered-Parallel Reaction Monitoring (IS-PRM)*

Gallien

Kim

Domon

2015

Molecular & Cellular Proteomics

186

185

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“…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).…”

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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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“…For NCI-60 experiments, minimum and maximum elution order (EO) indexes (Bailey et al, 2014) were placed in the fields reversed for the start and stop retention time values. These EO indexes were used so that only ~17 trigger peptides were considered at any given point in the experiment.…”

Section: Methodsmentioning

confidence: 99%

A Strategy to Combine Sample Multiplexing with Targeted Proteomics Assays for High-Throughput Protein Signature Characterization

et al. 2017

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SUMMARY Targeted mass spectrometry assays for protein quantitation monitor peptide surrogates, which are easily multiplexed to target many peptides in a single assay. However, these assays have generally not taken advantage of sample multiplexing which allows up to 10 analyses to occur in parallel. We present a two-dimensional multiplexing workflow that utilizes synthetic peptides for each protein to prompt the simultaneous quantification of >100 peptides from up to 10 mixed sample conditions. We demonstrate that targeted analysis of unfractionated lysates (2-hr) accurately reproduces the quantification of fractionated lysates (72-hr analysis), while obviating the need for peptide detection prior to quantification. We targeted 131 peptides corresponding to 69 proteins across all 60 National Cancer Institute cell lines in biological triplicate, analyzing 180 samples in only 48 hours (the equivalent of 16 min/sample). These data further elucidated a correlation between the expression of key proteins and their cellular response to drug treatment.

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Intelligent Data Acquisition Blends Targeted and Discovery Methods

Cited by 50 publications

References 58 publications

Large-Scale Targeted Proteomics Using Internal Standard Triggered-Parallel Reaction Monitoring (IS-PRM)*

Large-Scale Targeted Proteomics Using Internal Standard Triggered-Parallel Reaction Monitoring (IS-PRM)*

DeMix-Q: Quantification-Centered Data Processing Workflow

A Strategy to Combine Sample Multiplexing with Targeted Proteomics Assays for High-Throughput Protein Signature Characterization

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