Literature DB >> 31436101

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

Yan Chen, Joel M Guenther1, Jennifer W Gin, Leanne Jade G Chan, Zak Costello, Tadeusz L Ogorzalek, Huu M Tran1, Jacquelyn M Blake-Hedges, Jay D Keasling2,3,4, Paul D Adams, Héctor García Martín5, Nathan J Hillson, Christopher J Petzold.   

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.

Entities:  

Keywords:  automation; bacteria; biotechnology; fungi; high-throughput; microbes; proteomics; sample preparation

Mesh:

Substances:

Year:  2019        PMID: 31436101     DOI: 10.1021/acs.jproteome.9b00455

Source DB:  PubMed          Journal:  J Proteome Res        ISSN: 1535-3893            Impact factor:   4.466


  4 in total

1.  Functional genetics of human gut commensal Bacteroides thetaiotaomicron reveals metabolic requirements for growth across environments.

Authors:  Hualan Liu; Anthony L Shiver; Morgan N Price; Hans K Carlson; Valentine V Trotter; Yan Chen; Veronica Escalante; Jayashree Ray; Kelsey E Hern; Christopher J Petzold; Peter J Turnbaugh; Kerwyn Casey Huang; Adam P Arkin; Adam M Deutschbauer
Journal:  Cell Rep       Date:  2021-03-02       Impact factor: 9.423

2.  Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering.

Authors:  Somtirtha Roy; Tijana Radivojevic; Mark Forrer; Jose Manuel Marti; Vamshi Jonnalagadda; Tyler Backman; William Morrell; Hector Plahar; Joonhoon Kim; Nathan Hillson; Hector Garcia Martin
Journal:  Front Bioeng Biotechnol       Date:  2021-02-09

3.  Modular automated bottom-up proteomic sample preparation for high-throughput applications.

Authors:  Yan Chen; Nurgul Kaplan Lease; Jennifer W Gin; Tadeusz L Ogorzalek; Paul D Adams; Nathan J Hillson; Christopher J Petzold
Journal:  PLoS One       Date:  2022-02-25       Impact factor: 3.240

4.  Toward Zero Variance in Proteomics Sample Preparation: Positive-Pressure FASP in 96-Well Format (PF96) Enables Highly Reproducible, Time- and Cost-Efficient Analysis of Sample Cohorts.

Authors:  Stefan Loroch; Dominik Kopczynski; Adriana C Schneider; Cornelia Schumbrutzki; Ingo Feldmann; Eleftherios Panagiotidis; Yvonne Reinders; Roman Sakson; Fiorella A Solari; Alicia Vening; Frauke Swieringa; Johan W M Heemskerk; Maria Grandoch; Thomas Dandekar; Albert Sickmann
Journal:  J Proteome Res       Date:  2022-03-22       Impact factor: 4.466

  4 in total

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