bims-metlip 2024-03-03 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2024‒03‒03
24 papers selected by
Sofia Costa, Matterworks

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.
HILIC-IM-MS for Simultaneous Lipid and Metabolite Profiling of Bacteria.
Therapeutic drug monitoring of clozapine in human serum by high-throughput paper spray mass spectrometry.
An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of phenobarbital in human serum and plasma.
High-Throughput Lipidomic and Metabolomic Profiling for Brain Tissue and Biofluid Samples in Neurodegenerative Disorders.
An HPLC-ESI-QTOF method to analyze polar heteroatomic species in aviation turbine fuel via hydrophilic interaction chromatography.
Comparison of Amine-Modified Polymeric Stationary Phases for Polar Metabolomic Analysis Based on Unified-Hydrophilic Interaction/Anion Exchange Liquid Chromatography/High-Resolution Mass Spectrometry (Unified-HILIC/AEX/HRMS).
DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool.
UPLC-Orbitrap-HRMS application for analysis of plasma sterols.
Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research.
An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics.
A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method.
Ion entropy and accurate entropy-based FDR estimation in metabolomics.
RT-Transformer: Retention time prediction for metabolite annotation to assist in metabolite identification.
Impact of Source Conditions on Collision Cross Section Determination by Trapped Ion Mobility Spectrometry.
Endogenous monoclonal immunoglobulins analyzed using the EXENT® solution and LC-MS.
Mass Spectrometry Imaging and Histology for the Analysis of Budding Intestinal Organoids.
Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia.
Enhancing Quantitative Analysis of Xenobiotics in Blood Plasma through Cross-Matrix Calibration and Bayesian Hierarchical Modeling.
Parts-Per-Million Carbonate Mineral Quantification with Thermogravimetric Analysis-Mass Spectrometry.
Direct Analysis of Marine Dissolved Organic Matter Using LC-FT-ICR MS.
Co-stimulatory pathway competitive assay development using Liquid chromatography-tandem mass spectrometry (LC-MS/MS).
Mass Spectrometry Applications to Study Human Microbiome.
Development of a three-dimensional HPLC system for the determination of serine, threonine and allo-threonine enantiomers in the plasma of patients with chronic kidney disease.

bioRxiv. 2024 Feb 14. pii: 2024.02.13.580048. [Epub ahead of print]

Common data models to streamline metabolomics processing and annotation, and implementation in a Python pipeline.

Joshua M Mitchell, Yuanye Chi, Maheshwor Thapa, Zhiqiang Pang, Jianguo Xia, Shuzhao Li.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential is have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.Author Summary: All life processes involve the consumption, creation, and interconversion of metabolites. Metabolomics is the comprehensive study of these small molecules, often using mass spectrometry, to provide critical information of health and disease. Automated processing of such metabolomics data is desired, especially for the bioinformatics community with familiar tools and infrastructures. Despite of Python's popularity in bioinformatics and machine learning, the Python ecosystem in computational metabolomics still misses a complete data pipeline. We have developed an end-to-end computational metabolomics data processing pipeline, based on the raw data preprocessor Asari [1]. Our pipeline takes experimental data in .mzML or .raw format and outputs annotated feature tables for subsequent biological interpretation. We demonstrate the application of this pipeline to multiple metabolomics and lipidomics datasets. Accompanying the pipeline, we have designed a set of reusable data structures, released as the MetDataModel package, which shall promote more consistent terminology and software interoperability in this area.

DOI: https://doi.org/10.1101/2024.02.13.580048
ACS Meas Sci Au. 2024 Feb 21. 4(1): 104-116

HILIC-IM-MS for Simultaneous Lipid and Metabolite Profiling of Bacteria.

Jana M Carpenter, Hannah M Hynds, Kingsley Bimpeh, Kelly M Hines.

Although MALDI-ToF platforms for microbial identifications have found great success in clinical microbiology, the sole use of protein fingerprints for the discrimination of closely related species, strain-level identifications, and detection of antimicrobial resistance remains a challenge for the technology. Several alternative mass spectrometry-based methods have been proposed to address the shortcomings of the protein-centric approach, including MALDI-ToF methods for fatty acid/lipid profiling and LC-MS profiling of metabolites. However, the molecular diversity of microbial pathogens suggests that no single "ome" will be sufficient for the accurate and sensitive identification of strain- and susceptibility-level profiling of bacteria. Here, we describe the development of an alternative approach to microorganism profiling that relies upon both metabolites and lipids rather than a single class of biomolecule. Single-phase extractions based on butanol, acetonitrile, and water (the BAW method) were evaluated for the recovery of lipids and metabolites from Gram-positive and -negative microorganisms. We found that BAW extraction solutions containing 45% butanol provided optimal recovery of both molecular classes in a single extraction. The single-phase extraction method was coupled to hydrophilic interaction liquid chromatography (HILIC) and ion mobility-mass spectrometry (IM-MS) to resolve similar-mass metabolites and lipids in three dimensions and provide multiple points of evidence for feature annotation in the absence of tandem mass spectrometry. We demonstrate that the combined use of metabolites and lipids can be used to differentiate microorganisms to the species- and strain-level for four of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Acinetobacter baumannii, and Pseudomonas aeruginosa) using data from a single ionization mode. These results present promising, early stage evidence for the use of multiomic signatures for the identification of microorganisms by liquid chromatography, ion mobility, and mass spectrometry that, upon further development, may improve upon the level of identification provided by current methods.

DOI: https://doi.org/10.1021/acsmeasuresciau.3c00051
J Mass Spectrom Adv Clin Lab. 2024 Apr;32 41-46

Therapeutic drug monitoring of clozapine in human serum by high-throughput paper spray mass spectrometry.

A Saatchi, T M Zarkovic, S A Borden, J Palaty, C G Gill.

  Introduction: Monitoring the atypical antipsychotic drug clozapine is crucial to ensure patient safety. This article showcases a high-throughput analytical method for measuring clozapine and its primary metabolite norclozapine (N-desmethylclozapine) in serum using paper spray mass spectrometry (PS-MS).Objectives: This study aimed to assess the viability of a PS-MS method for the rapid measurement of clozapine and norclozapine in human serum samples as an alternative to liquid chromatography mass spectrometry (LC-MS).
Methods: Serum samples were processed by protein precipitation followed by deposition of the supernatant containing labelled internal standards onto paper spray substrates mounted in cartridges. Analytes were then analyzed using a triple quadrupole mass spectrometer equipped with a commercial paper spray ionization source. The results obtained from the patient samples were compared to those from a validated LC-MS assay.
Results: PS-MS calibrations for clozapine and norclozapine were linear (R2 > 0.99) over five days. Between-run precision was below 8 %, and within-run precision did not exceed 10 %. When compared to a validated LC-MS method, the mean bias for 39 patient samples was -9% for clozapine and -1% for norclozapine, with no outliers. Mass spectrometry ion ratio comparisons indicated no interference for patient samples above the lower limit of quantification. There was less than 7 % change in the measured concentrations of both analytes over five days for samples dried on paper substrates. Notably, virtually no maintenance of the MS source was required during this study.
Conclusion: This study illustrates the potential of PS-MS for serum drug monitoring in the clinical laboratory.

Keywords:  Clozapine and norclozapine; High-throughput; Paper spray mass spectrometry; Tandem mass spectrometry; Therapeutic drug monitoring

DOI:  https://doi.org/10.1016/j.jmsacl.2024.02.003
Clin Chem Lab Med. 2024 Feb 27.

An isotope dilution-liquid chromatography-tandem mass spectrometry (ID-LC-MS/MS)-based candidate reference measurement procedure (RMP) for the quantification of phenobarbital in human serum and plasma.

Tobias Schierscher, Linda Salzmann, Neeraj Singh, Martina Bachmann, Anja Kobel, Janik Wild, Friederike Bauland, Andrea Geistanger, Lorenz Risch, Christian Geletneky, Christoph Seger, Judith Taibon.

  OBJECTIVES: Phenobarbital serves as an antiepileptic drug (AED) and finds application in the treatment of epilepsy either as monotherapy or adjunctive therapy. This drug exhibits various pharmacodynamic properties that account for its beneficial effects as well as potential side effects. Accurate measurement of its concentration is critical for optimizing AED therapy through appropriate dose adjustments. Therefore, our objective was to develop and validate a new reference measurement procedure (RMP) for the accurate quantification of phenobarbital levels in human serum and plasma.METHODS: A sample preparation protocol based on protein precipitation followed by a high dilution step was established in combination with a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method using a C8 column to separate target analytes from known and unknown interferences. Assay validation and determination of measurement uncertainty were performed based on current guidelines. Selectivity and Specificity were assessed using spiked serum and plasma samples; to investigate possible matrix effects (MEs) a post-column infusion experiment and a comparison of standard line slopes was performed. Precision and accuracy were determined within a multiday precision experiment.
RESULTS: The RMP was shown to be highly selective and specific, with no evidence of matrix interferences. It can be used to quantify phenobarbital in the range of 1.92 to 72.0 μg/mL. Intermediate precision was less than 3.2 %, and repeatability coefficient of variation (CV) ranged from 1.3 to 2.0 % across all concentration levels. The relative mean bias ranged from -3.0 to -0.7 % for native serum levels, and from -2.8 to 0.8 % for Li-heparin plasma levels. The measurement uncertainties (k=1) for single measurements and target value assignment were 1.9 to 3.3 % and 0.9 to 1.6 %, respectively.
CONCLUSIONS: A novel LC-MS/MS-based candidate RMP for the quantification of phenobarbital in human serum and plasma is presented which can be used for the standardization of routine assays and the evaluation of clinically relevant samples.

Keywords:  ID-LC-MS/MS; SI units; phenobarbital; reference measurement procedure; traceability

DOI:  https://doi.org/10.1515/cclm-2023-1104
Methods Mol Biol. 2024 ;2785 221-260

High-Throughput Lipidomic and Metabolomic Profiling for Brain Tissue and Biofluid Samples in Neurodegenerative Disorders.

Bonne M Thompson, Giuseppe Astarita.

  Recent research has revealed the potential of lipidomics and metabolomics in identifying new biomarkers and mechanistic insights for neurodegenerative disorders. To contribute to this promising area, we present a detailed protocol for conducting an integrated lipidomic and metabolomic profiling of brain tissue and biofluid samples. In this method, a single-phase methanol extraction is employed for extracting both nonpolar and highly polar lipids and metabolites from each biological sample. The extracted samples are then subjected to liquid chromatography-mass spectrometry-based assays to provide relative or semiquantitative measurements for hundreds of selected lipids and metabolites per sample. This high-throughput approach enables the generation of new hypotheses regarding the mechanistic and functional significance of lipid and metabolite alterations in neurodegenerative disorders while also facilitating the discovery of new biomarkers to support drug development.

Keywords:  Biomarker; Drug development; Drug discovery; Lipidomics; Lipids; Metabolomics; Neurodegeneration; Omics

DOI:  https://doi.org/10.1007/978-1-0716-3774-6_14
J Chromatogr A. 2024 Feb 19. pii: S0021-9673(24)00127-4. [Epub ahead of print]1719 464754

An HPLC-ESI-QTOF method to analyze polar heteroatomic species in aviation turbine fuel via hydrophilic interaction chromatography.

Amanda M Arts, Paul J Wrzesinski, Zachary J West.

  Aviation turbine fuel is a complex mixture of thousands of compounds. An analytical method using hydrophilic interaction liquid chromatography (HILIC) coupled with electrospray ionization and quadrupole time-of-flight mass spectrometry (ESI-QTOF) was developed for the identification of heteroatomic, polar compounds in aviation turbine fuel. Although compounds containing oxygen, nitrogen, and sulfur functional groups are each found at low levels (<0.1 % by mass) in fuels, their presence can generate significant effects on fuel properties. The HILIC-ESI-QTOF method is a combined separation and detection technique that possesses many advantages including a fast and simple sample preparation-requiring no extraction step therefore ensuring no loss of compounds of interest-and the ability to acquire high-fidelity compound data for chemometric analysis of heteroatomic species in aviation turbine fuel. In the development of the method, it was found that the chromatographic conditions and nature of the injection sample had a significant effect on separation efficiency and repeatability. For a sample dataset optimized using a singular aviation turbine fuel, retention time shift was able to be reduced from 0.4 min to 2.0 % relative standard deviation (RSD) to approximately 0.1 min with RSD of 0.4 % using the newly developed method. In addition, a high number of untargeted molecular features (944) and targeted amines (121) were able to be identified when utilizing optimal method conditions. The specific benefits and limitations of utilizing HILIC techniques with HPLC-ESI-QTOF are also discussed herein. This new method is currently being expanded to include analysis of all heteroatoms and is being applied to real fuel sets. The results of these studies are forthcoming.

Keywords:  Aviation turbine fuel; HILIC; Heteroatoms; Polar stationary phase

DOI:  https://doi.org/10.1016/j.chroma.2024.464754
Mass Spectrom (Tokyo). 2024 ;13(1): A0143

Comparison of Amine-Modified Polymeric Stationary Phases for Polar Metabolomic Analysis Based on Unified-Hydrophilic Interaction/Anion Exchange Liquid Chromatography/High-Resolution Mass Spectrometry (Unified-HILIC/AEX/HRMS).

Kazuki Ikeda, Masatomo Takahashi, Takeshi Bamba, Yoshihiro Izumi.

  In metabolomic analysis, one of the most commonly used techniques to support the detection sensitivity and quantitation of mass spectrometry is combining it with liquid chromatography. Recently, we developed a method that enables comprehensive single-run measurement of hydrophilic metabolites using unified-hydrophilic interaction/anion exchange liquid chromatography/high-resolution mass spectrometry (unified-HILIC/AEX/HRMS) with a polymer-based mixed amines column (Gelpack GL-HilicAex). However, the importance of stationary phase functional groups and mobile phase conditions for the separation mechanisms and sensitive detection in unified-HILIC/AEX/HRMS is not yet fully understood. This study aimed to understand the importance of the mobile and stationary phases in unified-HILIC/AEX/HRMS. Two different alkali-resistant polymer-based amines-modified columns (Gelpack GL-HilicAex, primary, secondary, tertiary, and quaternary amine-modified polyglycerol dimethacrylate gel; Asahipak NH2P-50 2D, secondary amine-modified polyvinyl alcohol gel) and two eluents (acetonitrile and ammonium bicarbonate solution, pH 9.8) were used for comparative validation. A comparison of mobile phase conditions using both columns confirmed that the two-step separation from HILIC to AEX characteristic of unified-HILIC/AEX requires a linear gradient condition from acetonitrile to nearly 50% water and AEX with up to 40 mM bicarbonate ions. We found that when alkali-resistant hydrophilic polymer packing materials are modified with amines, unified-HILIC/AEX separation can be reproduced if at least one secondary amine associated with the amine series is present in the stationary phase. Furthermore, the difference in sensitivity in the HILIC and AEX modes owing to the different columns indicates the need for further improvements in the mobile phase composition and stationary phase.

Keywords:  amine polymer column; hydrophilic metabolite; metabolome; metabolomics; non-targeted analysis

DOI:  https://doi.org/10.5702/massspectrometry.A0143
J Chem Inf Model. 2024 Feb 27.

DEIMoS GUI: An Open-Source User Interface for a High-Dimensional Mass Spectrometry Data Processing Tool.

Marjolein T Oostrom, Sean M Colby, Thomas O Metz.

We report here the creation of a graphical user interface (GUI) for the Data Extraction for Integrated Multidimensional Spectrometry (DEIMoS) tool. DEIMoS is a Python package that processes data from high-dimensional mass spectrometry measurements. It is divided into several modules, each representing a data processing step such as peak detection, alignment, and tandem mass spectra extraction and deconvolution. The inputs for and outputs from DEIMoS can include millions of N-dimensional data points, which can be challenging to visualize in a way that is interactive, informative, and responsive. Here, we used the HoloViz Python data visualization stack, including DataShader and Param, to create an interactive visualization of the mass spectrometry data. We believe the GUI will increase the accessibility of DEIMoS and that the visualization methods could be useful for other open-source mass spectrometry tools.

DOI: https://doi.org/10.1021/acs.jcim.3c01222
Anal Chim Acta. 2024 Apr 01. pii: S0003-2670(24)00148-X. [Epub ahead of print]1296 342347

UPLC-Orbitrap-HRMS application for analysis of plasma sterols.

Maria van der Ham, Johan Gerrits, Berthil Prinsen, Peter van Hasselt, Sabine Fuchs, Judith Jans, Anke Willems, Monique de Sain-van der Velden.

  Correct identification and quantification of different sterol biomarkers can be used as a first-line diagnostic approach for inherited metabolic disorders (IMD). The main drawbacks of current methodologies are related to lack of selectivity and sensitivity for some of these compounds. To address this, we developed and validated two sensitive and selective assays for quantification of six cholesterol biosynthesis pathway intermediates (total amount (free and esterified form) of 7-dehydrocholesterol (7-DHC), 8-dehydrocholesterol (8-DHC), desmosterol, lathosterol, lanosterol and cholestanol), two phytosterols (total amount (free and esterified form) of campesterol and sitosterol) and free form of two oxysterols (7-ketocholesterol (7-KC) and 3β,5α,6β-cholestane-triol (C-triol). For quantification of four cholesterol intermediates we based our analytical approach on sterol derivatization with 4-phenyl-1,2,4-triazoline-3,5-dione (PTAD). Quantification of all analytes is performed using UPLC coupled to an Orbitrap high resolution mass spectrometry (HRMS) system, with detection of target ions through full scan acquisition using positive atmospheric pressure chemical ionization (APCI) mode. UPLC and MS parameters were optimized to achieve high sensitivity and selectivity. Analog stable isotope labeled for each compound was used for proper quantification and correction for recovery, matrix effects and process efficiency. Precision (2.4%-12.3% inter-assay variation), lower limit of quantification (0.027 nM-50.5 nM) and linearity (5.5 μM (R2 0.999) - 72.3 μM (R2 0.997)) for phyto- and oxysterols were determined. The diagnostic potential of these two assays in a cohort of patients (n = 31, 50 samples) diagnosed with IMD affecting cholesterol and lysosomal/peroxisomal homeostasis is demonstrated.

Keywords:  Cholesterol intermediates; Inherited metabolic diseases; Oxysterols; PTAD derivatization; Phytosterols; UPLC-Orbitrap-HRMS

DOI:  https://doi.org/10.1016/j.aca.2024.342347
Methods Mol Biol. 2024 ;2785 75-86

Mass Spectrometry-Based Metabolomics Multi-platform for Alzheimer's Disease Research.

Álvaro González-Domínguez, Ana Sayago, Ángeles Fernández-Recamales, Raúl González-Domínguez.

  The integration of complementary analytical platforms is nowadays the most common strategy for comprehensive metabolomics analysis of complex biological systems. In this chapter, we describe methods and tips for the application of a mass spectrometry multi-platform in Alzheimer's disease research, based on the combination of direct mass spectrometry and orthogonal hyphenated approaches, namely, reversed-phase ultrahigh-performance liquid chromatography and gas chromatography. These procedures have been optimized for the analysis of multiple biological samples from human patients and transgenic animal models, including blood serum, various brain regions (e.g., hippocampus, cortex, cerebellum, striatum, olfactory bulbs), and other peripheral organs (e.g., liver, kidney, spleen, thymus).

Keywords:  Alzheimer’s disease; Direct MS analysis; Gas chromatography; Mass spectrometry; Metabolomics; Multi-platform; Ultrahigh-performance liquid chromatography

DOI:  https://doi.org/10.1007/978-1-0716-3774-6_6
Sci Rep. 2024 02 28. 14(1): 4841

An assessment of AcquireX and Compound Discoverer software 3.3 for non-targeted metabolomics.

Bret Cooper, Ronghui Yang.

We used the Exploris 240 mass spectrometer for non-targeted metabolomics on Saccharomyces cerevisiae strain BY4741 and tested AcquireX software for increasing the number of detectable compounds and Compound Discoverer 3.3 software for identifying compounds by MS2 spectral library matching. AcquireX increased the number of potentially identifiable compounds by 50% through six iterations of MS2 acquisition. On the basis of high-scoring MS2 matches made by Compound Discoverer, there were 483 compounds putatively identified from nearly 8000 candidate spectra. Comparisons to 20 amino acid standards, however, revealed instances whereby compound matches could be incorrect despite strong scores. Situations included the candidate with the top score not being the correct compound, matching the same compound at two different chromatographic peaks, assigning the highest score to a library compound much heavier than the mass for the parent ion, and grouping MS2 isomers to a single parent ion. Because the software does not calculate false positive and false discovery rates at these multiple levels where such errors can propagate, we conclude that manual examination of findings will be required post software analysis. These results will interest scientists who may use this platform for metabolomics research in diverse disciplines including medical science, environmental science, and agriculture.

DOI: https://doi.org/10.1038/s41598-024-55356-3
J Pharm Biomed Anal. 2024 Feb 23. pii: S0731-7085(24)00108-0. [Epub ahead of print]243 116068

A GC×GC-MS method based on solid-state modulator for non-targeted metabolomics: Comparison with traditional GC-MS method.

Yueyi Zhang, Haihui Ren, Xiao Tang, Qiaorong Liu, Wen Xiao, Zunjian Zhang, Yuan Tian.

  The formidable challenge posed by the presence of extremely high amounts of compounds and large differences in concentrations in plasma significantly complicates non-targeted metabolomics analyses. In this study, a comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry (GC×GC-qMS) method with a solid-state modulator (SSM) for non-targeted metabolomics in beagle plasma was first established based on a GC-MS method, and the qualitative and quantitative performance of the two platforms were compared. Identification of detected compounds was accomplished utilizing NIST database match scores, retention indices (RIs) and standards. Semi-quantification involved the calculation of peak area ratios to internal standards. Metabolite identification sheets were generated for plasma samples on both analytical platforms, featuring 22 representative metabolites chosen for validating qualitative accuracy, and for conducting comparisons of linearity, accuracy, precision, and sensitivity. The outcomes revealed a threefold increase in the number of identifiable metabolites on the GC×GC-MS platform, with lower limits of quantitation (LLOQs) reduced to 0.5-0.05 times those achieved on the GC-MS platform. Accuracy in quantification for both GC×GC-MS and GC-MS fell within the range of 85-115%, and the vast majority of intra- and inter-day precisions were within the range of 20%. These findings underscore that relative to the conventional GC-MS method, the GC×GC-MS method developed in this study, combined with SSM, exhibits enhanced qualitative capabilities, heightened sensitivity, and comparable accuracy and precision, rendering it more suitable for non-targeted metabolomics analyses.

Keywords:  Comprehensive two-dimensional gas chromatography-mass spectrometry; GC-MS; High throughput analysis method; Non-targeted metabolomics; Solid-state modulator

DOI:  https://doi.org/10.1016/j.jpba.2024.116068
Brief Bioinform. 2024 Jan 22. pii: bbae056. [Epub ahead of print]25(2):

Ion entropy and accurate entropy-based FDR estimation in metabolomics.

Shaowei An, Miaoshan Lu, Ruimin Wang, Jinyin Wang, Hengxuan Jiang, Cong Xie, Junjie Tong, Changbin Yu.

  Accurate metabolite annotation and false discovery rate (FDR) control remain challenging in large-scale metabolomics. Recent progress leveraging proteomics experiences and interdisciplinary inspirations has provided valuable insights. While target-decoy strategies have been introduced, generating reliable decoy libraries is difficult due to metabolite complexity. Moreover, continuous bioinformatics innovation is imperative to improve the utilization of expanding spectral resources while reducing false annotations. Here, we introduce the concept of ion entropy for metabolomics and propose two entropy-based decoy generation approaches. Assessment of public databases validates ion entropy as an effective metric to quantify ion information in massive metabolomics datasets. Our entropy-based decoy strategies outperform current representative methods in metabolomics and achieve superior FDR estimation accuracy. Analysis of 46 public datasets provides instructive recommendations for practical application.

Keywords:  FDR estimation; entropy; mass spectrometry; metabolite annotation; metabolomics

DOI:  https://doi.org/10.1093/bib/bbae056
Bioinformatics. 2024 Feb 24. pii: btae084. [Epub ahead of print]

RT-Transformer: Retention time prediction for metabolite annotation to assist in metabolite identification.

Jun Xue, Bingyi Wang, Hongchao Ji, WeiHua Li.

MOTIVATION: Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in non-targeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another.RESULTS: Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification.
AVAILABILITY AND IMPLEMENTATION: The source code for the model is available at https://github.com/01dadada/RT-Transformer.The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btae084
J Am Soc Mass Spectrom. 2024 Mar 02.

Impact of Source Conditions on Collision Cross Section Determination by Trapped Ion Mobility Spectrometry.

Anaïs C George, Isabelle Schmitz, Benoit Colsch, Carlos Afonso, François Fenaille, Corinne Loutelier-Bourhis.

  Collision cross section (CCS) values determined in ion mobility-mass spectrometry (IM-MS) are increasingly employed as additional descriptors in metabolomics studies. CCS values must therefore be reproducible and the causes of deviations must be carefully known and controlled. Here, we analyzed lipid standards by trapped ion mobility spectrometry-mass spectrometry (TIMS-MS) to evaluate the effects of solvent and flow rate in flow injection analysis (FIA), as well as electrospray source parameters including nebulizer gas pressure, drying gas flow rate, and temperature, on the ion mobility and CCS values. The stability of ion mobility experiments was studied over 10 h, which established the need for a delay-time of 20 min to stabilize source parameters (mostly pressure and temperature). Modifications of electrospray source parameters induced shifts of ion mobility peaks and even the occurrence of an additional peak in the ion mobility spectra. This behavior could be essentially explained by ion-solvent cluster formation. Changes in source parameters were also found to impact CCS value measurements, resulting in deviations up to 0.8%. However, internal calibration with the Tune Mix calibrant reduced the CCS deviations to 0.1%. Thus, optimization of source parameters is essential to achieve a good desolvation of lipid ions and avoid misinterpretation of peaks in ion mobility spectra due to solvent effects. This work highlights the importance of internal calibration to ensure interoperable CCS values, usable in metabolomics annotation.

Keywords:  Collision cross section; Ion mobility-mass spectrometry; Lipids; Solvent−ion cluster; Trapped ion mobility spectrometry

DOI:  https://doi.org/10.1021/jasms.3c00361
J Mass Spectrom Adv Clin Lab. 2024 Apr;32 31-40

Endogenous monoclonal immunoglobulins analyzed using the EXENT® solution and LC-MS.

David Barnidge, Derek Troske, Simon North, Gregg Wallis, Mark Perkins, Stephen Harding.

  Introduction: The EXENT® Solution, a fully automated system, is a recent advancement for identifying and quantifying monoclonal immunoglobulins in serum. It combines immunoprecipitation with MALDI-TOF mass spectrometry. Compared to gel-based methods, like SPEP and IFE, it has demonstrated the ability to detect monoclonal immunoglobulins in serum at lower levels. In this study, samples that tested negative using EXENT® were reflexed to LC-MS to determine if the more sensitive LC-MS method could identify monoclonal immunoglobulins missed by EXENT®.Objectives: To assess whether monoclonal immunoglobulins that are not detected by EXENT® can be detected by LC-MS using a low flow LC system coupled to a Q-TOF mass spectrometer.
Methods: Samples obtained from patients confirmed to have multiple myeloma (MM) were diluted with pooled polyclonal human serum and analyzed using EXENT®. If a specific monoclonal immunoglobulin was not detected by EXENT®, the sample was then subjected to analysis by LC-MS. For the LC-MS analysis, the sample eluate, obtained after the MALDI-TOF MS spotting step, was collected and transferred to an autosampler tray for subsequent analysis using LC-MS.
Conclusion: LC-MS has the capability to detect monoclonal immunoglobulins that are no longer detected by EXENT®. Reflexing samples to LC-MS for analysis does not involve additional sample handling, allowing for a faster time-to-result compared to current approaches, such as Next-Generation Sequencing, Next-Generation Flow, and clonotypic peptide methods. Notably, LC-MS offers equivalent sensitivity in detecting these specific monoclonal immunoglobulins.

Keywords:  LC-MS; MALDI-TOF MS; Mass spectrometry; Monoclonal immunoglobulin; Multiple myeloma

DOI:  https://doi.org/10.1016/j.jmsacl.2024.02.002
Anal Chem. 2024 Mar 01.

Mass Spectrometry Imaging and Histology for the Analysis of Budding Intestinal Organoids.

Emily R Sekera, Kubra B Akkaya-Colak, Arbil Lopez, Maria M Mihaylova, Amanda B Hummon.

Three-dimensional (3D) organoids have been at the forefront of regenerative medicine and cancer biology fields for the past decade. However, the fragile nature of organoids makes their spatial analysis challenging due to their budding structures and composition of single layer of cells. The standard sample preparation approaches can collapse the organoid morphology. Therefore, in this study, we evaluated several approaches to optimize a method compatible with both mass spectrometry imaging (MSI) and immunohistological techniques. Murine intestinal organoids were used to evaluate embedding in gelatin, carboxymethylcellulose (CMC)-gelatin-CMC-sucrose, or hydroxypropyl methylcellulose (HPMC) and polyvinylpyrrolidone (PVP) solutions. Organoids were assessed with and without aldehyde fixation and analyzed for lipid distributions by MSI coupled with hematoxylin and eosin (H&E) staining and immunofluorescence (IF) in consecutive sections from the same sample. While chemical fixation preserves morphology for better histological outcomes, it can lead to suppression of the matrix-assisted laser desorption/ionization (MALDI) lipid signal. By contrast, leaving organoid samples unfixed enhanced MALDI lipid signal. The method that performed best for both MALDI and histological analysis was embedding unfixed samples in HPMC and PVP. This approach allowed assessment of cell proliferation by Ki67 while also identifying putative phosphatidylethanolamine (PE(18:0/18:1)), which was confirmed further by tandem MS approaches. Overall, these protocols will be amenable to multiplexing imaging mass spectrometry analysis with several histological assessments and help advance our understanding of the biological processes that take place in district subsets of cells in budding organoid structures.

DOI: https://doi.org/10.1021/acs.analchem.3c05725
Anal Chem. 2024 Mar 01.

Technical Note: mzML and imzML Libraries for Processing Mass Spectrometry Data with the High-Performance Programming Language Julia.

Ignacio Rosas-Román, Héctor Guillén-Alonso, Abigail Moreno-Pedraza, Robert Winkler.

Julia combines the virtues of high-level and low-level programming languages: The code is human-readable, and the performance of the created binaries competes with machine-orientated compilers. Thus, Julia is popular in "Big Data" sciences. Reading mass spectrometry (MS) data with Julia was impossible until now due to missing libraries. Here, we present a Julia library for importing mass spectrometry (MS) data in HUPO standard mzML and imzML formats and demonstrate its function with direct and ambient ionization MS, liquid chromatography-MS, and MS imaging data on standard platforms (Windows, Linux, and Mac OS). The processing speed of Julia for reading imzML MS imaging files was up to 214 times faster than the comparable code in R. Julia can remove bottlenecks for computationally demanding tasks in large-scale MS-Omics and MS imaging data processing workflows and supports their agile development. In addition, time-critical and complex data evaluation tasks become possible, such as following the real-time monitoring of biological processes and pattern recognition in large MS imaging projects. Our mzML/imzML libraries and code examples are available under the terms of the MIT license from https://github.com/CINVESTAV-LABI/julia_mzML_imzML.

DOI: https://doi.org/10.1021/acs.analchem.3c05853
ACS Meas Sci Au. 2024 Feb 21. 4(1): 127-135

Enhancing Quantitative Analysis of Xenobiotics in Blood Plasma through Cross-Matrix Calibration and Bayesian Hierarchical Modeling.

Nipunika H Godage, Song S Qian, Erasmus Cudjoe, Emanuela Gionfriddo.

This study addresses the challenges of matrix effects and interspecies plasma protein binding (PPB) on measurement variability during method validation across diverse plasma types (human, rat, rabbit, and bovine). Accurate measurements of small molecules in plasma samples often require matrix-matched calibration approaches with the use of specific plasma types, which may have limited availability or affordability. To mitigate the costs associated with human plasma measurements, we explore in this work the potential of cross-matrix-matched calibration using Bayesian hierarchical modeling (BHM) to correct for matrix effects associated with PPB. We initially developed a targeted quantitative approach utilizing biocompatible solid-phase microextraction coupled with liquid chromatography-mass spectrometry for xenobiotic analysis in plasma. The method was evaluated for absolute matrix effects across human, bovine, rat, and rabbit plasma comparing pre- and postmatrix extraction standards. Absolute matrix effects from 96 to 108% for most analytes across plasma sources indicate that the biocompatibility of the extraction phase minimizes interference coextraction. However, the extent of PPB in different media can still affect the accuracy of the measurement when the extraction of small molecules is carried out via free concentration, as in the case of microextraction techniques. In fact, while matrix-matched calibration revealed high accuracy, cross-matrix calibration (e.g., using a calibration curve generated from bovine plasma) proved inadequate for precise measurements in human plasma. A BHM was used to calculate correction factors for each analyte within each plasma type, successfully mitigating the measurement bias resulting from diverse calibration curve types used to quantify human plasma samples. This work contributes to the development of cost-effective, efficient calibration strategies for biofluids. Leveraging easily accessible plasma sources, like bovine plasma, for method optimization and validation prior to analyzing costly plasma (e.g., human plasma) holds substantial advantages applicable to biomonitoring and pharmacokinetic studies.

DOI: https://doi.org/10.1021/acsmeasuresciau.3c00049
Anal Chem. 2024 Feb 26.

Parts-Per-Million Carbonate Mineral Quantification with Thermogravimetric Analysis-Mass Spectrometry.

Madeline F Bartels, Quin R S Miller, Ruoshi Cao, Nabajit Lahiri, Jade E Holliman, C Heath Stanfield, H Todd Schaef.

Mitigating the deleterious effects of climate change requires the development and implementation of carbon capture and storage technologies. To expand the monitoring, verification, and reporting (MRV) capabilities of geologic carbon mineralization projects, we developed a thermogravimetric analysis-mass spectrometry (TGA-MS) methodology to enable quantification of <100 ppm calcite (CaCO3) in complex samples. We extended TGA-MS calcite calibration curves to enable a higher measurement resolution and lower limits of quantification for evolved CO2 from a calcite-corundum mixture. We demonstrated <100 ppm carbonate mineral quantification with TGA-MS for the first time, an outcome applicable across earth, environmental, and materials science fields. We applied this carbonate quantification method to a suite of Columbia River Basalt Group (CRBG) well cuttings recovered in 2009 from Pacific Northwest National Laboratory's Wallula #1 Well. Our execution of this new combined calcite and calcite-corundum calibration curve TGA-MS method on our CRBG sample suite indicated average carbonate contents of 0.050 wt % in flow interiors (caprocks) and 0.400 wt % in interflow zones (reservoirs) in the upper 1250 m of the Wallula #1 Well. By advancing our knowledge of continental flood basalt-hosted carbonates in the mafic subsurface and reaching new TGA-MS quantification limits for carbonate minerals, we expand MRV capabilities and support the commercial-scale deployment of carbon mineralization projects in the Pacific Northwest United States and beyond.

DOI: https://doi.org/10.1021/acs.analchem.3c03936
Environ Sci Technol. 2024 Mar 01.

Direct Analysis of Marine Dissolved Organic Matter Using LC-FT-ICR MS.

Oliver J Lechtenfeld, Jan Kaesler, Elaine K Jennings, Boris P Koch.

  Marine dissolved organic matter (DOM) is an important component of the global carbon cycle, yet its intricate composition and the sea salt matrix pose major challenges for chemical analysis. We introduce a direct injection, reversed-phase liquid chromatography ultrahigh resolution mass spectrometry approach to analyze marine DOM without the need for solid-phase extraction. Effective separation of salt and DOM is achieved with a large chromatographic column and an extended isocratic aqueous step. Postcolumn dilution of the sample flow with buffer-free solvents and implementing a counter gradient reduced salt buildup in the ion source and resulted in excellent repeatability. With this method, over 5,500 unique molecular formulas were detected from just 5.5 nmol carbon in 100 μL of filtered Arctic Ocean seawater. We observed a highly linear detector response for variable sample carbon concentrations and a high robustness against the salt matrix. Compared to solid-phase extracted DOM, our direct injection method demonstrated superior sensitivity for heteroatom-containing DOM. The direct analysis of seawater offers fast and simple sample preparation and avoids fractionation introduced by extraction. The method facilitates studies in environments, where only minimal sample volume is available e.g. in marine sediment pore water, ice cores, or permafrost soil solution. The small volume requirement also supports higher spatial (e.g., in soils) or temporal sample resolution (e.g., in culture experiments). Chromatographic separation adds further chemical information to molecular formulas, enhancing our understanding of marine biogeochemistry, chemodiversity, and ecological processes.

Keywords:  Fourier transform ion cyclotron resonance mass spectrometry; Natural organic matter; PPL, SPE; RP-LC-MS; Salt water

DOI:  https://doi.org/10.1021/acs.est.3c07219
J Pharm Biomed Anal. 2024 Feb 15. pii: S0731-7085(24)00074-8. [Epub ahead of print]242 116034

Co-stimulatory pathway competitive assay development using Liquid chromatography-tandem mass spectrometry (LC-MS/MS).

Abeer K Malkawi, Leanne Ohlund, Anas M Abdel Rahman, Lekha Sleno, Mohamed Siaj.

  T-cells play a significant role in the development of autoimmune diseases. The CD28-B7 costimulatory pathway is crucial for activating T-cells, and blocking this pathway is essential for treating autoimmune diseases. Therapeutic antibodies and fusion proteins that target costimulatory molecules like CD80, CD86, CTLA-4, and CD28 have been developed to explore the costimulation process and as targeted treatments. To advance our understanding of costimulation in autoimmunity and the inhibition of the costimulatory pathway, it is crucial to have an accurate, precise, and direct method for detecting and quantifying the soluble form of these molecules in body fluids and various biological systems. Herein, we developed a liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for quantifying the four costimulatory proteins depending on the signature peptides derived from the soluble isoform of these proteins in multiple reaction monitoring (MRM) mode. The method was validated using the US FDA guidelines. The LOQ was determined as ∼0.5 nM for the four analytes, with quantification extended to 20 nM with a correlation coefficient of R2>0.998. The developed MRM method was used to analyze on-bead digested protein mixtures to establish a competitive assay for the CD28-B7 costimulatory pathway using CTLA4-Ig (Abatacept ™) as an FDA-approved drug for rheumatoid arthritis. The IC50 was determined to be 2.99 and 159.8 nM for sCD80 and sCD86, respectively. A straightforward MRM-based competitive assay will advance the knowledge about the costimulatory role in autoimmunity and the autoimmune therapeutic drug discovery, with the need for broad application on different in vitro and in vivo models to discover new targeted inhibitors.

Keywords:  Abatacept; Competitive assay; Costimulatory molecules; Method validation; Multiple reaction monitoring (MRM)

DOI:  https://doi.org/10.1016/j.jpba.2024.116034
Adv Exp Med Biol. 2024 ;1443 87-101

Mass Spectrometry Applications to Study Human Microbiome.

Mohamed A Raslan, Sara A Raslan, Eslam M Shehata, Amr S Mahmoud, Marcus Vinicius Canário Viana, Flávia Aburjaile, Debmalya Barh, Nagwa A Sabri, Vasco Azevedo.

  Microbiotas are an adaptable component of ecosystems, including human ecology. Microorganisms influence the chemistry of their specialized niche, such as the human gut, as well as the chemistry of distant surroundings, such as other areas of the body. Metabolomics based on mass spectrometry (MS) is one of the primary methods for detecting and identifying small compounds generated by the human microbiota, as well as understanding the functional significance of these microbial metabolites. This book chapter gives basic knowledge on the kinds of untargeted mass spectrometry as well as the data types that may be generated in the context of microbiome study. While data analysis remains a barrier, the emphasis is on data analysis methodologies and integrative analysis, particularly the integration of microbiome sequencing data. Mass spectrometry (MS)-based techniques have resurrected culture methods for studying the human gut microbiota, filling in the gaps left by high-throughput sequencing methods in terms of culturing minor populations.

Keywords:  Gut microbiota; MALDI-TOF MS; Mass spectrometry; Metabolomics; Microbiome

DOI:  https://doi.org/10.1007/978-3-031-50624-6_5
J Chromatogr A. 2024 Feb 14. pii: S0021-9673(24)00112-2. [Epub ahead of print]1719 464739

Development of a three-dimensional HPLC system for the determination of serine, threonine and allo-threonine enantiomers in the plasma of patients with chronic kidney disease.

Mai Oyaide, Chiharu Ishii, Takeyuki Akita, Tomonori Kimura, Shinsuke Sakai, Masayuki Mizui, Masashi Mita, Tomomi Ide, Yoshitaka Isaka, Kenji Hamase.

  A highly-selective three-dimensional high-performance liquid chromatographic (3D-HPLC) system was developed for the determination of serine (Ser), threonine (Thr) and allo-threonine (aThr) enantiomers in human plasma to screen the new biomarker of chronic kidney disease (CKD). d-Ser has been reported to be the candidate biomarker of CKD, however, multiple biomarkers are still required. Therefore, Ser analogs of hydroxy amino acids are the focus in the present study. For the sensitive analysis, the amino acids were derivatized with 4-fluoro-7-nitro-2,1,3-benzoxadiazole and detected by their fluorescence. The 3D-HPLC system consisted of a reversed-phase column (Singularity RP18, 1.0 × 250 mm), an anion-exchange column (Singularity AX, 1.0 × 150 mm) and a Pirkle-type chiral stationary phase (Singularity CSP-013S, 1.5 × 250 mm). The developed method was validated and applied to the human plasma samples obtained from 15 healthy volunteers and 165 CKD patients. The concentrations of the d-forms were 1.13-2.26 (Ser), 0.01-0.03 (Thr) and 0.04-0.10 μM (aThr) for the healthy volunteers and 0.95-19.0 (Ser), 0-0.57 (Thr) and 0.04-1.02 μM (aThr) for the CKD patients. The concentrations and the %d values of all the target d-amino acids were increased along with the decreasing of renal function and further investigation for clinical applications are expected.

Keywords:  Chronic kidney disease; Enantiomer separation; Hydroxy amino acids; Three-dimensional HPLC

DOI:  https://doi.org/10.1016/j.chroma.2024.464739