bims-mascan 2024-05-05 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2024–05–05
nineteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.
High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.
Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples.
MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization.
Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.
MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization.
Reinvestigating the Correctness of Decoy-Based False Discovery Rate Control in Proteomics Tandem Mass Spectrometry.
Advances in Oxygen Isotope Analysis of Phosphate by Electrospray Orbitrap Mass Spectrometry for Studying the Microbial Metabolism of Microorganisms.
A Workflow for Improved Analysis of Cross-Linking Mass Spectrometry Data Integrating Parallel Accumulation-Serial Fragmentation with MeroX and Skyline.
Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.
NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.
SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.
Cycling back to folate metabolism in cancer.
Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO.
Modern Electrospray Ionization Mass Spectrometry Techniques for the Characterization of Supramolecules and Coordination Compounds.
Disorders of fatty acid homeostasis.
Protocol to identify biomarkers in patients with post-COVID condition using multi-omics and machine learning analysis of human plasma.
Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study.
Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma.

Nat Commun. 2024 May 01. 15(1): 3675

MetaboAnalystR 4.0: a unified LC-MS workflow for global metabolomics.

Zhiqiang Pang, Lei Xu, Charles Viau, Yao Lu, Reza Salavati, Niladri Basu, Jianguo Xia.

The wide applications of liquid chromatography - mass spectrometry (LC-MS) in untargeted metabolomics demand an easy-to-use, comprehensive computational workflow to support efficient and reproducible data analysis. However, current tools were primarily developed to perform specific tasks in LC-MS based metabolomics data analysis. Here we introduce MetaboAnalystR 4.0 as a streamlined pipeline covering raw spectra processing, compound identification, statistical analysis, and functional interpretation. The key features of MetaboAnalystR 4.0 includes an auto-optimized feature detection and quantification algorithm for LC-MS1 spectra processing, efficient MS2 spectra deconvolution and compound identification for data-dependent or data-independent acquisition, and more accurate functional interpretation through integrated spectral annotation. Comprehensive validation studies using LC-MS1 and MS2 spectra obtained from standards mixtures, dilution series and clinical metabolomics samples have shown its excellent performance across a wide range of common tasks such as peak picking, spectral deconvolution, and compound identification with good computing efficiency. Together with its existing statistical analysis utilities, MetaboAnalystR 4.0 represents a significant step toward a unified, end-to-end workflow for LC-MS based global metabolomics in the open-source R environment.

DOI: https://doi.org/10.1038/s41467-024-48009-6
Chem Sci. 2024 May 01. 15(17): 6314-6320

High-throughput single-cell mass spectrometry enables metabolic network analysis by resolving phospholipid C[double bond, length as m-dash]C isomers.

Simin Cheng, Chenxi Cao, Yao Qian, Huan Yao, Xiaoyun Gong, Xinhua Dai, Zheng Ouyang, Xiaoxiao Ma.

Single-cell mass spectrometry (MS) is an essential technology for sensitive and multiplexed analysis of metabolites and lipids for cell phenotyping and pathway studies. However, the structural elucidation of lipids from single cells remains a challenge, especially in the high-throughput scenario. Technically, there is a contradiction between the inadequate sample amount (i.e. a single cell, 0.5-20 pL) for replicate or multiple analysis, on the one hand, and the high metabolite coverage and multidimensional structure analysis that needs to be performed for each single cell, on the other hand. Here, we have developed a high-throughput single-cell MS platform that can perform both lipid profiling and lipid carbon-carbon double bond (C[double bond, length as m-dash]C) location isomer resolution analysis, aided by C[double bond, length as m-dash]C activation in unsaturated lipids by the Paternò-Büchi (PB) reaction and tandem MS, termed single-cell structural lipidomics analysis. The method can achieve a single-cell analysis throughput of 51 cells per minute. A total of 145 lipids were structurally characterized at the subclass level, of which the relative abundance of 17 isomeric lipids differing in the location of C[double bond, length as m-dash]C from 5 lipid precursors was determined. While cell-to-cell variations in MS1-based lipid profiling can be large, an advantage of quantifying lipid C[double bond, length as m-dash]C location isomers is the significantly improved quantitation accuracy. For example, the relative standard deviations (RSDs) of the relative amounts of PC 34:1 C[double bond, length as m-dash]C position isomers in MDA-MB-468 cells are half smaller than those measured for PC 34:1 as a whole by MS1 abundance profiling. Taken together, the developed method can be effectively used for in-depth structural lipid metabolism network analysis by high-throughput analysis of 142 MDA-MB-468 human breast cancer cells.

DOI: https://doi.org/10.1039/d3sc06573a
J Proteome Res. 2024 May 01.

Improved Data Acquisition Settings on Q Exactive HF-X and Fusion Lumos Tribrid Orbitrap-Based Mass Spectrometers for Proteomic Analysis of Limited Samples.

Michal Greguš, Antonius Koller, Somak Ray, Alexander R Ivanov.

  Deep proteomic profiling of complex biological and medical samples available at low nanogram and subnanogram levels is still challenging. Thorough optimization of settings, parameters, and conditions in nanoflow liquid chromatography-tandem mass spectrometry (MS)-based proteomic profiling is crucial for generating informative data using amount-limited samples. This study demonstrates that by adjusting selected instrument parameters, e.g., ion injection time, automated gain control, and minimally altering the conditions for resuspending or storing the sample in solvents of different compositions, up to 15-fold more thorough proteomic profiling can be achieved compared to conventionally used settings. More specifically, the analysis of 1 ng of the HeLa protein digest standard by Q Exactive HF-X Hybrid Quadrupole-Orbitrap and Orbitrap Fusion Lumos Tribrid mass spectrometers yielded an increase from 1758 to 5477 (3-fold) and 281 to 4276 (15-fold) peptides, respectively, demonstrating that higher protein identification results can be obtained using the optimized methods. While the instruments applied in this study do not belong to the latest generation of mass spectrometers, they are broadly used worldwide, which makes the guidelines for improving performance desirable to a wide range of proteomics practitioners.

Keywords:  Orbitrap; data acquisition settings; limited samples; mass spectrometry; proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c00181
Anal Chem. 2024 May 01.

MobiLipid: A Tool for Enhancing CCS Quality Control of Ion Mobility-Mass Spectrometry Lipidomics by Internal Standardization.

Felina Hildebrand, Gunda Koellensperger, Tim Causon.

Ion mobility-mass spectrometry (IM-MS) offers benefits for lipidomics by obtaining IM-derived collision cross sections (CCS), a conditional property of an ion that can enhance lipid identification. While drift tube (DT) IM-MS retains a direct link to the primary experimental method to derive CCS values, other IM technologies rely solely on external CCS calibration, posing challenges due to dissimilar chemical properties between lipids and calibrants. To address this, we introduce MobiLipid, a novel tool facilitating the CCS quality control of IM-MS lipidomics workflows by internal standardization. MobiLipid utilizes a newly established DTCCSN2 library for uniformly (U)13C-labeled lipids, derived from a U13C-labeled yeast extract, containing 377 DTCCSN2 values. This automated open-source R Markdown tool enables internal monitoring and straightforward compensation for CCSN2 biases. It supports lipid class- and adduct-specific CCS corrections, requiring only three U13C-labeled lipids per lipid class-adduct combination across 10 lipid classes without requiring additional external measurements. The applicability of MobiLipid is demonstrated for trapped IM (TIM)-MS measurements of an unlabeled yeast extract spiked with U13C-labeled lipids. Monitoring the CCSN2 biases of TIMCCSN2 values compared to DTCCSN2 library entries utilizing MobiLipid resulted in mean absolute biases of 0.78% and 0.33% in positive and negative ionization mode, respectively. By applying the CCS correction integrated into the tool for the exemplary data set, the mean absolute CCSN2 biases of 10 lipid classes could be reduced to approximately 0%.

DOI: https://doi.org/10.1021/acs.analchem.4c01253
Anal Chem. 2024 May 01.

Strategy to Empower Nontargeted Metabolomics by Triple-Dimensional Combinatorial Derivatization with MS-TDF Software.

Caixia Yuan, Ying Jin, Hairong Zhang, Simian Chen, Jiajin Yi, Qiang Xie, Jiyang Dong, Caisheng Wu.

Chemical derivatization is a widely employed strategy in metabolomics to enhance metabolite coverage by improving chromatographic behavior and increasing the ionization rates in mass spectroscopy (MS). However, derivatization might complicate MS data, posing challenges for data mining due to the lack of a corresponding benchmark database. To address this issue, we developed a triple-dimensional combinatorial derivatization strategy for nontargeted metabolomics. This strategy utilizes three structurally similar derivatization reagents and is supported by MS-TDF software for accelerated data processing. Notably, simultaneous derivatization of specific metabolite functional groups in biological samples produced compounds with stable but distinct chromatographic retention times and mass numbers, facilitating discrimination by MS-TDF, an in-house MS data processing software. In this study, carbonyl analogues in human plasma were derivatized using a combination of three hydrazide-based derivatization reagents: 2-hydrazinopyridine, 2-hydrazino-5-methylpyridine, and 2-hydrazino-5-cyanopyridine (6-hydrazinonicotinonitrile). This approach was applied to identify potential carbonyl biomarkers in lung cancer. Analysis and validation of human plasma samples demonstrated that our strategy improved the recognition accuracy of metabolites and reduced the risk of false positives, providing a useful method for nontargeted metabolomics studies. The MATLAB code for MS-TDF is available on GitHub at https://github.com/CaixiaYuan/MS-TDF.

DOI: https://doi.org/10.1021/acs.analchem.4c00527
J Proteome Res. 2024 Apr 29.

MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization.

Justin C Sing, Joshua Charkow, Mohammed AlHigaylan, Ira Horecka, Leon Xu, Hannes L Röst.

  With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

Keywords:  data-independent-acquisition; mass-spectrometry; optimization; prototyping; validation; visualization

DOI:  https://doi.org/10.1021/acs.jproteome.4c00026
J Proteome Res. 2024 Apr 30.

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

Jack Freestone, William Stafford Noble, Uri Keich.

  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.

Keywords:  FDR control; PeptideProphet; Percolator; group-wise analysis; narrow search; open search; peptide analysis

DOI:  https://doi.org/10.1021/acs.jproteome.3c00902
Chimia (Aarau). 2024 Apr 24. 78(4): 256-260

Advances in Oxygen Isotope Analysis of Phosphate by Electrospray Orbitrap Mass Spectrometry for Studying the Microbial Metabolism of Microorganisms.

Nora M Bernet, Thomas B Hofstetter.

  Understanding the impact of human activities on the metabolic state of soil and aquatic environments is of paramount importance to implement measures for maintaining ecosystem services. Variations of natural abundance 18O/16O ratios in phosphate have been proposed as proxies for the holistic assessment of metabolic activity given the crucial importance of phosphoryl transfer reactions in fundamental biological processes. However, instrumental and procedural limitations inherent to oxygen isotope analysis in phosphate and organophosphorus compounds have so far limited the stable isotope-based evaluation of metabolic processes. Here, we discuss how recent developments in Orbitrap high resolution mass spectrometry enable measurements of 18O/16O ratios in phosphate and outline the critical mass spectrometry parameters for accurate and precise analysis. Subsequently, we evaluate the types of 18O kinetic isotope effects of phosphoryl transfer reactions and illustrate how novel analytical approaches will give rise to an improved understanding of 18O/16O ratio variations from biochemical processes affecting the microbial phosphorus metabolism.

Keywords:  Microbial metabolism; Orbitrap mass spectrometry; Oxygen isotope ratios; Phosphate; Phosphoryl transfer reactions

DOI:  https://doi.org/10.2533/chimia.2024.256
Anal Chem. 2024 May 02.

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

Juan Camilo Rojas Echeverri, Frank Hause, Claudio Iacobucci, Christian H Ihling, Dirk Tänzler, Nicholas Shulman, Michael Riffle, Brendan X MacLean, Andrea Sinz.

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.

DOI: https://doi.org/10.1021/acs.analchem.4c00829
J Sep Sci. 2024 Apr;47(8): e2300848

Combinatory data-independent acquisition and parallel reaction monitoring method for revealing the lipid metabolism biomarkers of coronary heart disease and its comorbidities.

Hao Wu, Lijuan Yang, Dabing Ren, Ying Gu, Xiaoxue Ding, Yan Zhao, Guanghui Fu, Hong Zhang, Lunzhao Yi.

  Disorders of lipid metabolism are a common cause of coronary heart disease (CHD) and its comorbidities. In this study, ultra-performance liquid chromatography-high-resolution mass spectrometry in data-independent acquisition (DIA) mode was applied to collect abundant tandem mass spectrometry data, which provided valuable information for lipid annotation. For the lipid isomers that could not be completely separated by chromatography, parallel reaction monitoring (PRM) mode was used for quantification. A total of 223 plasma lipid metabolites were annotated, and 116 of them were identified for their fatty acyl chain composition and location. In addition, 152 plasma lipids in patients with CHD and its comorbidities were quantitatively analyzed. Multivariate statistical analysis and metabolic pathway analysis demonstrated that glycerophospholipid and sphingolipid metabolism deserved more attention for CHD. This study proposed a method combining DIA and PRM for high-throughput characterization of plasma lipids. The results also improved our understanding of metabolic disorders of CHD and its comorbidities, which can provide valuable suggestions for medical intervention.

Keywords:  UHPLC‐HRMS; coronary heart disease; data‐independent acquisition; lipidomics; parallel reaction monitoring

DOI:  https://doi.org/10.1002/jssc.202300848
Anal Chem. 2024 May 03.

NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.

Cristina F Bazzano, Rafael de Felicio, Luiz Fernando Giolo Alves, Jonas Henrique Costa, Raquel Ortega, Bruna Domingues Vieira, Raquel Peres Morais-Urano, Luciana Costa Furtado, Everton L F Ferreira, Juliana R Gubiani, Roberto G S Berlinck, Leticia V Costa-Lotufo, Guilherme P Telles, Daniela B B Trivella.

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

DOI: https://doi.org/10.1021/acs.analchem.3c05829
Brief Bioinform. 2024 Mar 27. pii: bbae189. [Epub ahead of print]25(3):

SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Guihong Wan, Zoltan Maliga, Boshen Yan, Tuulia Vallius, Yingxiao Shi, Sara Khattab, Crystal Chang, Ajit J Nirmal, Kun-Hsing Yu, David Liu, Christine G Lian, Mia S DeSimone, Peter K Sorger, Yevgeniy R Semenov.

  Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Keywords:  multiplexed single-cell data; region-based profiling; spatial analysis; spatial omics; tumor immune infiltration; tumor microenvironment

DOI:  https://doi.org/10.1093/bib/bbae189
Nat Cancer. 2024 May 02.

Cycling back to folate metabolism in cancer.

Younghwan Lee, Karen H Vousden, Marc Hennequart.

Metabolic changes contribute to cancer initiation and progression through effects on cancer cells, the tumor microenvironment and whole-body metabolism. Alterations in serine metabolism and the control of one-carbon cycles have emerged as critical for the development of many tumor types. In this Review, we focus on the mitochondrial folate cycle. We discuss recent evidence that, in addition to supporting nucleotide synthesis, mitochondrial folate metabolism also contributes to metastasis through support of antioxidant defense, mitochondrial protein synthesis and the overflow of excess formate. These observations offer potential therapeutic opportunities, including the modulation of formate metabolism through dietary interventions and the use of circulating folate cycle metabolites as biomarkers for cancer detection.

DOI: https://doi.org/10.1038/s43018-024-00739-8
Cell Rep Methods. 2024 Apr 24. pii: S2667-2375(24)00091-2. [Epub ahead of print] 100760

Systematic analysis of proteome turnover in an organoid model of pancreatic cancer by dSILO.

Alison B Ross, Darvesh Gorhe, Jenny Kim Kim, Stefanie Hodapp, Lela DeVine, Karina M Chan, Iok In Christine Chio, Marko Jovanovic, Marina Ayres Pereira.

  The role of protein turnover in pancreatic ductal adenocarcinoma (PDA) metastasis has not been previously investigated. We introduce dynamic stable-isotope labeling of organoids (dSILO): a dynamic SILAC derivative that combines a pulse of isotopically labeled amino acids with isobaric tandem mass-tag (TMT) labeling to measure proteome-wide protein turnover rates in organoids. We applied it to a PDA model and discovered that metastatic organoids exhibit an accelerated global proteome turnover compared to primary tumor organoids. Globally, most turnover changes are not reflected at the level of protein abundance. Interestingly, the group of proteins that show the highest turnover increase in metastatic PDA compared to tumor is involved in mitochondrial respiration. This indicates that metastatic PDA may adopt alternative respiratory chain functionality that is controlled by the rate at which proteins are turned over. Collectively, our analysis of proteome turnover in PDA organoids offers insights into the mechanisms underlying PDA metastasis.

Keywords:  CP: Biotechnology; CP: Cancer biology; PDA; SILAC; dSILO; metastases; protein half-life; protein turnover; respirasome

DOI:  https://doi.org/10.1016/j.crmeth.2024.100760
Anal Chem. 2024 Apr 30.

Modern Electrospray Ionization Mass Spectrometry Techniques for the Characterization of Supramolecules and Coordination Compounds.

Niklas Geue.

Mass spectrometry is routinely used for myriad applications in clinical, industrial, and research laboratories worldwide. Developments in the areas of ionization sources, high-resolution mass analyzers, tandem mass spectrometry, and ion mobility have significantly extended the repertoire of mass spectrometrists; however, for coordination compounds and supramolecules, mass spectrometry remains underexplored and arguably underappreciated. Here, the reader is guided through different tools of modern electrospray ionization mass spectrometry that are suitable for larger inorganic complexes. All steps, from sample preparation and technical details to data analysis and interpretation are discussed. The main target audience of this tutorial is synthetic chemists as well as technicians/mass spectrometrists with little experience in characterizing labile inorganic compounds.

DOI: https://doi.org/10.1021/acs.analchem.4c01028
J Inherit Metab Dis. 2024 May 01.

Disorders of fatty acid homeostasis.

Frédéric M Vaz, Sacha Ferdinandusse, Gajja S Salomons, Ronald J A Wanders.

  Humans derive fatty acids (FA) from exogenous dietary sources and/or endogenous synthesis from acetyl-CoA, although some FA are solely derived from exogenous sources ("essential FA"). Once inside cells, FA may undergo a wide variety of different modifications, which include their activation to their corresponding CoA ester, the introduction of double bonds, the 2- and ω-hydroxylation and chain elongation, thereby generating a cellular FA pool which can be used for the synthesis of more complex lipids. The biological properties of complex lipids are very much determined by their molecular composition in terms of the FA incorporated into these lipid species. This immediately explains the existence of a range of genetic diseases in man, often with severe clinical consequences caused by variants in one of the many genes coding for enzymes responsible for these FA modifications. It is the purpose of this review to describe the current state of knowledge about FA homeostasis and the genetic diseases involved. This includes the disorders of FA activation, desaturation, 2- and ω-hydroxylation, and chain elongation, but also the disorders of FA breakdown, including disorders of peroxisomal and mitochondrial α- and β-oxidation.

Keywords:  (phospho)lipid metabolism; 2‐hydroxylation; fatty acid elongation; mitochondrial disorders; peroxisomal disorders; sphingolipid metabolism; ω‐hydroxylation

DOI:  https://doi.org/10.1002/jimd.12734
STAR Protoc. 2024 Apr 27. pii: S2666-1667(24)00206-5. [Epub ahead of print]5(2): 103041

Protocol to identify biomarkers in patients with post-COVID condition using multi-omics and machine learning analysis of human plasma.

Mobin Khoramjoo, Karthik Srinivasan, Kaiming Wang, David Wishart, Vinay Prasad, Gavin Y Oudit.

  Here, we present a workflow for analyzing multi-omics data of plasma samples in patients with post-COVID condition (PCC). Applicable to various diseases, we outline steps for data preprocessing and integrating diverse assay datasets. Then, we detail statistical analysis to unveil plasma profile changes and identify biomarker-clinical variable associations. The last two steps discuss machine learning techniques for unsupervised clustering of patients based on their inherent molecular similarities and feature selection to identify predictive biomarkers. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Keywords:  Bioinformatics; Clinical Protocol; Metabolomics; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2024.103041
PeerJ. 2024 ;12 e17272

Plasma-based lipidomics reveals potential diagnostic biomarkers for esophageal squamous cell carcinoma: a retrospective study.

Yang Chen, Yixuan Gu, Jinhua Rong, Luyin Xu, Xiancong Huang, Jing Zhu, Zhongjian Chen, Weimin Mao.

   Background: Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms.
Methods: Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software.
Results: A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.

Keywords:  Esophageal squamous cell carcinoma; Lipidomics; Machine learning; Plasma-based diagnostic model

DOI:  https://doi.org/10.7717/peerj.17272
Metabolomics. 2024 Apr 30. 20(3): 49

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma.

Selina Hemmer, Sascha K Manier, Lea Wagmann, Markus R Meyer.

   INTRODUCTION: Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose.
OBJECTIVES: The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices.
METHODS: Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others.
RESULTS: All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column.
CONCLUSION: The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.

Keywords:  Hydrophilic interaction chromatography columns; LC-HRMS; Quality assurance; Reversed-phase columns; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s11306-024-02115-0