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Title: Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Abstract

Selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), is a data acquisition technique used in targeted analysis of molecules, including targeted proteomic studies. It exploits the unique capability of triple-quadrupole (QQQ) mass spectrometers to monitor the predefined precursor and fragment ion pairs of specific molecules of interest throughout a liquid chromatography (LC) elution profile. Compared to shotgun proteomics, targeted proteomics using SRM has high selectivity, high sensitivity, and a wide linear dynamic range,(1-3) which makes it especially useful in the accurate and reproducible quantification of low-abundance proteins in highly complex biological samples. SRM has been widely used in the fields of biomarker discovery,(4-7) analysis of protein post-translational modifications(8) and characterization of biological protein networks.(4,9) In the recent years, multiple technical advances have greatly improved the throughput of SRM analyses, allowing for the quantification of hundreds of peptides in a single analysis.(6,7,10) For example, a single 800-plex SRM assay (e.g., 400 unlabeled and heavy-labeled peptide pairs and 2400 transitions with retention time scheduling) using ultrahigh-performance liquid chromatography (UHPLC) has been developed to quantify proteins in plasma.(11) Advanced labeling techniques utilizing in vitro proteome-assisted MRM for protein absolute quantification (iMPAQT) demonstrated the capability of SRM in genome-wide protein quantificationmore » of over 18?000 human proteins.(12) Moreover, the scan speed of QQQ instrumentation has been greatly improved in recent releases of commercial instrumentation. The newly developed TSQ Altis (released in 2017) can scan more than 600 transitions per second, which is 6 times more than a traditional QQQ scan speed of 100 transitions per second. The breadth of measurement enabled by these technological improvements to QQQ mass spectrometry has increased the feasibility and popularity of large scale targeted proteomics studies,(13) a major application of which will be in clinical studies in which up to hundreds of protein candidates need to be quantified in hundreds of clinical specimens.(14) Quantitative accuracy is a primary motivating factor for utilizing a targeted proteomics protocol. The precise and reproducible absolute quantification produced by SRM assays is essential to many clinical and laboratory experiments.(15,16) Because the abundance of an endogenous peptide is calculated from the measurement of the spiked-in reference standard, it is essential to assess the data quality of these references.(17) In early applications of targeted proteomics, when instrument speed greatly limited the number of transitions that could be monitored, much of this quality assessment was done manually. However, recent improvements in instrument performance and experimental design have enabled a dramatic increase in the number of target peptides and associated SRM transitions, which makes manual quality assessment an untenable and laborious task. A variety of computational tools assist in SRM experiment design and data analysis. The first task in creating an SRM experiment is the choice of proteins and representative peptides to monitor. Achieving a reliable protein abundance requires appropriately choosing peptides that have a strong signal and are free from interferences in the biological matrix. Numerous computational tools exist to facilitate assay design by identifying peptides and refining SRM transitions.(18-21) To help share these assays and eliminate time spent designing the same transitions at multiple institutions, community portals have begun to host well-designed and vetted assays.(22-24) Analyzing the experimental data requires significant computational effort to align files across replicates and experimental conditions, pick peaks and produce quantitative values, normalize data and perform statistical tests, etc.(25-27)« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]
  1. Biological Sciences Division, Pacific Northwest National Laboratory, Richland Washington 99336, United States
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1501856
Report Number(s):
PNNL-SA-137457
Journal ID: ISSN 1535-3893
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Proteome Research
Additional Journal Information:
Journal Volume: 18; Journal Issue: 2; Journal ID: ISSN 1535-3893
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English

Citation Formats

Gibbons, Bryson C., Fillmore, Thomas L., Gao, Yuqian, Moore, Ronald J., Liu, Tao, Nakayasu, Ernesto S., Metz, Thomas O., and Payne, Samuel H. Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards. United States: N. p., 2018. Web. doi:10.1021/acs.jproteome.8b00688.
Gibbons, Bryson C., Fillmore, Thomas L., Gao, Yuqian, Moore, Ronald J., Liu, Tao, Nakayasu, Ernesto S., Metz, Thomas O., & Payne, Samuel H. Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards. United States. doi:10.1021/acs.jproteome.8b00688.
Gibbons, Bryson C., Fillmore, Thomas L., Gao, Yuqian, Moore, Ronald J., Liu, Tao, Nakayasu, Ernesto S., Metz, Thomas O., and Payne, Samuel H. Fri . "Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards". United States. doi:10.1021/acs.jproteome.8b00688.
@article{osti_1501856,
title = {Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards},
author = {Gibbons, Bryson C. and Fillmore, Thomas L. and Gao, Yuqian and Moore, Ronald J. and Liu, Tao and Nakayasu, Ernesto S. and Metz, Thomas O. and Payne, Samuel H.},
abstractNote = {Selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), is a data acquisition technique used in targeted analysis of molecules, including targeted proteomic studies. It exploits the unique capability of triple-quadrupole (QQQ) mass spectrometers to monitor the predefined precursor and fragment ion pairs of specific molecules of interest throughout a liquid chromatography (LC) elution profile. Compared to shotgun proteomics, targeted proteomics using SRM has high selectivity, high sensitivity, and a wide linear dynamic range,(1-3) which makes it especially useful in the accurate and reproducible quantification of low-abundance proteins in highly complex biological samples. SRM has been widely used in the fields of biomarker discovery,(4-7) analysis of protein post-translational modifications(8) and characterization of biological protein networks.(4,9) In the recent years, multiple technical advances have greatly improved the throughput of SRM analyses, allowing for the quantification of hundreds of peptides in a single analysis.(6,7,10) For example, a single 800-plex SRM assay (e.g., 400 unlabeled and heavy-labeled peptide pairs and 2400 transitions with retention time scheduling) using ultrahigh-performance liquid chromatography (UHPLC) has been developed to quantify proteins in plasma.(11) Advanced labeling techniques utilizing in vitro proteome-assisted MRM for protein absolute quantification (iMPAQT) demonstrated the capability of SRM in genome-wide protein quantification of over 18?000 human proteins.(12) Moreover, the scan speed of QQQ instrumentation has been greatly improved in recent releases of commercial instrumentation. The newly developed TSQ Altis (released in 2017) can scan more than 600 transitions per second, which is 6 times more than a traditional QQQ scan speed of 100 transitions per second. The breadth of measurement enabled by these technological improvements to QQQ mass spectrometry has increased the feasibility and popularity of large scale targeted proteomics studies,(13) a major application of which will be in clinical studies in which up to hundreds of protein candidates need to be quantified in hundreds of clinical specimens.(14) Quantitative accuracy is a primary motivating factor for utilizing a targeted proteomics protocol. The precise and reproducible absolute quantification produced by SRM assays is essential to many clinical and laboratory experiments.(15,16) Because the abundance of an endogenous peptide is calculated from the measurement of the spiked-in reference standard, it is essential to assess the data quality of these references.(17) In early applications of targeted proteomics, when instrument speed greatly limited the number of transitions that could be monitored, much of this quality assessment was done manually. However, recent improvements in instrument performance and experimental design have enabled a dramatic increase in the number of target peptides and associated SRM transitions, which makes manual quality assessment an untenable and laborious task. A variety of computational tools assist in SRM experiment design and data analysis. The first task in creating an SRM experiment is the choice of proteins and representative peptides to monitor. Achieving a reliable protein abundance requires appropriately choosing peptides that have a strong signal and are free from interferences in the biological matrix. Numerous computational tools exist to facilitate assay design by identifying peptides and refining SRM transitions.(18-21) To help share these assays and eliminate time spent designing the same transitions at multiple institutions, community portals have begun to host well-designed and vetted assays.(22-24) Analyzing the experimental data requires significant computational effort to align files across replicates and experimental conditions, pick peaks and produce quantitative values, normalize data and perform statistical tests, etc.(25-27)},
doi = {10.1021/acs.jproteome.8b00688},
journal = {Journal of Proteome Research},
issn = {1535-3893},
number = 2,
volume = 18,
place = {United States},
year = {2018},
month = {12}
}