Tandem Mass Spectrometry Datasets Research Articles

Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks.

Tandem mass spectrometry is helpful in diagnosing amino acid metabolism disorders, organic acidemias, and fatty acid oxidation disorders and can provide rapid and accurate diagnosis for inborn errors of metabolism. The aim of this study was to predict inborn errors of metabolism in children with the help of artificial neural networks using tandem mass spectrometry data. Forty-seven and 13 parameters of tandem mass spectrometry datasets obtained from 2938 different patients were respectively taken into account to train and test the artificial neural networks. Different artificial neural network models were established to obtain better prediction performances. The obtained results were compared with each other for fair comparisons. The best results were obtained by using the rectified linear unit activation function. One, two, and three hidden layers were considered for artificial neural network models established with both 47 and 13 parameters. The sensitivity of model B2 for definitive inherited metabolic disorders was found to be 80%. The accuracy rates of model A3 and model B2 are 99.3% and 99.2%, respectively. The area under the curve value of model A3 was 0.87, while that of model B2 was 0.90. The results showed that the proposed artificial neural networks are capable of predicting inborn errors of metabolism very accurately. Therefore, developing new technologies to identify and predict inborn errors of metabolism will be very useful.

From MS/MS library implementation to molecular networks: Exploring oxylipin diversity with NEO-MSMS

Oxylipins, small polar molecules derived from the peroxidation of polyunsaturated fatty acids (PUFAs), serve as biomarkers for many diseases and play crucial roles in human physiology and inflammation. Despite their significance, many non-enzymatic oxygenated metabolites of PUFAs (NEO-PUFAs) remain poorly reported, resulting in a lack of public datasets of experimental data and limiting their dereplication in further studies. To overcome this limitation, we constructed a high-resolution tandem mass spectrometry (MS/MS) dataset comprising pure NEO-PUFAs (both commercial and self-synthesized) and in vitro free radical-induced oxidation of diverse PUFAs. By employing molecular networking techniques with this dataset and the existent ones in public repositories, we successfully mapped a wide range of NEO-PUFAs, expanding the strategies for annotating oxylipins, and NEO-PUFAs and offering a novel workflow for profiling these molecules in biological samples.

Mix24X, a Lab-Assembled Reference to Evaluate Interpretation Procedures for Tandem Mass Spectrometry Proteotyping of Complex Samples.

Correct identification of the microorganisms present in a complex sample is a crucial issue. Proteotyping based on tandem mass spectrometry can help establish an inventory of organisms present in a sample. Evaluation of bioinformatics strategies and tools for mining the recorded datasets is essential to establish confidence in the results obtained and to improve these pipelines in terms of sensitivity and accuracy. Here, we propose several tandem mass spectrometry datasets recorded on an artificial reference consortium comprising 24 bacterial species. This assemblage of environmental and pathogenic bacteria covers 20 different genera and 5 bacterial phyla. The dataset comprises difficult cases, such as the Shigella flexneri species, which is closely related to Escherichia coli, and several highly sequenced clades. Different acquisition strategies simulate real-life scenarios: from rapid survey sampling to exhaustive analysis. We provide access to individual proteomes of each bacterium separately to provide a rational basis for evaluating the assignment strategy of MS/MS spectra when recorded from complex mixtures. This resource should provide an interesting common reference for developers who wish to compare their proteotyping tools and for those interested in evaluating protein assignment when dealing with complex samples, such as microbiomes.

CompareMS2 2.0: An Improved Software for Comparing Tandem Mass Spectrometry Datasets.

It has long been known that biological species can be identified from mass spectrometry data alone. Ten years ago, we described a method and software tool, compareMS2, for calculating a distance between sets of tandem mass spectra, as routinely collected in proteomics. This method has seen use in species identification and mixture characterization in food and feed products, as well as other applications. Here, we present the first major update of this software, including a new metric, a graphical user interface and additional functionality. The data have been deposited to ProteomeXchange with dataset identifier PXD034932.

Non-targeted tandem mass spectrometry enables the visualization of organic matter chemotype shifts in coastal seawater

Urbanization along coastlines alters marine ecosystems including contributing molecules of anthropogenic origin to the coastal dissolved organic matter (DOM) pool. A broad assessment of the nature and extent of anthropogenic impacts on coastal ecosystems is urgently needed to inform regulatory guidelines and ecosystem management. Recently, non-targeted tandem mass spectrometry approaches are gaining momentum for the analysis of global organic matter composition (chemotypes) including a wide array of natural and anthropogenic compounds. In line with these efforts, we developed a non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) workflow that utilizes advanced data analysis approaches such as feature-based molecular networking and repository-scale spectrum searches. This workflow allows the scalable comparison and mapping of seawater chemotypes from large-scale spatial surveys as well as molecular family level annotation of unknown compounds. As a case study, we visualized organic matter chemotype shifts in coastal environments in northern San Diego, USA, after notable rain fall in winter 2017/2018 and highlight potential anthropogenic impacts. The observed seawater chemotype, consisting of 4384 LC-MS/MS features, shifted significantly after a major rain event. Molecular drivers of this shift could be attributed to multiple anthropogenic compounds, including pesticides (Imazapyr and Isoxaben), cleaning products (Benzyl-tetradecyl-dimethylammonium) and chemical additives (Hexa (methoxymethyl)melamine) and potential degradation products. By expanding the search of identified xenobiotics to other public tandem mass spectrometry datasets, we further contextualized their possible origin and show their importance in other ecosystems. The mass spectrometry and data analysis pipelines applied here offer a scalable framework for future molecular mapping and monitoring of marine ecosystems, which will contribute to a deliberate assessment of how chemical pollution impacts our oceans.

A Metabolome- and Metagenome-Wide Association Network Reveals Microbial Natural Products and Microbial Biotransformation Products from the Human Microbiota.

The human microbiome consists of thousands of different microbial species, and tens of thousands of bioactive small molecules are associated with them. These associated molecules include the biosynthetic products of microbiota and the products of microbial transformation of host molecules, dietary components, and pharmaceuticals. The existing methods for characterization of these small molecules are currently time consuming and expensive, and they are limited to the cultivable bacteria. Here, we propose a method for detecting microbiota-associated small molecules based on the patterns of cooccurrence of molecular and microbial features across multiple microbiomes. We further map each molecule to the clade in a phylogenetic tree that is responsible for its production/transformation. We applied our proposed method to the tandem mass spectrometry and metagenomics data sets collected by the American Gut Project and to microbiome isolates from cystic fibrosis patients and discovered the genes in the human microbiome responsible for the production of corynomycolenic acid, which serves as a ligand for human T cells and induces a specific immune response against infection. Moreover, our method correctly associated pseudomonas quinolone signals, tyrvalin, and phevalin with their known biosynthetic gene clusters.IMPORTANCE Experimental advances have enabled the acquisition of tandem mass spectrometry and metagenomics sequencing data from tens of thousands of environmental/host-oriented microbial communities. Each of these communities contains hundreds of microbial features (corresponding to microbial species) and thousands of molecular features (corresponding to microbial natural products). However, with the current technology, it is very difficult to identify the microbial species responsible for the production/biotransformation of each molecular feature. Here, we develop association networks, a new approach for identifying the microbial producer/biotransformer of natural products through cooccurrence analysis of metagenomics and mass spectrometry data collected on multiple microbiomes.

TagGraph reveals vast protein modification landscapes from large tandem mass spectrometry datasets.

Although mass spectrometry is well-suited to identifying thousands of possible protein post-translational modifications (PTMs), it has historically been biased towards just a few. To measure the entire set of PTMs across diverse proteomes, software must overcome the dual challenges of searching enormous search spaces and distinguishing correct from incorrect spectrum interpretations. Here, we describe TagGraph, a computational tool that overcomes both challenges with an unrestricted string-based search method that is as much as 350-fold faster than existing approaches, and a probabilistic validation model we optimized for PTM assignments. We applied TagGraph to a published human proteomic data set of 25 million mass spectra and tripled confident spectrum identifications compared its original analysis. We identified thousands of modification types on almost one million sites in the proteome. We show new contexts for highly abundant yet understudied PTMs such as proline hydroxylation, and its unexpected association with cancer mutations. By enabling broad PTM characterization TagGraph informs how their functions and regulation intersect.

Protein Identification from Tandem Mass Spectra by Database Searching.

Protein identification from tandem mass spectra is one of the most versatile and widely used proteomics workflows, able to identify proteins, characterize post-translational modifications, and provide semiquantitative measurements of relative protein abundance. This manuscript describes the concepts, prerequisites, and methods required to analyze a tandem mass spectrometry dataset in order to identify its proteins, by using a tandem mass spectrometry search engine to search protein sequence databases. The discussion includes instructions for extraction, preparation, and formatting of spectral datafiles, selection of appropriate search parameter settings, and basic interpretation of the results.

TAILS N-terminomic and proteomic datasets of healthy human dental pulp.

The Data described here provide the in depth proteomic assessment of the human dental pulp proteome and N-terminome (Eckhard et al., 2015) [1]. A total of 9 human dental pulps were processed and analyzed by the positional proteomics technique TAILS (Terminal Amine Isotopic Labeling of Substrates) N-terminomics. 38 liquid chromatography tandem mass spectrometry (LC-MS/MS) datasets were collected and analyzed using four database search engines in combination with statistical downstream evaluation, to yield the by far largest proteomic and N-terminomic dataset of any dental tissue to date. The raw mass spectrometry data and the corresponding metadata have been deposited in ProteomeXchange with the PXD identifier <PXD002264>; Supplementary Tables described in this article are available via Mendeley Data (10.17632/555j3kk4sw.1).

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.

GlyQ-IQ: glycomics quintavariate-informed quantification with high-performance computing and GlycoGrid 4D visualization.

Glycomics quintavariate-informed quantification (GlyQ-IQ) is a biologically guided glycomics analysis tool for identifying N-glycans in liquid chromatography–mass spectrometry (LC–MS) data. Glycomics LC–MS data sets have convoluted extracted ion chromatograms that are challenging to deconvolve with existing software tools. LC deconvolution into constituent pieces is critical in glycomics data sets because chromatographic peaks correspond to different intact glycan structural isomers. The biological targeted analysis approach offers several key advantages to traditional LC–MS data processing. A priori glycan information about the individual target’s elemental composition allows for improved sensitivity by utilizing the exact isotope profile information to focus chromatogram generation and LC peak fitting on the isotopic species having the highest intensity. Glycan target annotation utilizes glycan family relationships and in source fragmentation in addition to high specificity feature LC–MS detection to improve the specificity of the analysis. The GlyQ-IQ software was developed in this work and evaluated in the context of profiling the N-glycan compositions from human serum LC–MS data sets. A case study is presented to demonstrate how GlyQ-IQ identifies and removes confounding chromatographic peaks from high mannose glycan isomers from human blood serum. In addition, GlyQ-IQ was used to generate a broad human serum N-glycan profile from a high resolution nanoelectrospray-liquid chromatography–tandem mass spectrometry (nESI-LC–MS/MS) data set. A total of 156 glycan compositions and 640 glycan isomers were detected from a single sample. Over 99% of the GlyQ-IQ glycan-feature assignments passed manual validation and are backed with high-resolution mass spectra.

Authentication of Fish Products by Large-Scale Comparison of Tandem Mass Spectra

Authentication of food is a major concern worldwide to ensure that food products are correctly labeled in terms of which animals are actually processed for consumption. Normally authentication is based on species recognition by comparison of selected sequences of DNA or protein. We here present a new robust, proteome-wide tandem mass spectrometry method for species recognition and food product authentication. The method does not use or require any genome sequences or selection of tandem mass spectra but uses all acquired data. The experimental steps were performed in a simple, standardized workflow including protein extraction, digestion, and data analysis. First, a set of reference spectral libraries was generated using unprocessed muscle tissue from 22 different fish species. Query tandem mass spectrometry data sets from "unknown" fresh muscle tissue samples were then searched against the reference libraries. The number of matching spectra could unambiguously identify the origin of all fresh samples. A number of processed samples were also analyzed to further test the robustness and applicability of the method. The results clearly show that the method is also able to correctly identify heavily processed samples.

Investigation of Scrambled Ions in Tandem Mass Spectra, Part 2. On the Influence of the Ions on Peptide Identification

A comprehensive investigation was performed to understand the influence of sequence scrambling in peptide ions on peptide identification results. To achieve this, four tandem mass spectrometry datasets with scrambled ions included and with them excluded were analyzed by Crux, X!Tandem, SpectraST, Lutefisk, and PepNovo. While the different algorithms differed in their performance, an increase in the number of correctly identified peptides was generally observed when removing scrambled ions, with the exception of the SpectraST algorithm. However, the variation of the match scores upon removal was unpredictable. Following these investigations, an interpretation was given on how the scrambled ions affect peptide identification. Lastly, a simulated theoretical mass spectral library derived from the NIST peptide Libraries was constructed and searched by SpectraST to study whether scrambled ions in predicted mass spectra could affect peptide identification. Consistent with the peptide library search results, no significant variations for dot product scores as well as peptide identification results were observed when these ions were included in the theoretical MS/MS spectra. From the five adopted algorithms, the SpectraST and Crux provided the most robust results, whereas X!Tandem, PepNovo, and Lutefisk were sensitive to the existence of the scrambled ions, especially the latter two de novo sequencing algorithms.

Proteomic Classification of Acute Leukemias by Alignment-Based Quantitation of LC–MS/MS Data Sets

Despite immense interest in the proteome as a source of biomarkers in cancer, mass spectrometry has yet to yield a clinically useful protein biomarker for tumor classification. To explore the potential of a particular class of mass spectrometry-based quantitation approaches, label-free alignment of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) data sets, for the identification of biomarkers for acute leukemias, we asked whether a label-free alignment algorithm could distinguish known classes of leukemias on the basis of their proteomes. This approach to quantitation involves (1) computational alignment of MS1 peptide peaks across large numbers of samples; (2) measurement of the relative abundance of peptides across samples by integrating the area under the curve of the MS1 peaks; and (3) assignment of peptide IDs to those quantified peptide peaks on the basis of the corresponding MS2 spectra. We extracted proteins from blasts derived from four patients with acute myeloid leukemia (AML, acute leukemia of myeloid lineage) and five patients with acute lymphoid leukemia (ALL, acute leukemia of lymphoid lineage). Mobilized CD34+ cells purified from peripheral blood of six healthy donors and mononuclear cells (MNC) from the peripheral blood of two healthy donors were used as healthy controls. Proteins were analyzed by LC-MS/MS and quantified with a label-free alignment-based algorithm developed in our laboratory. Unsupervised hierarchical clustering of blinded samples separated the samples according to their known biological characteristics, with each sample group forming a discrete cluster. The four proteins best able to distinguish CD34+, AML, and ALL were all either known biomarkers or proteins whose biological functions are consistent with their ability to distinguish these classes. We conclude that alignment-based label-free quantitation of LC-MS/MS data sets can, at least in some cases, robustly distinguish known classes of leukemias, thus opening the possibility that large scale studies using such algorithms can lead to the identification of clinically useful biomarkers.

Quantile map: Simultaneous visualization of patterns in many distributions with application to tandem mass spectrometry

High-throughput experiments have become more and more prevalent in biomedical research. The resulting high-dimensional data have brought new challenges. Effective data reduction, summarization and visualization are important keys to initial exploration in data mining. In this paper, we introduce a visualization tool, namely a quantile map, to present information contained in a probabilistic distribution. We demonstrate its use as an effective visual analysis tool through the application of a tandem mass spectrometry data set. Information of quantiles of a distribution is presented in gradient colors by concentric doughnuts. The width of the doughnuts is proportional to the Fisher information of the distribution to present unbiased visualization effect. A parametric empirical Bayes (PEB) approach is shown to improve the simple maximum likelihood estimate (MLE) approach when estimating the Fisher information. In the motivating example from tandem mass spectrometry data, multiple probabilistic distributions are to be displayed in two-dimensional grids. A hierarchical clustering to reorder rows and columns and a gradient color selection from a Hue–Chroma–Luminance model, similar to that commonly applied in heatmaps of microarray analysis, are adopted to improve the visualization. Both simulations and the motivating example show superior performance of the quantile map in summarization and visualization of such high-throughput data sets.

Tandem mass spectrometry data quality assessment by self-convolution.

BackgroundMany algorithms have been developed for deciphering the tandem mass spectrometry (MS) data sets. They can be essentially clustered into two classes. The first performs searches on theoretical mass spectrum database, while the second based itself on de novo sequencing from raw mass spectrometry data. It was noted that the quality of mass spectra affects significantly the protein identification processes in both instances. This prompted the authors to explore ways to measure the quality of MS data sets before subjecting them to the protein identification algorithms, thus allowing for more meaningful searches and increased confidence level of proteins identified.ResultsThe proposed method measures the qualities of MS data sets based on the symmetric property of b- and y-ion peaks present in a MS spectrum. Self-convolution on MS data and its time-reversal copy was employed. Due to the symmetric nature of b-ions and y-ions peaks, the self-convolution result of a good spectrum would produce a highest mid point intensity peak. To reduce processing time, self-convolution was achieved using Fast Fourier Transform and its inverse transform, followed by the removal of the "DC" (Direct Current) component and the normalisation of the data set. The quality score was defined as the ratio of the intensity at the mid point to the remaining peaks of the convolution result. The method was validated using both theoretical mass spectra, with various permutations, and several real MS data sets. The results were encouraging, revealing a high percentage of positive prediction rates for spectra with good quality scores.ConclusionWe have demonstrated in this work a method for determining the quality of tandem MS data set. By pre-determining the quality of tandem MS data before subjecting them to protein identification algorithms, spurious protein predictions due to poor tandem MS data are avoided, giving scientists greater confidence in the predicted results. We conclude that the algorithm performs well and could potentially be used as a pre-processing for all mass spectrometry based protein identification tools.

Use of a Designed Peptide Array To Infer Dissociation Trends for Nontryptic Peptides in Quadrupole Ion Trap and Quadrupole Time-of-Flight Mass Spectrometry

Observed peptide gas-phase fragmentation patterns are a complex function of many variables. To systematically probe this phenomenon, an array of 40 peptides was synthesized for study. The array of sequences was designed to hold certain variables (peptide length) constant and randomize or balance others (peptide amino acid distribution and position). A high-quality tandem mass spectrometry (MS/MS) data set was acquired for each peptide for all observed charge states on multiple MS instruments, quadrupole-time-of-flight and quadrupole ion trap. The data were analyzed as a function of total charge state and number of mobile protons. Previously known dissociation trends were observed, validating our approach. In addition, the general influence of basic amino acids on dissociation could be determined because, in contrast to the more widely studied tryptic peptides, the amino acids H, K, and R were positionally distributed. Interestingly, our results suggest that cleavage at all basic amino acids is suppressed when a mobile proton is available. Cleavage at H becomes favored only under conditions where a partially mobile proton is present, a caveat to the previously reported trend of enhanced cleavage at H. Finally, all acquired data were used as a benchmark to determine how well these sequences would have been identified in a database search using a common algorithm, Mascot.

EBP, a Program for Protein Identification Using Multiple Tandem Mass Spectrometry Datasets

MS/MS combined with database search methods can identify the proteins present in complex mixtures. High throughput methods that infer probable peptide sequences from enzymatically digested protein samples create a challenge in how best to aggregate the evidence for candidate proteins. Typically the results of multiple technical and/or biological replicate experiments must be combined to maximize sensitivity. We present a statistical method for estimating probabilities of protein expression that integrates peptide sequence identifications from multiple search algorithms and replicate experimental runs. The method was applied to create a repository of 797 non-homologous zebrafish (Danio rerio) proteins, at an empirically validated false identification rate under 1%, as a resource for the development of targeted quantitative proteomics assays. We have implemented this statistical method as an analytic module that can be integrated with an existing suite of open-source proteomics software.

PhosTShunter: A Fast and Reliable Tool to Detect Phosphorylated Peptides in Liquid Chromatography Fourier Transform Tandem Mass Spectrometry Data Sets

A database independent search algorithm for the detection of phosphopeptides is described. The program interrogates the tandem mass spectra of LC-MS/MS data sets regarding the presence of phosphorylation specific signatures. To achieve maximum informational content, the complementary fragmentation techniques electron capture dissociation (ECD) and collisionally activated dissociation (CAD) are used independently for peptide fragmentation. Several criteria characteristic for peptides phosphorylated on either serine or threonine residues were evaluated. The final algorithm searches for product ions generated by either the neutral loss of phosphoric acid or the combined neutral loss of phosphoric acid and water. Various peptide mixtures were used to evaluate the program. False positive results were not observed because the program utilizes the parts-per-million mass accuracy of Fourier transform ion cyclotron resonance mass spectrometry. Additionally, false negative results were not generated owing to the high sensitivity of the chosen criteria. The limitations of database dependent data interpretation tools are discussed and the potential of the novel algorithm to overcome these limitations is illustrated.

Biomarker Discovery, Disease Classification, and Similarity Query Processing on High-Throughput MS/MS Data of Inborn Errors of Metabolism

In newborn errors of metabolism, biomarkers are urgently needed for disease screening, diagnosis, and monitoring of therapeutic interventions. This article describes a 2-step approach to discover metabolic markers, which involves (1) the identification of marker candidates and (2) the prioritization of them based on expert knowledge of disease metabolism. For step 1, the authors developed a new algorithm, the biomarker identifier (BMI), to identify markers from quantified diseased versus normal tandem mass spectrometry data sets. BMI produces a ranked list of marker candidates and discards irrelevant metabolites based on a quality measure, taking into account the discriminatory performance, discriminatory space, and variance of metabolites' concentrations at the state of disease. To determine the ability of identified markers to classify subjects, the authors compared the discriminatory performance of several machine-learning paradigms and described a retrieval technique that searches and classifies abnormal metabolic profiles from a screening database. Seven inborn errors of metabolism-- phenylketonuria (PKU), glutaric acidemia type I (GA-I), 3-methylcrotonylglycinemia deficiency (3-MCCD), methylmalonic acidemia (MMA), propionic acidemia (PA), medium-chain acylCoAdehydrogenase deficiency (MCADD), and 3-OH long-chain acyl CoA dehydrogenase deficiency (LCHADD)-were investigated. All primarily prioritized marker candidates could be confirmed by literature. Some novel secondary candidates were identified (i.e., C16:1 and C4DC for PKU, C4DC for GA-I, and C18:1 forMCADD), which require further validation to confirm their biochemical role during health and disease.