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

Abstract

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.

Authors:
 [1];  [2];  [1];  [1];  [1];  [1];  [2];  [3]; ORCiD logo [4];  [5]; ORCiD logo [6]; ORCiD logo [1]; ORCiD logo [1]
  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); Sandia National Lab. (SNL-CA), Livermore, 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)
  4. 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)
  5. Joint BioEnergy Inst. (JBEI), Emeryville, CA (United States); Univ. of California, Berkeley, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  6. 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)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
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
OSTI Identifier:
1592414
Alternate Identifier(s):
OSTI ID: 1569459
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Proteome Research
Additional Journal Information:
Journal Volume: 18; Journal Issue: 10; Journal ID: ISSN 1535-3893
Publisher:
American Chemical Society (ACS)
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; automation; sample preparation; proteomics; bacteria; fungi; microbes; biotechnology; high-throughput

Citation Formats

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., and Petzold, Christopher J. Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes. United States: N. p., 2019. Web. doi:10.1021/acs.jproteome.9b00455.
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. Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes. United States. https://doi.org/10.1021/acs.jproteome.9b00455
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., and Petzold, Christopher J. Thu . "Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes". United States. https://doi.org/10.1021/acs.jproteome.9b00455. https://www.osti.gov/servlets/purl/1592414.
@article{osti_1592414,
title = {Automated “Cells-To-Peptides” Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes},
author = {Chen, Yan and Guenther, Joel M. and Gin, Jennifer W. and Chan, Leanne Jade G. and Costello, Zak and Ogorzalek, Tadeusz L. and Tran, Huu M. and Blake-Hedges, Jacquelyn M. and Keasling, Jay D. and Adams, Paul D. and García Martín, Héctor and Hillson, Nathan J. and Petzold, Christopher J.},
abstractNote = {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.},
doi = {10.1021/acs.jproteome.9b00455},
journal = {Journal of Proteome Research},
number = 10,
volume = 18,
place = {United States},
year = {Thu Aug 22 00:00:00 EDT 2019},
month = {Thu Aug 22 00:00:00 EDT 2019}
}

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Works referenced in this record:

Validation of a high-throughput fermentation system based on online monitoring of biomass and fluorescence in continuously shaken microtiter plates
journal, January 2009

  • Kensy, Frank; Zang, Emerson; Faulhammer, Christian
  • Microbial Cell Factories, Vol. 8, Issue 1
  • DOI: 10.1186/1475-2859-8-31

Bioprocess Control in Microscale: Scalable Fermentations in Disposable and User-Friendly Microfluidic Systems
journal, January 2010

  • Funke, Matthias; Buchenauer, Andreas; Mokwa, Wilfried
  • Microbial Cell Factories, Vol. 9, Issue 1
  • DOI: 10.1186/1475-2859-9-86

Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry
journal, December 2015

  • Cominetti, Ornella; Núñez Galindo, Antonio; Corthésy, John
  • Journal of Proteome Research, Vol. 15, Issue 2
  • DOI: 10.1021/acs.jproteome.5b00901

Universal sample preparation method for proteome analysis
journal, April 2009

  • Wiśniewski, Jacek R.; Zougman, Alexandre; Nagaraj, Nagarjuna
  • Nature Methods, Vol. 6, Issue 5
  • DOI: 10.1038/nmeth.1322

Mass-spectrometric exploration of proteome structure and function
journal, September 2016


A quality control of proteomic experiments based on multiple isotopologous internal standards
journal, September 2015


A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids
journal, April 1984


Qualis-SIS: Automated Standard Curve Generation and Quality Assessment for Multiplexed Targeted Quantitative Proteomic Experiments with Labeled Standards
journal, December 2014

  • Mohammed, Yassene; Percy, Andrew J.; Chambers, Andrew G.
  • Journal of Proteome Research, Vol. 14, Issue 2
  • DOI: 10.1021/pr5010955

Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum
journal, September 2012

  • Shi, T.; Fillmore, T. L.; Sun, X.
  • Proceedings of the National Academy of Sciences, Vol. 109, Issue 38
  • DOI: 10.1073/pnas.1204366109

Quick 96FASP for high throughput quantitative proteome analysis
journal, August 2017


Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells
journal, February 2014

  • Kulak, Nils A.; Pichler, Garwin; Paron, Igor
  • Nature Methods, Vol. 11, Issue 3
  • DOI: 10.1038/nmeth.2834

A kinetic-based approach to understanding heterologous mevalonate pathway function in E. coli : A Kinetic-Based Approach to Understanding Heterologous
journal, August 2014

  • Weaver, Lane J.; Sousa, Mirta M. L.; Wang, George
  • Biotechnology and Bioengineering, Vol. 112, Issue 1
  • DOI: 10.1002/bit.25323

Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry
journal, November 2017


A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins
journal, November 2014


A modified FASP protocol for high-throughput preparation of protein samples for mass spectrometry
journal, July 2017


Calibration Using a Single-Point External Reference Material Harmonizes Quantitative Mass Spectrometry Proteomics Data between Platforms and Laboratories
journal, October 2018


Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach
journal, January 2014

  • Carr, Steven A.; Abbatiello, Susan E.; Ackermann, Bradley L.
  • Molecular & Cellular Proteomics, Vol. 13, Issue 3
  • DOI: 10.1074/mcp.m113.036095

A high-throughput sample preparation method for cellular proteomics using 96-well filter plates
journal, September 2013

  • Switzar, Linda; van Angeren, Jordy; Pinkse, Martijn
  • PROTEOMICS, Vol. 13, Issue 20
  • DOI: 10.1002/pmic.201300080

MStern Blotting–High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates
journal, October 2015

  • Berger, Sebastian T.; Ahmed, Saima; Muntel, Jan
  • Molecular & Cellular Proteomics, Vol. 14, Issue 10
  • DOI: 10.1074/mcp.o115.049650

Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow
journal, May 2016


Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics
journal, April 2019


A rapid methods development workflow for high-throughput quantitative proteomic applications
journal, February 2019


Development of an Automated, High-throughput Sample Preparation Protocol for Proteomics Analysis: Automation of Filter-aided Sample Preparation
journal, June 2015

  • Arul, Albert-Baskar; Byambadorj, Munkhtsolmon; Han, Na-Young
  • Bulletin of the Korean Chemical Society, Vol. 36, Issue 7
  • DOI: 10.1002/bkcs.10338

Mass Spectrometry-Based Label-Free Quantitative Proteomics
journal, January 2010

  • Zhu, Wenhong; Smith, Jeffrey W.; Huang, Chun-Ming
  • Journal of Biomedicine and Biotechnology, Vol. 2010
  • DOI: 10.1155/2010/840518

Comprehensive and Scalable Highly Automated MS-Based Proteomic Workflow for Clinical Biomarker Discovery in Human Plasma
journal, July 2014

  • Dayon, Loïc; Núñez Galindo, Antonio; Corthésy, John
  • Journal of Proteome Research, Vol. 13, Issue 8
  • DOI: 10.1021/pr500635f

Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
journal, December 2008

  • Huang, Da Wei; Sherman, Brad T.; Lempicki, Richard A.
  • Nature Protocols, Vol. 4, Issue 1
  • DOI: 10.1038/nprot.2008.211

Works referencing / citing this record:

Parallelized disruption of prokaryotic and eukaryotic cells via miniaturized and automated bead mill
text, January 2020

  • Jansen, Roman P.; Müller, Moritz Fabian; Schröter, Sophie Edith
  • RWTH Aachen University
  • DOI: 10.18154/rwth-2020-06212

Parallelized disruption of prokaryotic and eukaryotic cells via miniaturized and automated bead mill
journal, May 2020

  • Jansen, Roman P.; Müller, Moritz Fabian; Schröter, Sophie Edith
  • Engineering in Life Sciences, Vol. 20, Issue 8
  • DOI: 10.1002/elsc.202000002