bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024–09–15
eleven papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Tracing de novo Lipids using Stable Isotope Labeling LC-TIMS-TOF MS/MS.
  2. Combining Data Independent Acquisition with Spike-in SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.
  3. Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition.
  4. Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer.
  5. A Framework for Quality Control in Quantitative Proteomics.
  6. Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.
  7. MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method.
  8. High-resolution quadrupole improves spectral purity and reduces interference from non-target ions in isobaric multiplexed quantitative proteomics.
  9. GlyCombo Enables Rapid, Complete Glycan Composition Identification across Diverse Glycomic Sample Types.
  10. Glutaminolysis provides nucleotides and amino acids to regulate osteoclast differentiation in mice.
  11. Quantifying Phospholipids in Organic Samples Using a Hydrophilic Interaction Liquid Chromatography-Inductively Coupled Plasma High-Resolution Mass Spectrometry (HILIC-ICP-HRMS) Method.
  1. J Vis Exp. 2024 Aug 23.
    Tracing de novo Lipids using Stable Isotope Labeling LC-TIMS-TOF MS/MS.
    Katherine D Castro, Lilian Valadares Tose, Matthew Willetts, Melvin A Park, Marcela Nouzova, Fernando G Noriega, Francisco Fernandez-Lima.
      Lipids are highly diverse, and small changes in lipid structures and composition can have profound effects on critical biological functions. Stable isotope labeling (SIL) offers several advantages for the study of lipid distribution, mobilization, and metabolism, as well as de novo lipid synthesis. The successful implementation of the SIL technique requires the removal of interferences from endogenous molecules. In the present work, we describe a high-throughput analytical protocol for the screening of SIL lipids from biological samples; examples will be shown of lipid de novo identification during mosquito ovary development. The use of complementary liquid chromatography trapped ion mobility spectrometry and mass spectrometry allows for the separation and lipids assignment from a single sample in a single scan (<1 h). The described approach takes advantage of recent developments in data-dependent acquisition and data-independent acquisition, using parallel accumulation in the mobility trap followed by sequential fragmentation and collision-induced dissociation. The measurement of SIL at the fatty acid chain level reveals changes in lipid dynamics during the ovary development of mosquitoes. The lipids de novo structures are confidently assigned based on their retention time, mobility, and fragmentation pattern.
    DOI:  https://doi.org/10.3791/65590
  2. Mol Cell Proteomics. 2024 Sep 11. pii: S1535-9476(24)00129-4. [Epub ahead of print] 100839
    Combining Data Independent Acquisition with Spike-in SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.
    Anna Sophie Welter, Maximilian Gerwien, Robert Kerridge, Keziban Merve Alp, Philipp Mertins, Matthias Selbach.
      Data Independent Acquisition (DIA) is increasingly preferred over Data Dependent Acquisition (DDA) due to its higher throughput and fewer missing values. Whereas DDA often utilizes stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mTRAQ and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC methods utilize an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA with spike-in SILAC (DIA-SiS), leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded (FFPE) tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate and comprehensive proteome profiling.
    DOI:  https://doi.org/10.1016/j.mcpro.2024.100839
  3. J Am Soc Mass Spectrom. 2024 Sep 10.
    Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition.
    Ka Yang, Joao A Paulo, Steven P Gygi, Qing Yu.
      Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.
    DOI:  https://doi.org/10.1021/jasms.4c00234
  4. Nat Metab. 2024 Sep 09.
    Spatial single-cell isotope tracing reveals heterogeneity of de novo fatty acid synthesis in cancer.
    Elena Buglakova, Måns Ekelöf, Michaela Schwaiger-Haber, Lisa Schlicker, Martijn R Molenaar, Mohammed Shahraz, Lachlan Stuart, Andreas Eisenbarth, Volker Hilsenstein, Gary J Patti, Almut Schulze, Marteinn T Snaebjornsson, Theodore Alexandrov.
      While heterogeneity is a key feature of cancer, understanding metabolic heterogeneity at the single-cell level remains a challenge. Here we present 13C-SpaceM, a method for spatial single-cell isotope tracing that extends the previously published SpaceM method with detection of 13C6-glucose-derived carbons in esterified fatty acids. We validated 13C-SpaceM on spatially heterogeneous models using liver cancer cells subjected to either normoxia-hypoxia or ATP citrate lyase depletion. This revealed substantial single-cell heterogeneity in labelling of the lipogenic acetyl-CoA pool and in relative fatty acid uptake versus synthesis hidden in bulk analyses. Analysing tumour-bearing brain tissue from mice fed a 13C6-glucose-containing diet, we found higher glucose-dependent synthesis of saturated fatty acids and increased elongation of essential fatty acids in tumours compared with healthy brains. Furthermore, our analysis uncovered spatial heterogeneity in lipogenic acetyl-CoA pool labelling in tumours. Our method enhances spatial probing of metabolic activities in single cells and tissues, providing insights into fatty acid metabolism in homoeostasis and disease.
    DOI:  https://doi.org/10.1038/s42255-024-01118-4
  5. J Proteome Res. 2024 Sep 09.
    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, Jesse D Canterbury, Eric Huang, Michael Riffle, Vagisha Sharma, Brendan X MacLean, Josh Eckels, Christine C Wu, Michael S 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 vignettes 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 the protein and peptide levels 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 (Skyline), longitudinal QC metrics (AutoQC), and server-based data deposition (PanoramaWeb). We propose that this integrated approach to QC is a useful 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. Data are available on Panorama Public and ProteomeXchange under the identifier PXD051318.
    Keywords:  DDA; DIA; PRM; liquid chromatography; mass spectrometry; proteomics; quality control; quantitative results; sample preparation; system suitability
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00363
  6. J Proteome Res. 2024 Sep 10.
    Analysis and Visualization of Quantitative Proteomics Data Using FragPipe-Analyst.
    Yi Hsiao, Haijian Zhang, Ginny Xiaohe Li, Yamei Deng, Fengchao Yu, Hossein Valipour Kahrood, Joel R Steele, Ralf B Schittenhelm, Alexey I Nesvizhskii.
      The FragPipe computational proteomics platform is gaining widespread popularity among the proteomics research community because of its fast processing speed and user-friendly graphical interface. Although FragPipe produces well-formatted output tables that are ready for analysis, there is still a need for an easy-to-use and user-friendly downstream statistical analysis and visualization tool. FragPipe-Analyst addresses this need by providing an R shiny web server to assist FragPipe users in conducting downstream analyses of the resulting quantitative proteomics data. It supports major quantification workflows, including label-free quantification, tandem mass tags, and data-independent acquisition. FragPipe-Analyst offers a range of useful functionalities, such as various missing value imputation options, data quality control, unsupervised clustering, differential expression (DE) analysis using Limma, and gene ontology and pathway enrichment analysis using Enrichr. To support advanced analysis and customized visualizations, we also developed FragPipeAnalystR, an R package encompassing all FragPipe-Analyst functionalities that is extended to support site-specific analysis of post-translational modifications (PTMs). FragPipe-Analyst and FragPipeAnalystR are both open-source and freely available.
    Keywords:  AP-MS; FragPipe; LiP-MS; bioinformatics; downstream analysis; open source; protein phosphorylation; software
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00294
  7. Anal Chim Acta. 2024 Oct 09. pii: S0003-2670(24)00925-5. [Epub ahead of print]1325 343124
    MassLite: An integrated python platform for single cell mass spectrometry metabolomics data pretreatment with graphical user interface and advanced peak alignment method.
    Zhu Zou, Zongkai Peng, Deepti Bhusal, Shakya Wije Munige, Zhibo Yang.
      Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency.
    DOI:  https://doi.org/10.1016/j.aca.2024.343124
  8. Anal Chim Acta. 2024 Oct 09. pii: S0003-2670(24)00936-X. [Epub ahead of print]1325 343135
    High-resolution quadrupole improves spectral purity and reduces interference from non-target ions in isobaric multiplexed quantitative proteomics.
    Shen Zhang, J C Yves Le Blanc, Brett Larsen, Karen Colwill, Lyle Burton, Mircea Guna, Anne-Claude Gingras, Stephen Tate.
       BACKGROUND: Mass spectrometry (MS)-based proteomics is a powerful tool for identifying and quantifying proteins. However, chimeric spectra caused by the fragmentation of multiple precursors within the same isolation window impair the accuracy of peptide identification and isobaric mass tag-based quantification. While there have been advances in computational deconvolution of chimeric spectra and methods to further separate the peptides by ion mobility or through MSn, the use of narrower isolation windows to decrease the fraction of chimeric species remains to be fully explored.
    RESULTS: We present results obtained on a SCIEX TripleTOF instrument where the quadrupole was optimized and tuned for precursor isolation at 0.1 Da (FWHH). Using a three-proteome model (trypsin digest of protein lysates from yeast, human and E. coli) and 8-plex iTRAQ labeling to document the interference effect, we investigated the impact of co-fragmentation on spectral purity, identification accuracy and quantification accuracy. The narrow quadrupole isolation window significantly improved the spectral purity and reduced the interference of non-target precursors on quantification accuracy. The high-resolution isolation strategy also reduced the number of false identifications caused by chimeric spectra. While these improvements came at the cost of sensitivity loss, combining high-resolution isolation with other advanced techniques, including ion mobility, may result in improved accuracy in identification and quantification.
    SIGNIFICANCE: Compared to standard-resolution quadrupole isolation (0.7 Da), high-resolution quadrupole isolation (0.1 Da) significantly improved the spectral purity and quantification accuracy while reducing the number of potential false identifications caused by chimeric spectra, thus showing excellent potential for further development to analyze clinical proteomics samples, for which high accuracy is essential.
    Keywords:  Chimeric spectra; High-resolution quadrupole; Isobaric labeling; Quantitative proteomics; Spectral purity
    DOI:  https://doi.org/10.1016/j.aca.2024.343135
  9. J Am Soc Mass Spectrom. 2024 Sep 13.
    GlyCombo Enables Rapid, Complete Glycan Composition Identification across Diverse Glycomic Sample Types.
    Maia I Kelly, Christopher Ashwood.
      Glycans are sugar-based polymers found to modify biomolecules, including lipids and proteins, as well as occur unconjugated as free polysaccharides. Due to their ubiquitous cellular presentation, glycans mediate crucial biological processes and are frequently sought after as biomarkers for a wide range of diseases. Identification of glycans present in samples acquired with mass spectrometry (MS) is a cornerstone of glycomics research; thus, the ability to rapidly identify glycans in each acquisition is integral to glycomics analysis pipelines. Here we introduce GlyCombo (https://github.com/Protea-Glycosciences/GlyCombo), an open-source, freely available software tool designed to rapidly assign monosaccharide combinations to glycan precursor masses including those subjected to MS2 in LC-MS/MS experiments. GlyCombo was evaluated across six diverse data sets, demonstrating MS vendor, derivatization, and glycan-type neutrality. Compositional assignments using GlyCombo are shown to be faster than the current predominant approach, GlycoMod, a closed-source web application. Two unique features of GlyCombo, multiple adduct search and off-by-one error anticipation, reduced unassigned MS2 scans in a benchmark data set by 40%. Finally, the comprehensiveness of glycan feature identification is exhibited in Skyline, a software that requires predefined transitions that are derived from GlyCombo output files.
    DOI:  https://doi.org/10.1021/jasms.4c00188
  10. EMBO Rep. 2024 Sep 13.
    Glutaminolysis provides nucleotides and amino acids to regulate osteoclast differentiation in mice.
    Guoli Hu, Yilin Yu, Yinshi Ren, Robert J Tower, Guo-Fang Zhang, Courtney M Karner.
      Osteoclasts are bone resorbing cells that are essential to maintain skeletal integrity and function. While many of the growth factors and molecular signals that govern osteoclastogenesis are well studied, how the metabolome changes during osteoclastogenesis is unknown. Using a multifaceted approach, we identified a metabolomic signature of osteoclast differentiation consisting of increased amino acid and nucleotide metabolism. Maintenance of the osteoclast metabolic signature is governed by elevated glutaminolysis. Mechanistically, glutaminolysis provides amino acids and nucleotides which are essential for osteoclast differentiation and bone resorption in vitro. Genetic experiments in mice found that glutaminolysis is essential for osteoclastogenesis and bone resorption in vivo. Highlighting the therapeutic implications of these findings, inhibiting glutaminolysis using CB-839 prevented ovariectomy induced bone loss in mice. Collectively, our data provide strong genetic and pharmacological evidence that glutaminolysis is essential to regulate osteoclast metabolism, promote osteoclastogenesis and modulate bone resorption in mice.
    Keywords:  Amino Acids; Glutaminolysis; Nucleotides; Osteoclast; Osteoporosis
    DOI:  https://doi.org/10.1038/s44319-024-00255-x
  11. Anal Chem. 2024 Sep 12.
    Quantifying Phospholipids in Organic Samples Using a Hydrophilic Interaction Liquid Chromatography-Inductively Coupled Plasma High-Resolution Mass Spectrometry (HILIC-ICP-HRMS) Method.
    Wladimir Ruiz, Stéphanie Fontagné-Dicharry, Sylvain Verdier, David C Dayton, Julie Guillemant, Rémi Moulian, Pierre Giusti, Caroline Barrère-Mangote, Brice Bouyssiere.
      In this study, a novel method using hydrophilic interaction liquid chromatography (HILIC) coupled with inductively coupled plasma high-resolution mass spectrometry (ICP-HRMS) was introduced for the quantification of phospholipids in oil samples. The method employed a bridged ethyl hybrid (BEH) stationary phase HILIC column with a tetrahydrofuran (THF)/water mobile phase, enhancing the solubility and detection of phospholipids. During the study, a gradient/matrix effect on ICP-HRMS sensitivity was observed and successfully compensated for experimentally, ensuring reliable quantification results. This approach has proven effective for a wide range of different oil samples including vegetable oils, animal fats, and phospholipid supplements. Notably, this method allowed the direct quantification of phospholipids in oil samples, bypassing the need for prior sample preparation methods, such as solid phase extraction (SPE), thereby streamlining the analytical process. The precision, accuracy, and reduced need for extensive sample preparation offered by this method mark a significant advancement in lipids analysis. Its robustness and broad applicability have substantial implications for industries such as food and renewable energy production, where both efficient and accurate lipid identification and quantification are crucial.
    DOI:  https://doi.org/10.1021/acs.analchem.4c01883
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