bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024–02–25
twelve papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.
  2. Targeted metabolomics in human and animal biofluids and tissues using liquid chromatography coupled with tandem mass spectrometry.
  3. Hyperacetylated histone H4 is a source of carbon contributing to lipid synthesis.
  4. 1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry.
  5. Imputation of plasma lipid species to facilitate integration of lipidomic datasets.
  6. Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.
  7. Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry.
  8. Recent Analytical Methodologies in Lipid Analysis.
  9. Concise synthesis of deuterium-labelled proanthocyanidin metabolite 5-(3',4'-dihydroxyphenyl)-γ-valerolactone as an analytical tool.
  10. Optimized Mass Spectrometry Detection of Thyroid Hormones and Polar Metabolites in Rodent Cerebrospinal Fluid.
  11. Inserting Pre-Analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics With Sample Multiplexing.
  12. Accurate Clinical Detection of Vitamin D by Mass Spectrometry: A Review.
  1. Metabolites. 2024 Feb 15. pii: 125. [Epub ahead of print]14(2):
    metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics.
    Hani Habra, Jennifer L Meijer, Tong Shen, Oliver Fiehn, David A Gaul, Facundo M Fernández, Kaitlin R Rempfert, Thomas O Metz, Karen E Peterson, Charles R Evans, Alla Karnovsky.
      Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.
    Keywords:  LC-MS; R package; alignment; chromatography; metabolomics; software
    DOI:  https://doi.org/10.3390/metabo14020125
  2. STAR Protoc. 2024 Feb 16. pii: S2666-1667(24)00049-2. [Epub ahead of print]5(1): 102884
    Targeted metabolomics in human and animal biofluids and tissues using liquid chromatography coupled with tandem mass spectrometry.
    Jill A Willency, Yanzhu Lin, Valentina Pirro.
      Here, we present a targeted polar metabolomics protocol for the analysis of biofluids and frozen tissue biopsies using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We describe steps for sample pretreatment, liquid-liquid extraction, and isolation of polar metabolites. We then detail procedures for target LC-MS/MS analysis. In this protocol, we focus on the analysis of plasma and serum samples. We also provide brief instructions on how to process other biological matrices as supplemental information. For complete details on the use and execution of this protocol, please refer to Coskun et al. (2022).1.
    Keywords:  Cell-based Assays; Chemistry; Metabolomics
    DOI:  https://doi.org/10.1016/j.xpro.2024.102884
  3. EMBO J. 2024 Feb 21.
    Hyperacetylated histone H4 is a source of carbon contributing to lipid synthesis.
    Evelina Charidemou, Roberta Noberini, Chiara Ghirardi, Polymnia Georgiou, Panayiota Marcou, Andria Theophanous, Katerina Strati, Hector Keun, Volker Behrends, Tiziana Bonaldi, Antonis Kirmizis.
      Histone modifications commonly integrate environmental cues with cellular metabolic outputs by affecting gene expression. However, chromatin modifications such as acetylation do not always correlate with transcription, pointing towards an alternative role of histone modifications in cellular metabolism. Using an approach that integrates mass spectrometry-based histone modification mapping and metabolomics with stable isotope tracers, we demonstrate that elevated lipids in acetyltransferase-depleted hepatocytes result from carbon atoms derived from deacetylation of hyperacetylated histone H4 flowing towards fatty acids. Consistently, enhanced lipid synthesis in acetyltransferase-depleted hepatocytes is dependent on histone deacetylases and acetyl-CoA synthetase ACSS2, but not on the substrate specificity of the acetyltransferases. Furthermore, we show that during diet-induced lipid synthesis the levels of hyperacetylated histone H4 decrease in hepatocytes and in mouse liver. In addition, overexpression of acetyltransferases can reverse diet-induced lipogenesis by blocking lipid droplet accumulation and maintaining the levels of hyperacetylated histone H4. Overall, these findings highlight hyperacetylated histones as a metabolite reservoir that can directly contribute carbon to lipid synthesis, constituting a novel function of chromatin in cellular metabolism.
    Keywords:  Acetylation; Epigenetics; Histone Reservoirs; Lipid Metabolism
    DOI:  https://doi.org/10.1038/s44318-024-00053-0
  4. bioRxiv. 2024 Feb 08. pii: 2024.02.06.579213. [Epub ahead of print]
    1.4 min Plasma Proteome Profiling via Nanoparticle Protein Corona and Direct Infusion Mass Spectrometry.
    Yuming Jiang, Jesse G Meyer.
      Non-invasive detection of protein biomarkers in plasma is crucial for clinical purposes. Liquid chromatography mass spectrometry (LC-MS) is the gold standard technique for plasma proteome analysis, but despite recent advances, it remains limited by throughput, cost, and coverage. Here, we introduce a new hybrid method, which integrates direct infusion shotgun proteome analysis (DISPA) with nanoparticle (NP) protein coronas enrichment for high throughput and efficient plasma proteomic profiling. We realized over 280 protein identifications in 1.4 minutes collection time, which enables a potential throughput of approximately 1,000 samples daily. The identified proteins are involved in valuable pathways and 44 of the proteins are FDA approved biomarkers. The robustness and quantitative accuracy of this method were evaluated across multiple NPs and concentrations with a mean coefficient of variation at 17%. Moreover, different protein corona profiles were observed among various nanoparticles based on their distinct surface modifications, and all NP protein profiles exhibited deeper coverage and better quantification than neat plasma. Our streamlined workflow merges coverage and throughput with precise quantification, leveraging both DISPA and NP protein corona enrichments. This underscores the significant potential of DISPA when paired with NP sample preparation techniques for plasma proteome studies.
    DOI:  https://doi.org/10.1101/2024.02.06.579213
  5. Nat Commun. 2024 Feb 20. 15(1): 1540
    Imputation of plasma lipid species to facilitate integration of lipidomic datasets.
    LIPID Study Investigators
      Recent advancements in plasma lipidomic profiling methodology have significantly increased specificity and accuracy of lipid measurements. This evolution, driven by improved chromatographic and mass spectrometric resolution of newer platforms, has made it challenging to align datasets created at different times, or on different platforms. Here we present a framework for harmonising such plasma lipidomic datasets with different levels of granularity in their lipid measurements. Our method utilises elastic-net prediction models, constructed from high-resolution lipidomics reference datasets, to predict unmeasured lipid species in lower-resolution studies. The approach involves (1) constructing composite lipid measures in the reference dataset that map to less resolved lipids in the target dataset, (2) addressing discrepancies between aligned lipid species, (3) generating prediction models, (4) assessing their transferability into the targe dataset, and (5) evaluating their prediction accuracy. To demonstrate our approach, we used the AusDiab population-based cohort (747 lipid species) as the reference to impute unmeasured lipid species into the LIPID study (342 lipid species). Furthermore, we compared measured and imputed lipids in terms of parameter estimation and predictive performance, and validated imputations in an independent study. Our method for harmonising plasma lipidomic datasets will facilitate model validation and data integration efforts.
    DOI:  https://doi.org/10.1038/s41467-024-45838-3
  6. Anal Chem. 2024 Feb 23.
    Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.
    Zixuan Zhang, Huaxu Yu, Ethan Wong-Ma, Pouneh Dokouhaki, Ahmed Mostafa, Jay S Shavadia, Fang Wu, Tao Huan.
      Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.com/HuanLab/AVIR). The rationale is that metabolic features with computational variation have a poor correlation between chromatographic peak area and peak height-based quantifications across the samples in a study. AVIR was trained on a set of 696 manually curated metabolic features and achieved an accuracy of 94% in a 10-fold cross-validation. When tested on various external data sets from public metabolomics repositories, AVIR demonstrated an accuracy range of 84%-97%. Finally, tested on a large-scale metabolomics study, AVIR clearly indicated features with computational variation and thus guided us to manually correct them. Our results show that 75.3% of the samples with computational variation had a relative intensity difference of over 20% after correction. This demonstrates the critical role of AVIR in reducing computational variation to improve quantitative certainty in untargeted metabolomics analysis.
    DOI:  https://doi.org/10.1021/acs.analchem.3c04046
  7. J Chromatogr B Analyt Technol Biomed Life Sci. 2024 Feb 16. pii: S1570-0232(24)00054-0. [Epub ahead of print]1235 124046
    Optimizing and integrating depletion and precipitation methods for plasma proteomics through data-independent acquisition-mass spectrometry.
    Yue Zhou, Helong Zheng, Zengqi Tan, Enci Kang, Peng Xue, Xiang Li, Feng Guan.
      The application of plasma proteomics is a reliable approach for the discovery of biomarkers. However, the utilization of mass spectrometry-based proteomics in plasma encounters limitations due to the presence of high-abundant proteins (HAPs) and the vast dynamic range. To address this issue, we conducted an optimization and integration of depletion and precipitation strategies eliminating interference from HAPs. The optimized procedure involved utilizing 40 µL of beads for the removal of 1 µL of plasma, and maintaining a ratio of 1:1:1 between plasma, urea, and trichloroacetic acid for the precipitation of 50 µL of plasma. To facilitate high-throughput processing, experimental procedures were carried out utilizing 96-well plates. The depletion method identified a total of 1510 proteins, whereas the precipitated method yielded a total of 802 proteins. The integration of these methods yielded a total of 1794 proteins, including a wide concentration range spanning over 8 orders of magnitude. Furthermore, these approaches exhibited a commendable level of reproducibility, as indicated by median coefficients of variation of 14.7 % and 21.1 % for protein intensities, respectively. The integrative method was found to be effective in precisely quantifying yeast proteins that were intentionally spiked in plasma at predetermined rations of 5, 2, 0.5, and 0.2 with a high genuine positive recovery with a range of 71 % to 91 % of all yeast proteins. The use of a complementary and finely tuned approach involving depletion and precipitation demonstrates tremendous potential in the field of discovering protein biomarkers from large-scale cohort studies.
    Keywords:  Data-independent acquisition; Depletion; Plasma proteomics; Precipitation; Quantitative proteomics
    DOI:  https://doi.org/10.1016/j.jchromb.2024.124046
  8. Int J Mol Sci. 2024 Feb 13. pii: 2249. [Epub ahead of print]25(4):
    Recent Analytical Methodologies in Lipid Analysis.
    Ivana Gerhardtova, Timotej Jankech, Petra Majerova, Juraj Piestansky, Dominika Olesova, Andrej Kovac, Josef Jampilek.
      Lipids represent a large group of biomolecules that are responsible for various functions in organisms. Diseases such as diabetes, chronic inflammation, neurological disorders, or neurodegenerative and cardiovascular diseases can be caused by lipid imbalance. Due to the different stereochemical properties and composition of fatty acyl groups of molecules in most lipid classes, quantification of lipids and development of lipidomic analytical techniques are problematic. Identification of different lipid species from complex matrices is difficult, and therefore individual analytical steps, which include extraction, separation, and detection of lipids, must be chosen properly. This review critically documents recent strategies for lipid analysis from sample pretreatment to instrumental analysis and data interpretation published in the last five years (2019 to 2023). The advantages and disadvantages of various extraction methods are covered. The instrumental analysis step comprises methods for lipid identification and quantification. Mass spectrometry (MS) is the most used technique in lipid analysis, which can be performed by direct infusion MS approach or in combination with suitable separation techniques such as liquid chromatography or gas chromatography. Special attention is also given to the correct evaluation and interpretation of the data obtained from the lipid analyses. Only accurate, precise, robust and reliable analytical strategies are able to bring complex and useful lipidomic information, which may contribute to clarification of some diseases at the molecular level, and may be used as putative biomarkers and/or therapeutic targets.
    Keywords:  data analysis; lipid analysis; lipids; liquid chromatography; mass spectrometry; sample treatment
    DOI:  https://doi.org/10.3390/ijms25042249
  9. RSC Adv. 2024 Feb 14. 14(9): 6410-6415
    Concise synthesis of deuterium-labelled proanthocyanidin metabolite 5-(3',4'-dihydroxyphenyl)-γ-valerolactone as an analytical tool.
    Angelica Artasensi, Sarah Mazzotta, Giovanna Baron, Giancarlo Aldini, Laura Fumagalli.
      Deuterated proanthocyanidin metabolite 5-(3',4'-dihydroxyphenyl)-γ-valerolactone has been successfully produced. This metabolite is responsible for several proanthocyanidin protective effects in the field of cancer chemoprevention, skin wrinkle-prevention, and antimicrobials. The synthetic approach applied employs a short reaction sequence and allows the incorporation of four deuterium atoms on non-exchangeable sites, making it an attractive strategy to produce a stable isotopically labeled internal standard for quantitative mass spectrometry isotope dilution-based methods, as demonstrated by developing an LC-MS/MS method to quantify DHPV in urine samples. Overall, this efficient synthesis provides a valuable analytical tool for the study of the metabolic conversion of proanthocyanidins thus helping to investigate the biological effect and establishing the active dose of the key catabolite 5-(3',4'-dihydroxyphenyl)-γ-valerolactone.
    DOI:  https://doi.org/10.1039/d3ra08665h
  10. Metabolites. 2024 Jan 23. pii: 79. [Epub ahead of print]14(2):
    Optimized Mass Spectrometry Detection of Thyroid Hormones and Polar Metabolites in Rodent Cerebrospinal Fluid.
    Ryann M Fame, Ilhan Ali, Maria K Lehtinen, Naama Kanarek, Boryana Petrova.
      Thyroid hormones (TH) are required for brain development and function. Cerebrospinal fluid (CSF), which bathes the brain and spinal cord, contains TH as free hormones or as bound to transthyretin (TTR). Tight TH level regulation in the central nervous system is essential for developmental gene expression, which governs neurogenesis, myelination, and synaptogenesis. This integrated function of TH highlights the importance of developing precise and reliable methods for assessing TH levels in CSF. We report an optimized liquid chromatography-mass spectrometry (LC-MS)-based method to measure TH in rodent CSF and serum, applicable to both fresh and frozen samples. Using this new method, we find distinct differences in CSF TH in pregnant dams vs. non-pregnant adults and in embryonic vs. adult CSF. Further, targeted LC-MS metabolic profiling uncovers distinct central carbon metabolism in the CSF of these populations. TH detection and metabolite profiling of related metabolic pathways open new avenues of rigorous research into CSF TH and will inform future studies on metabolic alterations in CSF during normal development.
    Keywords:  cerebrospinal fluid; development; mass spectrometry method; metabolomics; reverse-phase chromatography; rodent; thyroid hormone
    DOI:  https://doi.org/10.3390/metabo14020079
  11. bioRxiv. 2024 Feb 08. pii: 2024.02.08.579551. [Epub ahead of print]
    Inserting Pre-Analytical Chromatographic Priming Runs Significantly Improves Targeted Pathway Proteomics With Sample Multiplexing.
    Steven R Shuken, Qing Yu, Steven P Gygi.
      GoDig, a recent platform for targeted pathway proteomics without the need for manual assay scheduling or synthetic standard peptides, is a relatively flexible and easy-to-use method that uses tandem mass tags (TMT) to increase sample throughput up to 18-fold relative to label-free targeted proteomics. Though the protein quantification success rate of GoDig is generally high, the peptide-level success rate is more limited, hampering the extension of GoDig to assays of harder-to-quantify proteins and site-specific phenomena. In order to guide the optimization of GoDig assays as well as improvements to the GoDig platform, we created GoDigViewer, a new stand-alone software that provides detailed visualizations of GoDig runs. GoDigViewer guided the implementation of "priming runs," an acquisition mode with significantly higher success rates due to improved elution order calibration. In this mode, one or more chromatographic priming runs are automatically performed to determine accurate and precise target elution orders, followed by analytical runs which quantify targets. Using priming runs, peptide-level quantification success rates exceeded 97% for a list of 400 peptide targets and 95% for a list of 200 targets that are usually not quantified using untargeted mass spectrometry. We used priming runs to establish a quantitative assay of 125 macroautophagy proteins that had a >95% success rate and revealed differences in macroautophagy protein expression profiles across four human cell lines.
    DOI:  https://doi.org/10.1101/2024.02.08.579551
  12. Crit Rev Anal Chem. 2024 Feb 20. 1-25
    Accurate Clinical Detection of Vitamin D by Mass Spectrometry: A Review.
    Zhilong Xu, Kai Yu, Meng Zhang, Yun Ju, Jing He, Yanxiao Jiang, Yunuo Li, Jie Jiang.
      Vitamin D deficiency is thought to be associated with a wide range of diseases, including diabetes, cancer, depression, neurodegenerative diseases, and cardiovascular and cerebrovascular diseases. This vitamin D deficiency is a global epidemic affecting both developing and developed countries and therefore qualitative and quantitative analysis of vitamin D in a clinical context is essential. Mass spectrometry has played an increasingly important role in the clinical analysis of vitamin D because of its accuracy, sensitivity, specificity, and the ability to detect multiple substances at the same time. Despite their many advantages, mass spectrometry-based methods are not without analytical challenges. Front-end and back-end challenges such as protein precipitation, analyte extraction, derivatization, mass spectrometer functionality, must be carefully considered to provide accurate and robust analysis of vitamin D through a well-designed approach with continuous control by internal and external quality control. Therefore, the aim of this review is to provide a comprehensive overview of the development of mass spectrometry methods for vitamin D accurate analysis, including emphasis on status markers, deleterious effects of biological matrices, derivatization reactions, effects of ionization sources, contribution of epimers, standardization of assays between laboratories.
    Keywords:  C3 epimers; Mass spectrometry; accurate detection; derivatization method; measurement standardization; sample pretreatment; vitamin D
    DOI:  https://doi.org/10.1080/10408347.2024.2316237
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