Mass Spectrometry Data Analysis Research Articles

Garciniaxanthone E and 12b-Hydroxy-des-D-garcigerrin A from The Tree Bark Garcinia dulcis and their Inhibitory Properties against Receptor Tyrosine Kinases

Two xanthone derivatives, garciniaxanthone E (1) and 12b-Hydroxy-des-D-garcigerrin A (2) have been isolated from ethyl acetate extract of the tree bark of Garcinia dulcis. The Ultraviolet (UV), Infrared (IR), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS) data analysis elucidated the structure of the isolated compounds. This study represents the first evaluation of compounds 1 and 2 in terms of their efficacy against receptor tyrosine kinases. The results showed that compound 1 exhibited weak activity with 12% inhibition against Insulin Receptor (InsR), while compound 2 showed moderate activity with 29% inhibition against epidermal growth factor receptor (EGFR). A molecular docking study targeting EGFR-TK suggests that the hydroxyl group at C-4 on compound 2 can be demolished to raise the inhibitory activity in future research.

Improving Targeted Mass Spectrometry Data Analysis with Nested Active Machine Learning

Targeted mass spectrometry (MS) holds promise for precise protein and protein‐representative peptide identification and quantification, enhancing disease diagnosis. However, its clinical application is hindered by complex data analysis and expert review requirements. It is hypothesized that machine learning (ML) models can automate data analysis to accelerate the clinical application of MS. The approach involves an ML‐driven pipeline that extracts statistical and morphological features from an MS target region and feeds these features into ML algorithms to generate and assess predictive models. The findings demonstrate ML prediction models exhibit superior performance when trained on extracted features versus raw spectra intensity data and that random forest models exhibit robust classification performance in both internal and external validation datasets. These models remain effective across varying training dataset sizes and positive sample rates and are enhanced by a nested active learning approach. This approach can thus revolutionize clinical MS applications.

A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline.

Cross-linking mass spectrometry (XL-MS) has evolved into a pivotal technique for probing protein interactions. This study describes the implementation of Parallel Accumulation-Serial Fragmentation (PASEF) on timsTOF instruments, enhancing the detection and analysis of protein interactions by XL-MS. Addressing the challenges in XL-MS, such as the interpretation of complex spectra, low abundant cross-linked peptides, and a data acquisition bias, our current study integrates a peptide-centric approach for the analysis of XL-MS data and presents the foundation for integrating data-independent acquisition (DIA) in XL-MS with a vendor-neutral and open-source platform. A novel workflow is described for processing data-dependent acquisition (DDA) of PASEF-derived information. For this, software by Bruker Daltonics is used, enabling the conversion of these data into a format that is compatible with MeroX and Skyline software tools. Our approach significantly improves the identification of cross-linked products from complex mixtures, allowing the XL-MS community to overcome current analytical limitations.

Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.

Traditional database search methods for the analysis of bottom-up proteomics tandem mass spectrometry (MS/MS) data are limited in their ability to detect peptides with post-translational modifications (PTMs). Recently, "open modification" database search strategies, in which the requirement that the mass of the database peptide closely matches the observed precursor mass is relaxed, have become popular as ways to find a wider variety of types of PTMs. Indeed, in one study, Kong et al. reported that the open modification search tool MSFragger can achieve higher statistical power to detect peptides than a traditional "narrow window" database search. We investigated this claim empirically and, in the process, uncovered a potential general problem with false discovery rate (FDR) control in the machine learning postprocessors Percolator and PeptideProphet. This problem might have contributed to Kong et al.'s report that their empirical results suggest that false discovery (FDR) control in the narrow window setting might generally be compromised. Indeed, reanalyzing the same data while using a more standard form of target-decoy competition-based FDR control, we found that, after accounting for chimeric spectra as well as for the inherent difference in the number of candidates in open and narrow searches, the data does not provide sufficient evidence that FDR control in proteomics MS/MS database search is inherently problematic.

Analysis of Tandem Mass Spectrometry Data with CONGA: Combining Open and Narrow Searches with Group-Wise Analysis.

Searching for tandem mass spectrometry proteomics data against a database is a well-established method for assigning peptide sequences to observed spectra but typically cannot identify peptides harboring unexpected post-translational modifications (PTMs). Open modification searching aims to address this problem by allowing a spectrum to match a peptide even if the spectrum's precursor mass differs from the peptide mass. However, expanding the search space in this way can lead to a loss of statistical power to detect peptides. We therefore developed a method, called CONGA (combining open and narrow searches with group-wise analysis), that takes into account results from both types of searches─a traditional "narrow window" search and an open modification search─while carrying out rigorous false discovery rate control. The result is an algorithm that provides the best of both worlds: the ability to detect unexpected PTMs without a concomitant loss of power to detect unmodified peptides.

Enhancing mass spectrometry data analysis: A novel framework for calibration, outlier detection, and classification

Mass spectrometry (MS) is a powerful analytical technique in metabolomics, enabling the identification and quantification of metabolites. However, analyzing MS data poses challenges such as batch effects, outliers, and high-dimensional data. In this paper, we propose a comprehensive framework for MS data analysis. The framework integrates data calibration, outlier detection, and automatic classification modules. Data calibration is performed using a deep autoencoder to remove batch effects. Outlier detection combines multiple algorithms through ensemble learning to identify and remove outliers. Automatic classification utilizes a transformer model to handle high-dimensional data and capture global feature relationships. Experimental results on myocardial infarction (MI) and coronary heart disease (CHD) datasets demonstrate the effectiveness of the framework. It outperforms traditional classification models and achieves higher accuracy. The proposed framework provides a robust solution for MS data analysis, facilitating more accurate classification and enabling reliable biological insights in metabolomics research.

Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data

Liquid chromatography (LC) or gas chromatography (GC) coupled to high-resolution mass spectrometry (HRMS) is a versatile analytical method for the analysis of thousands of chemical pollutants that can be found in environmental and biological samples. While the tools for handling such complex datasets have improved, there are still no fully automated workflows for targeted screening analysis. Here we present an R-based workflow that is able to cope with challenging data like noisy ion chromatograms, retention time shifts, and multiple peak patterns. The workflow can be applied to batches of HRMS data recorded after GC with electron ionization (GC-EI) and LC coupled to electrospray ionization in both negative and positive mode (LC-ESIneg/LC-ESIpos) to perform peak annotation and quantitation fully unsupervised. We used Orbitrap HRMS data of surface water extracts to compare the Automated Target Screening (ATS) workflow with data evaluations performed with the vendor software TraceFinder and the established semi-automated analysis workflow in the MZmine software. The ATS approach increased the overall evaluation performance of the peak annotation compared to the established MZmine module without the need for any post-hoc corrections. The overall accuracy increased from 0.80 to 0.86 (LC-ESIpos), from 0.77 to 0.83 (LC-ESIneg), and from 0.67 to 0.76 (GC-EI). The mean average percentage errors for quantification of ATS were around 30% compared to the manual quantification with TraceFinder. The ATS workflow enables time-efficient analysis of GC- and LC-HRMS data and accelerates and improves the applicability of target screening in studies with a large number of analytes and sample sizes without the need for manual intervention.Graphical

MS2Rescore 3.0 Is a Modular, Flexible, and User-Friendly Platform to Boost Peptide Identifications, as Showcased with MS Amanda 3.0.

Rescoring of peptide-spectrum matches (PSMs) has emerged as a standard procedure for the analysis of tandem mass spectrometry data. This emphasizes the need for software maintenance and continuous improvement for such algorithms. We introduce MS2Rescore 3.0, a versatile, modular, and user-friendly platform designed to increase peptide identifications. Researchers can install MS2Rescore across various platforms with minimal effort and benefit from a graphical user interface, a modular Python API, and extensive documentation. To showcase this new version, we connected MS2Rescore 3.0 with MS Amanda 3.0, a new release of the well-established search engine, addressing previous limitations on automatic rescoring. Among new features, MS Amanda now contains additional output columns that can be used for rescoring. The full potential of rescoring is best revealed when applied on challenging data sets. We therefore evaluated the performance of these two tools on publicly available single-cell data sets, where the number of PSMs was substantially increased, thereby demonstrating that MS2Rescore offers a powerful solution to boost peptide identifications. MS2Rescore's modular design and user-friendly interface make data-driven rescoring easily accessible, even for inexperienced users. We therefore expect the MS2Rescore to be a valuable tool for the wider proteomics community. MS2Rescore is available at https://github.com/compomics/ms2rescore.

Registration of hyperspectral images and mass spectrometry data for the correlation of tissue optical spectra and molecular profiles.

Hyperspectral imaging (HSI) is a label-free imaging modality that is emerging for non-invasive detection of various diseases including cancers. HSI provides high-resolution spatial images where each pixel has a spectral curve with numerous wavelength bands from the visible to infrared ranges. The rich spatial and spectral information can be used to discriminate various types of tissues and pathophysiological conditions. However, it can be difficult to explain spectral data with respect to the underline cellular and molecular mechanism. In this study, we developed an approach that registers hyperspectral images and mass spectrometry (MS) data where MS provides tissue molecular profiles. Human prostate tissues that were obtained after prostatectomy were used in the experiments. The whole prostate was first sliced every six mm. A customized hyperspectral surgical microscope was used to acquire HSI data from the sliced tissue. For MS data analysis, the sliced tissue of the prostate was divided into 51 small regions and then processed separately for each region. The immediately adjacent tissue was sliced and processed histologically for H&E staining. The MS molecular profiles were correlated with the hyperspectral images in this study.

Neat plasma proteomics: getting the best out of the worst

Plasma proteomics holds immense potential for clinical research and biomarker discovery, serving as a non-invasive "liquid biopsy" for tissue sampling. Mass spectrometry (MS)-based proteomics, thanks to improvement in speed and robustness, emerges as an ideal technology for exploring the plasma proteome for its unbiased and highly specific protein identification and quantification. Despite its potential, plasma proteomics is still a challenge due to the vast dynamic range of protein abundance, hindering the detection of less abundant proteins. Different approaches can help overcome this challenge. Conventional depletion methods face limitations in cost, throughput, accuracy, and off-target depletion. Nanoparticle-based enrichment shows promise in compressing dynamic range, but cost remains a constraint. Enrichment strategies for extracellular vesicles (EVs) can enhance plasma proteome coverage dramatically, but current methods are still too laborious for large series. Neat plasma remains popular for its cost-effectiveness, time efficiency, and low volume requirement. We used a test set of 33 plasma samples for all evaluations. Samples were digested using S-Trap and analyzed on Evosep One and nanoElute coupled to a timsTOF Pro using different elution gradients and ion mobility ranges. Data were mainly analyzed using library-free searches using DIA-NN. This study explores ways to improve proteome coverage in neat plasma both in MS data acquisition and MS data analysis. We demonstrate the value of sampling smaller hydrophilic peptides, increasing chromatographic separation, and using library-free searches. Additionally, we introduce the EV boost approach, that leverages on the extracellular vesicle fraction to enhance protein identification in neat plasma samples. Globally, our optimized analysis workflow allows the quantification of over 1000 proteins in neat plasma with a 24SPD throughput. We believe that these considerations can be of help independently of the LC–MS platform used.

Tissue Usage Preference and Intrinsically Disordered Region Remodeling of Alternative Splicing Derived Proteoforms in the Heart.

A computational analysis of mass spectrometry data was performed to uncover alternative splicing derived protein variants across chambers of the human heart. Evidence for 216 non-canonical isoforms was apparent in the atrium and the ventricle, including 52 isoforms not documented on SwissProt and recovered using an RNA sequencing derived database. Among non-canonical isoforms, 29 show signs of regulation based on statistically significant preferences in tissue usage, including a ventricular enriched protein isoform of tensin-1 (TNS1) and an atrium-enriched PDZ and LIM Domain 3 (PDLIM3) isoform 2 (PDLIM3-2/ALP-H). Examined variant regions that differ between alternative and canonical isoforms are highly enriched with intrinsically disordered regions. Moreover, over two-thirds of such regions are predicted to function in protein binding and RNA binding. The analysis here lends further credence to the notion that alternative splicing diversifies the proteome by rewiring intrinsically disordered regions, which are increasingly recognized to play important roles in the generation of biological function from protein sequences.

Retinestatin, a Polyol Polyketide from a Termite Nest-Derived Streptomyces sp.

A new polyol polyketide, named retinestatin (1), was obtained and characterized from the culture of a Streptomyces strain, which was isolated from a subterranean nest of the termite Reticulitermes speratus kyushuensis Morimoto. The planar structure of 1 was elucidated on the basis of the cumulative analysis of ultraviolet, infrared, mass spectrometry, and nuclear magnetic resonance spectroscopic data. The absolute configuration of 1 at 12 chiral centers was successfully assigned by employing a J-based configuration analysis in combination with ROESY correlations, a quantum mechanics-based computational approach to calculate NMR chemical shifts, and a 3 min flash esterification by Mosher's reagents followed by NMR analysis. Biological evaluation of retinestatin (1) using an in vitro model of Parkinson's disease revealed that 1 protected SH-SY5Y dopaminergic cells from MPP+-induced cytotoxicity, indicating its neuroprotective effects.

Data-Independent Acquisition and Label-Free Quantification for Quantitative Proteomics Analysis of Human Cerebrospinal Fluid.

This article presents a practical guide to mass spectrometry-based data-independent acquisition and label-free quantification for proteomics analysis applied to cerebrospinal fluid, offering a robust and scalable approach to probing the proteomic composition of the central nervous system. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Cerebrospinal fluid sample collection and preparation for mass spectrometry analysis Basic Protocol 2: Mass spectrometry sample analysis with data-independent acquisition Support Protocol: Data-dependent mass spectrometry and spectral library construction Basic Protocol 3: Analysis of mass spectrometry data.

A metabolomics approach for monitoring thermal processing temperature of bovine milk using ultra-performance liquid chromatography tandem Q-Exactive mass spectrometry and multivariate data analysis

A metabolomics approach for monitoring thermal processing temperature of bovine milk using ultra-performance liquid chromatography tandem Q-Exactive mass spectrometry and multivariate data analysis

Recent Developments in Machine Learning for Mass Spectrometry.

Statistical analysis and modeling of mass spectrometry (MS) data have a long and rich history with several modern MS-based applications using statistical and chemometric methods. Recently, machine learning (ML) has experienced a renaissance due to advents in computational hardware and the development of new algorithms for artificial neural networks (ANN) and deep learning architectures. Moreover, recent successes of new ANN and deep learning architectures in several areas of science, engineering, and society have further strengthened the ML field. Importantly, modern ML methods and architectures have enabled new approaches for tasks related to MS that are now widely adopted in several popular MS-based subdisciplines, such as mass spectrometry imaging and proteomics. Herein, we aim to provide an introductory summary of the practical aspects of ML methodology relevant to MS. Additionally, we seek to provide an up-to-date review of the most recent developments in ML integration with MS-based techniques while also providing critical insights into the future direction of the field.

Advances in Mass Spectrometry-Based Blood Metabolomics Profiling for Non-Cancer Diseases: A Comprehensive Review.

Blood metabolomics profiling using mass spectrometry has emerged as a powerful approach for investigating non-cancer diseases and understanding their underlying metabolic alterations. Blood, as a readily accessible physiological fluid, contains a diverse repertoire of metabolites derived from various physiological systems. Mass spectrometry offers a universal and precise analytical platform for the comprehensive analysis of blood metabolites, encompassing proteins, lipids, peptides, glycans, and immunoglobulins. In this comprehensive review, we present an overview of the research landscape in mass spectrometry-based blood metabolomics profiling. While the field of metabolomics research is primarily focused on cancer, this review specifically highlights studies related to non-cancer diseases, aiming to bring attention to valuable research that often remains overshadowed. Employing natural language processing methods, we processed 507 articles to provide insights into the application of metabolomic studies for specific diseases and physiological systems. The review encompasses a wide range of non-cancer diseases, with emphasis on cardiovascular disease, reproductive disease, diabetes, inflammation, and immunodeficiency states. By analyzing blood samples, researchers gain valuable insights into the metabolic perturbations associated with these diseases, potentially leading to the identification of novel biomarkers and the development of personalized therapeutic approaches. Furthermore, we provide a comprehensive overview of various mass spectrometry approaches utilized in blood metabolomics research, including GC-MS, LC-MS, and others discussing their advantages and limitations. To enhance the scope, we propose including recent review articles supporting the applicability of GC×GC-MS for metabolomics-based studies. This addition will contribute to a more exhaustive understanding of the available analytical techniques. The Integration of mass spectrometry-based blood profiling into clinical practice holds promise for improving disease diagnosis, treatment monitoring, and patient outcomes. By unraveling the complex metabolic alterations associated with non-cancer diseases, researchers and healthcare professionals can pave the way for precision medicine and personalized therapeutic interventions. Continuous advancements in mass spectrometry technology and data analysis methods will further enhance the potential of blood metabolomics profiling in non-cancer diseases, facilitating its translation from the laboratory to routine clinical application.

Analysis of mitochondrial targeting signal cleavage and protein processing by mass spectrometry.

The majority of mitochondrial proteins are encoded in the nucleus, synthesized in the cytosol and imported into mitochondria mediated by an N-terminal mitochondrial targeting sequences (MTS). After import, the MTS is cleaved off by the mitochondrial processing peptidase (MPP) and subsets of the imported proteins are further processed by the aminopeptidase intermediate cleaving peptidase 55 (ICP55), the mitochondrial intermediate peptidase (MIP), octapeptidyl aminopeptidase 1 (Oct1) or other proteolytic enzymes. Mutations that impair the mitochondrial processing machinery or mitochondrial protein degradation result in rare but severe human diseases. In addition, aging and various stress conditions are associated with altered proteolysis of mitochondrial proteins. Enrichment of protein terminal peptides in combination with mass spectrometry-based identification and quantification has become the method of choice to study proteolytic processing. Here, we describe an updated step-by-step protocol for the enrichment of N-terminal peptides by Hypersensitive Undecanal-mediated Enrichment of N-Terminal peptides (HUNTER). We describe analysis of mass spectrometry data acquired for HUNTER samples and present a suite of dedicated Python and R scripts for HUNTER quality control, classification of the enriched peptides, annotation of mitochondrial processing sites and quantitative evaluation. The scripts are freely available at https://github.com/FabianStockert/mito_annotation.

Making MS Omics Data ML-Ready: SpeCollate Protocols.

The increasing complexity and volume of mass spectrometry (MS) data have presented new challenges and opportunities for proteomics data analysis and interpretation. In this chapter, we provide a comprehensive guide to transforming MS data for machine learning (ML) training, inference, and applications. The chapter is organized into three parts. The first part describes the data analysis needed for MS-based experiments and a general introduction to our deep learning model SpeCollate-which we will use throughout the chapter for illustration. The second part of the chapter explores the transformation of MS data for inference, providing a step-by-step guide for users to deduce peptides from their MS data. This section aims to bridge the gap between data acquisition and practical applications by detailing the necessary steps for data preparation and interpretation. In the final part, we present a demonstrative example of SpeCollate, a deep learning-based peptide database search engine that overcomes the problems of simplistic simulation of theoretical spectra and heuristic scoring functions for peptide-spectrum matches by generating joint embeddings for spectra and peptides. SpeCollate is a user-friendly tool with an intuitive command-line interface to perform the search, showcasing the effectiveness of the techniques and methodologies discussed in the earlier sections and highlighting the potential of machine learning in the context of mass spectrometry data analysis. By offering a comprehensive overview of data transformation, inference, and ML model applications for mass spectrometry, this chapter aims to empower researchers and practitioners in leveraging the power of machine learning to unlock novel insights and drive innovation in the field of mass spectrometry-based omics.

Evaluation of Anti-Trypanosoma cruzi Activity of Chemical Constituents from Baccharis sphenophylla Isolated Using High-Performance Countercurrent Chromatography.

Endemic in 21 countries, Chagas disease, also known as American Trypanosomiasis, is a neglected tropical disease (NTD) caused by the protozoan parasite Trypanosoma cruzi. The available drugs for the treatment of this disease, benznidazole and nifurtimox, are outdated and display severe side effects. Thus, the discovery of new drugs is crucial. Based on our continuous studies aiming towards the discovery of natural products with anti-T. cruzi potential, the MeOH extract from aerial parts of Baccharis sphenophylla Dusén ex. Malme (Asteraceae) displayed activity against this parasite and was subjected to high-performance countercurrent chromatography (HPCCC), to obtain one unreported syn-labdane diterpene - sphenophyllol (1) - as well as the known compounds gaudichaudol C (2), ent-kaurenoic acid (3), hispidulin (4), eupafolin (5), and one mixture of di-O-caffeoylquinic acids (6-8). Compounds 1-8 were characterized by analysis of nuclear magnetic resonance (NMR) and mass spectrometry (MS) data. When tested against trypomastigote forms, isolated labdane diterpenes 1 and 2 displayed potent activity, with EC50 values of 20.1 μM and 2.9 μM, respectively. The mixture of chlorogenic acids 6-8, as well as the isolated flavones 4 and 5, showed significant activity against the clinically relevant amastigotes, with EC50 values of 24.9, 12.8, and 2.7 μM, respectively. Nonetheless, tested compounds 1-8 displayed no cytotoxicity against mammalian cells (CC50 > 200 μM). These results demonstrate the application of HPCCC as an important tool to isolate bioactive compounds from natural sources, including the antitrypanosomal extract from B. sphenophylla, allowing for the development of novel strategic molecular prototypes against tropical neglected diseases.

Sucrosephenylpropanoid esters and isoflavonoids isolated from Belamcanda chinensis roots and their potential anti-osteoclastogenic activity

Repeated chromatography of the CH2Cl2 and EtOAc soluble fractions from the methanol extract of Belamcanda chinensis root yielded six new sucrosephenylpropanoid esters (1–6) and twenty-one known compounds (7–27). The structures of 1–6 were elucidated using diverse nuclear magnetic resonance (NMR) techniques and high-resolution mass spectrometry (HRMS) data analysis, together with chemical methods. All the twenty-seven isolated compounds were evaluated for their anti-osteoclastogenic activities. Preliminary screening results revealed that compounds 1 and 19 exhibited strong effects against RANKL-induced osteoclast formation in RAW264.7 cells. In addition, the treatment of mouse bone marrow macrophages (BMMs) with compounds 1 and 19 significantly decreased RANKL-induced TRAP-positive multinucleated osteoclast formation in a concentration-dependent manner without affecting cell viability. Further bioassay investigation showed that compounds 1 and 19 inhibited the expression of some osteoclast-specific marker genes and the transcription factor nuclear factor of activated T cells cytoplasmic 1 (NFATc1) in response to RANKL. To the best of our knowledge, this is the first investigation of anti-osteoclastogenic activity for compounds isolated from B. chinensis.