bims-metlip 2021-04-04 papers

bims-metlip

Biomed News

on Methods and protocols in metabolomics and lipidomics

Issue of 2021–04–04
nineteen papers selected by
Sofia Costa, Cold Spring Harbor Laboratory

SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics.
Quantitative analysis of n-3 polyunsaturated fatty acids and their metabolites by chemical isotope labeling coupled with liquid chromatography - mass spectrometry.
Calibration-Curve-Locking Database for Semi-Quantitative Metabolomics by Gas Chromatography/Mass Spectrometry.
Improved Sensitivity in Hydrophilic Interaction Liquid Chromatography-Electrospray-Mass Spectrometry after Removal of Sodium and Potassium Ions from Biological Samples.
Single-Cell Metabolomics by Mass Spectrometry Imaging.
Novel Fast Chromatography-Tandem Mass Spectrometric Quantitative Approach for the Determination of Plant-Extracted Phytosterols and Tocopherols.
Determination of selected water-soluble vitamins (thiamine, riboflavin, nicotinamide and pyridoxine) from a food matrix using hydrophilic interaction liquid chromatography coupled with mass spectroscopy.
Chemical Isotope Labeling LC-MS for Metabolomics.
Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research.
Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics.
NMR Methods for Determining Lipid Turnover via Stable Isotope Resolved Metabolomics.
Methods of Lipidomic Analysis: Extraction, Derivatization, Separation, and Identification of Lipids.
Optical Microscopy-Guided Laser Ablation Electrospray Ionization Ion Mobility Mass Spectrometry: Ambient Single Cell Metabolomics with Increased Confidence in Molecular Identification.
Capillary Electrophoresis-Mass Spectrometry for Cancer Metabolomics.
NMR-Based Metabolomics in Cancer Research.
Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics.
TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization.
Ion Chromatography with Mass Spectrometry for Metabolomic Analysis.
Tracer-Based Cancer Metabolomic Analysis.

Anal Chem. 2021 Mar 30.

SteroidXtract: Deep Learning-Based Pattern Recognition Enables Comprehensive and Rapid Extraction of Steroid-Like Metabolic Features for Automated Biology-Driven Metabolomics.

Shipei Xing, Yibo Jiao, Melody Salehzadeh, Kiran K Soma, Tao Huan.

Despite the vast amount of metabolic information that can be captured in untargeted metabolomics, many biological applications are looking for a biology-driven metabolomics platform that targets a set of metabolites that are relevant to the given biological question. Steroids are a class of important molecules that play critical roles in many physiological systems and diseases. Besides known steroids, there are a large number of unknown steroids that have not been reported in the literature. The ability to rapidly detect and quantify both known and unknown steroid molecules in a biological sample can greatly accelerate a broad range of steroid-focused life science research. This work describes the development and application of SteroidXtract, a convolutional neural network (CNN)-based bioinformatics tool that can recognize steroid molecules in mass spectrometry (MS)-based untargeted metabolomics using their unique tandem MS (MS2) spectral patterns. SteroidXtract was trained using a comprehensive set of standard MS2 spectra from MassBank of North America (MoNA) and an in-house steroid library. Data augmentation strategies, including intensity thresholding and Gaussian noise addition, were created and applied to minimize data overfitting caused by the limited number of standard steroid MS2 spectra. The CNN model embedded in SteroidXtract was further compared with random forest and XGBoost using nested cross-validations to demonstrate its performance. Finally, SteroidXtract was applied in several metabolomics studies to demonstrate its sensitivity, specificity, and robustness. Compared to conventional statistics-driven metabolomics data interpretation, our work offers a novel automated biology-driven approach to interpreting untargeted metabolomics data, prioritizing biologically important molecules with high throughput and sensitivity.

DOI: https://doi.org/10.1021/acs.analchem.0c04834
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Mar 17. pii: S1570-0232(21)00146-X. [Epub ahead of print]1172 122666

Quantitative analysis of n-3 polyunsaturated fatty acids and their metabolites by chemical isotope labeling coupled with liquid chromatography - mass spectrometry.

Rujie Yang, Fangbo Xia, Huanxing Su, Jian-Bo Wan.

  n-3 polyunsaturated fatty acids (PUFAs) and their metabolites play the crucial role in a wide range of physiologic and pathologic processes, including cardiovascular, neurodegenerative diseases, and inflammation-associated disorders. However, the quantitative analysis of n-3 PUFAs and their metabolites, oxylipins, is obstructed by high structural similarity, poor ionization efficiency and low abundance. In this study, a sensitive method was developed to quantify 28 n-3 PUFAs/oxylipins using chemical isotope labeling coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS). Standards labeled with cholamine-d9 were used as one-to-one internal standards to achieve accurate quantification. The cholamine-d0-derivatized biological samples were mixed with cholamine d9-labeled standards for LC-MS/MS with multiple reaction monitoring. After cholamine derivatization, both MS sensitivity and chromatographic performance of n-3 PUFAs/oxylipins were substantially improved. Furthermore, the relationship between retention time and substituent position of regioisomers, and their fragmentation patterns were investigated, which may facilitate the identification of unknown oxylipins. Additionally, the developed method was applied to quantify the target n-3 PUFAs/oxylipins in serum and brain tissue from fish oil-supplemented mice, which exhibited its great potential and practicability. Collectively, this sensitive and reliable method may facilitate the elucidation of the roles of n-3 PUFAs/oxylipins in the physiological and pathological processes.

Keywords:  Chemical isotope labeling; Cholamine; LC-MS/MS; Oxylipins; n-3 polyunsaturated fatty acids

DOI:  https://doi.org/10.1016/j.jchromb.2021.122666
Metabolites. 2021 Mar 30. pii: 207. [Epub ahead of print]11(4):

Calibration-Curve-Locking Database for Semi-Quantitative Metabolomics by Gas Chromatography/Mass Spectrometry.

Kosuke Hata, Yuki Soma, Toshiyuki Yamashita, Masatomo Takahashi, Kuniyo Sugitate, Takeshi Serino, Hiromi Miyagawa, Kenichi Suzuki, Kayoko Yamada, Takatomo Kawamukai, Teruhisa Shiota, Yoshihiro Izumi, Takeshi Bamba.

  Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ionization (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after derivatization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.

Keywords:  GC/MS; Standard Reference Material (SRM) 1950; calibration-curve-locking-database; data integration; metabolomics; quantification

DOI:  https://doi.org/10.3390/metabo11040207
Metabolites. 2021 Mar 15. pii: 170. [Epub ahead of print]11(3):

Improved Sensitivity in Hydrophilic Interaction Liquid Chromatography-Electrospray-Mass Spectrometry after Removal of Sodium and Potassium Ions from Biological Samples.

Ida Erngren, Marika Nestor, Curt Pettersson, Mikael Hedeland.

  Inorganic ions, such as sodium and potassium, are present in all biological matrices and are sometimes also added during sample preparation. However, these inorganic ions are known to hamper electrospray ionization -mass spectrometry (ESI-MS) applications, especially in hydrophilic interaction liquid chromatography (HILIC) where they are retained and can be detected as adducts and clusters with mobile phase components or analytes. The retention of inorganic ions leads to co-elution with analytes and as a result ion-suppression, extensive adduct formation and problems with reproducibility. In the presented work, a sample preparation method using cation exchange solid phase extraction (SPE) was developed to trap Na+ and K+ ions from human blood plasma and head and neck cancer cells for the analysis of small cationic, anionic as well as neutral organic analytes. The investigated analytes were small, hydrophilic compounds typically in focus in metabolomics studies. The samples were analyzed using full-scan HILIC-ESI-quadrupole time of flight (QTOF)-MS with an untargeted, screening approach. Method performance was evaluated using multivariate data analysis as well as relative quantifications, spiking of standards to evaluate linearity of response and post-column infusion to study ion-suppression. In blood plasma, the reduction of sodium and potassium ion concentration resulted in improved sensitivity increased signal intensity for 19 out of 28 investigated analytes, improved linearity of response, reduced ion-suppression and reduced cluster formation as well as adduct formation. Thus, the presented method has significant potential to improve data quality in metabolomics studies.

Keywords:  alkali metal ions; hydrophilic interaction liquid chromatography; ion suppression; mass spectrometry; matrix effects; metabolomics; sample preparation

DOI:  https://doi.org/10.3390/metabo11030170
Adv Exp Med Biol. 2021 ;1280 69-82

Single-Cell Metabolomics by Mass Spectrometry Imaging.

Maria Emilia Dueñas, Young Jin Lee.

  Multicellular organisms achieve their complex living activities through the highly organized metabolic interplay of individual cells and tissues. This complexity has driven the need to spatially resolve metabolomics down to the cellular and subcellular level. Recent technological advances have enabled mass spectrometry imaging (MSI), especially matrix-assisted laser desorption/ionization (MALDI), to become a powerful tool for the visualization of molecular species down to subcellular spatial resolution. In the present chapter, we summarize recent advances in the field of MALDI-MSI, with respect to single-cell level resolution metabolomics directly on tissue. In more detail, we focus on advancements in instrumentation for MSI at single-cell resolution, and the applications towards metabolomic scale imaging. Finally, we discuss new computational tools to aid in metabolite identification, future perspective, and the overall direction that the field of single-cell metabolomics directly on tissue may take in the years to come.

Keywords:  MALDI imaging; Mass spectrometry imaging; Single-cell metabolomics

DOI:  https://doi.org/10.1007/978-3-030-51652-9_5
Molecules. 2021 Mar 05. pii: 1402. [Epub ahead of print]26(5):

Novel Fast Chromatography-Tandem Mass Spectrometric Quantitative Approach for the Determination of Plant-Extracted Phytosterols and Tocopherols.

George Gachumi, Alice Demelenne, Asmita Poudel, Zafer Dallal Bashi, Anas El-Aneed.

  Phytosterols and tocopherols are commonly used in food and pharmaceutical industries for their health benefits. Current analysis methods rely on conventional liquid chromatography, using an analytical column, which can be tedious and time consuming. However, simple, and fast analytical methods can facilitate their qualitative and quantitative analysis. In this study, a fast chromatography-tandem mass spectrometric (FC-MS/MS) method was developed and validated for the quantitative analysis of phytosterols and tocopherols. Omitting chromatography by employing flow injection analysis-mass spectrometry (FIA-MS) failed in the quantification of target analytes due to analyte-to-analyte interferences from phytosterols. These interferences arise from their ambiguous MS fingerprints that would lead to false identification and inaccurate quantification. Therefore, a C18 guard column with a 1.9 µm particle size was employed for FC-MS/MS under isocratic elution using acetonitrile/methanol (99:1 v/v) at a flow rate of 600 µL/min. Analyte-to-analyte interferences were identified and eliminated. The false peaks could then be easily identified due to chromatographic separation. In addition, two internal standards were evaluated, namely cholestanol and deuterated cholesterol. Both internal standards contributed to the observed analyte-to-analyte interferences; however, adequate shift in the retention time for deuterated cholesterol eliminated its interferences and allowed for an accurate quantification. The method is fast (1.3 min) compared to published methods and can distinguish false peaks observed in FIA-MS. Seven analytes were quantified simultaneously, namely brassicasterol, campesterol, stigmasterol, β-sitosterol, α-tocopherol, δ-tocopherol, and γ-tocopherol. The method was successfully applied in the quantitative analysis of phytosterols and tocopherols present in the unsaponifiable matter of canola oil deodorizer distillate (CODD). β-sitosterol and γ-tocopherol were the most abundant phytosterols and tocopherols, respectively.

Keywords:  canola deodorizer distillate; fast chromatography-mass spectrometry; phytosterols; tocopherols

DOI:  https://doi.org/10.3390/molecules26051402
J Chromatogr B Analyt Technol Biomed Life Sci. 2021 Jan 12. pii: S1570-0232(21)00020-9. [Epub ahead of print]1171 122541

Determination of selected water-soluble vitamins (thiamine, riboflavin, nicotinamide and pyridoxine) from a food matrix using hydrophilic interaction liquid chromatography coupled with mass spectroscopy.

Kate Porter, John K Lodge.

  Water-soluble vitamins are essential dietary components with a multitude of important functions that require quantification from food sources to characterise the nutritional status of food. In this study, we have developed a hydrophilic interaction chromatography (HILIC) based method coupled to single-quadrupole mass spectrometry (MS) for the analysis of selected water-soluble vitamins. Due to their involvement in energy release from macronutrients, the quantification of thiamine (B1), riboflavin (B2), nicotinamide (B3) and pyridoxine (B6) offers significant value in food analysis. A commercially available vegetable soup was selected as the food matrix for this study and utilised to develop an efficient extraction procedure for the vitamins of interest. Vitamins were extracted using meta-phosphoric acid coupled with a reducing agent, DL-dithiothreitol (DTT) to produce the parent compound. The extracted vitamins were then analysed using an LC-MS system with electrospray - atmospheric pressure ionization (ES-API) source, operated in positive single ion monitoring (SIM) mode. The MS provided good linearity within the investigated range from 5 to 400 ng/mL with coefficient of determination (r2) ranging from 0.98 to 0.99. Retention times (0.65-9.04 min) were reproducible and no coelution between vitamins was observed. Limit of detection (LOD) varied from 2.4 to 9.0 ng/mL and limit of quantification (LOQ) was from 8 to 30 ng/mL, comparable to previously published studies. The extraction method provided good intra-day (%CV 1.56-6.56) and inter-day precision (%CV 8.07-10.97). Standard injections were used as part of quality control measures and provided excellent reproducibility (%CV 0.9-3.4). The overall runtime of this method was 19 min, including column reconditioning. Using this method, the quantity of thiamine (67 ± 7 ng/g), riboflavin (423 ± 39 ng/g), nicotinamide (856 ± 77 ng/g) and pyridoxine (133 ± 11 ng/g) was determined from a complex food matrix. In conclusion, we have developed a rapid and reliable, HILIC-single quad MS method utilising SIM for the low-level quantification of four B vitamins in a vegetable soup matrix in under 20 min. This method has shown excellent linearity, intra- and inter-day reproducibility and is directly applicable to other plant-based food matrices.

Keywords:  Analysis; Food matrix; HILIC; LC-MS; Water-soluble vitamins

DOI:  https://doi.org/10.1016/j.jchromb.2021.122541
Adv Exp Med Biol. 2021 ;1280 1-18

Chemical Isotope Labeling LC-MS for Metabolomics.

Shuang Zhao, Liang Li.

  Due to the great diversity of chemical and physical properties of metabolites as well as a wide range of concentrations of metabolites present in metabolomic samples, performing comprehensive and quantitative metabolome analysis is a major analytical challenge. Conventional approach of combining various techniques and methods with each detecting a fraction of the metabolome can lead to the increase in overall metabolomic coverage. However, this approach requires extensive investment in equipment and analytical expertise with still relatively low coverage and low sample throughput. Chemical isotope labeling (CIL) liquid chromatography mass spectrometry (LC-MS) offers an alternative means of increasing metabolomic coverage while maintaining high quantification precision and accuracy. This chapter describes the CIL LC-MS method and its key features for metabolomic analysis.

Keywords:  Chemical isotope labeling; Global metabolomics; Liquid chromatography with mass spectrometry

DOI:  https://doi.org/10.1007/978-3-030-51652-9_1
Adv Exp Med Biol. 2021 ;1280 39-55

Mass Spectrometry-Based Shotgun Lipidomics for Cancer Research.

Jianing Wang, Chunyan Wang, Xianlin Han.

  Shotgun lipidomics is an analytical approach for large-scale and systematic analysis of the composition, structure, and quantity of cellular lipids directly from lipid extracts of biological samples by mass spectrometry. This approach possesses advantages of high throughput and quantitative accuracy, especially in absolute quantification. As cancer research deepens at the level of quantitative biology and metabolomics, the demand for lipidomics approaches such as shotgun lipidomics is becoming greater. In this chapter, the principles, approaches, and some applications of shotgun lipidomics for cancer research are overviewed.

Keywords:  Cancer lipidomics; Direct infusion-based shotgun lipidomics; Imaging lipidomics; Mass spectrometry; Shotgun lipidomics

DOI:  https://doi.org/10.1007/978-3-030-51652-9_3
Adv Exp Med Biol. 2021 ;1280 57-67

Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometry-Based Metabolomics.

Md Aminul Islam Prodhan, Craig McClain, Xiang Zhang.

  Compared to one-dimensional gas chromatography with mass spectrometry (GC-MS), GC × GC-MS provides significantly increased peak capacity, resolution, and sensitivity for analysis of complex biological samples. In the last decade, GC × GC-MS has been increasingly applied to the discovery of metabolite biomarkers and elucidation of metabolic mechanisms in human diseases. The recent development of coupling GC × GC with a high-resolution mass spectrometer further accelerates these metabolomic applications. In this chapter, we will briefly review the instrumentation, sample preparation, data analysis, and applications of GC × GC-MS-based metabolomic analysis.

Keywords:  Column configuration; GC × GC-MS; Metabolomics; Sample preparation

DOI:  https://doi.org/10.1007/978-3-030-51652-9_4
Metabolites. 2021 Mar 29. pii: 202. [Epub ahead of print]11(4):

NMR Methods for Determining Lipid Turnover via Stable Isotope Resolved Metabolomics.

Penghui Lin, Li Dai, Daniel R Crooks, Leonard M Neckers, Richard M Higashi, Teresa W-M Fan, Andrew N Lane.

  Lipids comprise diverse classes of compounds that are important for the structure and properties of membranes, as high-energy fuel sources and as signaling molecules. Therefore, the turnover rates of these varied classes of lipids are fundamental to cellular function. However, their enormous chemical diversity and dynamic range in cells makes detailed analysis very complex. Furthermore, although stable isotope tracers enable the determination of synthesis and degradation of complex lipids, the numbers of distinguishable molecules increase enormously, which exacerbates the problem. Although LC-MS-MS (Liquid Chromatography-Tandem Mass Spectrometry) is the standard for lipidomics, NMR can add value in global lipid analysis and isotopomer distributions of intact lipids. Here, we describe new developments in NMR analysis for assessing global lipid content and isotopic enrichment of mixtures of complex lipids for two cell lines (PC3 and UMUC3) using both 13C6 glucose and 13C5 glutamine tracers.

Keywords:  Nuclear Magnetic Resonance; isotopomer distributions; lipid 13C incorporation; stable isotope tracers

DOI:  https://doi.org/10.3390/metabo11040202
Adv Exp Med Biol. 2021 ;1280 173-187

Methods of Lipidomic Analysis: Extraction, Derivatization, Separation, and Identification of Lipids.

Ya Xie, Zongyuan Wu, Zuojian Qin, Bangfu Wu, Xin Lv, Fang Wei, Hong Chen.

  Lipidomics refers to the large-scale study of pathways and networks of cellular lipids in biological systems. A lipidomic analysis often involves the identification and quantification of the thousands of cellular lipid molecular species within a complex biological sample and therefore requires a well optimized method for lipid profiling. In this chapter, the methods for lipidomic analysis, including sample collection and preparation, lipid derivatization and separation, mass spectrometric identification of lipids, data processing and interpretation, and quality control, are overviewed.

Keywords:  Chromatographic separation of lipids; Lipid derivatization; Lipid extraction; Lipidomics; Mass spectrometry

DOI:  https://doi.org/10.1007/978-3-030-51652-9_12
Metabolites. 2021 Mar 27. pii: 200. [Epub ahead of print]11(4):

Optical Microscopy-Guided Laser Ablation Electrospray Ionization Ion Mobility Mass Spectrometry: Ambient Single Cell Metabolomics with Increased Confidence in Molecular Identification.

Michael J Taylor, Sara Mattson, Andrey Liyu, Sylwia A Stopka, Yehia M Ibrahim, Akos Vertes, Christopher R Anderton.

  Single cell analysis is a field of increasing interest as new tools are continually being developed to understand intercellular differences within large cell populations. Laser-ablation electrospray ionization mass spectrometry (LAESI-MS) is an emerging technique for single cell metabolomics. Over the years, it has been validated that this ionization technique is advantageous for probing the molecular content of individual cells in situ. Here, we report the integration of a microscope into the optical train of the LAESI source to allow for visually informed ambient in situ single cell analysis. Additionally, we have coupled this 'LAESI microscope' to a drift-tube ion mobility mass spectrometer to enable separation of isobaric species and allow for the determination of ion collision cross sections in conjunction with accurate mass measurements. This combined information helps provide higher confidence for structural assignment of molecules ablated from single cells. Here, we show that this system enables the analysis of the metabolite content of Allium cepa epidermal cells with high confidence structural identification together with their spatial locations within a tissue.

Keywords:  ambient analysis; collisional cross section; drift tube ion mobility separation; in situ metabolomics; laser ablation electrospray ionization; mass spectrometry

DOI:  https://doi.org/10.3390/metabo11040200
Adv Exp Med Biol. 2021 ;1280 189-200

Capillary Electrophoresis-Mass Spectrometry for Cancer Metabolomics.

Xiangdong Xu.

  This chapter presents the fundamentals, instrumentation, methodology, and applications of capillary electrophoresis-mass spectrometry (CE-MS) for cancer metabolomics. CE offers fast and high-resolution separation of charged analytes from a very small amount of sample. When coupled to MS, it represents a powerful analytical technique enabling identification and quantification of metabolites in biological samples. Several issues need to be addressed when combining CE with MS, especially the interface between CE and MS and the selection of a proper separation methodology, sample pretreatment, and capillary coatings. We will discuss these aspects of CE-MS and detail representative applications for cancer metabolomic analysis.

Keywords:  CE-MS interface; Cancer metabolomics; Capillary coating; Capillary electrophoresis; Mass spectrometry

DOI:  https://doi.org/10.1007/978-3-030-51652-9_13
Adv Exp Med Biol. 2021 ;1280 201-218

NMR-Based Metabolomics in Cancer Research.

Rui Hu, Tao Li, Yunhuang Yang, Yuan Tian, Limin Zhang.

  Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Keywords:  Cancer metabolomics; Data analysis; Microcoil NMR; Nuclear magnetic resonance (NMR); Sample preparation

DOI:  https://doi.org/10.1007/978-3-030-51652-9_14
Anal Chem. 2021 Apr 02.

Extension of Diagnostic Fragmentation Filtering for Automated Discovery in DNA Adductomics.

Kevin J Murray, Erik S Carlson, Alessia Stornetta, Emily P Balskus, Peter W Villalta, Silvia Balbo.

Development of high-resolution/accurate mass liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) methodology enables the characterization of covalently modified DNA induced by interaction with genotoxic agents in complex biological samples. Constant neutral loss monitoring of 2'-deoxyribose or the nucleobases using data-dependent acquisition represents a powerful approach for the unbiased detection of DNA modifications (adducts). The lack of available bioinformatics tools necessitates manual processing of acquired spectral data and hampers high throughput application of these techniques. To address this limitation, we present an automated workflow for the detection and curation of putative DNA adducts by using diagnostic fragmentation filtering of LC-MS/MS experiments within the open-source software MZmine. The workflow utilizes a new feature detection algorithm, DFBuilder, which employs diagnostic fragmentation filtering using a user-defined list of fragmentation patterns to reproducibly generate feature lists for precursor ions of interest. The DFBuilder feature detection approach readily fits into a complete small-molecule discovery workflow and drastically reduces the processing time associated with analyzing DNA adductomics results. We validate our workflow using a mixture of authentic DNA adduct standards and demonstrate the effectiveness of our approach by reproducing and expanding the results of a previously published study of colibactin-induced DNA adducts. The reported workflow serves as a technique to assess the diagnostic potential of novel fragmentation pattern combinations for the unbiased detection of chemical classes of interest.

DOI: https://doi.org/10.1021/acs.analchem.0c04895
J Am Soc Mass Spectrom. 2021 Mar 29.

TopMSV: A Web-Based Tool for Top-Down Mass Spectrometry Data Visualization.

In Kwon Choi, Tianze Jiang, Sreekanth Reddy Kankara, Si Wu, Xiaowen Liu.

  Top-down mass spectrometry (MS) investigates intact proteoforms for proteoform identification, characterization, and quantification. Data visualization plays an essential role in top-down MS data analysis because proteoform identification and characterization often involve manual data inspection to determine the molecular masses of highly charged ions and validate unexpected alterations in identified proteoforms. While many software tools have been developed for MS data visualization, there is still a lack of web-based visualization software designed for top-down MS. Here, we present TopMSV, a web-based tool for top-down MS data processing and visualization. TopMSV provides interactive views of top-down MS data using a web browser. It integrates software tools for spectral deconvolution and proteoform identification and uses analysis results of the tools to annotate top-down MS data.

Keywords:  data visualization; software tools; top-down mass spectrometry

DOI:  https://doi.org/10.1021/jasms.0c00460
Adv Exp Med Biol. 2021 ;1280 149-159

Ion Chromatography with Mass Spectrometry for Metabolomic Analysis.

Eoon Hye Ji, Jason Lee, Shen Hu.

  Ion chromatography (IC) represents an important technique for separation of charged and polar compounds. Traditionally, IC is often used for the analysis of small inorganic ions. Due to the development of eluent suppression technology that allows continuous online desalting and conversion of high-salt eluents into pure water, IC has been coupled with mass spectrometry (MS) for the analysis of more diverse range of anionic and cationic analytes. Recent studies have demonstrated that IC-MS is a powerful technique with exquisite detection sensitivity, high reproducibility, and quantitative capability for metabolomic analysis. In this chapter, we provide a brief overview of IC principles and IC-MS for metabolomic analysis.

Keywords:  Ion; Ion chromatography; Mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1007/978-3-030-51652-9_10
Adv Exp Med Biol. 2021 ;1280 115-130

Tracer-Based Cancer Metabolomic Analysis.

Jianzhou Liu, Jing Huang, Gary Guishan Xiao.

  Metabolic rewiring/reprogramming is an essential hallmark of cancer. Alteration of metabolic phenotypes is occurred in cancer cells in response to a harsh condition to support cancer cell proliferation, survival, and metastasis. Stable isotope can be used as a tracer to investigate the redistribution of the carbons labeled in glucose in order to elucidate the detailed mechanisms of cellular rewiring and reprogramming in tumor microenvironment. Stable isotope-resolved metabolomics (SIRM) is an analytical method inferring metabolic networking by using advanced nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) to analyze the fate of a single atom from a stable isotope-enriched precursor to a product metabolite. This methodology has been demonstrated for a wide range of biological applications, including cancer metabolomic analysis. The basic principle and platforms of SIRM and its implication for cancer metabolism research will be addressed in this chapter.

Keywords:  Cancer metabolomics; MS; NMR; SIRM; Stable isotope-based tracer

DOI:  https://doi.org/10.1007/978-3-030-51652-9_8