bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024‒04‒28
eighteen papers selected by
Giovanny Rodriguez Blanco, University of Edinburgh
  1. Stable Isotope Labeling and Quantification of Photosynthetic Metabolites.
  2. SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.
  3. TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics.
  4. Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.
  5. Selected Ion Monitoring for Orbitrap-Based Metabolomics.
  6. Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.
  7. Aza-Prilezhaev Aziridination-Enabled Multidimensional Analysis of Isomeric Lipids via High-Resolution U-Shaped Mobility Analyzer-Mass Spectrometry.
  8. Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection.
  9. Cancer cell metabolism and antitumour immunity.
  10. DIMet: An open-source tool for Differential analysis of targeted Isotope-labeled Metabolomics data.
  11. Revisiting liver metabolism through acetyl-CoA carboxylase inhibition.
  12. ACSM1 and ACSM3 regulate fatty acid metabolism to support prostate cancer growth and constrain ferroptosis.
  13. Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows.
  14. Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel.
  15. An Efficient, Amine-Specific, and Cost-Effective Method for TMT 6/11-plex Labeling Improves the Proteome Coverage, Quantitative Accuracy and Precision.
  16. Metabolomics-driven discovery of therapeutic targets for cancer cachexia.
  17. A guide to precise measurements of isotope abundance by ESI-Orbitrap MS.
  18. A framework for quality control in quantitative proteomics.
  1. Methods Mol Biol. 2024 ;2790 439-466
    Stable Isotope Labeling and Quantification of Photosynthetic Metabolites.
    Chiara Baccolini, Stéphanie Arrivault.
      Stable isotope labeling with 13CO2 coupled with mass spectrometry allows monitoring the incorporation of 13C into photosynthetic intermediates and is a powerful technique for the investigation of the metabolic dynamics of photosynthesis. We describe here a protocol for 13CO2 labeling of large leaved plants and of Arabidopsis thaliana rosette, and a method for quantitative mass spectrometry analyses to uncover the labeling pattern of Calvin-Benson cycle sucrose, and starch synthesis as well as carbon-concentrating mechanism metabolites.
    Keywords:  13C enrichment; 13CO2 labeling; Calvin-Benson Cycle; Carbon-concentrating mechanism; Isotopomer distribution; LC-MS/MS; Mass spectrometry; Metabolite quantification; Photosynthesis; Pulse-chase
    DOI:  https://doi.org/10.1007/978-1-0716-3790-6_24
  2. Anal Chem. 2024 Apr 20.
    SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.
    Luis F Ponce, Stephanie L Bishop, Soren Wacker, Ryan A Groves, Ian A Lewis.
      Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.
    DOI:  https://doi.org/10.1021/acs.analchem.3c04988
  3. bioRxiv. 2024 Apr 09. pii: 2024.04.05.588302. [Epub ahead of print]
    TopDIA: A Software Tool for Top-Down Data-Independent Acquisition Proteomics.
    Abdul Rehman Basharat, Xingzhao Xiong, Tian Xu, Yong Zang, Liangliang Sun, Xiaowen Liu.
      Top-down mass spectrometry is widely used for proteoform identification, characterization, and quantification owing to its ability to analyze intact proteoforms. In the last decade, top-down proteomics has been dominated by top-down data-dependent acquisition mass spectrometry (TD-DDA-MS), and top-down data-independent acquisition mass spectrometry (TD-DIA-MS) has not been well studied. While TD-DIA-MS produces complex multiplexed tandem mass spectrometry (MS/MS) spectra, which are challenging to confidently identify, it selects more precursor ions for MS/MS analysis and has the potential to increase proteoform identifications compared with TD-DDA-MS. Here we present TopDIA, the first software tool for proteoform identification by TD-DIA-MS. It generates demultiplexed pseudo MS/MS spectra from TD-DIA-MS data and then searches the pseudo MS/MS spectra against a protein sequence database for proteoform identification. We compared the performance of TD-DDA-MS and TD-DIA-MS using Escherichia coli K-12 MG1655 cells and demonstrated that TD-DIA-MS with TopDIA increased proteoform and protein identifications compared with TD-DDA-MS.
    DOI:  https://doi.org/10.1101/2024.04.05.588302
  4. Proc IEEE Int Symp Bioinformatics Bioeng. 2023 Dec;2023 28-35
    Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.
    Shiva Ebrahimi, Xuan Guo.
      Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce Casanovo-DIA, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our Casanovo-DIA model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/Casanovo-DIA.
    Keywords:  De novo peptide sequencing; data-independent acquisition (DIA); mass spectrometry; transformer
    DOI:  https://doi.org/10.1109/bibe60311.2023.00013
  5. Metabolites. 2024 Mar 25. pii: 184. [Epub ahead of print]14(4):
    Selected Ion Monitoring for Orbitrap-Based Metabolomics.
    Wenyun Lu, Matthew J McBride, Won Dong Lee, Xi Xing, Xincheng Xu, Xi Li, Anna M Oschmann, Yihui Shen, Caroline Bartman, Joshua D Rabinowitz.
      Orbitrap mass spectrometry in full scan mode enables the simultaneous detection of hundreds of metabolites and their isotope-labeled forms. Yet, sensitivity remains limiting for many metabolites, including low-concentration species, poor ionizers, and low-fractional-abundance isotope-labeled forms in isotope-tracing studies. Here, we explore selected ion monitoring (SIM) as a means of sensitivity enhancement. The analytes of interest are enriched in the orbitrap analyzer by using the quadrupole as a mass filter to select particular ions. In tissue extracts, SIM significantly enhances the detection of ions of low intensity, as indicated by improved signal-to-noise (S/N) ratios and measurement precision. In addition, SIM improves the accuracy of isotope-ratio measurements. SIM, however, must be deployed with care, as excessive accumulation in the orbitrap of similar m/z ions can lead, via space-charge effects, to decreased performance (signal loss, mass shift, and ion coalescence). Ion accumulation can be controlled by adjusting settings including injection time and target ion quantity. Overall, we suggest using a full scan to ensure broad metabolic coverage, in tandem with SIM, for the accurate quantitation of targeted low-intensity ions, and provide methods deploying this approach to enhance metabolome coverage.
    Keywords:  SIM; fluxomics; full scan; isotope labeling; isotope tracing; metabolomics; orbitrap; relative standard deviation; selected ion monitoring; signal-to-noise ratio
    DOI:  https://doi.org/10.3390/metabo14040184
  6. ArXiv. 2024 Apr 09. pii: arXiv:2402.11363v2. [Epub ahead of print]
    Transformer-based de novo peptide sequencing for data-independent acquisition mass spectrometry.
    Shiva Ebrahimi, Xuan Guo.
      Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and non-targeted fragmentation of precursor ions. The DIA-generated MS/MS spectra present a formidable obstacle due to their inherent high multiplexing nature. Each spectrum encapsulates fragmented product ions originating from multiple precursor peptides. This intricacy poses a particularly acute challenge in de novo peptide/protein sequencing, where current methods are ill-equipped to address the multiplexing conundrum. In this paper, we introduce DiaTrans, a deep-learning model based on transformer architecture. It deciphers peptide sequences from DIA mass spectrometry data. Our results show significant improvements over existing STOA methods, including DeepNovo-DIA and PepNet. Casanovo-DIA enhances precision by 15.14% to 34.8%, recall by 11.62% to 31.94% at the amino acid level, and boosts precision by 59% to 81.36% at the peptide level. Integrating DIA data and our DiaTrans model holds considerable promise to uncover novel peptides and more comprehensive profiling of biological samples. Casanovo-DIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/DiaTrans.
  7. Anal Chem. 2024 Apr 22.
    Aza-Prilezhaev Aziridination-Enabled Multidimensional Analysis of Isomeric Lipids via High-Resolution U-Shaped Mobility Analyzer-Mass Spectrometry.
    Yuling Li, Yiming Wang, Kang Guo, Kuo-Feng Tseng, Xiaoqiang Zhang, Wenjian Sun.
      Unsaturated lipids constitute a significant portion of the lipidome, serving as players of multifaceted functions involving cellular signaling, membrane structure, and bioenergetics. While derivatization-assisted liquid chromatography tandem mass spectrometry (LC-MS/MS) remains the gold standard technique in lipidome, it mainly faces challenges in efficiently labeling the carbon-carbon double bond (C═C) and differentiating isomeric lipids in full dimension. This presents a need for new orthogonal methodologies. Herein, a metal- and additive-free aza-Prilezhaev aziridination (APA)-enabled ion mobility mass spectrometric method is developed for probing multiple levels of unsaturated lipid isomerization with high sensitivity. Both unsaturated polar and nonpolar lipids can be efficiently labeled in the form of N-H aziridine without significant side reactions. The signal intensity can be increased by up to 3 orders of magnitude, achieving the nM detection limit. Abundant site-specific fragmentation ions indicate C═C location and sn-position in MS/MS spectra. Better yet, a stable monoaziridination product is dominant, simplifying the spectrum for lipids with multiple double bonds. Coupled with a U-shaped mobility analyzer, identification of geometric isomers and separation of different lipid classes can be achieved. Additionally, a unique pseudo MS3 mode with UMA-QTOF MS boosts the sensitivity for generating diagnostic fragments. Overall, the current method provides a comprehensive solution for deep-profiling lipidomics, which is valuable for lipid marker discovery in disease monitoring and diagnosis.
    DOI:  https://doi.org/10.1021/acs.analchem.4c00481
  8. Anal Chem. 2024 Apr 23.
    Single-Cell Untargeted Lipidomics Using Liquid Chromatography and Data-Dependent Acquisition after Live Cell Selection.
    Johanna von Gerichten, Kyle D G Saunders, Anastasia Kontiza, Carla F Newman, George Mayson, Dany J V Beste, Eirini Velliou, Anthony D Whetton, Melanie J Bailey.
      We report the development and validation of an untargeted single-cell lipidomics method based on microflow chromatography coupled to a data-dependent mass spectrometry method for fragmentation-based identification of lipids. Given the absence of single-cell lipid standards, we show how the methodology should be optimized and validated using a dilute cell extract. The methodology is applied to dilute pancreatic cancer and macrophage cell extracts and standards to demonstrate the sensitivity requirements for confident assignment of lipids and classification of the cell type at the single-cell level. The method is then coupled to a system that can provide automated sampling of live, single cells into capillaries under microscope observation. This workflow retains the spatial information and morphology of cells during sampling and highlights the heterogeneity in lipid profiles observed at the single-cell level. The workflow is applied to show changes in single-cell lipid profiles as a response to oxidative stress, coinciding with expanded lipid droplets. This demonstrates that the workflow is sufficiently sensitive to observing changes in lipid profiles in response to a biological stimulus. Understanding how lipids vary in single cells will inform future research into a multitude of biological processes as lipids play important roles in structural, biophysical, energy storage, and signaling functions.
    DOI:  https://doi.org/10.1021/acs.analchem.3c05677
  9. Nat Rev Immunol. 2024 Apr 22.
    Cancer cell metabolism and antitumour immunity.
    Mara De Martino, Jeffrey C Rathmell, Lorenzo Galluzzi, Claire Vanpouille-Box.
      Accumulating evidence suggests that metabolic rewiring in malignant cells supports tumour progression not only by providing cancer cells with increased proliferative potential and an improved ability to adapt to adverse microenvironmental conditions but also by favouring the evasion of natural and therapy-driven antitumour immune responses. Here, we review cancer cell-intrinsic and cancer cell-extrinsic mechanisms through which alterations of metabolism in malignant cells interfere with innate and adaptive immune functions in support of accelerated disease progression. Further, we discuss the potential of targeting such alterations to enhance anticancer immunity for therapeutic purposes.
    DOI:  https://doi.org/10.1038/s41577-024-01026-4
  10. Bioinformatics. 2024 Apr 24. pii: btae282. [Epub ahead of print]
    DIMet: An open-source tool for Differential analysis of targeted Isotope-labeled Metabolomics data.
    Johanna Galvis, Joris Guyon, Benjamin Dartigues, Helge Hecht, Björn Grüning, Florian Specque, Hayssam Soueidan, Slim Karkar, Thomas Daubon, Macha Nikolski.
      MOTIVATION: Many diseases, such as cancer, are characterized by an alteration of cellular metabolism allowing cells to adapt to changes in the microenvironment. Stable isotope-resolved metabolomics and downstream data analyses are widely used techniques for unraveling cells' metabolic activity to understand the altered functioning of metabolic pathways in the diseased state. While a number of bioinformatic solutions exist for the differential analysis of Stable Isotope-Resolved Metabolomics data, there is currently no available resource providing a comprehensive toolbox.RESULTS: In this work, we present DIMet, a one-stop comprehensive tool for differential analysis of targeted tracer data. DIMet accepts metabolite total abundances, isotopologue contributions, and isotopic mean enrichment, and supports differential comparison (pairwise and multi-group), time-series analyses, and labeling profile comparison. Moreover, it integrates transcriptomics and targeted metabolomics data through network-based metabolograms. We illustrate the use of DIMet in real SIRM datasets obtained from Glioblastoma P3 cell-line samples. DIMet is open-source, and is readily available for routine downstream analysis of isotope-labeled targeted metabolomics data, as it can be used both in the command line interface or as a complete toolkit in the public Galaxy Europe and Workfow4Metabolomics web platforms.
    AVAILABILITY: DIMet is freely available at https://github.com/cbib/DIMet, and through https://usegalaxy.eu and https://workflow4metabolomics.usegalaxy.fr. All the datasets are available at Zenodo https://zenodo.org/records/10925786.
    SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
    DOI:  https://doi.org/10.1093/bioinformatics/btae282
  11. Trends Endocrinol Metab. 2024 Apr 24. pii: S1043-2760(24)00093-6. [Epub ahead of print]
    Revisiting liver metabolism through acetyl-CoA carboxylase inhibition.
    Armando Jesús Pérez-Díaz, María Ángeles Núñez-Sánchez, Bruno Ramos-Molina.
      Liver-targeted acetyl-coenzyme A (CoA) carboxylase (ACC) inhibitors in metabolic dysfunction-associated steatotic liver disease (MASLD) trials reveal notable secondary effects: hypertriglyceridemia and altered glucose metabolism, paradoxically with reduced hepatic steatosis. In their study, Deja et al. explored how hepatic ACC influences metabolism using different pharmacological and genetic methods, coupled with targeted metabolomics and stable isotope-based tracing techniques.
    Keywords:  acetyl-CoA carboxylase; autophagy; lipogenesis; liver metabolism; malonyl-CoA
    DOI:  https://doi.org/10.1016/j.tem.2024.04.010
  12. Cancer Res. 2024 Apr 24.
    ACSM1 and ACSM3 regulate fatty acid metabolism to support prostate cancer growth and constrain ferroptosis.
    Raj Kumar Shrestha, Zeyad D Nassar, Adrienne R Hanson, Richard Iggo, Scott L Townley, Jonas Dehairs, Chui Yan Mah, Madison Helm, Mohammadreza Alizadeh-Ghodsi, Marie Pickering, Bart Ghesquiere, Matthew J Watt, Lake-Ee Quek, Andrew J Hoy, Wayne D Tilley, Johannes V Swinnen, Lisa M Butler, Luke A Selth.
      Solid tumors are highly reliant on lipids for energy, growth, and survival. In prostate cancer, the activity of the androgen receptor (AR) is associated with reprogramming of lipid metabolic processes. Here, we identified acyl-CoA synthetase medium chain family members 1 and 3 (ACSM1 and ACSM3) as AR-regulated mediators of prostate cancer metabolism and growth. ACSM1 and ACSM3 were upregulated in prostate tumors compared to non-malignant tissues and other cancer types. Both enzymes enhanced proliferation and protected prostate cancer cells from death in vitro, while silencing ACSM3 led to reduced tumor growth in an orthotopic xenograft model. ACSM1 and ACSM3 were major regulators of the prostate cancer lipidome and enhanced energy production via fatty acid oxidation. Metabolic dysregulation caused by loss of ACSM1/3 led to mitochondrial oxidative stress, lipid peroxidation and cell death by ferroptosis. Conversely, elevated ACSM1/3 activity enabled prostate cancer cells to survive toxic levels of medium chain fatty acids and promoted resistance to ferroptosis-inducing drugs and AR antagonists. Collectively, this study reveals a tumor-promoting function for medium chain acyl-CoA synthetases and positions ACSM1 and ACSM3 as key players in prostate cancer progression and therapy resistance.
    DOI:  https://doi.org/10.1158/0008-5472.CAN-23-1489
  13. J Proteome Res. 2024 Apr 26.
    Benefit of In Silico Predicted Spectral Libraries in Data-Independent Acquisition Data Analysis Workflows.
    An Staes, Teresa Mendes Maia, Sara Dufour, Robbin Bouwmeester, Ralf Gabriels, Lennart Martens, Kris Gevaert, Francis Impens, Simon Devos.
      Data-independent acquisition (DIA) has become a well-established method for MS-based proteomics. However, the list of options to analyze this type of data is quite extensive, and the use of spectral libraries has become an important factor in DIA data analysis. More specifically the use of in silico predicted libraries is gaining more interest. By working with a differential spike-in of human standard proteins (UPS2) in a constant yeast tryptic digest background, we evaluated the sensitivity, precision, and accuracy of the use of in silico predicted libraries in data DIA data analysis workflows compared to more established workflows. Three commonly used DIA software tools, DIA-NN, EncyclopeDIA, and Spectronaut, were each tested in spectral library mode and spectral library-free mode. In spectral library mode, we used independent spectral library prediction tools PROSIT and MS2PIP together with DeepLC, next to classical data-dependent acquisition (DDA)-based spectral libraries. In total, we benchmarked 12 computational workflows for DIA. Our comparison showed that DIA-NN reached the highest sensitivity while maintaining a good compromise on the reproducibility and accuracy levels in either library-free mode or using in silico predicted libraries pointing to a general benefit in using in silico predicted libraries.
    Keywords:  DIA data analysis; benchmarking; data-independent acquisition (DIA); in silico spectral libraries
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00048
  14. Nat Protoc. 2024 Apr 26.
    Deep-time phylogenetic inference by paleoproteomic analysis of dental enamel.
    Alberto J Taurozzi, Patrick L Rüther, Ioannis Patramanis, Claire Koenig, Ryan Sinclair Paterson, Palesa P Madupe, Florian Simon Harking, Frido Welker, Meaghan Mackie, Jazmín Ramos-Madrigal, Jesper V Olsen, Enrico Cappellini.
      In temperate and subtropical regions, ancient proteins are reported to survive up to about 2 million years, far beyond the known limits of ancient DNA preservation in the same areas. Accordingly, their amino acid sequences currently represent the only source of genetic information available to pursue phylogenetic inference involving species that went extinct too long ago to be amenable for ancient DNA analysis. Here we present a complete workflow, including sample preparation, mass spectrometric data acquisition and computational analysis, to recover and interpret million-year-old dental enamel protein sequences. During sample preparation, the proteolytic digestion step, usually an integral part of conventional bottom-up proteomics, is omitted to increase the recovery of the randomly degraded peptides spontaneously generated by extensive diagenetic hydrolysis of ancient proteins over geological time. Similarly, we describe other solutions we have adopted to (1) authenticate the endogenous origin of the protein traces we identify, (2) detect and validate amino acid variation in the ancient protein sequences and (3) attempt phylogenetic inference. Sample preparation and data acquisition can be completed in 3-4 working days, while subsequent data analysis usually takes 2-5 days. The workflow described requires basic expertise in ancient biomolecules analysis, mass spectrometry-based proteomics and molecular phylogeny. Finally, we describe the limits of this approach and its potential for the reconstruction of evolutionary relationships in paleontology and paleoanthropology.
    DOI:  https://doi.org/10.1038/s41596-024-00975-3
  15. J Proteome Res. 2024 Apr 25.
    An Efficient, Amine-Specific, and Cost-Effective Method for TMT 6/11-plex Labeling Improves the Proteome Coverage, Quantitative Accuracy and Precision.
    Yan Cai, Chenchen Chang, Qin Yang, Rijing Liao.
      Tandem mass tags (TMT) are widely used in proteomics to simultaneously quantify multiple samples in a single experiment. The tags can be easily added to the primary amines of peptides/proteins through chemical reactions. In addition to amines, TMT reagents also partially react with the hydroxyl groups of serine, threonine, and tyrosine residues under alkaline conditions, which significantly compromises the analytical sensitivity and precision. Under alkaline conditions, reducing the TMT molar excess can partially mitigate overlabeling of histidine-free peptides, but has a limited effect on peptides containing histidine and hydroxyl groups. Here, we present a method under acidic conditions to suppress overlabeling while efficiently labeling amines, using only one-fifth of the TMT amount recommended by the manufacturer. In a deep-scale analysis of a yeast/human two-proteome sample, we systematically evaluated our method against the manufacturer's method and a previously reported TMT-reduced method. Our method reduced overlabeled peptides by 9-fold and 6-fold, respectively, resulting in the substantial enhancement in peptide/protein identification rates. More importantly, the quantitative accuracy and precision were improved as overlabeling was reduced, endowing our method with greater statistical power to detect 42% and 12% more statistically significant yeast proteins compared to the standard and TMT-reduced methods, respectively. Mass spectrometric data have been deposited in the ProteomeXchange Consortium via the iProX partner repository with the data set identifier PXD047052.
    Keywords:  isobaric labeling; labeling specificity; overlabeling; proteome quantification; tandem mass tags
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00129
  16. J Cachexia Sarcopenia Muscle. 2024 Apr 21.
    Metabolomics-driven discovery of therapeutic targets for cancer cachexia.
    Pengfei Cui, Xiaoyi Li, Caihua Huang, Donghai Lin.
      Cancer cachexia (CC) is a devastating metabolic syndrome characterized by skeletal muscle wasting and body weight loss, posing a significant burden on the health and survival of cancer patients. Despite ongoing efforts, effective treatments for CC are still lacking. Metabolomics, an advanced omics technique, offers a comprehensive analysis of small-molecule metabolites involved in cellular metabolism. In CC research, metabolomics has emerged as a valuable tool for identifying diagnostic biomarkers, unravelling molecular mechanisms and discovering potential therapeutic targets. A comprehensive search strategy was implemented to retrieve relevant articles from primary databases, including Web of Science, Google Scholar, Scopus and PubMed, for CC and metabolomics. Recent advancements in metabolomics have deepened our understanding of CC by uncovering key metabolic signatures and elucidating underlying mechanisms. By targeting crucial metabolic pathways including glucose metabolism, amino acid metabolism, fatty acid metabolism, bile acid metabolism, ketone body metabolism, steroid metabolism and mitochondrial energy metabolism, it becomes possible to restore metabolic balance and alleviate CC symptoms. This review provides a comprehensive summary of metabolomics studies in CC, focusing on the discovery of potential therapeutic targets and the evaluation of modulating specific metabolic pathways for CC treatment. By harnessing the insights derived from metabolomics, novel interventions for CC can be developed, leading to improved patient outcomes and enhanced quality of life.
    Keywords:  Cancer cachexia; Metabolic pathway; Metabolomics; Therapeutic target
    DOI:  https://doi.org/10.1002/jcsm.13465
  17. Nat Protoc. 2024 Apr 23.
    A guide to precise measurements of isotope abundance by ESI-Orbitrap MS.
    Kristýna Kantnerová, Nils Kuhlbusch, Dieter Juchelka, Andreas Hilkert, Sebastian Kopf, Cajetan Neubauer.
      Stable isotopes of carbon, hydrogen, nitrogen, oxygen and sulfur are widespread in nature. Nevertheless, their relative abundance is not the same everywhere. This is due to kinetic isotope effects in enzymes and other physical principles such as equilibrium thermodynamics. Variations in isotope ratios offer unique insights into environmental pollution, trophic relationships in ecology, metabolic disorders and Earth history including climate history. Although classical isotope ratio mass spectrometry (IRMS) techniques still struggle to access intramolecular information like site-specific isotope abundance, electrospray ionization-Orbitrap mass spectrometry can be used to achieve precise and accurate intramolecular quantification of isotopically substituted molecules ('isotopocules'). This protocol describes two procedures. In the first one, we provide a step-by-step beginner's guide for performing multi-elemental, intramolecular and site-specific stable isotope analysis in unlabeled polar solutes by direct infusion. Using a widely available calibration solution, isotopocules of trifluoroacetic acid and immonium ions from the model peptide MRFA are quantified. In the second approach, nitrate is used as a simple model for a flow injection routine that enables access to a diverse range of naturally occurring isotopic signatures in inorganic oxyanions. Each procedure takes 2-3 h to complete and requires expertise only in general mass spectrometry. The workflows use optimized Orbitrap IRMS data-extraction and -processing software and are transferable to various analytes amenable to soft ionization, including metabolites, peptides, drugs and environmental pollutants. Optimized mass spectrometry systems will enable intramolecular isotope research in many areas of biology.
    DOI:  https://doi.org/10.1038/s41596-024-00981-5
  18. bioRxiv. 2024 Apr 13. pii: 2024.04.12.589318. [Epub ahead of print]
    A framework for quality control in quantitative proteomics.
    Kristine A Tsantilas, Gennifer E Merrihew, Julia E Robbins, Richard S Johnson, Jea Park, Deanna L Plubell, Eric Huang, Michael Riffle, Vagisha Sharma, Brendan X MacLean, Josh Eckels, Michael B Bereman, Sandra E Spencer, Andrew N Hoofnagle, Michael J MacCoss.
      A thorough evaluation of the quality, reproducibility, and variability of bottom-up proteomics data is necessary at every stage of a workflow from planning to analysis. We share real-world case studies applying adaptable quality control (QC) measures to assess sample preparation, system function, and quantitative analysis. System suitability samples are repeatedly measured longitudinally with targeted methods, and we share examples where they are used on three instrument platforms to identify severe system failures and track function over months to years. Internal QCs incorporated at protein and peptide-level allow our team to assess sample preparation issues and to differentiate system failures from sample-specific issues. External QC samples prepared alongside our experimental samples are used to verify the consistency and quantitative potential of our results during batch correction and normalization before assessing biological phenotypes. We combine these controls with rapid analysis using Skyline, longitudinal QC metrics using AutoQC, and server-based data deposition using PanoramaWeb. We propose that this integrated approach to QC be used as a starting point for groups to facilitate rapid quality control assessment to ensure that valuable instrument time is used to collect the best quality data possible.
    DOI:  https://doi.org/10.1101/2024.04.12.589318
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