bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2022–03–20
fiveteen papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.
  2. A concise review on lipidomics analysis in biological samples.
  3. Regulation of Ferroptosis by Amino Acid Metabolism in Cancer.
  4. Targeting stearoyl-CoA desaturase in solid tumors.
  5. Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics.
  6. Optimized TMT-Based Quantitative Cross-Linking Mass Spectrometry Strategy for Large-Scale Interactomic Studies.
  7. Need for more focus on lipid species in studies of biological and model membranes.
  8. Profiling Urinary Sulfate Metabolites With Mass Spectrometry.
  9. Enrichment of Tyrosine Phosphorylated Peptides for Quantitative Mass Spectrometry Analysis of RTK Signaling Dynamics.
  10. NAD+ depletion enhances reovirus-induced oncolysis in multiple myeloma.
  11. Single cell mass spectrometry analysis of drug-resistant cancer cells: Metabolomics studies of synergetic effect of combinational treatment.
  12. UNRAVELLING THE COMPLEXITY OF THE EXTRACELLULAR VESICLE LANDSCAPE WITH ADVANCED PROTEOMICS.
  13. Clinical Utility and Analytical Aspects of Direct Measurements of Free Hormones Using Mass Spectrometry-Based Methods.
  14. Ultra-High-Throughput Ambient MS: Direct Analysis at 22 Samples per Second by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry.
  15. Altered Fecal Metabolomics and Potential Biomarkers of Psoriatic Arthritis Differing From Rheumatoid Arthritis.
  1. Nat Commun. 2022 Mar 15. 13(1): 1347
    DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics.
    Oliver Alka, Premy Shanthamoorthy, Michael Witting, Karin Kleigrewe, Oliver Kohlbacher, Hannes L Röst.
      The extraction of meaningful biological knowledge from high-throughput mass spectrometry data relies on limiting false discoveries to a manageable amount. For targeted approaches in metabolomics a main challenge is the detection of false positive metabolic features in the low signal-to-noise ranges of data-independent acquisition results and their filtering. Another factor is that the creation of assay libraries for data-independent acquisition analysis and the processing of extracted ion chromatograms have not been automated in metabolomics. Here we present a fully automated open-source workflow for high-throughput metabolomics that combines data-dependent and data-independent acquisition for library generation, analysis, and statistical validation, with rigorous control of the false-discovery rate while matching manual analysis regarding quantification accuracy. Using an experimentally specific data-dependent acquisition library based on reference substances allows for accurate identification of compounds and markers from data-independent acquisition data in low concentrations, facilitating biomarker quantification.
    DOI:  https://doi.org/10.1038/s41467-022-29006-z
  2. ADMET DMPK. 2021 ;9(1): 1-22
    A concise review on lipidomics analysis in biological samples.
    Ramani Venkata Addepalli, Ramesh Mullangi.
      Lipids are a complex and critical heterogeneous molecular entity, playing an intricate and key role in understanding biological activities and disease processes. Lipidomics aims to quantitatively define the lipid classes, including their molecular species. The analysis of the biological tissues and fluids are challenging due to the extreme sample complexity and occurrence of the molecular species as isomers or isobars. This review documents the overview of lipidomics workflow, beginning from the approaches of sample preparation, various analytical techniques and emphasizing the state-of-the-art mass spectrometry either by shotgun or coupled with liquid chromatography. We have considered the latest ion mobility spectroscopy technologies to deal with the vast number of structural isomers, different imaging techniques. All these techniques have their pitfalls and we have discussed how to circumvent them after reviewing the power of each technique with examples..
    Keywords:  LC-MS/MS; Lipids; bioanalysis; samples processing
    DOI:  https://doi.org/10.5599/admet.913
  3. Int J Biol Sci. 2022 ;18(4): 1695-1705
    Regulation of Ferroptosis by Amino Acid Metabolism in Cancer.
    Jian Yang, Xinyu Dai, Huanji Xu, Qiulin Tang, Feng Bi.
      Ferroptosis, a new form of programmed necrosis characterized by iron-dependent lethal accumulation of lipid hydroperoxides, is associated with many human diseases. Targeting amino acid (AA) availability can selectively suppress tumor growth and has been a promising therapeutic strategy for cancer therapy. Compelling studies have indicated that AA metabolism is also involved in ferroptosis, closely regulating its initiation and execution. This manuscript systematically summarizes the latest advances of AA metabolism in regulating ferroptosis and discusses the potential combination of therapeutic strategies that simultaneously target AA metabolism and ferroptosis in cancer to eliminate tumors or limit their invasiveness.
    Keywords:  amino acid metabolism; cancer; combinatorial therapy; ferroptosis
    DOI:  https://doi.org/10.7150/ijbs.64982
  4. Cancer Res. 2022 Mar 16. pii: canres.4044.2021. [Epub ahead of print]
    Targeting stearoyl-CoA desaturase in solid tumors.
    Casie S Kubota, Peter J Espenshade.
      Cancer cells are demarcated from normal cells by distinct biological hallmarks, including the reprogramming of metabolic processes. One of the key players involved in metabolic reprogramming is stearoyl-CoA desaturase (SCD), which converts saturated fatty acids to monounsaturated fatty acids in an oxygen-dependent reaction that is crucial for maintaining fatty acid homeostasis. As such, SCD has been identified as a potential therapeutic target in numerous types of cancers, and its inhibition suppresses cancer cell growth in vitro and in vivo. This review summarizes the evidence implicating SCD in cancer progression and proposes novel therapeutic strategies for targeting SCD in solid tumors.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-21-4044
  5. Anal Chem. 2022 Mar 15.
    Deep Learning-Assisted Peak Curation for Large-Scale LC-MS Metabolomics.
    Yoann Gloaguen, Jennifer A Kirwan, Dieter Beule.
      Available automated methods for peak detection in untargeted metabolomics suffer from poor precision. We present NeatMS, which uses machine learning based on a convoluted neural network to reduce the number and fraction of false peaks. NeatMS comes with a pre-trained model representing expert knowledge in the differentiation of true chemical signal from noise. Furthermore, it provides all necessary functions to easily train new models or improve existing ones by transfer learning. Thus, the tool improves peak curation and contributes to the robust and scalable analysis of large-scale experiments. We show how to integrate it into different liquid chromatography-mass spectrometry (LC-MS) analysis workflows, quantify its performance, and compare it to various other approaches. NeatMS software is available as open source on github under permissive MIT license and is also provided as easy-to-install PyPi and Bioconda packages.
    DOI:  https://doi.org/10.1021/acs.analchem.1c02220
  6. Anal Chem. 2022 Mar 15.
    Optimized TMT-Based Quantitative Cross-Linking Mass Spectrometry Strategy for Large-Scale Interactomic Studies.
    Max Ruwolt, Lennart Schnirch, Diogo Borges Lima, Michal Nadler-Holly, Rosa Viner, Fan Liu.
      Cross-linking mass spectrometry (XL-MS) is a powerful method for the investigation of protein-protein interactions (PPI) from highly complex samples. XL-MS combined with tandem mass tag (TMT) labeling holds the promise of large-scale PPI quantification. However, a robust and efficient TMT-based XL-MS quantification method has not yet been established due to the lack of a benchmarking dataset and thorough evaluation of various MS parameters. To tackle these limitations, we generate a two-interactome dataset by spiking in TMT-labeled cross-linked Escherichia coli lysate into TMT-labeled cross-linked HEK293T lysate using a defined mixing scheme. Using this benchmarking dataset, we assess the efficacy of cross-link identification and accuracy of cross-link quantification using different MS acquisition strategies. For identification, we compare various MS2- and MS3-based XL-MS methods, and optimize stepped higher energy collisional dissociation (HCD) energies for TMT-labeled cross-links. We observed a need for notably higher fragmentation energies compared to unlabeled cross-links. For quantification, we assess the quantification accuracy and dispersion of MS2-, MS3-, and synchronous precursor selection-MS3-based methods. We show that a stepped HCD-MS2 method with stepped collision energies 36-42-48 provides a vast number of quantifiable cross-links with high quantification accuracy. This widely applicable method paves the way for multiplexed quantitative PPI characterization from complex biological systems.
    DOI:  https://doi.org/10.1021/acs.analchem.1c04812
  7. Prog Lipid Res. 2022 Mar 11. pii: S0163-7827(22)00015-7. [Epub ahead of print] 101160
    Need for more focus on lipid species in studies of biological and model membranes.
    Tore Skotland, Kirsten Sandvig.
      The lipid composition of cellular membranes can impact a number of physiological processes such as signaling, cell migration, endocytosis and intracellular transport. In this article we focus on some aspects concerning analysis of lipids and research on lipid structure and function in mammalian cells that in our opinion have not obtained sufficient attention. This includes interleaflet coupling between the two layers of the membrane, and the role of lipid species, i.e. the role of the complete structure of the lipids, including lipid chain length and the position of double bonds. We highlight the role of PS species for membrane function. We also discuss the large diversity of PS species in different biological samples and the possible functional consequences, and we provide an overview of PS species from 40 different samples. Furthermore, recent studies show that there seems to be a coregulation concerning the levels of sphingolipids and ether lipids. We review and discuss the published data indicating such a coregulation. Moreover, we point to some of the pitfalls in the field of lipidomics and present suggestions for improvement. Finally, we discuss the importance of using asymmetric membrane models with a composition of lipid species that are common in biological membranes.
    Keywords:  Asymmetric membranes; Ether lipids; Interleaflet coupling; Lipidomic analysis; PS species; Sphingolipids
    DOI:  https://doi.org/10.1016/j.plipres.2022.101160
  8. Front Mol Biosci. 2022 ;9 829511
    Profiling Urinary Sulfate Metabolites With Mass Spectrometry.
    Christopher C J Fitzgerald, Rikard Hedman, Dimanthi R Uduwela, Bettina Paszerbovics, Adam J Carroll, Teresa Neeman, Adam Cawley, Lance Brooker, Malcolm D McLeod.
      The study of urinary phase II sulfate metabolites is central to understanding the role and fate of endogenous and exogenous compounds in biological systems. This study describes a new workflow for the untargeted metabolic profiling of sulfated metabolites in a urine matrix. Analysis was performed using ultra-high-performance liquid chromatography-high resolution tandem mass spectrometry (UHPLC-HRMS/MS) with data dependent acquisition (DDA) coupled to an automated script-based data processing pipeline and differential metabolite level analysis. Sulfates were identified through k-means clustering analysis of sulfate ester derived MS/MS fragmentation intensities. The utility of the method was highlighted in two applications. Firstly, the urinary metabolome of a thoroughbred horse was examined before and after administration of the anabolic androgenic steroid (AAS) testosterone propionate. The analysis detected elevated levels of ten sulfated steroid metabolites, three of which were identified and confirmed by comparison with synthesised reference materials. This included 5α-androstane-3β,17α-diol 3-sulfate, a previously unreported equine metabolite of testosterone propionate. Secondly, the hydrolytic activity of four sulfatase enzymes on pooled human urine was examined. This revealed that Pseudomonas aeruginosa arylsulfatases (PaS) enzymes possessed higher selectivity for the hydrolysis of sulfated metabolites than the commercially available Helix pomatia arylsulfatase (HpS). This novel method provides a rapid tool for the systematic, untargeted metabolic profiling of sulfated metabolites in a urinary matrix.
    Keywords:  anti-doping; mass spectrometry; metabolomics; steroid; sulfatase; sulfate ester; sulfation
    DOI:  https://doi.org/10.3389/fmolb.2022.829511
  9. Bio Protoc. 2022 Feb 05. 12(3): e4311
    Enrichment of Tyrosine Phosphorylated Peptides for Quantitative Mass Spectrometry Analysis of RTK Signaling Dynamics.
    Vidyasiri Vemulapalli, Stephen C Blacklow, Steven P Gygi, Alison Erickson.
      Cells sense and respond to mitogens by activating a cascade of signaling events, primarily mediated by tyrosine phosphorylation (pY). Because of its key roles in cellular homeostasis, deregulation of this signaling is often linked to oncogenesis. To understand the mechanisms underlying these signaling pathway aberrations, it is necessary to quantify tyrosine phosphorylation on a global scale in cancer cell models. However, the majority of the protein phosphorylation events occur on serine (86%) and threonine (12%) residues, whereas only 2% of phosphorylation events occur on tyrosine residues ( Olsen et al., 2006 ). The low stoichiometry of tyrosine phosphorylation renders it difficult to quantify cellular pY events comprehensively with high mass accuracy and reproducibility. Here, we describe a detailed protocol for isolating and quantifying tyrosine phosphorylated peptides from drug-perturbed, growth factor-stimulated cancer cells, using immunoaffinity purification and tandem mass tags (TMT) coupled with mass spectrometry.
    Keywords:  Growth factor stimulation; Phosphoproteomics; Phosphotyrosine enrichment; Receptor tyrosine kinases; Signal transduction; Tandem mass tag labeling; Tyrosine phosphorylation
    DOI:  https://doi.org/10.21769/BioProtoc.4311
  10. Mol Ther Oncolytics. 2022 Mar 17. 24 695-706
    NAD+ depletion enhances reovirus-induced oncolysis in multiple myeloma.
    Barry E Kennedy, Michael Giacomantonio, J Patrick Murphy, Samuel Cutler, Maryanne Sadek, Prathyusha Konda, Joao A Paulo, Gopal P Pathak, Saskia H J Renkens, Stacy Grieve, Jonathan Pol, Steven P Gygi, Christopher Richardson, Daniel Gaston, Anthony Reiman, Guido Kroemer, Manal O Elnenaei, Shashi A Gujar.
      Cancer cell energy metabolism plays an important role in dictating the efficacy of oncolysis by oncolytic viruses. To understand the role of multiple myeloma metabolism in reovirus oncolysis, we performed semi-targeted mass spectrometry-based metabolomics on 12 multiple myeloma cell lines and revealed a negative correlation between NAD+ levels and susceptibility to oncolysis. Likewise, a negative correlation was observed between the activity of the rate-limiting NAD+ synthesis enzyme NAMPT and oncolysis. Indeed, depletion of NAD+ levels by pharmacological inhibition of NAMPT using FK866 sensitized several myeloma cell lines to reovirus-induced killing. The myelomas that were most sensitive to this combination therapy expressed a functional p53 and had a metabolic and transcriptomic profile favoring mitochondrial metabolism over glycolysis, with the highest synergistic effect in KMS12 cells. Mechanistically, U-13C-labeled glucose flux, extracellular flux analysis, multiplex proteomics, and cell death assays revealed that the reovirus + FK866 combination caused mitochondrial dysfunction and energy depletion, leading to enhanced autophagic cell death in KMS12 cells. Finally, the combination of reovirus and NAD+ depletion achieved greater antitumor effects in KMS12 tumors in vivo and patient-derived CD138+ multiple myeloma cells. These findings identify NAD+ depletion as a potential combinatorial strategy to enhance the efficacy of oncolytic virus-based therapies in multiple myeloma.
    Keywords:  FK866; NAD+; NAMPT; aerobic glycolysis; autophagy; cancer metabolism; mitochondrial metabolism; oncolytic virus; p53; reovirus
    DOI:  https://doi.org/10.1016/j.omto.2022.02.017
  11. Anal Chim Acta. 2022 Apr 08. pii: S0003-2670(22)00192-1. [Epub ahead of print]1201 339621
    Single cell mass spectrometry analysis of drug-resistant cancer cells: Metabolomics studies of synergetic effect of combinational treatment.
    Xingxiu Chen, Mei Sun, Zhibo Yang.
      Irinotecan (IRI), a topoisomerase I inhibitor blocking DNA synthesis, is a widely used chemotherapy drug for metastatic colorectal cancer. Despite being an effective chemotherapy drug, its clinical effectiveness is limited by both intrinsic and acquired drug resistance. Previous studies indicate IRI induces cancer stemness in irinotecan-resistant (IRI-resistant) cells. Metformin, an oral antidiabetic drug, was recently reported for anticancer effects, likely due to its selective killing of cancer stem cells (CSCs). Given IRI-resistant cells exhibiting high cancer stemness, we hypothesize metformin can sensitize IRI-resistant cells and rescue the therapeutic effect. In this work, we utilized the Single-probe mass spectrometry technique to analyze live IRI-resistant cells under different treatment conditions. We discovered that metformin treatment was associated with the downregulation of lipids and fatty acids, potentially through the inhibition of fatty acid synthase (FASN). Importantly, certain species can be only detected from cells in their living status. The level of synergistic effect of metformin and IRI in their co-treatment of IRI-resistant cells was evaluated using Chou-Talalay combinational index. Using enzymatic activity assay, we determined that the co-treatment exhibit the highest FASN inhibition compared with the mono-treatment of IRI or metformin. To our knowledge, this is the first single-cell MS metabolomics study demonstrating metformin-IRI synergistic effect overcoming drug resistance in IRI-resistant cells.
    Keywords:  Drug-resistant cancer cells; Fatty-acid synthase; Lipidomics; Metabolomics; Metformin; Single cell mass spectrometry
    DOI:  https://doi.org/10.1016/j.aca.2022.339621
  12. Expert Rev Proteomics. 2022 Mar 15.
    UNRAVELLING THE COMPLEXITY OF THE EXTRACELLULAR VESICLE LANDSCAPE WITH ADVANCED PROTEOMICS.
    Julia Morales-Sanfrutos, Javier Munoz.
       INTRODUCTION: The field of extracellular vesicles (EVs) is rapidly advancing. This progress is fuelled by the potential applications of these agents as biomarkers and also as an attractive source to encapsulate therapeutics and other agents to target specific cells.
    AREAS COVERED: Different types of EVs, including exosomes, and other nanoparticles have been identified in the last years with key regulatory functions in cell-cell communication. However, the techniques used for their purification possess inherent limitations, resulting in heterogeneous preparations contaminated by other EVs subtypes and nano-size structures. It is therefore urgent to deconvolute the molecular constituents present in each type of EVs in order to accurately ascribe their specific functions. In this context, proteomics can profile, not only the lumen proteins and surface markers, but also their post-translational modifications, which will inform on the mechanisms of cargo selection and sorting.
    EXPERT OPINION: Mass spectrometry-based proteomics is now a mature technique and has started to deliver new insights in the EV field. Here, we review recent developments in sample preparation, mass spectrometry (MS) and computational analysis and discuss how these technological advances, in conjunction with improved purification protocols, could impact the proteomic characterization of the complex landscape of EVs and other secreted nanoparticles.
    Keywords:  Biomarkers; Exosomes; Extracellular Vesicles; Mass Spectrometry; Proteomics; Purification techniques
    DOI:  https://doi.org/10.1080/14789450.2022.2052849
  13. J Appl Lab Med. 2022 Mar 17. pii: jfac010. [Epub ahead of print]
    Clinical Utility and Analytical Aspects of Direct Measurements of Free Hormones Using Mass Spectrometry-Based Methods.
    Mark M Kushnir, Heather A Nelson, Kelly Doyle.
       BACKGROUND: The free hormone (FH) hypothesis states that hormone action and the corresponding biological effects are mediated by the unbound (free) fraction of hormone in circulation. The in vivo relationship between protein-bound and FH is complex and dynamic. In most individuals, measurement of total hormone (TH) is usually adequate to reflect the hormone status; however, certain physiological conditions and/or medications can affect protein binding and alter FH concentration. In these cases, measurement of FH will provide a better measure of the bioactive hormone status than measurement of the TH. Measurement of FH presents many challenges, as the concentrations are very low and there are number of pitfalls, which may affect the measured concentrations.
    CONTENT: In this review, we discuss techniques used in the separation and direct quantitation of FH concentrations in biological samples using mass spectrometry for analysis. We also highlight clinical situations in which FH analysis is warranted and when mass spectrometry should be the preferred methodology over immunoassays.
    SUMMARY: Equilibrium dialysis, ultrafiltration, or size-exclusion separation coupled with liquid chromatography-tandem mass spectrometry provides a sensitive and specific method to measure FH concentrations. These direct methods are useful in iatrogenic or physiological states that alter hormone binding or metabolism.
    Keywords:  laboratory methods and tools; mass spectrometry; steroids and steroid hormones; thyroid hormones
    DOI:  https://doi.org/10.1093/jalm/jfac010
  14. Anal Chem. 2022 Mar 15.
    Ultra-High-Throughput Ambient MS: Direct Analysis at 22 Samples per Second by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry.
    Andrew J Radosevich, Fan Pu, David Chang-Yen, James W Sawicki, Nari N Talaty, Nathaniel L Elsen, Jon D Williams, Jeffrey Y Pan.
      Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry is an ambient-direct sampling method that is being developed for high-throughput, label-free, biochemical screening of large-scale compound libraries. Here, we report the development of an ultra-high-throughput continuous motion IR-MALDESI sampling approach capable of acquiring data at rates up to 22.7 samples per second in a 384-well microtiter plate. At top speed, less than 1% analyte carryover is observed from well-to-well, and signal intensity relative standard deviations (RSD) of 11.5% and 20.9% for 3 μM 1-hydroxymidazolam and 12 μM dextrorphan, respectively, are achieved. The ability to perform parallel kinetics studies on 384 samples with a ∼30 s time resolution using an isocitrate dehydrogenase 1 (IDH1) enzyme assay is shown. Finally, we demonstrate the repeatability and throughput of our approach by measuring 115200 samples from 300 microtiter plate reads consecutively over 5.54 h with RSDs under 8.14% for each freshly introduced plate. Taken together, these results demonstrate the use of IR-MALDESI at sample acquisition rates that surpass other currently reported direct sampling mass spectrometry approaches used for high-throughput compound screening.
    DOI:  https://doi.org/10.1021/acs.analchem.1c04605
  15. Front Immunol. 2022 ;13 812996
    Altered Fecal Metabolomics and Potential Biomarkers of Psoriatic Arthritis Differing From Rheumatoid Arthritis.
    Nan Wang, Linjiao Yang, Lili Shang, Zhaojun Liang, Yanlin Wang, Min Feng, Shuting Yu, Xiaoying Li, Chong Gao, Zhenyu Li, Jing Luo.
      Psoriatic arthritis (PsA) is a chronic inflammatory joint disease, and the diagnosis is quite difficult due to the unavailability of reliable clinical markers. This study aimed to investigate the fecal metabolites in PsA by comparison with rheumatoid arthritis (RA), and to identify potential diagnostic biomarkers for PsA. The metabolic profiles of the fecal samples from 27 PsA and 29 RA patients and also 36 healthy controls (HCs) were performed on ultra-high-performance liquid chromatography coupled with hybrid triple quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). And differentially altered metabolites were screened and assessed using multivariate analysis for exploring the potential biomarkers of PsA. The results showed that 154 fecal metabolites were significantly altered in PsA patients when compared with HCs, and 45 metabolites were different when compared with RA patients. A total of 14 common differential metabolites could be defined as candidate biomarkers. Furthermore, a support vector machines (SVM) model was performed to distinguish PsA from RA patients and HCs, and 5 fecal metabolites, namely, α/β-turmerone, glycerol 1-hexadecanoate, dihydrosphingosine, pantothenic acid and glutamine, were determined as biomarkers for PsA. Through the metabolic pathways analysis, we found that the abnormality of amino acid metabolism, bile acid metabolism and lipid metabolism might contribute to the occurrence and development of PsA. In summary, our research provided ideas for the early diagnosis and treatment of PsA by identifying fecal biomarkers and analyzing metabolic pathways.
    Keywords:  UHPLC-Q-TOF-MS; biomarker; feces; metabolomics; psoriatic arthritis
    DOI:  https://doi.org/10.3389/fimmu.2022.812996
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