Mass Spectrometry Data Processing Research Articles

MSnbase, Efficient and Elegant R-Based Processing and Visualization of Raw Mass Spectrometry Data.

We present version 2 of the MSnbase R/Bioconductor package. MSnbase provides infrastructure for the manipulation, processing, and visualization of mass spectrometry data. We focus on the new on-disk infrastructure, that allows the handling of large raw mass spectrometry experiments on commodity hardware and illustrate how the package is used for elegant data processing, method development, and visualization.

Food Phenotyping: Recording and Processing of Non-Targeted Liquid Chromatography Mass Spectrometry Data for Verifying Food Authenticity.

Experiments based on metabolomics represent powerful approaches to the experimental verification of the integrity of food. In particular, high-resolution non-targeted analyses, which are carried out by means of liquid chromatography-mass spectrometry systems (LC-MS), offer a variety of options. However, an enormous amount of data is recorded, which must be processed in a correspondingly complex manner. The evaluation of LC-MS based non-targeted data is not entirely trivial and a wide variety of strategies have been developed that can be used in this regard. In this paper, an overview of the mandatory steps regarding data acquisition is given first, followed by a presentation of the required preprocessing steps for data evaluation. Then some multivariate analysis methods are discussed, which have proven to be particularly suitable in this context in recent years. The publication closes with information on the identification of marker compounds.

Multiplexed Proximity Biotinylation Coupled to Mass Spectrometry for Defining Integrin Adhesion Complexes.

BioID, a proximity biotinylation technique, offers a valuable approach to examine the interactions occurring within protein complexes that complements traditional protein biochemical methods. BioID has various advantages that are beneficial to the study of complexes, including an ability to detect insoluble and transient proteins. We have applied BioID to the study of integrin adhesion complexes (IACs), which are located at the junction between the plasma membrane and actin cytoskeleton. The use of multiple BioID baits enables a complex-wide, spatial annotation of IACs, which in turn facilitates the detection of novel proximal interactors and provides insights into IAC architecture. This article describes the labeling and affinity purification of IAC-proximal proteins and their analysis by label-free quantitative mass spectrometry. The article also outlines steps to identify high-confidence proximity interactors, and to interrogate the topology and functional relevance of proximity interaction networks through bioinformatic analyses. © 2020 The Authors. Basic Protocol 1: Proximity biotinylation of integrin adhesion complex components Basic Protocol 2: Mass spectrometry data processing by MaxQuant and detection of high-confidence proximal interactors Basic Protocol 3: Bioinformatic analysis and data visualization.

Mass Spectrometric Method for the Unambiguous Profiling of Cellular Dynamic Glycosylation

Various biological processes at the cellular level are regulated by glycosylation which is a highly microheterogeneous post-translational modification (PTM) on proteins and lipids. The dynamic nature of glycosylation can be studied through metabolic incorporation of non-natural sugars into glycan epitopes and their detection using bio-orthogonal probes. However, this approach possesses a significant drawback due to nonspecific background reactions and ambiguity of non-natural sugar metabolism. Here, we report a probe-free strategy for their direct detection by glycoproteomics and glycomics using mass spectrometry (MS). The method dramatically simplifies the detection of non-natural functional group bearing monosaccharides installed through promiscuous sialic acid, N-acetyl-d-galactosamine (GalNAc) and N-acetyl-d-glucosamine (GlcNAc) biosynthetic pathways. Multistage enrichment of glycoproteins by cellular fractionation, subsequent ZIC-HILIC (zwitterionic-hydrophilic interaction chromatography) based glycopeptide enrichment, and a spectral enrichment algorithm for the MS data processing enabled direct detection of non-natural monosaccharides that are incorporated at low abundance on the N/O-glycopeptides along with their natural counterparts. Our approach allowed the detection of both natural and non-natural sugar bearing glycopeptides, N- and O-glycopeptides, differentiation of non-natural monosaccharide types on the glycans and also their incorporation efficiency through quantitation. Through this, we could deduce interconversion of monosaccharides during their processing through glycan salvage pathway and subsequent incorporation into glycan chains. The study of glycosylation dynamics through this method can be conducted in high throughput, as few sample processing steps are involved, enabling understanding of glycosylation dynamics under various external stimuli and thereby could bolster the use of metabolic glycan engineering in glycosylation functional studies.

Symbolic Aggregate Approximation Improves Gap Filling in High-Resolution Mass Spectrometry Data Processing.

Nontargeted mass spectrometry (MS) is widely used in life sciences and environmental chemistry to investigate large sets of samples. A major problem for larger-scale MS studies is data gaps or missing values in aligned data sets. The main causes for these data gaps are the absence of the compound from the sample, issues related to chromatography or mass spectrometry (for example, broad peaks, early eluting peaks, ion suppression, low ionization efficiency), and issues related to software (mainly limitations of peak detection algorithms). While those algorithms are heuristic by necessity and should be used with strict settings to minimize the number of false positive and negative peaks in a data set, gap filling may be used to reduce missing data in single samples remaining after peak detection. In this study, we present a new gap filling algorithm. The method is based on the symbolic aggregation approximation (SAX) algorithm that was developed for the evaluation and classification of time series in data mining studies. We adopted SAX for liquid chromatography high-resolution MS nontarget screening to support the detection of missing peaks in aligned mass spectral data sets. The SAX-based algorithm improves the detection efficiency considerably compared to existing gap filling methods including the Peak Finder algorithm provided in MZmine.

DropMS: Petroleomics Data Treatment Based in Web Server for High-Resolution Mass Spectrometry.

We have built an online tool with a user-friendly and browser-based interface to facilitate the processing of high resolution and precision oil mass spectrometry data. DropMS does not require software installations. Mass spectra are sent and processed by the server using various algorithms reported in the literature, such as S/N ratio filters, recalibrations, chemical formula assimilations, and data visualization using graphs and diagrams popularly known in mass spectrometry as Van Krevelen and Kendrick diagrams and DBE vs C#. To validate the algorithms used and the processing results, the same mass spectrum of a typical Brazilian oil sample was analyzed by ESI(+)-FT-ICR/MS and processed using Sierra Analytics DropMS and Composer to obtain good agreement between the heteroatomic classes found and the number of compounds assigned. The MS has chemical information spread over the entire spectrum. The PLS multivariate regression has the main objective of decomposing the most important information into latent variables in order to quantify the most evaluated properties. Finally, 12 processed petroleum FT-ICR MS spectra were used for a partial least-squares regression with seven latent variables for R2 = 0.971 and RMSEC of 0.997 for API density property with a reference value range of 21-42.

ICBM-OCEAN: Processing Ultrahigh-Resolution Mass Spectrometry Data of Complex Molecular Mixtures.

Untargeted molecular analyses of complex mixtures are relevant for many fields of research, including geochemistry, pharmacology, and medicine. Ultrahigh-resolution mass spectrometry is one of the most powerful tools in this context. The availability of open scripts and online tools for specific data processing steps such as noise removal or molecular formula assignment is growing, but an integrative tool where all crucial steps are reproducibly evaluated and documented is lacking. We developed a novel, server-based tool (ICBM-OCEAN, Institute for Chemistry and Biology of the Marine Environment, Oldenburg-complex molecular mixtures, evaluation & analysis) that integrates published and novel approaches for standardized processing of ultrahigh-resolution mass spectrometry data of complex molecular mixtures. Different from published approaches, we offer diagnostic and validation tools for all relevant steps. Among other features, we included objective and reproducible reduction of noise and systematic errors, spectra recalibration and alignment, and identification of likeliest molecular formulas. With 15 chemical elements, the tool offers high flexibility in formula attribution. Alignment of mass spectra among different samples prior to molecular formula assignment improves mass error and facilitates molecular formula confirmation with the help of isotopologues. The online tool and the detailed instruction manual are freely accessible at www.icbm.de/icbm-ocean.

Identification of potential cervical cancer serum biomarkers in Thai patients

Cervical cancer is one of the most common causes of cancer-associated mortality in females worldwide. Serum biomarkers are important tools for diagnosis, disease staging, monitoring treatment and detecting recurrence in different types of cancer. However, only a small number of established biomarkers have been used for clinical diagnosis of cervical cancer. Therefore, the identification of minimally invasive, sensitive and highly specific biomarkers for detection of cervical cancer may improve outcomes. In the present pilot study, changes in disease-relevant proteins in 31 patients with cervical cancer were compared with 16 healthy controls. The Human 14 Multiple Affinity Removal system was used to deplete the 14 most abundant serum proteins to decrease sample complexity and to enrich proteins that exhibited decreased levels of abundance in the serum samples. Immunoaffinity-depleted serum samples were analyzed by in-gel digestion, followed by liquid chromatography mass spectrometry analysis and data processing. Automated quantitative western blot assays and receiver operating characteristic (ROC) curves were used to evaluate the differential protein expression levels between the two groups. Capillary electrophoresis-based western blot analysis was performed to quantitatively determine serum levels of the candidate biomarkers. Significantly increased levels of α-1-antitrypsin (A1AT) and pyrroline-5-carboxylate reductase 2 (PYCR2) were detected, whereas the levels of transthyretin (TTR), apolipoprotein A-I (ApoA-I), vitamin D binding protein (VDBP) and multimerin-1 (MMRN1) were significantly decreased in patients with cervical cancer compared with the healthy controls. ROC curve analysis indicated that the sensitivity and specificity was improved through the combination of the 6 candidate biomarkers. In summary, the results demonstrated that 6 candidate biomarkers (A1AT, PYCR2, TTR, ApoA-I, VDBP and MMRN1) exhibited significantly different expression between serum samples from healthy controls and patients with cervical cancer. These proteins may represent potential biomarkers for distinguishing patients with cervical cancer from healthy controls and for differentiation of patient subgroups.

A European proposal for quality control and quality assurance of tandem mass spectral libraries

BackgroundHigh resolution mass spectrometry (HRMS) is being used increasingly in the context of suspect and non-targeted screening for the identification of bioorganic molecules. There is correspondingly increasing awareness that higher confidence identification will require a systematic, group effort to increase the fraction of compounds with tandem mass spectra available in central, publicly available resources. While typical suspect screening efforts will only result in tentative annotations with a moderate level of confidence, library spectral matches will yield higher confidence or even full confirmation of the identity if the reference standards are available.ResultsThis article first explores representative percent coverage of measured tandem mass spectra in selected major environmental suspect databases of interest in the context of human biomonitoring, demonstrating the current extensive gap between the number of potential substances of interest (up to hundreds of thousands) and measured spectra (0.57–3.6% of the total chemicals have spectral information available). Furthermore, certain datasets are benchmarked, based on previous efforts, to show the extent to which acquired experimental data were comparable between laboratories, even with HRMS instruments based on different technologies (i.e., quadrupole–quadrupole-time of flight versus ion trap/quadrupole-Orbitrap). Instruments and settings that are less comparable are also revealed, primarily linear ion trap instruments, which show distinctly lower comparability.ConclusionsBased on these efforts, harmonization guidelines for the acquisition and processing of tandem mass spectrometry data are proposed to enable European (and ideally worldwide) laboratories to contribute to common resources, without requiring extensive changes to their current in house methods.

Automatic peak assignment and visualisation of copolymer mass spectrometry data using the 'genetic algorithm'.

Copolymer analysis is vitally important as the materials have a wide variety of applications due to their tunable properties. Processing mass spectrometry data for copolymer samples can be very complex due to the increase in the number of species when the polymer chains are formed by two or more monomeric units. In this paper, we describe the use of the genetic algorithm for automated peak assignment of copolymers synthesised by a variety of polymerisation methods. We find that in using this method we are able to easily assign copolymer spectra in a few minutes and visualise them into heat maps. These heat maps allow us to look qualitatively at the distribution of the chains, by showing how they alter with different polymerisation techniques, and by changing the initial copolymer composition. This methodology is simple to use and requires little user input, which makes it well suited for use by less expert users. The data outputted by the automatic assignment may also allow for more complex data processing in the future.

Skyline for Small Molecules: A Unifying Software Package for Quantitative Metabolomics.

Vendor-independent software tools for quantification of small molecules and metabolites are lacking, especially for targeted analysis workflows. Skyline is a freely available, open-source software tool for targeted quantitative mass spectrometry method development and data processing with a 10 year history supporting six major instrument vendors. Designed initially for proteomics analysis, we describe the expansion of Skyline to data for small molecule analysis, including selected reaction monitoring, high-resolution mass spectrometry, and calibrated quantification. This fundamental expansion of Skyline from a peptide-sequence-centric tool to a molecule-centric tool makes it agnostic to the source of the molecule while retaining Skyline features critical for workflows in both peptide and more general biomolecular research. The data visualization and interrogation features already available in Skyline, such as peak picking, chromatographic alignment, and transition selection, have been adapted to support small molecule data, including metabolomics. Herein, we explain the conceptual workflow for small molecule analysis using Skyline, demonstrate Skyline performance benchmarked against a comparable instrument vendor software tool, and present additional real-world applications. Further, we include step-by-step instructions on using Skyline for small molecule quantitative method development and data analysis on data acquired with a variety of mass spectrometers from multiple instrument vendors.

An Integrated Mass Spectroscopy Data Processing Strategy for Fast Identification, In-Depth, and Reproducible Quantification of Protein O-Glycosylation in a Large Cohort of Human Urine Samples.

Protein O-glycosylation has long been recognized to be closely associated with many diseases, particularly with tumor proliferation, invasion, and metastasis. The ability to efficiently profile the variation of O-glycosylation in large-scale clinical samples provides an important approach for the development of biomarkers for cancer diagnosis and for therapeutic response evaluation. Therefore, mass spectrometry (MS)-based techniques for high throughput, in-depth and reliable elucidation of protein O-glycosylation in large clinical cohorts are in high demand. However, the wide existence of serine and threonine residues in the proteome and the tens of mammalian O-glycan types lead to extremely large searching space composed of millions of theoretical combinations of peptides and O-glycans for intact O-glycopeptide database searching. As a result, an exceptionally long time is required for database searching, which is a major obstacle in O-glycoproteome studies of large clinical cohorts. More importantly, because of the low abundance and poor ionization of intact O-glycopeptides and the stochastic nature of data-dependent MS2 acquisition, substantially elevated missing data levels are inevitable as the sample number increases, which undermines the quantitative comparison across samples. Therefore, we report a new MS data processing strategy that integrates glycoform-specific database searching, reference library-based MS1 feature matching and MS2 identification propagation for fast identification, in-depth, and reproducible label-free quantification of O-glycosylation of human urinary proteins. This strategy increases the database searching speeds by up to 20-fold and leads to a 30%-40% enhanced intact O-glycopeptide quantification in individual samples with an obviously improved reproducibility. In total, we identified 1300 intact O-glycopeptides in 36 healthy human urine samples with a 30%-40% reduction in the amount of missing data. This is currently the largest dataset of urinary O-glycoproteome and demonstrates the application potential of this new strategy in large-scale clinical investigations.

Comparison of Software Tools for Liquid Chromatography-High-Resolution Mass Spectrometry Data Processing in Nontarget Screening of Environmental Samples.

The field of high-resolution mass spectrometry has undergone a rapid progress in the last years due to instrumental improvements leading to a higher sensitivity and selectivity of instruments. A variety of qualitative screening approaches, summarized as nontarget screening, have been introduced and have successfully extended the environmental monitoring of organic micropollutants. Several automated data processing workflows have been developed to handle the immense amount of data that are recorded in short time frames by these methods. Most data processing workflows include similar steps, but underlying algorithms and implementation of different processing steps vary. In this study the consistency of data processing with different software tools was investigated. For this purpose, the same raw data files were processed with the software packages MZmine2, enviMass, Compound Discoverer, and XCMS online and resulting feature lists were compared. Results show a low coherence between different processing tools, as overlap of features between all four programs was around 10%, and for each software between 40% and 55% of features did not match with any other program. The implementation of replicate and blank filter was identified as one of the sources of observed divergences. However, there is a need for a better understanding and user instructions on the influence of different algorithms and settings on feature extraction and following filtering steps. In future studies it would be of interest to investigate how final data interpretation is influenced by different processing software. With this work we want to encourage more awareness on data processing as a crucial step in the workflow of nontarget screening.

Spectrum_utils: A Python Package for Mass Spectrometry Data Processing and Visualization.

Given the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https://github.com/bittremieux/spectrum_utils .

Mass Spectrometry-Based Plasma Proteomics: Considerations from Sample Collection to Achieving Translational Data.

The proteomic analysis of human blood and blood-derived products (e.g., plasma) offers an attractive avenue to translate research progress from the laboratory into the clinic. However, due to its unique protein composition, performing proteomics assays with plasma is challenging. Plasma proteomics has regained interest due to recent technological advances, but challenges imposed by both complications inherent to studying human biology (e.g., interindividual variability) and analysis of biospecimens (e.g., sample variability), as well as technological limitations remain. As part of the Human Proteome Project (HPP), the Human Plasma Proteome Project (HPPP) brings together key aspects of the plasma proteomics pipeline. Here, we provide considerations and recommendations concerning study design, plasma collection, quality metrics, plasma processing workflows, mass spectrometry (MS) data acquisition, data processing, and bioinformatic analysis. With exciting opportunities in studying human health and disease though this plasma proteomics pipeline, a more informed analysis of human plasma will accelerate interest while enhancing possibilities for the incorporation of proteomics-scaled assays into clinical practice.

Methods and Challenges for Computational Data Analysis for DNA Adductomics.

Frequent exposure to chemicals in the environment, diet, and endogenous electrophiles leads to chemical modification of DNA and the formation of DNA adducts. Some DNA adducts can induce mutations during cell division and, when occurring in critical regions of the genome, can lead to the onset of disease, including cancer. The targeted analysis of DNA adducts over the past 30 years has revealed that the human genome contains many types of DNA damages. However, a long-standing limitation in conducting DNA adduct measurements has been the inability to screen for the total complement of DNA adducts derived from a wide range of chemicals in a single assay. With the advancement of high-resolution mass spectrometry (MS) instrumentation and new scanning technologies, nontargeted "omics" approaches employing data-dependent acquisition and data-independent acquisition methods have been established to simultaneously screen for multiple DNA adducts, a technique known as DNA adductomics. However, notable challenges in data processing must be overcome for DNA adductomics to become a mature technology. DNA adducts occur at low abundance in humans, and current softwares do not reliably detect them when using common MS data acquisition methods. In this perspective, we discuss contemporary computational tools developed for feature finding of MS data widely utilized in the disciplines of proteomics and metabolomics and highlight their limitations for conducting nontargeted DNA-adduct biomarker discovery. Improvements to existing MS data processing software and new algorithms for adduct detection are needed to develop DNA adductomics into a powerful tool for the nontargeted identification of potential cancer-causing agents.

Speeding Up Percolator.

The processing of peptide tandem mass spectrometry data involves matching observed spectra against a sequence database. The ranking and calibration of these peptide-spectrum matches can be improved substantially using a machine learning postprocessor. Here, we describe our efforts to speed up one widely used postprocessor, Percolator. The improved software is dramatically faster than the previous version of Percolator, even when using relatively few processors. We tested the new version of Percolator on a data set containing over 215 million spectra and recorded an overall reduction to 23% of the running time as compared to the unoptimized code. We also show that the memory footprint required by these speedups is modest relative to that of the original version of Percolator.

PepFoot: A Software Package for Semiautomated Processing of Protein Footprinting Data.

Covalent footprinting of proteins using reactive intermediates such as radicals and carbenes is emerging as a valuable tool for mapping surface accessibility, and hence binding sites of proteins. The approach generates a significant amount of mass spectrometry (MS) data, which can be time-consuming to process manually. PepFoot, a software package that allows semiautomated processing of MS data from footprinting experiments, is described. By using the open source .mz5 file format, it is able to accept data from all the major instrument manufacturers. Following manual user interrogation of one data file within a user-friendly GUI, the software then automates determination of the degree of fractional modification ( fm) with the footprinting agent across a batch of experimental data. This greatly increases efficiency and throughput compared to manual analysis of each file, and provides initial scrutiny and confidence compared to fully automated analysis. Histogram plots of fm for each peptide from the footprinted protein may be displayed within PepFoot and mapped onto an imported protein structure to reveal differential labeling patterns and hence binding sites. The software has been tested on data from carbene and hydroxyl radical labeling experiments to demonstrate its broad utility. PepFoot is released under the LGPL version 3 license, and is available for Windows, MacOS, and Linux systems at github.com/jbellamycarter/pepfoot .

Suspect and non-target screening workflows to investigate the in vitro and in vivo metabolism of the synthetic cannabinoid 5-Cl-THJ-018

Use of synthetic cannabinoids cause similar effects as Δ 9 -tetrahydrocannabinol. Long-term and acute (ab)use can lead to health hazards and fatal intoxications respectively. As most investigated synthetic cannabinoids undergo extensive biotransformation, almost no parent compound can be detected in urine, which hampers forensic investigations [1] . Limited information about biotransformation products of new synthetic cannabinoids makes the detection of these drugs in various biological matrices challenging. This study aimed to identify the main in vitro biotransformation products of 5-Cl-THJ-018 and compare the in vitro findings with authentic urine samples of 5-Cl-THJ-018 users. 5-Cl-THJ-018 was incubated with pooled human liver microsomes and cytosol to simulate phase I and phase II biotransformations. Samples were analyzed with liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-QTOF-MS). Three different data-analysis workflows were applied for identifying the biotransformation products. The suspect screening workflow used a database built from literature and in silico biotransformation predictions by the Meteor Nexus software (Lhasa Limited). The two non-target screening Methods used the MassProfiler Professional (Agilent) and the open source software for mass spectrometry data processing MZmine 2.29 respectively. The obtained m/z features were further processed and visualized using open source R software. A total of 23 biotransformation products were identified. Phase I biotransformation included hydroxylation, oxidative dechlorination and dihydrodiol formation pathways. Also, five glucuronidated and three sulphated phase II conjugates have been identified. Seven biotransformation products were also identified in the in vivo sample. The application of non-target screening workflows in parallel to suspect screening increased the number of metabolites being identified. As only dechlorinated metabolites were identified in the in vivo urine sample, no specific biomarkers could be proposed to distinguish 5-Cl-THJ-018 use from the use of similar (non-) halogenated analogues.

El-MAVEN: A Fast, Robust, and User-Friendly Mass Spectrometry Data Processing Engine for Metabolomics.

Analysis of large metabolomic datasets is becoming commonplace with the increased realization of the role that metabolites play in biology and pathophysiology. While there are many open-source analysis tools to extract peaks from liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and tandem mass spectrometry (LC-MS/MS) data, these tools are not very interactive and are suboptimal when a large number of samples are to be analyzed. El-MAVEN is an open-source analysis platform that extends MAVEN and provides fast, powerful, and interactive analysis capabilities especially for datasets containing over 100 samples. The El-MAVEN workflow is easy to use with just four steps from loading data to exporting of the results. Advanced analysis and software techniques such as multiprocessing, machine learning, and reduction of memory leaks are implemented so as to provide a seamless and interactive user experience. Results from El-MAVEN can be exported in a range of formats allowing continued analysis on other platforms. Additionally, El-MAVEN is also fully integrated with Polly™, a cloud-based analysis platform that provides a range of tools for flux analysis and integrative-omics analysis. El-MAVEN is a powerful tool that enables fast and efficient analysis of large metabolomic datasets to accelerate the process of gaining insight from raw data.