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PLoS One. 2019 Feb 14;14(2):e0211582. doi: 10.1371/journal.pone.0211582. eCollection 2019.

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

Chen Y1,2, Vu J1,2, Thompson MG1,2,3, Sharpless WA1,2, Chan LJG1,2, Gin JW1,2, Keasling JD1,2,4,5,6, Adams PD1,4,7, Petzold CJ1,2.

Author information

Joint BioEnergy Institute, Lawrence Berkeley National Laboratory, Emeryville, CA, United States of America.
Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.
Department of Plant and Microbial Biology, University of California, Berkeley, CA, United States of America.
Department of Bioengineering, University of California Berkeley, Berkeley, CA, United States of America.
Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA, United States of America.
The Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark.
Molecular Biophysics and Bioimaging, Lawrence Berkeley National Laboratory, Berkeley, CA, United States of America.


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.

Conflict of interest statement

J.D.K. has financial interests in Amyris, Lygos, Constructive Biology, Demetrix, and Napigen. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

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