Mass Spectrometry-based Proteomics Research Research Articles

Mass Spectrometry-Based Proteomics Research to Fight COVID-19: An Expert Review on Hopes and Challenges.

The COVID-19 pandemic caused by the severe acute respiratory syndrome (SARS)-CoV-2 infection is a systemic disease and a major planetary health burden. While SARS-CoV-2 impacts host biology extensively, our knowledge of these alterations from a systems perspective remains incomplete. Moreover, there is currently only a limited description of this systemic disease. For precision diagnosis and treatment of SARS-CoV-2, multiomics technologies and systems science research offer significant prospects. This expert review offers a critical analysis of the prospects and challenges of the emerging mass spectrometry-based proteomics approaches to the study of COVID-19 as seen through a systems medicine lens. We also discuss the ways in which proteomics is poised to offer hope for diagnostics and therapeutics innovation on SARS-CoV-2 infection as the disease transitions from a pandemic to an endemic disease, and thus further challenging the health systems and services worldwide in the coming decade. Proteomics is an important high-throughput technology platform to achieve a functional overview of the ways in which COVID-19 changes host biology, and hence, can help identify possible points of entry for innovation in medicines and vaccines, among others.

Proteomics study of colorectal cancer and adenomatous polyps identifies TFR1, SAHH, and HV307 as potential biomarkers for screening

Colorectal cancer (CRC) is a malignant tumour with high morbidity and mortality worldwide. Efficient screening strategies for CRC and pre-cancerous lesions can promote early medical intervention and treatment, thereby reducing morbidity and mortality. Proteins are generally considered key biomarkers of cancer. Herein, we performed a quantitative, original-tissue proteomics study in a cohort of ninety patients from pre-cancerous to cancerous conditions via liquid chromatography-tandem mass spectrometry. In total, 134,812 peptides, 8697 proteins, 2355 union differentially expressed proteins (DEPs), and 409 shared DEPs (compared with adjacent tissues) were identified. The number of DEPs indicated a positive correlation with increasing severity of illness. The union and shared DEPs were both enriched in the KEGG pathway of focal adhesion, metabolism of xenobiotics by cytochrome P450, and drug metabolism by cytochrome P450. Among the 2355 union DEPs, 32 were selected for identification and validation by multiple reaction monitoring from twenty plasma specimens. Of these, three proteins, transferrin receptor protein 1 (TFR1), adenosylhomocysteinase (SAHH), and immunoglobulin heavy variable 3–7 (HV307), were significantly differentially expressed and displayed the same expression pattern in plasma as observed in the tissue data. In conclusion, TFR1, SAHH, and HV307 may be considered as potential biomarkers for CRC screening. SignificanceAlthough CRC is a malignant tumour with high morbidity and mortality worldwide, efficient screening strategies for CRC and pre-cancerous lesions can play an important role in addressing these issues. Screening of molecular biomarkers provide a non-invasive, cost-effective, and efficient approach. Proteins are generally considered key molecular biomarkers of cancer. Our study reports a quantitative proteomics analysis of protein biomarkers for colorectal cancer (CRC) and adenomatous polyps, and identifies TFR1, SAHH, and HV307 as potential biomarkers for screening. This research makes a significant contribution to the literature as although mass spectrometry-based proteomics research has been widely used for clinical research, its application to clinical translation as parallel specimens ranging from pre-cancerous to cancerous tissues—according to the degree of disease progression—has not been readily assessed.

C18-functionalized magnetic nanocomposites fabricated by one-step aqueous coating of tailored oligopeptides for enrichment of low-abundance peptides

Screening and monitoring endogenous peptides from complicated biosamples is still a major challenge in mass spectrometry-based proteomics research, mainly due to their low concentration and the interference of high-abundance proteins and other contaminants in biological samples. Herein, a facile and novel approach was described for rapid fabrication of C18-functionalized magnetic nanocomposites (C18-MNCs) based on one-step aqueous coating of C18-Val-Lys-Val-Lys-Val-Lys (C18-VK-VI) for the highly selective enrichment of low-abundance endogenous peptides from biological samples. C18-VK-VI can readily self-assemble into complete monolayers mainly composed of β-sheets with C18 hydrophobic chains erecting on the surface of GO@Fe3O4 MNCs under the physiological conditions. The resulting C18VK-VI-GO@Fe3O4 MNCs exhibited good performance for peptides enrichment from digests of standard protein (myoglobin, MYO) and human serum, such as high sensitivity (0.05 fmol μL−1) and selectivity (mass ratio of MYO digests and MYO = 1:500), rapid separation, and good reproducibility. Such a simple mild and rapid one-step aqueous coating method on the basis of oligopeptides self-assembly showed great potential in surface functionalization of various nanoadsorbents for proteome/peptidome researches.

YPED: An Integrated Bioinformatics Suite and Database for Mass Spectrometry-based Proteomics Research

We report a significantly-enhanced bioinformatics suite and database for proteomics research called Yale Protein Expression Database (YPED) that is used by investigators at more than 300 institutions worldwide. YPED meets the data management, archival, and analysis needs of a high-throughput mass spectrometry-based proteomics research ranging from a single laboratory, group of laboratories within and beyond an institution, to the entire proteomics community. The current version is a significant improvement over the first version in that it contains new modules for liquid chromatography–tandem mass spectrometry (LC–MS/MS) database search results, label and label-free quantitative proteomic analysis, and several scoring outputs for phosphopeptide site localization. In addition, we have added both peptide and protein comparative analysis tools to enable pairwise analysis of distinct peptides/proteins in each sample and of overlapping peptides/proteins between all samples in multiple datasets. We have also implemented a targeted proteomics module for automated multiple reaction monitoring (MRM)/selective reaction monitoring (SRM) assay development. We have linked YPED’s database search results and both label-based and label-free fold-change analysis to the Skyline Panorama repository for online spectra visualization. In addition, we have built enhanced functionality to curate peptide identifications into an MS/MS peptide spectral library for all of our protein database search identification results.

Processing Shotgun Proteomics Data on the Amazon Cloud with the Trans-Proteomic Pipeline

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost.

Repeatedly identified differentially expressed proteins (RIDEPs) from antibody microarray proteomic analysis

Antibody microarrays are powerful new tools in the field of comparative proteomics. The success of the biomarker discovery pipeline relies on the quality of data generated in the discovery phase and careful selection of proteins for the verification phase. Recent meta-analyses found a number of repeatedly identified differentially expressed proteins (RIDEPs) from mass spectrometry-based proteomics research in a range of species. We aimed to assess RIDEPs based on antibody microarray data-sets. A total of 13 independent experiments encompassing a range of oncology-related research on human tissue, cells or cell lines from 5 distinct sample groups were performed utilising a commercial 725 antibody microarray platform (Panorama XPRESS Profiler725; Sigma-Aldrich). Analysis of all microarray slides was carried out by the same individual to reduce inter-observer variability. Fold changes of ≥1.8 were considered significant. A total of 13 RIDEPs were seen, each appearing in at least 4/13 (30%) antibody microarray analyses from at least 2 out of 5 experimental sample groups. The phenomenon of RIDEPs may exist in antibody microarray proteomics and we report a preliminary list of 13 RIDEPs from the XPRESS Profiler725 platform. This information will be useful when interpreting experimental data and considering which DEPs should be prioritised for verification.

Variance Component Analysis of a Multi-Site Study for the Reproducibility of Multiple Reaction Monitoring Measurements of Peptides in Human Plasma

BackgroundIn the Addona et al. paper (Nature Biotechnology 2009), a large-scale multi-site study was performed to quantify Multiple Reaction Monitoring (MRM) measurements of proteins spiked in human plasma. The unlabeled signature peptides derived from the seven target proteins were measured at nine different concentration levels, and their isotopic counterparts were served as the internal standards.Methodology/Principal FindingsIn this paper, the sources of variation are analyzed by decomposing the variance into parts attributable to specific experimental factors: technical replicates, sites, peptides, transitions within each peptide, and higher-order interaction terms based on carefully built mixed effects models. The factors of peptides and transitions are shown to be major contributors to the variance of the measurements considering heavy (isotopic) peptides alone. For the light (12C) peptides alone, in addition to these factors, the factor of study*peptide also contributes significantly to the variance of the measurements. Heterogeneous peptide component models as well as influence analysis identify the outlier peptides in the study, which are then excluded from the analysis. Using a log-log scale transformation and subtracting the heavy/isotopic peptide [internal standard] measurement from the peptide measurements (i.e., taking the logarithm of the peak area ratio in the original scale establishes that), the MRM measurements are overall consistent across laboratories following the same standard operating procedures, and the variance components related to sites, transitions and higher-order interaction terms involving sites have greatly reduced impact. Thus the heavy peptides have been effective in reducing apparent inter-site variability. In addition, the estimates of intercepts and slopes of the calibration curves are calculated for the sub-studies.Conclusions/SignificanceThe MRM measurements are overall consistent across laboratories following the same standard operating procedures, and heavy peptides can be used as an effective internal standard for reducing apparent inter-site variability. Mixed effects modeling is a valuable tool in mass spectrometry-based proteomics research.

Multiplierz: an extensible API based desktop environment for proteomics data analysis

BackgroundEfficient analysis of results from mass spectrometry-based proteomics experiments requires access to disparate data types, including native mass spectrometry files, output from algorithms that assign peptide sequence to MS/MS spectra, and annotation for proteins and pathways from various database sources. Moreover, proteomics technologies and experimental methods are not yet standardized; hence a high degree of flexibility is necessary for efficient support of high- and low-throughput data analytic tasks. Development of a desktop environment that is sufficiently robust for deployment in data analytic pipelines, and simultaneously supports customization for programmers and non-programmers alike, has proven to be a significant challenge.ResultsWe describe multiplierz, a flexible and open-source desktop environment for comprehensive proteomics data analysis. We use this framework to expose a prototype version of our recently proposed common API (mzAPI) designed for direct access to proprietary mass spectrometry files. In addition to routine data analytic tasks, multiplierz supports generation of information rich, portable spreadsheet-based reports. Moreover, multiplierz is designed around a "zero infrastructure" philosophy, meaning that it can be deployed by end users with little or no system administration support. Finally, access to multiplierz functionality is provided via high-level Python scripts, resulting in a fully extensible data analytic environment for rapid development of custom algorithms and deployment of high-throughput data pipelines.ConclusionCollectively, mzAPI and multiplierz facilitate a wide range of data analysis tasks, spanning technology development to biological annotation, for mass spectrometry-based proteomics research.

Bifurcating Fragmentation Behavior of Gas-Phase Tryptic Peptide Dications in Collisional Activation

Collision-activated dissociation (CAD) of tryptic peptides is a cornerstone of mass spectrometry-based proteomics research. Principal component analysis of a database containing 15,000 high-resolution CAD mass spectra of gas-phase tryptic peptide dications revealed that they fall into two classes with a good separation between the classes. The main factor determining the class identity is the relative abundance of the peptide bond cleavage after the first two N-terminal residues. A possible scenario explaining this bifurcation involves trans- to cis-isomerization of the N-terminal peptide bond, which facilitates solvation of the N-terminal charge on the second backbone amide and formation of stable b(2) ions in the form of protonated diketopiperazines. Evidence supporting this scenario is derived from statistical analysis of the high-resolution CAD MS/MS database. It includes the observation of the strong deficit of a(3) ions and anomalous amino acid preferences for b(2) ion formation.