Generation of a mouse SWATH-MS spectral library to quantify 10148 proteins involved in cell reprogramming

Murine models are amongst the most widely used systems to study biology and pathology. Targeted quantitative proteomic analysis is a relatively new tool to interrogate such systems. Recently the need for relative quantification on hundreds to thousands of samples has driven the development of Data Independent Acquisition methods. One such technique is SWATH-MS, which in the main requires prior acquisition of mass spectra to generate an assay reference library. In stem cell research, it has been shown pluripotency can be induced starting with a fibroblast population. In so doing major changes in expressed proteins is inevitable. Here we have created a reference library to underpin such studies. This is inclusive of an extensively documented script to enable replication of library generation from the raw data. The documented script facilitates reuse of data and adaptation of the library to novel applications. The resulting library provides deep coverage of the mouse proteome. The library covers 29519 proteins (53% of the proteome) of which 7435 (13%) are supported by a proteotypic peptide.

Is It Possible to Find Needles in a Haystack? Meta-Analysis of 1000+ MS/MS Files Provided by the Russian Proteomic Consortium for Mining Missing Proteins

Despite direct or indirect efforts of the proteomic community, the fraction of blind spots on the protein map is still significant. Almost 11% of human genes encode missing proteins; the existence of which proteins is still in doubt. Apparently, proteomics has reached a stage when more attention and curiosity need to be exerted in the identification of every novel protein in order to expand the unusual types of biomaterials and/or conditions. It seems that we have exhausted the current conventional approaches to the discovery of missing proteins and may need to investigate alternatives. Here, we present an approach to deciphering missing proteins based on the use of non-standard methodological solutions and encompassing diverse MS/MS data, obtained for rare types of biological samples by members of the Russian Proteomic community in the last five years. These data were re-analyzed in a uniform manner by three search engines, which are part of the SearchGUI package. The study resulted in the identification of two missing and five uncertain proteins detected with two peptides. Moreover, 149 proteins were detected with a single proteotypic peptide. Finally, we analyzed the gene expression levels to suggest feasible targets for further validation of missing and uncertain protein observations, which will fully meet the requirements of the international consortium. The MS data are available on the ProteomeXchange platform (PXD014300).

Integrated modeling of peptide digestion and detection for the prediction of proteotypic peptides in targeted proteomics

ABSTRACTMotivationThe selection of proteotypic peptides, i.e., detectable unique representatives of proteins of interest, is a key step in targeted shotgun proteomics. To date, much effort has been made to predict proteotypic peptides in the absence of mass spectrometry data. However, the performance of existing tools is still unsatisfactory. One crucial reason is their neglect of the close relationship between protein proteolytic digestion and peptide detection.ResultsWe present an algorithm (named AP3) that firstly considers peptide digestion probability as a feature for proteotypic peptide prediction and demonstrated peptide digestion probability is the most important feature for accurate prediction of proteotypic peptides. AP3 showed higher accuracy than existing tools and accurately predicted the proteotypic peptides for a targeted proteomics assay, showing its great potential for assisting the design of targeted proteomics experiments.Availability and ImplementationFreely available at http://fugroup.amss.ac.cn/software/AP3/[email protected] or [email protected] InformationSupplementary data are available at Bioinformatics online.

Quantification of a Proteotypic Peptide from Protein C Inhibitor by Liquid Chromatography–Free SISCAPA-MALDI Mass Spectrometry: Application to Identification of Recurrence of Prostate Cancer

BACKGROUND Biomarker validation remains one of the most challenging constraints to the development of new diagnostic assays. To facilitate biomarker validation, we previously developed a chromatography-free stable isotope standards and capture by antipeptide antibodies (SISCAPA)-MALDI assay allowing rapid, high-throughput quantification of protein analytes in large sample sets. Here we applied this assay to the measurement of a surrogate proteotypic peptide from protein C inhibitor (PCI) in sera from patients with prostate cancer. METHODS A 2-plex SISCAPA-MALDI assay for quantification of proteotypic peptides from PCI and soluble transferrin receptor (sTfR) was used to measure these peptides in 159 trypsin-digested sera collected from 51 patients with prostate cancer. These patients had been treated with radiation with or without neoadjuvant androgen deprivation. RESULTS Patients who experienced biochemical recurrence of prostate cancer showed decreased serum concentrations of the PCI peptide analyte within 18 months of treatment. The PCI peptide concentrations remained increased in the sera of patients who did not experience cancer recurrence. Prostate-specific antigen concentrations had no predictive value during the same time period. CONCLUSIONS The high-throughput, liquid chromatography–free SISCAPA-MALDI assay is capable of rapid quantification of proteotypic PCI and sTfR peptide analytes in complex serum samples. Decreased serum concentrations of the PCI peptide were found to be related to recurrence of prostate cancer in patients treated with radiation with or without hormone therapy. However, a larger cohort of patients will be required for unequivocal validation of the PCI peptide as a biomarker for clinical use.