RATIONALE: High-throughput reliable data generation has become a substantial requirement in many "omics" investigations. In proteomics the sample preparation workflow consists of multiple steps adding more bias to the sample with each additional manual step. Especially for label-free quantification experiments, this drastically impedes reproducible quantification of proteins in replicates. Here, a positive pressure workstation was evaluated to increase automation of sample preparation and reduce workload as well as consumables. METHODS: Digested peptide samples were purified utilizing a new semi-automated sample preparation device, the Resolvex A200, followed by nanospray liquid chromatography/electrospray ionization (nLC/ESI) Orbitrap tandem mass spectrometry (MS/MS) measurements. In addition, the sorbents Maestro and WWP2 (available in conventional cartridge and dual-chamber narrow-bore extraction columns) were compared with Sep-Pak C18 cartridges. Raw data was analyzed by MaxQuant and Perseus software. RESULTS: The semi-automated workflow with the Resolvex A200 workstation and both new sorbents produced highly reproducible results within 10-300 μg of peptide starting material. The new workflow performed equally as well as the routinely conducted manual workflow with similar technical variability in MS/MS-based identifications of peptides and proteins. A first application of the system to a biological question contributed to highly reliable results, where time-resolved proteomic data was separated by principal component analysis (PCA) and hierarchical clustering. CONCLUSIONS: The new workstation was successfully established for proteolytic peptide purification in our proteomic workflow without any drawbacks. Highly reproducible results were obtained in decreased time per sample, which will facilitate further large-scale proteomic investigations.
RATIONALE: High-throughput reliable data generation has become a substantial requirement in many "omics" investigations. In proteomics the sample preparation workflow consists of multiple steps adding more bias to the sample with each additional manual step. Especially for label-free quantification experiments, this drastically impedes reproducible quantification of proteins in replicates. Here, a positive pressure workstation was evaluated to increase automation of sample preparation and reduce workload as well as consumables. METHODS: Digested peptide samples were purified utilizing a new semi-automated sample preparation device, the Resolvex A200, followed by nanospray liquid chromatography/electrospray ionization (nLC/ESI) Orbitrap tandem mass spectrometry (MS/MS) measurements. In addition, the sorbents Maestro and WWP2 (available in conventional cartridge and dual-chamber narrow-bore extraction columns) were compared with Sep-Pak C18 cartridges. Raw data was analyzed by MaxQuant and Perseus software. RESULTS: The semi-automated workflow with the Resolvex A200 workstation and both new sorbents produced highly reproducible results within 10-300 μg of peptide starting material. The new workflow performed equally as well as the routinely conducted manual workflow with similar technical variability in MS/MS-based identifications of peptides and proteins. A first application of the system to a biological question contributed to highly reliable results, where time-resolved proteomic data was separated by principal component analysis (PCA) and hierarchical clustering. CONCLUSIONS: The new workstation was successfully established for proteolytic peptide purification in our proteomic workflow without any drawbacks. Highly reproducible results were obtained in decreased time per sample, which will facilitate further large-scale proteomic investigations.
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