bims-metlip 2023-10-29 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2023–10–29
eightteen papers selected by
Sofia Costa, Matterworks

Program for Integration and Rapid Analysis of Mass Isotopomer Distributions (PIRAMID).
Methods for the relative quantitation of human plasma lipidome using liquid chromatography coupled with mass spectrometry using minimal sample manipulation.
An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.
Comparison of liquid chromatography-high-resolution tandem mass spectrometry (MS2) and multi-stage mass spectrometry (MS3) for screening toxic natural products.
Advancements in Preprocessing and Analysis of Nitrite and Nitrate since 2010 in Biological Samples: A Review.
A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry.
Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.
Development of a novel enantioselective high-performance liquid chromatography-mass spectrometry method for the differentiation of dextro- and levo-methorphan and their O-demethylated metabolites in human blood and its application to post-mortem samples.
Chemical profiling of botanical extracts obtained in NADES systems using centrifugal partition chromatography combined with 13 C NMR dereplication-Hypericum perforatum as a case study.
Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification.
Effects of Solvent Evaporation Methods and Short-Term Room Temperature Storage on High-Coverage Cellular Metabolome Analysis.
Development and Validation of LC-MS/MS Method for Determination of Cytisine in Human Serum and Saliva.
Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics.
A Guide to MALDI Imaging Mass Spectrometry for Tissues.
Ultrasound-assisted extraction followed by liquid chromatography coupled to tandem mass spectrometry for the simultaneous determination of multiclass herbicides in soil.
Imaging Plant Metabolism in situ.
A single extraction 96-well method for LC-MS/MS quantification of urinary eicosanoids, steroids and drugs.
Quantification of the lung cancer tumor marker CYFRA 21-1 using protein precipitation, immunoaffinity bottom-up LC-MS/MS.

Bioinformatics. 2023 Oct 27. pii: btad661. [Epub ahead of print]

Program for Integration and Rapid Analysis of Mass Isotopomer Distributions (PIRAMID).

Javier D Gomez, Martha L Wall, Mohsin Rahim, Shrikaar Kamphampati, Bradley S Evans, Doug K Allen, Maciek R Antoniewicz, Jamey D Young.

The analysis of stable isotope labeling experiments requires accurate, efficient, and reproducible quantification of mass isotopomer distributions (MIDs), which is not a core feature of general-purpose metabolomics software tools that are optimized to quantify metabolite abundance. Here we present PIRAMID, a MATLAB-based tool that addresses this need by offering a user-friendly, graphical user interface (GUI)-driven program to automate the extraction of isotopic information from mass spectrometry (MS) data sets. This tool can simultaneously extract ion chromatograms for various metabolites from multiple data files in common vendor-agnostic file formats, locate chromatographic peaks based on a targeted list of characteristic ions and retention times, and integrate MIDs for each target ion. These MIDs can be corrected for natural isotopic background based on the user-defined molecular formula of each ion. PIRAMID offers support for datasets acquired from low- or high-resolution (HR) MS, and single (MS) or tandem (MS/MS) instruments. It also enables the analysis of single or dual labeling experiments using a variety of isotopes (i.e., 2H, 13C, 15N, 18O, 34S).
AVAILABILITY: MATLAB p-code files are freely available for non-commercial use and can be downloaded from https://mfa.vueinnovations.com/. Commercial licenses are also available.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

DOI: https://doi.org/10.1093/bioinformatics/btad661
Rapid Commun Mass Spectrom. 2023 Oct 26. e9641

Methods for the relative quantitation of human plasma lipidome using liquid chromatography coupled with mass spectrometry using minimal sample manipulation.

Diane Taylor, Bebiana Sousa, Greg West, Aveline Neo Huipeng, Andrea F Lopez-Clavijo.

Extraction protocols and liquid chromatography coupled with mass spectrometry (LC-MS) and tandem mass spectrometry (LC-MS/MS) methods for the measurement of four lipid categories, namely glycerophospholipids, glycerolipids, sphingolipids and sterol lipids in human plasma, are described here. Step-by-step instructions are provided for the liquid-liquid extraction methods, including solution preparation and the non-endogenous lipid internal standards used. All instrumental conditions, chromatography columns and solutions required for the LC-MS and LC-MS/MS methods are also provided in detail.

DOI: https://doi.org/10.1002/rcm.9641
J Am Soc Mass Spectrom. 2023 Oct 24.

An Automated Workflow Composition System for Liquid Chromatography-Mass Spectrometry Metabolomics Data Processing.

Xinsong Du, Farhad Dastmalchi, Matthew A Diller, Mathias Brochhausen, Timothy J Garrett, William R Hogan, Dominick J Lemas.

Liquid chromatography-mass spectrometry (LC-MS) metabolomics studies produce high-dimensional data that must be processed by a complex network of informatics tools to generate analysis-ready data sets. As the first computational step in metabolomics, data processing is increasingly becoming a challenge for researchers to develop customized computational workflows that are applicable for LC-MS metabolomics analysis. Ontology-based automated workflow composition (AWC) systems provide a feasible approach for developing computational workflows that consume high-dimensional molecular data. We used the Automated Pipeline Explorer (APE) to create an AWC for LC-MS metabolomics data processing across three use cases. Our results show that APE predicted 145 data processing workflows across all the three use cases. We identified six traditional workflows and six novel workflows. Through manual review, we found that one-third of novel workflows were executable whereby the data processing function could be completed without obtaining an error. When selecting the top six workflows from each use case, the computational viable rate of our predicted workflows reached 45%. Collectively, our study demonstrates the feasibility of developing an AWC system for LC-MS metabolomics data processing.

DOI: https://doi.org/10.1021/jasms.3c00248
J Mass Spectrom Adv Clin Lab. 2023 Nov;30 38-44

Comparison of liquid chromatography-high-resolution tandem mass spectrometry (MS2) and multi-stage mass spectrometry (MS3) for screening toxic natural products.

Ruben Yiqi Luo, Kate Comstock, Caroline Ding, Alan H B Wu, Kara L Lynch.

   Background: Liquid chromatography-high-resolution mass spectrometry (LC-HR-MS) has emerged as a powerful analytical technology for compound screening in clinical toxicology. To evaluate the potential of LC-HR-MS3 in detecting toxic natural products, a spectral library of 85 natural products (79 alkaloids) that contains both MS2 and MS3 mass spectra was constructed and used to identify the natural products. Samples were analyzed using an LC-HR-MS3 method and the generated data were matched to the spectral library to identify the natural products.
Methods: To test the performance of the LC-HR-MS3 method in different sample matrices, the 85 natural product standards were divided into three groups to separate structural isomers and avoid ion suppression effects caused by co-elution of multiple analytes. The grouped analytes were spiked into drug-free serum and drug-free urine to produce contrived clinical samples.
Results: The compound identification results of the 85 natural products in urine and serum samples were obtained. The match scores using both MS2 and MS3 mass spectra and those using only MS2 mass spectra were compared at 10 different analyte concentrations. The two types of data analysis provided identical identification results for the majority of the analytes (96% in serum, 92% in urine), whereas, for the remaining analytes, the MS2-MS3 tree data analysis had better performance in identifying them at lower concentrations.
Conclusion: This study shows that in comparison to LC-HR-MS (MS2), LC-HR-MS3 can increase the performance in identification of a small group of the toxic natural products tested in serum and urine specimens.

Keywords:  Clinical toxicology; LC-HR-MS3; Natural products; Spectral library

DOI:  https://doi.org/10.1016/j.jmsacl.2023.09.002
Molecules. 2023 Oct 17. pii: 7122. [Epub ahead of print]28(20):

Advancements in Preprocessing and Analysis of Nitrite and Nitrate since 2010 in Biological Samples: A Review.

Guojie Liu, Honghui Guo, Wanlin Zhao, Hongmu Yan, Enze Zhang, Lina Gao.

  As a substance present in organisms, nitrite is a metabolite of nitric oxide and can also be ingested. Nitrate is the metabolite of nitrite. Therefore, it is necessary to measure it quickly, easily and accurately to evaluate the health status of humans. Although there have been several reviews on analytical methods for non-biological samples, there have been no reviews focused on both sample preparation and analytical methods for biological samples. First, rapid and accurate nitrite measurement has significant effects on human health. Second, the detection of nitrite in biological samples is problematic due to its very low concentration and matrix interferences. Therefore, the pretreatment plus measuring methods for nitrite and nitrate obtained from biological samples since 2010 are summarized in the present review, and their prospects for the future are proposed. The treatment methods include liquid-liquid microextraction, various derivatization reactions, liquid-liquid extraction, protein precipitation, solid phase extraction, and cloud point extraction. Analytical methods include spectroscopic methods, paper-based analytical devices, ion chromatography, liquid chromatography, gas chromatography-mass spectrometry, electrochemical methods, liquid chromatography-mass spectrometry and capillary electrophoresis. Derivatization reagents with rapid quantitative reactions and advanced extraction methods with high enrichment efficiency are also included. Nitrate and nitrate should be determined at the same time by the same analytical method. In addition, much exploration has been performed on formulating fast testing through microfluidic technology. In this review, the newest developments in nitrite and nitrate processing are a focus in addition to novel techniques employed in such analyses.

Keywords:  analytical methods; biological samples; nitrate; nitrite; sample treatment

DOI:  https://doi.org/10.3390/molecules28207122
Talanta. 2023 Oct 17. pii: S0039-9140(23)01069-X. [Epub ahead of print]268(Pt 1): 125318

A novel endogenous retention-index for minimizing retention-time variations in metabolomic analysis with reversed-phase ultrahigh-performance liquid-chromatography and mass spectrometry.

Qinsheng Chen, Qinwei Lu, Lianglong Zhang, Chenhan Zhang, Jingxian Zhang, Yu Gu, Qingxia Huang, Huiru Tang.

  Consistent retention time (tR) of metabolites is vital for identification in metabolomic analysis with ultrahigh-performance liquid-chromatography (UPLC). To minimize inter-experimental tR variations from the reversed-phase UPLC-MS, we developed an endogenous retention-index (endoRI) using in-sample straight-chain acylcarnitines with different chain-length (LC, C0-C26) without additives. The endoRI-corrections reduced the tR variations caused by the combined changes of mobile phases, gradients, flow-rates, elution time, columns and temperature from up to 5.1 min-0.2 min for most metabolites in a model metabolome consisting of 91 metabolites and multiple biological matrices including human serum, plasma, fecal, urine, A549 cells and rabbit liver extracts. The endoRI-corrections also reduced the inter-batch and inter-platform tR variations from 1.5 min to 0.15 min for 95 % of detected features in the above biological samples. We further established a quantitative model between tR and LC for predicting tR values of acylcarnitines when absent in samples. This makes it possible to compare metabolites' tR from different tR databases and the UPLC-based metabolomic data from different batches.

Keywords:  Endogenous retention-index; Metabolic profiling; Metabonomics/metabolomics; Straight-chain acylcarnitines; Ultrahigh-performance liquid-chromatography-mass spectrometry

DOI:  https://doi.org/10.1016/j.talanta.2023.125318
ArXiv. 2023 Oct 12. pii: arXiv:2310.07990v1. [Epub ahead of print]

Multi-View Variational Autoencoder for Missing Value Imputation in Untargeted Metabolomics.

Chen Zhao, Kuan-Jui Su, Chong Wu, Xuewei Cao, Qiuying Sha, Wu Li, Zhe Luo, Tian Qin, Chuan Qiu, Lan Juan Zhao, Anqi Liu, Lindong Jiang, Xiao Zhang, Hui Shen, Weihua Zhou, Hong-Wen Deng.

BACKGROUND: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies.
METHOD: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-view variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information.
RESULTS: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved r2-scores > 0.01 for 71.55% of metabolites.
CONCLUSION: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research.
J Pharm Biomed Anal. 2023 Oct 05. pii: S0731-7085(23)00538-1. [Epub ahead of print]237 115769

Development of a novel enantioselective high-performance liquid chromatography-mass spectrometry method for the differentiation of dextro- and levo-methorphan and their O-demethylated metabolites in human blood and its application to post-mortem samples.

Saba Jorbenadze, Tamar Khatiashvili, Aluda Chelidze, Alfredo Fabrizio Lo Faro, Tivadar Farkas, Anastasio Tini, Giorgia Sprega, Diletta Berardinelli, Francesco Paolo Busardò, Bezhan Chankvetadze.

  Recently we proposed an isocratic enantioselective high-performance liquid chromatography-tandem mass spectrometry (LC-MS/MS) method for the separation and quantitative determination of dextro- (DXM) and levo-methorphan (LVM) and their pharmacologically relevant metabolites, dextrorphan and levorphanol, respectively, in human blood samples. This method was based on the polysaccharide-based chiral column Lux AMP, a specialty column characterized with high stability in mobile phases of pH 11.0 and above. The use of a single-source column is a limitation for any analytical method. Therefore, the major goal of the present study was to develop an enantioselective method for the differentiation of dextro- and levo-methorphan, as well as their metabolites dextrorphan and levorphanol, using Lux Cellulose-3 as alternative chiral column with methanol containing 0.1 % diethylamine mobile phase. A newly developed method uses a chiral selector part of HPLC columns available from multiple manufacturers and a fairly common mobile phase. The method was validated and applied to post-mortem blood samples. Out of 50 analyzed new samples, dextromethorphan (DXM) was detected in 17 samples. Of these 17 cases DXM was accompanied with LVM in 7 samples. The proposed analytical method is relatively simple, accurate and fast and can be adopted for routine use in forensic and clinical toxicology laboratories.

Keywords:  Dextromethorphan; Dextrorphan; Enantioselective analysis; Levomethorphan; Levorphanol

DOI:  https://doi.org/10.1016/j.jpba.2023.115769
Phytochem Anal. 2023 Oct 27.

Chemical profiling of botanical extracts obtained in NADES systems using centrifugal partition chromatography combined with 13 C NMR dereplication-Hypericum perforatum as a case study.

Alexis Kotland, Joseph Thiery, Jane Hubert.

   INTRODUCTION: Natural deep eutectic solvents (NADES) have emerged as interesting extractants to develop botanical ingredients. They are nontoxic and biodegradable, nonflammable, easy to prepare, and able to solubilize a wide range of molecules. However, NADES extracts remain difficult to analyze because the metabolites of interest stay highly diluted in the nonvolatile viscous NADES matrix.
OBJECTIVE: This study presents a robust analytical workflow for the chemical profiling of NADES extracts. It is applied to Hypericum perforatum aerial parts extracted with the neutral mixture fructose/glycerol/water (3/1/1, w/w/w), and compared to the chemical profiling of a classical dry methanol extract.
METHODS: Exploiting polarity differences between metabolites, the H. perforatum NADES extract was partitioned in a liquid-liquid solvent system to trap the hydrophilic NADES constituents in the lower phase. The upper phase, containing a diversity of secondary metabolites from H. perforatum, was fractionated by centrifugal partition chromatography. All fractions were chemically investigated using a 13 C NMR dereplication method which involves hierarchical clustering analysis of the whole NMR dataset, a natural metabolite database for metabolite identification, and 2D NMR analyses for validation. Liquid chromatography-mass spectrometry (LC-MS) analyses were also performed to complete the identification process.
RESULTS: A range of 21 metabolites were unambiguously identified, including glycosylated flavonols, lactones, catechins, phenolic acids, lipids, and simple sugars, and 15 additional minor extract constituents were annotated by LC-MS based on exact mass measurements.
CONCLUSION: The proposed identification process is rapid and nondestructive and provides good prospects to deeply characterize botanical extracts obtained in nonvolatile and viscous NADES systems.

Keywords:  Hypericum perforatum; chemical profiling; dereplication; extraction; natural deep eutectic solvents; natural products; nuclear magnetic resonance

DOI:  https://doi.org/10.1002/pca.3297
Metabolites. 2023 Oct 21. pii: 1101. [Epub ahead of print]13(10):

Characterizing Families of Spectral Similarity Scores and Their Use Cases for Gas Chromatography-Mass Spectrometry Small Molecule Identification.

David J Degnan, Javier E Flores, Eva R Brayfindley, Vanessa L Paurus, Bobbie-Jo M Webb-Robertson, Chaevien S Clendinen, Lisa M Bramer.

  Metabolomics provides a unique snapshot into the world of small molecules and the complex biological processes that govern the human, animal, plant, and environmental ecosystems encapsulated by the One Health modeling framework. However, this "molecular snapshot" is only as informative as the number of metabolites confidently identified within it. The spectral similarity (SS) score is traditionally used to identify compound(s) in mass spectrometry approaches to metabolomics, where spectra are matched to reference libraries of candidate spectra. Unfortunately, there is little consensus on which of the dozens of available SS metrics should be used. This lack of standard SS score creates analytic uncertainty and potentially leads to issues in reproducibility, especially as these data are integrated across other domains. In this work, we use metabolomic spectral similarity as a case study to showcase the challenges in consistency within just one piece of the One Health framework that must be addressed to enable data science approaches for One Health problems. Here, using a large cohort of datasets comprising both standard and complex datasets with expert-verified truth annotations, we evaluated the effectiveness of 66 similarity metrics to delineate between correct matches (true positives) and incorrect matches (true negatives). We additionally characterize the families of these metrics to make informed recommendations for their use. Our results indicate that specific families of metrics (the Inner Product, Correlative, and Intersection families of scores) tend to perform better than others, with no single similarity metric performing optimally for all queried spectra. This work and its findings provide an empirically-based resource for researchers to use in their selection of similarity metrics for GC-MS identification, increasing scientific reproducibility through taking steps towards standardizing identification workflows.

Keywords:  One Health; distance metrics; machine learning; metabolomics

DOI:  https://doi.org/10.3390/metabo13101101
Metabolites. 2023 Oct 05. pii: 1052. [Epub ahead of print]13(10):

Effects of Solvent Evaporation Methods and Short-Term Room Temperature Storage on High-Coverage Cellular Metabolome Analysis.

Xian Luo, Liang Li.

  Cellular metabolomics provides insights into the metabolic processes occurring within cells and can help researchers understand how these processes are regulated and how they relate to cellular function, health, and disease. In this technical note, we investigated the effects of solvent evaporation equipment and storage condition on high-coverage cellular metabolomics. We previously introduced a robust CIL LC-MS-based cellular metabolomics workflow that encompasses various steps, including cell harvest, metabolic quenching, cell lysis, metabolite extraction, differential chemical isotope labeling, and LC-MS analysis. This workflow has consistently served as the cornerstone of our collaborative research and service projects. As a core facility catering to users with diverse research needs and financial resources, we have encountered scenarios requiring short-term sample storage. For example, the need often arises to transport samples at room temperature from user sites to our core facility. Herein, we present a study in which we compared different solvent evaporation methods (specifically, the nitrogen blowdown evaporator, SpeedVac concentrator, and lyophilizer) and diverse storage conditions (including dried samples stored in a freezer, samples stored in a freezer with methanol, dried samples stored at room temperature, and samples stored at room temperature with methanol). Our findings indicate that the choice of solvent evaporation equipment did not significantly impact the cellular metabolome. However, we observed a noteworthy change in the metabolome after 7 days of storage when cells were stored with methanol, regardless of whether they were kept at -80 °C or room temperature, in contrast to cells that were dried and frozen. Importantly, we detected no significant alterations in cells that were dried and stored at room temperature. In conclusion, to ensure the production of high-quality CIL LC-MS metabolomics results, we strongly recommend that, in situations where low-temperature storage is not feasible, cell samples should be thoroughly dried before storage or shipment at room temperature.

Keywords:  LC-MS; chemical isotope labeling; metabolomics; sample shipment; sample storage

DOI:  https://doi.org/10.3390/metabo13101052
Int J Mol Sci. 2023 Oct 19. pii: 15364. [Epub ahead of print]24(20):

Development and Validation of LC-MS/MS Method for Determination of Cytisine in Human Serum and Saliva.

Karol Wróblewski, Małgorzata Szultka-Młyńska, Ryan J Courtney, Bogusław Buszewski, Piotr Tutka.

  Cytisine (CYT) is a quinolizidine alkaloid used for nicotine addiction treatment. Recent clinical trial data regarding cytisine confirm its high effectiveness and safety as a smoking cessation treatment. CYT's popularity is growing due to its increased availability and licensing in more countries worldwide. This increased use by smokers has also resulted in an urgent need for continued drug research, including developing appropriate analytical methods for analyzing the drug in biological samples. In this study, a simple, fast, and reliable method combining hydrophilic interaction liquid chromatography and electrospray ionization quadrupole time of flight mass spectrometry (HILIC/ESI-QTOF-MS) for the determination of CYT in human serum and saliva was developed and validated. This was undertaken after the previous pre-treatment of the sample using solid-phase extraction (SPE). A hydrophilic interaction liquid chromatography (HILIC) column with a silica stationary phase was used for chromatographic analysis. In a linear gradient, the mobile phase consisted of acetonitrile (ACN) and formate buffer at pH 4.0. The proposed method was fully validated and demonstrated its sensitivity, selectivity, precision, and accuracy. The method was successfully applied to determine CYT in serum and, for the first time, in saliva. The findings indicate that saliva could be a promising non-invasive alternative to measure the free concentration of CYT.

Keywords:  LC-ESI-QTOF-MS; cytisine; nicotine addiction; saliva; serum

DOI:  https://doi.org/10.3390/ijms242015364
BMC Bioinformatics. 2023 Oct 28. 24(1): 404

Picky with peakpicking: assessing chromatographic peak quality with simple metrics in metabolomics.

William Kumler, Bryna J Hazelton, Anitra E Ingalls.

   BACKGROUND: Chromatographic peakpicking continues to represent a significant bottleneck in automated LC-MS workflows. Uncontrolled false discovery rates and the lack of manually-calibrated quality metrics require researchers to visually evaluate individual peaks, requiring large amounts of time and breaking replicability. This problem is exacerbated in noisy environmental datasets and for novel separation methods such as hydrophilic interaction columns in metabolomics, creating a demand for a simple, intuitive, and robust metric of peak quality.
RESULTS: Here, we manually labeled four HILIC oceanographic particulate metabolite datasets to assess the performance of individual peak quality metrics. We used these datasets to construct a predictive model calibrated to the likelihood that visual inspection by an MS expert would include a given mass feature in the downstream analysis. We implemented two novel peak quality metrics, a custom signal-to-noise metric and a test of similarity to a bell curve, both calculated from the raw data in the extracted ion chromatogram, and found that these outperformed existing measurements of peak quality. A simple logistic regression model built on two metrics reduced the fraction of false positives in the analysis from 70-80% down to 1-5% and showed minimal overfitting when applied to novel datasets. We then explored the implications of this quality thresholding on the conclusions obtained by the downstream analysis and found that while only 10% of the variance in the dataset could be explained by depth in the default output from the peakpicker, approximately 40% of the variance was explained when restricted to high-quality peaks alone.
CONCLUSIONS: We conclude that the poor performance of peakpicking algorithms significantly reduces the power of both univariate and multivariate statistical analyses to detect environmental differences. We demonstrate that simple models built on intuitive metrics and derived from the raw data are more robust and can outperform more complex models when applied to new data. Finally, we show that in properly curated datasets, depth is a major driver of variability in the marine microbial metabolome and identify several interesting metabolite trends for future investigation.

Keywords:  Ground Truth Dataset; Marine environment; Mass-spectrometry; Metabolomics; Peakpicking; XCMS

DOI:  https://doi.org/10.1186/s12859-023-05533-4
J Proteome Res. 2023 Oct 25.

A Guide to MALDI Imaging Mass Spectrometry for Tissues.

Jessica L Moore, Georgia Charkoftaki.

  Imaging mass spectrometry is a well-established technology that can easily and succinctly communicate the spatial localization of molecules within samples. This review communicates the recent advances in the field, with a specific focus on matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) applied on tissues. The general sample preparation strategies for different analyte classes are explored, including special considerations for sample types (fresh frozen or formalin-fixed,) strategies for various analytes (lipids, metabolites, proteins, peptides, and glycans) and how multimodal imaging strategies can leverage the strengths of each approach is mentioned. This work explores appropriate experimental design approaches and standardization of processes needed for successful studies, as well as the various data analysis platforms available to analyze data and their strengths. The review concludes with applications of imaging mass spectrometry in various fields, with a focus on medical research, and some examples from plant biology and microbe metabolism are mentioned, to illustrate the breadth and depth of MALDI IMS.

Keywords:  MALDI; imaging mass spectrometry; spatialomics; tissues

DOI:  https://doi.org/10.1021/acs.jproteome.3c00167
Anal Bioanal Chem. 2023 Oct 23.

Ultrasound-assisted extraction followed by liquid chromatography coupled to tandem mass spectrometry for the simultaneous determination of multiclass herbicides in soil.

Ana Castiñeira-Landeira, Lua Vazquez, Helena Gonzalez-Leirado, María Llompart, Thierry Dagnac.

  An analytical methodology based on ultrasound-assisted extraction (UAE) followed by liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) has been developed for the identification and quantification of 9 authorized herbicides in soil (dimethenamid-P, imazamox, S-metolachlor, nicosulfuron, pendimethalin, prosulfuron, bentazone, terbuthylazine, and mesotrione). Preliminary experiments dealing with solvent extraction, the extraction technique, and herbicide response comparison in soil, with and without organic amendments, were carried out with the purpose of obtaining high sample throughput and sensitivity. UAE and the solvent mixture water:methanol demonstrated higher efficiency and they were selected as sample treatment and extraction solvent, respectively. Critical parameters affecting UAE were optimized by experimental design. In the present research, the extraction technique used in the official EPA microwave-assisted extraction (MAE) methodology (United States Environmental Protection Agency) and UAE optimized methodology were compared. The results indicated that the developed method showed better efficacy since microwave extraction gave very poor responses for nicosulfuron and prosulfuron. The temperature extraction was also optimized; room temperature was the most suitable to work with. Under the optimized conditions, the proposed UAE-LC-MS/MS method was assessed in terms of linearity (R2 ≥ 0.9912), accuracy (recoveries around 100%), and precision (relative standard deviation, RSD < 13%). The absence of significant matrix effects allowed quantification in real samples by external calibration with standards prepared in water:methanol. Method sustainability was also evaluated using the metric tool AGREEPrep. Finally, the analysis of real contaminated samples revealed the presence of 7 out of the 9 studied herbicides with S-metolachlor at high concentrations in all samples.

Keywords:  Herbicides; Liquid chromatography; Sample preparation; Soil; Tandem mass spectrometry; Ultrasound-assisted extraction

DOI:  https://doi.org/10.1007/s00216-023-04987-y
J Exp Bot. 2023 Oct 27. pii: erad423. [Epub ahead of print]

Imaging Plant Metabolism in situ.

Patrick J Horn, Kent D Chapman.

  Mass spectrometry imaging (MSI) has emerged as an invaluable analytical technique for investigating the spatial distribution of molecules within biological systems. In the realm of plant science, MSI is increasingly employed to explore metabolic processes across a wide array of plant tissues, including those in leaves, fruits, stems, roots, and seeds, spanning various plant systems such as model species, staple and energy crops, and medicinal plants. By generating spatial maps of metabolites, MSI has elucidated the distribution patterns of diverse metabolites and phytochemicals, encompassing lipids, carbohydrates, amino acids, organic acids, phenolics, terpenes, alkaloids, vitamins, pigments, and others, thereby providing insights into their metabolic pathways and functional roles. In this review, we present recent MSI studies that demonstrate the advances made in visualizing the plant spatial metabolome. Moreover, we emphasize the technical progresses that enhance the identification and interpretation of spatial metabolite maps. Within a mere decade since the inception of plant MSI studies, this robust technology is poised to continue as a vital tool for tackling complex challenges in plant metabolism.

Keywords:  Biochemistry; chemical imaging; desorption ionization mass spectrometry (DESI); mass spectrometry imaging (MSI); matrix-assisted laser desorption/ionization (MALDI); metabolism; metabolome; primary metabolism; spatial maps; specialized metabolism

DOI:  https://doi.org/10.1093/jxb/erad423
Prostaglandins Other Lipid Mediat. 2023 Oct 23. pii: S1098-8823(23)00086-2. [Epub ahead of print] 106789

A single extraction 96-well method for LC-MS/MS quantification of urinary eicosanoids, steroids and drugs.

Julia Sieminska, Johan Kolmert, Javier Zurita-Perez, Kurt Benkestock, Johanna Revol-Cavalier, Jacek Niklinski, Joanna Reszec, Sven-Erik Dahlén, Michal Ciborowski, Craig E Wheelock.

  Urinary eicosanoid concentrations reflect inflammatory processes in multiple diseases and have been used as biomarkers of disease as well as for stratification in precision medicine. However, implementation of urinary eicosanoid profiling in large-scale analyses is restricted due to sample preparation limits. Here we demonstrate a single solid-phase extraction of 300µL urine in 96-well-format for prostaglandins, thromboxanes, isoprostanes, cysteinyl-leukotriene E4 and the linoleic acid-derived dihydroxy-octadecenoic acids (9,10- and 12,13-DiHOME). A simultaneous screening protocol was also developed for cortisol/cortisone and 7 exogenous steroids as well as 3 cyclooxygenase-inhibitors. Satisfactory performance for quantification of eicosanoids with a proper internal standard was demonstrated for intra-plate analyses (CV=8.5-15.1%) as well as for inter-plate (n=35) from multiple studies (CV=22.1-34.9%). Storage stability was evaluated at -20 °C, and polar tetranors evidenced a 50% decrease after 5 months, while the remaining eicosanoids evidenced no significant degradation. All eicosanoids were stable over 3.5-years in urine stored at -80°C. This method will facilitate the implementation of urinary eicosanoid quantification in large scale screening.

Keywords:  COX-inhibitors; DiHOME; eicosanoid; inhaled corticosteroids; mass spectrometry; oral corticosteroids; solid-phase extraction; urine metabolites

DOI:  https://doi.org/10.1016/j.prostaglandins.2023.106789
Clin Chem Lab Med. 2023 Oct 30.

Quantification of the lung cancer tumor marker CYFRA 21-1 using protein precipitation, immunoaffinity bottom-up LC-MS/MS.

Sylvia A A M Genet, Sebastian A H van den Wildenberg, Maarten A C Broeren, Joost L J van Dongen, Luc Brunsveld, Volkher Scharnhorst, Daan van de Kerkhof.

   OBJECTIVES: Numerous studies have proven the potential of cytokeratin 19 fragment 21-1 (CYFRA 21-1) detection in the (early) diagnosis and treatment monitoring of non-small cell lung cancer (NSCLC). Conventional immunoassays for CYFRA 21-1 quantification are however prone to interferences and lack diagnostic sensitivity and standardization. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is an emerging approach based on a different, often superior, detection principle, which may improve the clinical applicability of CYFRA 21-1 in cancer diagnostics. Therefore, we developed and validated a protein precipitation, immunoaffinity (IA) LC-MS/MS assay for quantitative analysis of serum CYFRA 21-1.
METHODS: Selective sample preparation was performed using ammonium sulfate (AS) precipitation, IA purification, tryptic digestion and LC-MS/MS quantification using a signature peptide and isotopically labeled internal standard. The workflow was optimized and validated according to EMA guidelines and results were compared to a conventional immunoassay.
RESULTS: Significant interference effects were seen during IA purification, which were sufficiently solved by performing AS precipitation prior to IA purification. A linear calibration curve was obtained in the range of 1.0-100 ng/mL (R2=0.98). Accuracy and precision were well within acceptance criteria. In sera of patients suspected of lung cancer, the method showed good correlation with the immunoassay.
CONCLUSIONS: A robust AS precipitation-IA LC-MS/MS assay for the quantification of serum CYFRA 21-1 was developed. With this assay, the clinically added value of LC-MS/MS-based detection over immunoassays can be further explored.

Keywords:  cytokeratin 19 fragment 21-1; immunoaffinity (IA); liquid chromatography-tandem mass spectrometry (LC-MS/MS); lung cancer diagnostics; protein precipitation; tryptic digestion

DOI:  https://doi.org/10.1515/cclm-2023-0795