bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024–11–17
twelve papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Triple acquisition mass spectrometry (TRAM) combining targeted and non-targeted metabolomics in a single run.
  2. Strategies for Using Postcolumn Infusion of Standards to Correct for Matrix Effect in LC-MS-Based Quantitative Metabolomics.
  3. Autonomous Dissociation-type Selection for Glycoproteomics Using a Real-Time Library Search.
  4. High-throughput glycosaminoglycan extraction and UHPLC-MS/MS quantification in human biofluids.
  5. Hair and Saliva Endocannabinoid and Steroid Hormone Analysis by Liquid Chromatography Paired with Tandem Mass Spectrometry.
  6. Cancer-associated fibroblasts maintain critical pancreatic cancer cell lipid homeostasis in the tumor microenvironment.
  7. Bridging the Gap from Proteomics Technology to Clinical Application: Highlights from the 68th Benzon Foundation Symposium.
  8. A Micro-Flow Liquid Chromatography-Mass Spectrometry Method for the Quantification of Oxylipins in Volume-Limited Human Plasma.
  9. A complementary approach for detecting biological signals through a semi-automated feature selection tool.
  10. Lipidomic profiling of triple-negative breast cancer cells reveals distinct metabolic signatures associated with EpCAM expression.
  11. NRF2 signaling and amino acid metabolism in cancer.
  12. Protocol for unified metabolomics and proteomics analysis of formalin-fixed paraffin-embedded tissue.
  1. Anal Chim Acta. 2024 Dec 01. pii: S0003-2670(24)01115-2. [Epub ahead of print]1331 343314
    Triple acquisition mass spectrometry (TRAM) combining targeted and non-targeted metabolomics in a single run.
    Lisa Panzenboeck, Harald Schoeny, Bruno Stelzer, Elisabeth Foels, Marvin Glas, Marlene Pühringer, Dorian Hirschmann, Daniela Loetsch, Christian Dorfer, Evelyn Rampler, Gunda Koellensperger.
       BACKGROUND: We introduce TRAM, a triple acquisition strategy on a high-speed quadrupole time-of-flight mass spectrometer for merging non-targeted and targeted metabolomics into one run. TRAM stands for "quasi-simultaneous" acquisition of (1) a full scan MS1, (2) top 30 data-dependent MS2 (DDA), and (3) targeted scheduled MS2 for multiple reaction monitoring (MRM) within measurement cycles of ∼1 s. TRAM combines the selectivity and sensitivity of state-of-the-art targeted MRM-based methods with the full scope of non-targeted analysis enabled by high-resolution mass spectrometry.
    RESULTS: In this work, we deploy a workflow based on hydrophilic interaction liquid chromatography (HILIC). For a broad panel of metabolites, we provide chromatographic retention times, and optimized conditions as a basis for targeted MRM experiments, listing accurate masses and sum formulas for fragment ions (including fully 13C labeled analogs). Validation experiments showed that TRAM offered (1) linear working ranges and limits of quantification comparable to MRM-only methods, (2) enabled accurate quantification in SRM 1950 human plasma reference material, and (3) was equivalent to DDA-only approaches in non-targeted metabolomics. Metabolomics in human cerebrospinal fluid showcased the power of the strategy, emphasizing the need for high coverage/high throughput metabolomics in clinical studies.
    SIGNIFICANCE: Acquiring up to 30 data-dependent spectra per MS cycle while still offering gold standard absolute quantification down to low nanomolar concentrations, TRAM allows in-depth profiling and reduces required sample volume, time, cost, and environmental impact.
    Keywords:  Absolute quantification; HILIC; Liquid chromatography-mass spectrometry; Meningioma; Non-targeted metabolomics; Targeted metabolomics; ZenoTOF 7600
    DOI:  https://doi.org/10.1016/j.aca.2024.343314
  2. J Am Soc Mass Spectrom. 2024 Nov 15.
    Strategies for Using Postcolumn Infusion of Standards to Correct for Matrix Effect in LC-MS-Based Quantitative Metabolomics.
    Anne-Charlotte Dubbelman, Bo van Wieringen, Lesley Roman Arias, Michael van Vliet, Roel Vermeulen, Amy C Harms, Thomas Hankemeier.
      The matrix effect limits the accuracy of quantitation of the otherwise popular metabolomics technique liquid chromatography coupled to mass spectrometry (LC-MS). The gold standard to correct for this phenomenon, whereby compounds coeluting with the analyte of interest cause ionization enhancement or suppression, is to quantify an analyte based on the peak area ratio with an isotopologue added to the sample as an internal standard. However, these stable isotopes are expensive and sometimes unavailable. Here, we describe an alternative approach: matrix effect correction and quantifying analytes using a signal ratio with a postcolumn infused standard (PCIS). Using an LC-MS/MS method for eight endocannabinoids and related metabolites in plasma, we provide strategies to select, optimize, and evaluate PCIS candidates. Based on seven characteristics, the structural endocannabinoid analogue arachidonoyl-2'-fluoroethylamide was selected as a PCIS. Three methods to evaluate the PCIS correction vs no correction showed that PCIS correction improved values for the matrix effect, precision, and dilutional linearity of at least six of the analytes to within acceptable ranges. PCIS correction also resulted in parallelization of calibration curves in plasma and neat solution, for six of eight analytes even with higher accuracy than peak area ratio correction with their stable isotope labeled internal standard, i.e., the gold standard. This enables quantification based on neat solutions, which is a significant step toward absolute quantification. We conclude that PCIS has great, but so far underappreciated, potential in accurate LC-MS quantification.
    DOI:  https://doi.org/10.1021/jasms.4c00408
  3. J Proteome Res. 2024 Nov 12.
    Autonomous Dissociation-type Selection for Glycoproteomics Using a Real-Time Library Search.
    Emmajay Sutherland, Tim S Veth, William D Barshop, Jacob H Russell, Kathryn Kothlow, Jesse D Canterbury, Christopher Mullen, David Bergen, Jingjing Huang, Vlad Zabrouskov, Romain Huguet, Graeme C McAlister, Nicholas M Riley.
      Tandem mass spectrometry (MS/MS) is the gold standard for intact glycopeptide identification, enabling peptide sequence elucidation and site-specific localization of glycan compositions. Beam-type collisional activation is generally sufficient for N-glycopeptides, while electron-driven dissociation is crucial for site localization in O-glycopeptides. Modern glycoproteomic methods often employ multiple dissociation techniques within a single LC-MS/MS analysis, but this approach frequently sacrifices sensitivity when analyzing multiple glycopeptide classes simultaneously. Here we explore the utility of intelligent data acquisition for glycoproteomics through real-time library searching (RTLS) to match oxonium ion patterns for on-the-fly selection of the appropriate dissociation method. By matching dissociation method with glycopeptide class, this autonomous dissociation-type selection (ADS) generates equivalent numbers of N-glycopeptide identifications relative to traditional beam-type collisional activation methods while also yielding comparable numbers of site-localized O-glycopeptide identifications relative to conventional electron transfer dissociation-based methods. The ADS approach represents a step forward in glycoproteomics throughput by enabling site-specific characterization of both N-and O-glycopeptides within the same LC-MS/MS acquisition.
    Keywords:  Glycoproteomics; N-glycopeptides; O-glycopeptides; beam-type collisional dissociation; electron transfer dissociation; intelligent data acquisition; product-dependent triggering; real-time library search; tandem mass spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00723
  4. Nat Protoc. 2024 Nov 14.
    High-throughput glycosaminoglycan extraction and UHPLC-MS/MS quantification in human biofluids.
    Nicola Volpi, Fabio Galeotti, Francesco Gatto.
      Glycosaminoglycans (GAGs) are linear, unbranched heteropolysaccharides whose structural complexity determines their function. Accurate quantification of GAGs in biofluids at high throughput is relevant for numerous biomedical applications. However, because of the structural variability of GAGs in biofluids, existing protocols require complex pre-analytical procedures, have limited throughput and lack accuracy. Here, we describe the extraction and quantification of GAGs by using ultra-high-performance liquid chromatography coupled with triple-quadrupole mass spectrometry (UHPLC-MS/MS). Designed for 96-well plates, this method enables the processing of up to 82 study samples per plate, with the remaining 14 wells used for calibrators and controls. Key steps include the enzymatic depolymerization of GAGs, their derivatization with 2-aminoacridone and their quantification via UHPLC-MS/MS. Each plate can be analyzed in a single UHPLC-MS/MS run, offering the quantitative and scalable analysis of 17 disaccharides from chondroitin sulfate, heparan sulfate and hyaluronic acid, with a level of precision and reproducibility sufficient for their use as biomarkers. The procedure from sample thawing to initiating the UHPLC-MS/MS run can be completed in ~1.5 d plus 15 min of MS runtime per sample, and it is structured to fit within ordinary working shifts, thus making it a valuable tool for clinical laboratories seeking high-throughput analysis of GAGs. The protocol requires expertise in UHPLC-MS/MS.
    DOI:  https://doi.org/10.1038/s41596-024-01078-9
  5. Methods Mol Biol. 2025 ;2868 135-147
    Hair and Saliva Endocannabinoid and Steroid Hormone Analysis by Liquid Chromatography Paired with Tandem Mass Spectrometry.
    Madeline Jarvis, Khalisa Amir Hamzah, David Nichols, Luke J Ney.
      Endocannabinoids are lipid neurotransmitters that play an important part in human health. Recent methods have found that quantification of endocannabinoids in hair and saliva samples is possible using liquid chromatography paired with tandem mass spectrometry (LC-MS/MS). This chapter describes two simple sample preparation methods that can be used to prepare hair and saliva samples for analysis using LC-MS/MS. Our LC-MS/MS method can be applied to both hair and saliva samples and is sufficiently sensitive for endocannabinoid, as well as steroid hormone, quantification in both of these sample matrices. This chapter provides a comprehensive description of how this can be achieved and provides tips and tricks for troubleshooting problems users may experience.
    Keywords:  Endocannabinoids; Hair; Hormones; Mass spectrometry; Saliva
    DOI:  https://doi.org/10.1007/978-1-0716-4200-9_8
  6. Cell Rep. 2024 Nov 12. pii: S2211-1247(24)01323-8. [Epub ahead of print]43(11): 114972
    Cancer-associated fibroblasts maintain critical pancreatic cancer cell lipid homeostasis in the tumor microenvironment.
    Xu Han, Michelle Burrows, Laura C Kim, Jimmy P Xu, Will Vostrejs, Tran Ngoc Van Le, Carson Poltorack, Yanqing Jiang, Edna Cukierman, Ben Z Stanger, Kim A Reiss, Sydney M Shaffer, Clementina Mesaros, Brian Keith, M Celeste Simon.
      Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with abundant cancer-associated fibroblasts (CAFs) creating hallmark desmoplasia that limits oxygen and nutrient delivery. This study explores the importance of lipid homeostasis under stress. Exogenous unsaturated lipids, rather than de novo synthesis, sustain PDAC cell viability by relieving endoplasmic reticulum (ER) stress under nutrient scarcity. Furthermore, CAFs are less hypoxic than adjacent malignant cells in vivo, nominating them as a potential source of unsaturated lipids. CAF-conditioned medium promotes PDAC cell survival upon nutrient and oxygen deprivation, an effect reversed by delipidation. Lysophosphatidylcholines (LPCs) are particularly enriched in CAF-conditioned medium and preferentially taken up by PDAC cells, where they are converted to phosphatidylcholine (PC) to sustain membrane integrity. Blocking LPC-to-PC conversion inhibits PDAC cell survival and increases ER stress. These findings show a critical lipid "cross-feeding" mechanism that promotes PDAC cell survival, offering a potential metabolic target for treatment.
    Keywords:  CP: Cancer; CP: Metabolism; fibroblasts; hypoxia; lipids; pancreatic cancer; tumor microenvironment; unsaturated fatty acids
    DOI:  https://doi.org/10.1016/j.celrep.2024.114972
  7. Mol Cell Proteomics. 2024 Nov 08. pii: S1535-9476(24)00167-1. [Epub ahead of print] 100877
    Bridging the Gap from Proteomics Technology to Clinical Application: Highlights from the 68th Benzon Foundation Symposium.
    Vincent Albrecht, Johannes Müller-Reif, Thierry M Nordmann, Andreas Mund, Lisa Schweizer, Philipp E Geyer, Lili Niu, Juanjuan Wang, Frederik Post, Marc Oeller, Andreas Metousis, Annelaura Bach Nielsen, Medini Steger, Nicolai J Wewer Albrechtsen, Matthias Mann.
      The 68th Benzon Foundation Symposium brought together leading experts to explore the integration of mass spectrometry (MS)-based proteomics and artificial intelligence in revolutionizing personalized medicine. This report highlights key discussions on recent technological advances in MS-based proteomics, including improvements in sensitivity, throughput, and data analysis. Particular emphasis was placed on plasma proteomics and its potential for biomarker discovery across various diseases. The symposium addressed critical challenges in translating proteomic discoveries to clinical practice, including standardization, regulatory considerations and the need for robust 'business cases' to motivate adoption. Promising applications were presented in areas such as cancer diagnostics, neurodegenerative diseases, and cardiovascular health. The integration of proteomics with other omics technologies and imaging methods was explored, showcasing the power of multi-modal approaches in understanding complex biological systems. Artificial intelligence emerged as a crucial tool for the acquisition of large-scale proteomic datasets, extracting meaningful insights, and enhancing clinical decision-making. By fostering dialogue between academic researchers, industry leaders in proteomics technology, and clinicians, the symposium illuminated potential pathways for proteomics to transform personalized medicine, advancing the cause of more precise diagnostics and targeted therapies.
    DOI:  https://doi.org/10.1016/j.mcpro.2024.100877
  8. Electrophoresis. 2024 Nov 11.
    A Micro-Flow Liquid Chromatography-Mass Spectrometry Method for the Quantification of Oxylipins in Volume-Limited Human Plasma.
    Bingshu He, Rawi Ramautar, Marian Beekman, P Eline Slagboom, Amy Harms, Thomas Hankemeier.
      Oxylipins are well-known lipid mediators in various inflammatory conditions. Their endogenous concentrations range from low picomolar to nanomolar, and there are growing demands to determine their concentrations in low-volume matrices for pathological studies, including blood, cerebrospinal fluids from animal disease models, infants, and microsampling devices. Most of the published quantification methods for comprehensive profiling of oxylipins still require more than 50 µL plasma as a starting volume to detect these low levels. The aim of our study is to develop a sensitive and reliable method for the quantification of oxylipins in volume-limited human plasma samples. We established and validated a micro-liquid chromatography (LC)-mass spectrometry (MS)/MS method that requires only 5 µL of human plasma for the determination of 66 oxylipins. The optimized micro-LC-MS/MS method utilized a flow rate of 4 µL/min with a 0.3-mm inner diameter column. With an injection volume of 3 µL, our method provides limits of detection in the range from 0.1 to 91.9 pM, and limits of quantification range from 0.3 to 306.2 pM. The sensitivity enhancement compared to conventional flow ranged from 1.4 to 180.7 times for 51 compounds depending on their physical-chemical properties. After validation, the method was applied to analyze 40 plasma samples from a healthy aging study to demonstrate robustness and sensitivity.
    Keywords:  micro–liquid chromatography (LC)–mass spectrometry (MS); oxylipins; sensitivity enhancement; volume‐limited plasma
    DOI:  https://doi.org/10.1002/elps.202400151
  9. Front Chem. 2024 ;12 1477492
    A complementary approach for detecting biological signals through a semi-automated feature selection tool.
    Gabriel Santos Arini, Luiz Gabriel Souza Mencucini, Rafael de Felício, Luís Guilherme Pereira Feitosa, Paula Rezende-Teixeira, Henrique Marcel Yudi de Oliveira Tsuji, Alan Cesar Pilon, Danielle Rocha Pinho, Letícia Veras Costa Lotufo, Norberto Peporine Lopes, Daniela Barretto Barbosa Trivella, Ricardo Roberto da Silva.
       Introduction: Untargeted metabolomics is often used in studies that aim to trace the metabolic profile in a broad context, with the data-dependent acquisition (DDA) mode being the most commonly used method. However, this approach has the limitation that not all detected ions are fragmented in the data acquisition process, in addition to the lack of specificity regarding the process of fragmentation of biological signals. The present work aims to extend the detection of biological signals and contribute to overcoming the fragmentation limits of the DDA mode with a dynamic procedure that combines experimental and in silico approaches.
    Methods: Metabolomic analysis was performed on three different species of actinomycetes using liquid chromatography coupled with mass spectrometry. The data obtained were preprocessed by the MZmine software and processed by the custom package RegFilter.
    Results and Discussion: RegFilter allowed the coverage of the entire chromatographic run and the selection of precursor ions for fragmentation that were previously missed in DDA mode. Most of the ions selected by the tool could be annotated through three levels of annotation, presenting biologically relevant candidates. In addition, the tool offers the possibility of creating local spectral libraries curated according to the user's interests. Thus, the adoption of a dynamic analysis flow using RegFilter allowed for detection optimization and curation of potential biological signals, previously absent in the DDA mode, being a good complementary approach to the current mode of data acquisition. In addition, this workflow enables the creation and search of in-house tailored custom libraries.
    Keywords:  chemometrics; data dependent acquisition; mass spectrometry; natural products; untargeted metabolomics
    DOI:  https://doi.org/10.3389/fchem.2024.1477492
  10. Talanta. 2024 Oct 30. pii: S0039-9140(24)01506-6. [Epub ahead of print]283 127127
    Lipidomic profiling of triple-negative breast cancer cells reveals distinct metabolic signatures associated with EpCAM expression.
    Xiaoyue Huang, Xinyu Di, Melissa Celine Zuiderwijk, Lu Zhang, Hanneke Leegwater, Sam Davidse, Alida Kindt, Amy Harms, Thomas Hankemeier, Sylvia E Le Dévédec, Ahmed Ali.
      Lipid metabolism is essential at all stages of cancer progression, particularly for triple-negative breast cancer (TNBC) the deadliest cancer subtype for women patients. TNBC cells exhibit significant metabolic heterogeneity, which contributes to their aggressive behavior. Epithelial-to-mesenchymal transition (EMT), a key step in metastasis, is associated with distinct lipid profiles, where the epithelial cell adhesion molecule (EpCAM) was found to be decreased along the transition. To understand this link, we employed lipidomic profiling of the TNBC cell line SUM149PT, which exhibits high variability in EpCAM, an epithelial marker. Using EpCAM levels to categorize cells with high and low EpCAM expression using fluorescence-activated cell sorter, we performed targeted mass spectrometry analysis of various lipid classes (glycerophospholipids, glycerolipids, lysophospholipids, and sphingolipids) by a hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS)-based screening method. After correcting for cell size, we identified a unique lipid profile associated with each EpCAM expression level. Notably, cells with higher EpCAM expression displayed lower levels of lysophosphatidylethanolamine (LPE). This finding suggests a potential role for LPE in the regulation of EMT in TNBC.
    Keywords:  EMT; EpCAM; HILIC-MS/MS; LPE; Lipidomics; SUM149PT; TNBC
    DOI:  https://doi.org/10.1016/j.talanta.2024.127127
  11. Free Radic Res. 2024 Nov 14. 1-14
    NRF2 signaling and amino acid metabolism in cancer.
    Suji Ham, Bo-Hyun Choi, Mi-Kyoung Kwak.
      Alterations in amino acid metabolism have emerged as a critical component in cancer biology, influencing various aspects of tumor initiation, progression, and metastasis. This review explores how amino acids, beyond their role as protein building blocks, are essential for redox balance, cell proliferation, metastasis, signaling/epigenetic regulation, and tumor microenvironment modulation in cancer. We particularly focus on the intricate relationship between amino acid metabolism and nuclear factor erythroid 2-related factor 2 (NRF2) signaling, a master regulator of oxidative stress response that frequently hyperactivated in cancer. Increasing evidence indicates that NRF2 is a key player in amino acid metabolism, orchestrating metabolism of cysteine, glutamine, and serine/glycine to promote cancer cell survival and growth. This comprehensive analysis provides insights into potential therapeutic strategies targeting the NRF2-amino acid metabolism axis, offering new avenues for cancer treatment that address multiple aspects of tumor biology.
    Keywords:  NRF2; amino acid metabolism; cancer; cysteine; serine
    DOI:  https://doi.org/10.1080/10715762.2024.2423690
  12. STAR Protoc. 2024 Nov 09. pii: S2666-1667(24)00607-5. [Epub ahead of print]5(4): 103442
    Protocol for unified metabolomics and proteomics analysis of formalin-fixed paraffin-embedded tissue.
    Abisola R Isaiah, Laneke Luies, Du Toit Loots, Aurelia A Williams, Maré Vlok, Novel N Chegou, Marceline Tutu van Furth, Martijn van der Kuip, Shayne Mason.
      The use of archival formalin-fixed paraffin-embedded (FFPE) tissue samples for biochemical analyses is problematic because of the formation of a Schiff base, leading to low protein and metabolite yields during analytical extractions. Here, we overcome this issue using a unified protocol on FFPE tissue for metabolomics and proteomics analyses. Using 20 mg of wet mass tissue, this protocol consistently extracted more than 50 metabolites (across 11 classes of metabolites) and over 900 proteins.
    Keywords:  health sciences; metabolism; metabolomics; neuroscience; protein biochemistry; proteomics
    DOI:  https://doi.org/10.1016/j.xpro.2024.103442
This page is being maintained by Thomas Krichel. It was last updated on 2024‒12‒09 at 5:53.