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

Journal Article · · Journal of Proteome Research
 [1];  [2];  [3];  [4];  [3];  [3];  [2];  [3];  [5];  [3];  [6];  [3];  [3]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lawrence Berkeley National Laboratory
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  4. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Calico Biosciences, South San Francisco, CA (United States)
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Univ. of California, Berkeley, CA (United States); Technical Univ. of Denmark, Lyngby (Denmark); Center for Synthetic Biochemistry, Synthetic Biology Inst., Shenzhen Inst. for Advanced Technologies, Shenzhen (China)
  6. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); BCAM, Basque Center for Applied Mathematics, Bilbao (Spain); Labcyte Inc., San Jose, CA (United States)
+ Show Author Affiliations
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.
Research Organization:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
Sponsoring Organization:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Bioenergy Technologies Office (EE-3B); USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Grant/Contract Number:
AC02-05CH11231
OSTI ID:
1569459
Alternate ID(s):
OSTI ID: 1592414
Journal Information:
Journal of Proteome Research, Journal Name: Journal of Proteome Research Journal Issue: 10 Vol. 18; ISSN 1535-3893
Publisher:
American Chemical Society (ACS)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

59 BASIC BIOLOGICAL SCIENCES
automation
bacteria
biotechnology
fungi
high-throughput
microbes
proteomics
sample preparation