Mass Spectrometry Data Analysis Research Articles

Untangling the Complexities of Processing and Analysis for Untargeted LC-MS Data Using Open-Source Tools.

Untargeted metabolomics is a powerful tool for measuring and understanding complex biological chemistries. However, employment, bioinformatics and downstream analysis of mass spectrometry (MS) data can be daunting for inexperienced users. Numerous open-source and free-to-use data processing and analysis tools exist for various untargeted MS approaches, including liquid chromatography (LC), but choosing the 'correct' pipeline isn't straight-forward. This tutorial, in conjunction with a user-friendly online guide presents a workflow for connecting these tools to process, analyse and annotate various untargeted MS datasets. The workflow is intended to guide exploratory analysis in order to inform decision-making regarding costly and time-consuming downstream targeted MS approaches. We provide practical advice concerning experimental design, organisation of data and downstream analysis, and offer details on sharing and storing valuable MS data for posterity. The workflow is editable and modular, allowing flexibility for updated/changing methodologies and increased clarity and detail as user participation becomes more common. Hence, the authors welcome contributions and improvements to the workflow via the online repository. We believe that this workflow will streamline and condense complex mass-spectrometry approaches into easier, more manageable, analyses thereby generating opportunities for researchers previously discouraged by inaccessible and overly complicated software.

Structure and dynamics of the Arabidopsis O-fucosyltransferase SPINDLY

SPINDLY (SPY) in Arabidopsis thaliana is a novel nucleocytoplasmic protein O-fucosyltransferase (POFUT), which regulates diverse developmental processes. Sequence analysis indicates that SPY is distinct from ER-localized POFUTs and contains N-terminal tetratricopeptide repeats (TPRs) and a C-terminal catalytic domain resembling the O-linked-N-acetylglucosamine (GlcNAc) transferases (OGTs). However, the structural feature that determines the distinct enzymatic selectivity of SPY remains unknown. Here we report the cryo-electron microscopy (cryo-EM) structure of SPY and its complex with GDP-fucose, revealing distinct active-site features enabling GDP-fucose instead of UDP-GlcNAc binding. SPY forms an antiparallel dimer instead of the X-shaped dimer in human OGT, and its catalytic domain interconverts among multiple conformations. Analysis of mass spectrometry, co-IP, fucosylation activity, and cryo-EM data further demonstrates that the N-terminal disordered peptide in SPY contains trans auto-fucosylation sites and inhibits the POFUT activity, whereas TPRs 1–5 dynamically regulate SPY activity by interfering with protein substrate binding.

A simple TICT/ICT based molecular probe exhibiting ratiometric fluorescence Turn-On response in selective detection of Cu2+

The work reported herein describes photophysical behavior of a new fluorescent probe APDA. The probe exhibited sensitivity for Cu2+ ion in 80% aqueous acetonitrile medium. Probe shows dual emission due to TICT and ICT states and upon interaction with different metal ions Cu2+ induces hydrolysis of imine bond to rejuvenate aldehyde derivative with ratiometric “turn–On” fluorescence response, through ICT state while emission due to TICT state get diminished. Similarly, the synthesized model compound ADA under similar experimental condition supported hydrolysis of imine bond in the presence of copper. Jobs plot analysis, based on emission titration data suggested a 1:1 binding stoichiometry between probe APDA and Cu2+ ion. The limit of detection (LOD) was found to be 4.4 × 10-8 M (44 nM). The probe has been applied on test paper strip to detect Cu2+ ion with naked-eye sensitive response. Also, the cell imaging studies revealed the potential applicability of probe to detect Cu2+ ion in live cells. The mechanism of sensing was confirmed by 1H, 13C NMR, FTIR and mass spectrometry data analysis.

DeepDetect: Deep Learning of Peptide Detectability Enhanced by Peptide Digestibility and Its Application to DIA Library Reduction.

In tandem mass spectrometry-based proteomics, proteins are digested into peptides by specific protease(s), but generally only a fraction of peptides can be detected. To characterize detectable proteotypic peptides, we have developed a series of methods to predict peptide digestibility and detectability. Here, we propose a bidirectional long short-term memory (BiLSTM)-based algorithm, named DeepDetect, for the prediction of peptide detectability enhanced by peptide digestibility. Compared with existing algorithms, DeepDetect is featured by its improved prediction accuracy for a wide range of commonly used proteases, covering trypsin, ArgC, chymotrypsin, GluC, LysC, AspN, LysN, and LysargiNase. On 11 test data sets from E. coli, yeast, mouse, and human samples, DeepDetect achieved higher prediction accuracies than PepFormer, a state-of-the-art deep-learning-based peptide detectability prediction algorithm. The results further demonstrated that peptide digestibility can substantially enhance the performance of peptide detectability predictors. As an application, DeepDetect was used to reduce the in silico predicted spectral libraries in data-independent acquisition mass spectrometry data analysis. Experiments using DIA-NN software showed that DeepDetect can significantly accelerate the library search without loss of peptide and protein identification sensitivity.

A direct examination of microbial specialized metabolites associated with ocean sediments from Baffin Bay and the Gulf of Maine.

Specialized metabolites produced by microorganisms found in ocean sediments display a wide range of clinically relevant bioactivities, including antimicrobial, anticancer, antiviral, and anti-inflammatory. Due to limitations in our ability to culture many benthic microorganisms under laboratory conditions, their potential to produce bioactive compounds remains underexplored. However, the advent of modern mass spectrometry technologies and data analysis methods for chemical structure prediction has aided in the discovery of such metabolites from complex mixtures. In this study, ocean sediments were collected from Baffin Bay (Canadian Arctic) and the Gulf of Maine for untargeted metabolomics using mass spectrometry. A direct examination of prepared organic extracts identified 1468 spectra, of which ∼45% could be annotated using in silico analysis methods. A comparable number of spectral features were detected in sediments collected from both locations, but 16S rRNA gene sequencing revealed a significantly more diverse bacterial community in samples from Baffin Bay. Based on spectral abundance, 12 specialized metabolites known to be associated with bacteria were selected for discussion. The application of metabolomics directly on marine sediments provides an avenue for culture-independent detection of metabolites produced under natural settings. The strategy can help prioritize samples for novel bioactive metabolite discovery using traditional workflows.

FAMetA: a mass isotopologue-based tool for the comprehensive analysis of fatty acid metabolism.

The use of stable isotope tracers and mass spectrometry (MS) is the gold standard method for the analysis of fatty acid (FA) metabolism. Yet, current state-of-the-art tools provide limited and difficult-to-interpret information about FA biosynthetic routes. Here we present FAMetA, an R package and a web-based application (www.fameta.es) that uses 13C mass isotopologue profiles to estimate FA import, de novo lipogenesis, elongation and desaturation in a user-friendly platform. The FAMetA workflow covers the required functionalities needed for MS data analyses. To illustrate its utility, different in vitro and in vivo experimental settings are used in which FA metabolism is modified. Thanks to the comprehensive characterization of FA biosynthesis and the easy-to-interpret graphical representations compared to previous tools, FAMetA discloses unnoticed insights into how cells reprogram their FA metabolism and, when combined with FASN, SCD1 and FADS2 inhibitors, it enables the identification of new FAs by the metabolic reconstruction of their synthesis route.

DBDIpy: a Python library for processing of untargeted datasets from real-time plasma ionization mass spectrometry.

Plasma ionization is rapidly gaining popularity for mass spectrometry (MS)-based studies of volatiles and aerosols. However, data from plasma ionization are delicate to interpret as competing ionization pathways in the plasma create numerous ion species. There is no tool for detection of adducts and in-source fragments from plasma ionization data yet, which makes data evaluation ambiguous. We developed DBDIpy, a Python library for processing and formal analysis of untargeted, time-sensitive plasma ionization MS datasets. Its core functionality lies in the identification of in-source fragments and identification of rivaling ionization pathways of the same analytes in time-sensitive datasets. It further contains elementary functions for processing of untargeted metabolomics data and interfaces to an established ecosystem for analysis of MS data in Python. DBDIpy is implemented in Python (Version ≥ 3.7) and can be downloaded from PyPI the Python package repository (https://pypi.org/project/DBDIpy) or from GitHub (https://github.com/leopold-weidner/DBDIpy). Supplementary data are available at Bioinformatics online.

Multienzyme deep learning models improve peptide de novo sequencing by mass spectrometry proteomics.

Generating and analyzing overlapping peptides through multienzymatic digestion is an efficient procedure for de novo protein using from bottom-up mass spectrometry (MS). Despite improved instrumentation and software, de novo MS data analysis remains challenging. In recent years, deep learning models have represented a performance breakthrough. Incorporating that technology into de novo protein sequencing workflows require machine-learning models capable of handling highly diverse MS data. In this study, we analyzed the requirements for assembling such generalizable deep learning models by systemcally varying the composition and size of the training set. We assessed the generated models' performances using two test sets composed of peptides originating from the multienzyme digestion of samples from various species. The peptide recall values on the test sets showed that the deep learning models generated from a collection of highly N- and C-termini diverse peptides generalized 76% more over the termini-restricted ones. Moreover, expanding the training set's size by adding peptides from the multienzymatic digestion with five proteases of several species samples led to a 2-3 fold generalizability gain. Furthermore, we tested the applicability of these multienzyme deep learning (MEM) models by fully de novo sequencing the heavy and light monomeric chains of five commercial antibodies (mAbs). MEMs extracted over 10000 matching and overlapped peptides across six different proteases mAb samples, achieving a 100% sequence coverage for 8 of the ten polypeptide chains. We foretell that the MEMs' proven improvements to de novo analysis will positively impact several applications, such as analyzing samples of high complexity, unknown nature, or the peptidomics field.

Competition-based control of the false discovery proportion.

Recently, Barber and Candès laid the theoretical foundation for a general framework for false discovery rate (FDR) control based on the notion of "knockoffs." A closely related FDR control methodology has long been employed in the analysis of mass spectrometry data, referred to there as "target-decoy competition" (TDC). However, any approach that aims to control the FDR, which is defined as the expected value of the false discovery proportion (FDP), suffers from a problem. Specifically, even when successfully controlling the FDR at level α, the FDP in the list of discoveries can significantly exceed α. We offer FDP-SD, a new procedure that rigorously controls the FDP in the knockoff/TDC competition setup by guaranteeing that the FDP is bounded by α at a desired confidence level. Compared with the recently published framework of Katsevich and Ramdas, FDP-SD generally delivers more power and often substantially so in simulated and realdata.

Trans-Proteomic Pipeline: Robust Mass Spectrometry-Based Proteomics Data Analysis Suite.

The Trans-Proteomic Pipeline (TPP) mass spectrometry data analysis suite has been in continual development and refinement since its first tools, PeptideProphet and ProteinProphet, were published 20 years ago. The current release provides a large complement of tools for spectrum processing, spectrum searching, search validation, abundance computation, protein inference, and more. Many of the tools include machine-learning modeling to extract the most information from data sets and build robust statistical models to compute the probabilities that derived information is correct. Here we present the latest information on the many TPP tools, and how TPP can be deployed on various platforms from personal Windows laptops to Linux clusters and expansive cloud computing environments. We describe tutorials on how to use TPP in a variety of ways and describe synergistic projects that leverage TPP. We conclude with plans for continued development of TPP.

Shapley Value as a Quality Control for Mass Spectra of Human Glioblastoma Tissues

The automatic processing of high-dimensional mass spectrometry data is required for the clinical implementation of ambient ionization molecular profiling methods. However, complex algorithms required for the analysis of peak-rich spectra are sensitive to the quality of the input data. Therefore, an objective and quantitative indicator, insensitive to the conditions of the experiment, is currently in high demand for the automated treatment of mass spectrometric data. In this work, we demonstrate the utility of the Shapley value as an indicator of the quality of the individual mass spectrum in the classification task for human brain tumor tissue discrimination. The Shapley values are calculated on the training set of glioblastoma and nontumor pathological tissues spectra and used as feedback to create a random forest regression model to estimate the contributions for all spectra of each specimen. As a result, it is shown that the implementation of Shapley values significantly accelerates the data analysis of negative mode mass spectrometry data alongside simultaneous improving the regression models’ accuracy.

New Anti-Inflammatory β-Resorcylic Acid Lactones Derived from an Endophytic Fungus, Colletotrichum sp.

The endophytic fungus Colletotrichum gloeosprioides JS0419, isolated from the leaves of the halophyte Suaeda japonica, produced four new β-resorcylic acid derivatives, colletogloeopyrones A and B (1 and 2) and colletogloeolactones A and B (3 and 4), and seven known β-resorcylic acid lactones (RALs). The structures of these compounds were elucidated via analysis of the high-resolution mass spectrometry and nuclear magnetic resonance data. Compounds 1 and 2 showed a dihydrobenzopyranone ring with a linear C9 side chain, which is rarely observed in RALs. All isolated compounds were evaluated for their anti-inflammatory activities. Colletogloeopyrone A (1), monocillin II (5), and monocillin II glycoside (6) were effective in reducing nitric oxide production without cytotoxicity. They also inhibited the secretion of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), as demonstrated by the expression of mRNA corresponding to IL-6 and TNF-α. Mechanistically, compounds 5 and 6 significantly inhibited the protein expression of nuclear factor-κB, IκBα, IKKα/β, inducible nitric oxide synthase, and cyclooxygenase (COX)-2, whereas compound 1 only inhibited COX-2 expression. This study indicated that RAL-type compounds 1, 5, and 6 demonstrated potential anti-inflammatory activity by inhibiting the synthesis of pro-inflammatory cytokines.

Efficient Indexing of Peptides for Database Search Using Tide.

The first step in the analysis of protein tandem mass spectrometry data typically involves searching the observed spectra against a protein database. During database search, the search engine must digest the proteins in the database into peptides, subject to digestion rules that are under user control. The choice of these digestion parameters, as well as selection of post-translational modifications (PTMs), can dramatically affect the size of the search space and hence the statistical power of the search. The Tide search engine separates the creation of the peptide index from the database search step, thereby saving time by allowing a peptide index to be reused in multiple searches. Here we describe an improved implementation of the indexing component of Tide that consumes around four times less resources (CPU and RAM) than the previous version and can generate arbitrarily large peptide databases, limited by only the amount of available disk space. We use this improved implementation to explore the relationship between database size and the parameters controlling digestion and PTMs, as well as database size and statistical power. Our results can help guide practitioners in proper selection of these important parameters.

Improved Analysis of Cross-Linking Mass Spectrometry Data with Kojak 2.0, Advanced by Integration into the Trans-Proteomic Pipeline.

Fragmentation ion spectral analysis of chemically cross-linked proteins is an established technology in the proteomics research repertoire for determining protein interactions, spatial orientation, and structure. Here we present Kojak version 2.0, a major update to the original Kojak algorithm, which was developed to identify cross-linked peptides from fragment ion spectra using a database search approach. A substantially improved algorithm with updated scoring metrics, support for cleavable cross-linkers, and identification of cross-links between 15N-labeled homomultimers are among the newest features of Kojak 2.0 presented here. Kojak 2.0 is now integrated into the Trans-Proteomic Pipeline, enabling access to dozens of additional tools within that suite. In particular, the PeptideProphet and iProphet tools for validation of cross-links improve the sensitivity and accuracy of correct cross-link identifications at user-defined thresholds. These new features improve the versatility of the algorithm, enabling its use in a wider range of experimental designs and analysis pipelines. Kojak 2.0 remains open-source and multiplatform.

PmartR 2.0: A Quality Control, Visualization, and Statistics Pipeline for Multiple Omics Datatypes.

The pmartR (https://github.com/pmartR/pmartR) package was designed for the quality control (QC) and analysis of mass spectrometry data, tailored to specific characteristics of proteomic (isobaric or labeled), metabolomic, and lipidomic data sets. Since its initial release, the tool has been expanded to address the needs of its growing userbase and now includes QC and statistics for nuclear magnetic resonance metabolomic data, and leverages the DESeq2, edgeR, and limma-voom R packages for transcriptomic data analyses. These improvements have made progress toward a unified omics processing pipeline for ease of reporting and streamlined statistical purposes. The package's statistics and visualization capabilities have also been expanded by adding support for paired data and by integrating pmartR with the trelliscopejs R package for the quick creation of trellis displays (https://github.com/hafen/trelliscopejs). Here, we present relevant examples of each of these enhancements to pmartR and highlight how each new feature benefits the omics community.

The Crux Toolkit for Analysis of Bottom-Up Tandem Mass Spectrometry Proteomics Data.

The Crux tandem mass spectrometry data analysis toolkit provides a collection of algorithms for analyzing bottom-up proteomics tandem mass spectrometry data. Many publications have described various individual components of Crux, but a comprehensive summary has not been published since 2014. The goal of this work is to summarize the functionality of Crux, focusing on developments since 2014. We begin with empirical results demonstrating our recently implemented speedups to the Tide search engine. Other new features include a new score function in Tide, two new confidence estimation procedures, as well as three new tools: Param-medic for estimating search parameters directly from mass spectrometry data, Kojak for searching cross-linked mass spectra, and DIAmeter for searching data independent acquisition data against a sequence database.

Indian traditional rice variety "Gathuwan" suppresses T-cell-mediated immune responses via activation of ERK/Nrf2/HO-1 signalling pathway.

The impact of food on immune functions has been recognized for centuries and is now being increasingly explored for therapeutic applications. Rice, in addition to being the staple food in most developing countries, exhibits diverse complexities of phytochemicals among its wide germplasm repertoire, which supports its development as a functional food. In the present study, we have explored the immunomodulatory properties of Gathuwan rice, a local rice variety grown in Chhattisgarh, India, and traditionally used for the treatment of rheumatism. Methanolic Gathuwan Brown Rice Extract (BRE) inhibits T-cell activation and proliferation and cytokine secretion (IL-2, IL-4, IL-6 and IFN-γ) without inducing cell death. BRE exhibits radical scavenging activity in a cell-free system and decreases intracellular reactive oxygen species (ROS) and glutathione levels in lymphocytes. BRE induces nuclear translocation of the immune-regulatory transcription factor Nrf2 via activation of ERK and p-38 MAP kinase and up-regulates the expression of Nrf2-dependent genes (SOD, CAT, HO-1, GPx and TrxR) in lymphocytes. BRE treatment had no effect on cytokine secretion by lymphocytes from Nrf2 knockout mice, confirming the role of Nrf2 in the immunosuppressive effects of BRE. Feeding of Gathuwan brown rice to mice had no effect on the basal haematological parameters, but lymphocytes isolated from these mice were hypo-responsive to mitogenic stimuli. Treatment of allografts with BRE significantly prevented graft-versus-host disease (GVHD)-associated mortality and morbidity in mice. Metabolic pathway enrichment analysis of ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) data revealed a high enrichment ratio of amino acid and vitamin B metabolism pathways, and among metabolite sets, pyridoxamines, phytosphingosines, hydroxybenzaldehydes, hydroxycinnamic acids and indoles were highly enriched bioactive components. In conclusion, Gathuwan BRE suppresses T-cell-mediated immune responses by altering the cellular redox balance and activating the Nrf2 signalling pathway.

Machine learning-assisted structure annotation of natural products based on MS and NMR data.

Covering: up to March 2023Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

REVIEW ON PROTEIN FOOTPRINTING AS A TOOL IN STRUCTURAL BIOLOGY

Structural characterization of proteins is fundamental for understanding their functions and interactions within cellular environments. Protein footprinting has emerged as a powerful and versatile technique in structural biology, providing valuable insights into protein structure, dynamics, and conformational changes. This study comprehensively explores the principles, methodologies, and applications of protein footprinting as an indispensable tool in structural biology. Protein footprinting encompasses a range of innovative techniques, including hydroxyl radical footprinting, mass spectrometry-based footprinting, and chemical footprinting, each offering unique advantages for probing different aspects of protein structure. The study elucidates the underlying principles of these methods, discussing the mechanisms by which protein-footprinting agents interact with amino acid residues and elucidate structural details. The application of protein footprinting in studying protein-protein interactions, ligand binding, and conformational changes is highlighted through a detailed examination of key studies and experimental approaches. The review emphasizes the versatility of protein footprinting in investigating diverse protein systems, from small globular proteins to large multi-subunit complexes. Furthermore, the integration of protein footprinting with other structural biology techniques, such as X-ray crystallography, NMR spectroscopy, and cryo-electron microscopy, is explored to showcase the complementary nature of these approaches in achieving a comprehensive understanding of protein structure and function. The study addresses recent advancements and technological innovations in protein footprinting methodologies, including advancements in mass spectrometry instrumentation and data analysis strategies. Additionally, it discusses challenges associated with the technique, such as data interpretation and accessibility, and proposes future directions for overcoming these hurdles. Protein footprinting stands as a valuable tool in the structural biologist’s arsenal, providing unique perspectives on protein structure and dynamics. As the field continues to evolve, with ongoing refinements in methodologies and expanded applications, protein footprinting is poised to play a pivotal role in unraveling the complexities of biological macromolecules. This study serves as a comprehensive resource for researchers and practitioners seeking an in-depth understanding of protein footprinting and its contributions to advancing structural biology.

SigSel: A MATLAB package for the pre and post-treatment of high-resolution mass spectrometry signals using the ROIMCR methodology

The Regions of Interest Multivariate curve Resolution (ROIMCR) methodology has gained significance for analyzing mass spectrometry data. The new SigSel package improves the ROIMCR methodology by providing a filtering step to reduce computational costs and to identify chemical compounds giving low-intensity signals. SigSel allows the visualization and assessment of ROIMCR results and filters out components resolved as interferences and background noise. This improves the analysis of complex mixtures and facilitates the identification of chemical compounds for statistical or chemometrics analysis.SigSel has been tested using metabolomics samples of mussels exposed to the sulfamethoxazole antibiotic. It begins by analyzing the data according to their charge state, eliminating signals considered background noise, and reducing the size of the datasets. In the ROIMCR analysis, the resolution of 30 ROIMCR components was achieved. After evaluating these components, 24 were ultimately selected explaining 99.05% of the total data variance. From ROIMCR results, chemical annotation is performed using different methods:•Generating a list of signals and reanalyzing them in a data-dependent analysis.•Comparing the ROIMCR resolved mass spectra to those stored in online repositories.•Searching MS signals of chemical compounds in the ROIMCR resolved spectra profiles.