Automated "cells-To-Peptides" Sample Preparation Workflow for High-Throughput, Quantitative Proteomic Assays of Microbes

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

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
JournalJournal of Proteome Research
Volume18
Issue number10
Pages (from-to)3752-3761
Number of pages10
ISSN1535-3893
DOIs
Publication statusPublished - 2019

Keywords

  • Automation
  • Sample preparation
  • Proteomics
  • Bacteria
  • Fungi
  • Microbes
  • Biotechnology
  • High-throughput

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