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Extensive analysis of the cytoplasmic proteome of human erythrocytes using the Peptide ligand library technology and advanced mass spectrometry

The erythrocyte cytoplasmic proteome is composed of 98% hemoglobin; the remaining 2% is largely unexplored. Here we used a combinatorial library of hexameric peptides as a capturing agent to lower the signal of hemoglobin and amplify the signal of low to very low abundance proteins in the cytoplasm of human red blood cells (RBCs). Two types of hexapeptide library beads have been adopted: amino-terminal hexapeptide beads and beads in which the peptides have been further derivatized by carboxylation. The amplification of the signal of low abundance and suppression of the signal of high abundance species were fully demonstrated by two-dimensional gel maps and nano-LC-MSMS analysis. The effect of this new methodology on quantitative information also was explored. Moreover using this approach on an LTQ-Orbitrap mass spectrometer, we could identify with high confidence as many as 1578 proteins in the cytoplasmic fraction of a highly purified preparation of RBCs, allowing a deep exploration of the classical RBC pathways as well as the identification of unexpected minor proteins. In addition, we were able to detect the presence of eight different hemoglobin chains including embryonic and newly discovered globin chains. Thus, this extensive study provides a huge data set of proteins that are present in the RBC cytoplasm that may help to better understand the biology of this simplified cell and may open the way to further studies on blood pathologies using targeted approache

WOMBAT-P: benchmarking label-free proteomics data analysis workflows

The inherent diversity of approaches in proteomics research has led to a wide range of software solutions for data analysis. These software solutions encompass multiple tools, each employing different algorithms for various tasks such as peptide-spectrum matching, protein inference, quantification, statistical analysis, and visualization. To enable an unbiased comparison of commonly used bottom-up label-free proteomics workflows, we introduce WOMBAT-P, a versatile platform designed for automated benchmarking and comparison. WOMBAT-P simplifies the processing of public data by utilizing the sample and data relationship format for proteomics (SDRF-Proteomics) as input. This feature streamlines the analysis of annotated local or public ProteomeXchange data sets, promoting efficient comparisons among diverse outputs. Through an evaluation using experimental ground truth data and a realistic biological data set, we uncover significant disparities and a limited overlap in the quantified proteins. WOMBAT-P not only enables rapid execution and seamless comparison of workflows but also provides valuable insights into the capabilities of different software solutions. These benchmarking metrics are a valuable resource for researchers in selecting the most suitable workflow for their specific data sets. The modular architecture of WOMBAT-P promotes extensibility and customization. The software is available at https://github.com/wombat-p/WOMBAT-Pipelines.Proteomic

Data treatment for LC-MS untargeted analysis

Liquid chromatography-mass spectrometry (LC-MS) untargeted experiments require complex chemometrics strategies to extract information from the experimental data. Here we discuss “data preprocessing”, the set of procedures performed on the raw data to produce a data matrix which will be the starting point for the subsequent statistical analysis. Data preprocessing is a crucial step on the path to knowledge extraction, which should be carefully controlled and optimized in order to maximize the output of any untargeted metabolomics investigatio