An automated 'cells-to-peptides' sample preparation workflow for high-throughput, quantitative proteomic assays of microbes.

Mass spectrometry-based quantitative proteomic analysis has proven valuable for clinical and biotechnology related research and development. Driving this value have been improvements in the sensitivity, resolution, and robustness of mass analyzers. 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 two hours 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-SRM-MS acquisition methods. These results show that this automated sample preparation workflow provides robust, reproducible proteomic samples for high-throughput applications.

[1]  Jihyeon Lee,et al.  Cost-Effective Automated Preparation of Serum Samples for Reproducible Quantitative Clinical Proteomics. , 2019, Journal of proteome research.

[2]  Paul D. Adams,et al.  A rapid methods development workflow for high-throughput quantitative proteomic applications , 2019, PloS one.

[3]  William Stafford Noble,et al.  Calibration Using a Single-Point External Reference Material Harmonizes Quantitative Mass Spectrometry Proteomics Data between Platforms and Laboratories. , 2018, Analytical chemistry.

[4]  David H Perlman,et al.  Automated sample preparation for high-throughput single-cell proteomics , 2018, bioRxiv.

[5]  Qin Fu,et al.  Highly Reproducible Automated Proteomics Sample Preparation Workflow for Quantitative Mass Spectrometry. , 2018, Journal of proteome research.

[6]  Rembert Pieper,et al.  Quick 96FASP for high throughput quantitative proteome analysis. , 2017, Journal of proteomics.

[7]  J. Mulvenna,et al.  A modified FASP protocol for high-throughput preparation of protein samples for mass spectrometry , 2016, bioRxiv.

[8]  Ruedi Aebersold,et al.  Mass-spectrometric exploration of proteome structure and function , 2016, Nature.

[9]  Edward J. O'Brien,et al.  Characterizing Strain Variation in Engineered E. coli Using a Multi-Omics-Based Workflow. , 2016, Cell systems.

[10]  Loïc Dayon,et al.  Proteomic Biomarker Discovery in 1000 Human Plasma Samples with Mass Spectrometry. , 2016, Journal of proteome research.

[11]  B. Domon,et al.  A quality control of proteomic experiments based on multiple isotopologous internal standards , 2015 .

[12]  H. Steen,et al.  MStern Blotting–High Throughput Polyvinylidene Fluoride (PVDF) Membrane-Based Proteomic Sample Preparation for 96-Well Plates* , 2015, Molecular & Cellular Proteomics.

[13]  Hookeun Lee,et al.  Development of an Automated, High‐throughput Sample Preparation Protocol for Proteomics Analysis , 2015 .

[14]  Yassene Mohammed,et al.  Qualis-SIS: automated standard curve generation and quality assessment for multiplexed targeted quantitative proteomic experiments with labeled standards. , 2015, Journal of proteome research.

[15]  Edward Baidoo,et al.  A kinetic‐based approach to understanding heterologous mevalonate pathway function in E. coli , 2015, Biotechnology and bioengineering.

[16]  J. Keasling,et al.  A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins. , 2014, Metabolic engineering.

[17]  M. Kussmann,et al.  Comprehensive and Scalable Highly Automated MS-Based Proteomic Workflow for Clinical Biomarker Discovery in Human Plasma. , 2014, Journal of proteome research.

[18]  M. Mann,et al.  Minimal, encapsulated proteomic-sample processing applied to copy-number estimation in eukaryotic cells , 2014, Nature Methods.

[19]  Susan E. Abbatiello,et al.  Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach* , 2014, Molecular & Cellular Proteomics.

[20]  Martijn Pinkse,et al.  A high‐throughput sample preparation method for cellular proteomics using 96‐well filter plates , 2013, Proteomics.

[21]  Ronald J. Moore,et al.  Antibody-free, targeted mass-spectrometric approach for quantification of proteins at low picogram per milliliter levels in human plasma/serum , 2012, Proceedings of the National Academy of Sciences.

[22]  Wilfried Mokwa,et al.  Bioprocess Control in Microscale: Scalable Fermentations in Disposable and User-Friendly Microfluidic Systems , 2010, Microbial cell factories.

[23]  Frank Kensy,et al.  Scale-up from microtiter plate to laboratory fermenter: evaluation by online monitoring techniques of growth and protein expression in Escherichia coli and Hansenula polymorpha fermentations , 2009, Microbial cell factories.

[24]  Jeffrey W. Smith,et al.  Mass Spectrometry-Based Label-Free Quantitative Proteomics , 2009, Journal of biomedicine & biotechnology.

[25]  Frank Kensy,et al.  Validation of a high-throughput fermentation system based on online monitoring of biomass and fluorescence in continuously shaken microtiter plates , 2009, Microbial cell factories.

[26]  M. Mann,et al.  Universal sample preparation method for proteome analysis , 2009, Nature Methods.

[27]  D. Wessel,et al.  A method for the quantitative recovery of protein in dilute solution in the presence of detergents and lipids. , 1984, Analytical biochemistry.

[28]  C. Eyers Universal sample preparation method for proteome analysis , 2009 .

[29]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.