skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: A rapid methods development workflow for high-throughput quantitative proteomic applications

Abstract

Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in onlymore » a few minutes.« less

Authors:
 [1]; ORCiD logo [1];  [2];  [1];  [1];  [1];  [3];  [1]; ORCiD logo [1];  [4]
  1. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  2. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
  3. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark). Novo Nordisk Foundation Center for Biosustainability
  4. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1542348
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 14; Journal Issue: 2; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
02 PETROLEUM

Citation Formats

Chen, Yan, Vu, Jonathan, Thompson, Mitchell G., Sharpless, William A., Chan, Leanne Jade G., Gin, Jennifer W., Keasling, Jay D., Adams, Paul D., Petzold, Christopher J., and Jacobs, Jon M. A rapid methods development workflow for high-throughput quantitative proteomic applications. United States: N. p., 2019. Web. doi:10.1371/journal.pone.0211582.
Chen, Yan, Vu, Jonathan, Thompson, Mitchell G., Sharpless, William A., Chan, Leanne Jade G., Gin, Jennifer W., Keasling, Jay D., Adams, Paul D., Petzold, Christopher J., & Jacobs, Jon M. A rapid methods development workflow for high-throughput quantitative proteomic applications. United States. doi:10.1371/journal.pone.0211582.
Chen, Yan, Vu, Jonathan, Thompson, Mitchell G., Sharpless, William A., Chan, Leanne Jade G., Gin, Jennifer W., Keasling, Jay D., Adams, Paul D., Petzold, Christopher J., and Jacobs, Jon M. Thu . "A rapid methods development workflow for high-throughput quantitative proteomic applications". United States. doi:10.1371/journal.pone.0211582. https://www.osti.gov/servlets/purl/1542348.
@article{osti_1542348,
title = {A rapid methods development workflow for high-throughput quantitative proteomic applications},
author = {Chen, Yan and Vu, Jonathan and Thompson, Mitchell G. and Sharpless, William A. and Chan, Leanne Jade G. and Gin, Jennifer W. and Keasling, Jay D. and Adams, Paul D. and Petzold, Christopher J. and Jacobs, Jon M.},
abstractNote = {Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.},
doi = {10.1371/journal.pone.0211582},
journal = {PLoS ONE},
issn = {1932-6203},
number = 2,
volume = 14,
place = {United States},
year = {2019},
month = {2}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Save / Share: