Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes

Chen, Yan; Guenther, Joel M.; Gin, Jennifer W.; Chan, Leanne Jade G.; Costello, Zak; Ogorzalek, Tadeusz L.; Tran, Huu M.; Blake-Hedges, Jacquelyn M.; Keasling, Jay D.; Adams, Paul D.; García Martín, Héctor; Hillson, Nathan J.; Petzold, Christopher J.

doi:10.1021/acs.jproteome.9b00455

Title: Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes

Journal Article · Thu Aug 22 00:00:00 EDT 2019 · Journal of Proteome Research

DOI:https://doi.org/10.1021/acs.jproteome.9b00455· OSTI ID:1592414

Chen, Yan ^[1]; Guenther, Joel M. ^[2]; Gin, Jennifer W. ^[1]; Chan, Leanne Jade G. ^[1]; Costello, Zak ^[1]; Ogorzalek, Tadeusz L. ^[1]; Tran, Huu M. ^[2]; Blake-Hedges, Jacquelyn M. ^[3];

^[4]; Adams, Paul D. ^[5];

^[6];

^[1];

^[1]

Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States)
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); Shenzhen Inst. for Advanced Technologies (China)
Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Basque Center for Applied Mathematics (BCAM), Bilbao, Bizkaia (Spain)

Mass spectrometry-based quantitative proteomic analysis has proven valuable for clinical and biotechnology-related research and development. Improvements in sensitivity, resolution, and robustness of mass analyzers have also added value. However, manual sample preparation protocols are often a bottleneck for sample throughput and can lead to poor reproducibility, especially for applications where thousands of samples per month must be analyzed. To alleviate these issues, we developed a “cells-to-peptides” automated workflow for Gram-negative bacteria and fungi that includes cell lysis, protein precipitation, resuspension, quantification, normalization, and tryptic digestion. The workflow takes 2 h to process 96 samples from cell pellets to the initiation of the tryptic digestion step and can process 384 samples in parallel. We measured the efficiency of protein extraction from various amounts of cell biomass and optimized the process for standard liquid chromatography–mass spectrometry systems. The automated workflow was tested by preparing 96 Escherichia coli samples and quantifying over 600 peptides that resulted in a median coefficient of variation of 15.8%. Similar technical variance was observed for three other organisms as measured by highly multiplexed LC-MRM–MS acquisition methods. These results show that this automated sample preparation workflow provides robust, reproducible proteomic samples for high-throughput applications.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1592414

Alternate ID(s):: OSTI ID: 1569459

Journal Information:: Journal of Proteome Research, Vol. 18, Issue 10; ISSN 1535-3893

Publisher:: American Chemical Society (ACS)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 24 works

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