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Lu W, McBride MJ, Lee WD, Xing X, Xu X, Li X, Oschmann AM, Shen Y, Bartman C, Rabinowitz JD. Selected Ion Monitoring for Orbitrap-Based Metabolomics. Metabolites 2024; 14:184. [PMID: 38668312 PMCID: PMC11051813 DOI: 10.3390/metabo14040184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2024] [Revised: 03/08/2024] [Accepted: 03/18/2024] [Indexed: 04/28/2024] Open
Abstract
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.
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Affiliation(s)
- Wenyun Lu
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Princeton University, Princeton, NJ 08544, USA
| | - Matthew J. McBride
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Won Dong Lee
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
| | - Xi Xing
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Princeton University, Princeton, NJ 08544, USA
| | - Xincheng Xu
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Xi Li
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Princeton University, Princeton, NJ 08544, USA
| | - Anna M. Oschmann
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
| | - Yihui Shen
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Princeton University, Princeton, NJ 08544, USA
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Caroline Bartman
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Pharmacology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joshua D. Rabinowitz
- Lewis Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA; (W.L.)
- Department of Chemistry, Princeton University, Princeton, NJ 08544, USA
- DOE Center for Advanced Bioenergy and Bioproducts Innovation, Princeton University, Princeton, NJ 08544, USA
- Rutgers Cancer Institute of New Jersey (CINJ), Rutgers University, New Brunswick, NJ 08901, USA
- Ludwig Institute for Cancer Research, Princeton University, Princeton, NJ 08544, USA
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Schwaiger-Haber M, Stancliffe E, Anbukumar DS, Sells B, Yi J, Cho K, Adkins-Travis K, Chheda MG, Shriver LP, Patti GJ. Using mass spectrometry imaging to map fluxes quantitatively in the tumor ecosystem. Nat Commun 2023; 14:2876. [PMID: 37208361 DOI: 10.1038/s41467-023-38403-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 04/26/2023] [Indexed: 05/21/2023] Open
Abstract
Tumors are comprised of a multitude of cell types spanning different microenvironments. Mass spectrometry imaging (MSI) has the potential to identify metabolic patterns within the tumor ecosystem and surrounding tissues, but conventional workflows have not yet fully integrated the breadth of experimental techniques in metabolomics. Here, we combine MSI, stable isotope labeling, and a spatial variant of Isotopologue Spectral Analysis to map distributions of metabolite abundances, nutrient contributions, and metabolic turnover fluxes across the brains of mice harboring GL261 glioma, a widely used model for glioblastoma. When integrated with MSI, the combination of ion mobility, desorption electrospray ionization, and matrix assisted laser desorption ionization reveals alterations in multiple anabolic pathways. De novo fatty acid synthesis flux is increased by approximately 3-fold in glioma relative to surrounding healthy tissue. Fatty acid elongation flux is elevated even higher at 8-fold relative to surrounding healthy tissue and highlights the importance of elongase activity in glioma.
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Affiliation(s)
- Michaela Schwaiger-Haber
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Ethan Stancliffe
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Dhanalakshmi S Anbukumar
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Blake Sells
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Jia Yi
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Kevin Cho
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Kayla Adkins-Travis
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Milan G Chheda
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neurology, Washington University in St. Louis, St. Louis, MO, USA
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA
| | - Leah P Shriver
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, MO, USA.
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Medicine, Washington University in St. Louis, St. Louis, MO, USA.
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, MO, USA.
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3
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Zhang R, Chen B, Zhang H, Tu L, Luan T. Stable isotope-based metabolic flux analysis: A robust tool for revealing toxicity pathways of emerging contaminants. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2022.116909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
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4
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Lackner M, Neef SK, Winter S, Beer-Hammer S, Nürnberg B, Schwab M, Hofmann U, Haag M. Untargeted stable isotope-resolved metabolomics to assess the effect of PI3Kβ inhibition on metabolic pathway activities in a PTEN null breast cancer cell line. Front Mol Biosci 2022; 9:1004602. [PMID: 36310598 PMCID: PMC9614656 DOI: 10.3389/fmolb.2022.1004602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/28/2022] [Indexed: 11/17/2022] Open
Abstract
The combination of high-resolution LC-MS untargeted metabolomics with stable isotope-resolved tracing is a promising approach for the global exploration of metabolic pathway activities. In our established workflow we combine targeted isotopologue feature extraction with the non-targeted X13CMS routine. Metabolites, detected by X13CMS as differentially labeled between two biological conditions are subsequently integrated into the original targeted library. This strategy enables monitoring of changes in known pathways as well as the discovery of hitherto unknown metabolic alterations. Here, we demonstrate this workflow in a PTEN (phosphatase and tensin homolog) null breast cancer cell line (MDA-MB-468) exploring metabolic pathway activities in the absence and presence of the selective PI3Kβ inhibitor AZD8186. Cells were fed with [U-13C] glucose and treated for 1, 3, 6, and 24 h with 0.5 µM AZD8186 or vehicle, extracted by an optimized sample preparation protocol and analyzed by LC-QTOF-MS. Untargeted differential tracing of labels revealed 286 isotope-enriched features that were significantly altered between control and treatment conditions, of which 19 features could be attributed to known compounds from targeted pathways. Other 11 features were unambiguously identified based on data-dependent MS/MS spectra and reference substances. Notably, only a minority of the significantly altered features (11 and 16, respectively) were identified when preprocessing of the same data set (treatment vs. control in 24 h unlabeled samples) was performed with tools commonly used for label-free (i.e. w/o isotopic tracer) non-targeted metabolomics experiments (Profinder´s batch recursive feature extraction and XCMS). The structurally identified metabolites were integrated into the existing targeted isotopologue feature extraction workflow to enable natural abundance correction, evaluation of assay performance and assessment of drug-induced changes in pathway activities. Label incorporation was highly reproducible for the majority of isotopologues in technical replicates with a RSD below 10%. Furthermore, inter-day repeatability of a second label experiment showed strong correlation (Pearson R2 > 0.99) between tracer incorporation on different days. Finally, we could identify prominent pathway activity alterations upon PI3Kβ inhibition. Besides pathways in central metabolism, known to be changed our workflow revealed additional pathways, like pyrimidine metabolism or hexosamine pathway. All pathways identified represent key metabolic processes associated with cancer metabolism and therapy.
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Affiliation(s)
- Marcel Lackner
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Sylvia K. Neef
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Sandra Beer-Hammer
- Department of Pharmacology, Experimental Therapy and Toxicology, Institute for Experimental and Clinical Pharmacology and Pharmacogenomics, Interfaculty Center for Pharmacogenomics and Drug Research (ICePhA), University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180), Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
| | - Bernd Nürnberg
- Department of Pharmacology, Experimental Therapy and Toxicology, Institute for Experimental and Clinical Pharmacology and Pharmacogenomics, Interfaculty Center for Pharmacogenomics and Drug Research (ICePhA), University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
- Cluster of Excellence iFIT (EXC 2180), Image-Guided and Functionally Instructed Tumor Therapies, University of Tübingen, Tübingen, Germany
- Departments of Clinical Pharmacology and of Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
| | - Ute Hofmann
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
| | - Mathias Haag
- Dr. Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany
- University of Tübingen, Tübingen, Germany
- *Correspondence: Mathias Haag,
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Butin N, Bergès C, Portais JC, Bellvert F. An optimization method for untargeted MS-based isotopic tracing investigations of metabolism. Metabolomics 2022; 18:41. [PMID: 35713733 PMCID: PMC9205802 DOI: 10.1007/s11306-022-01897-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 05/17/2022] [Indexed: 11/29/2022]
Abstract
INTRODUCTION Stable isotope tracer studies are increasingly applied to explore metabolism from the detailed analysis of tracer incorporation into metabolites. Untargeted LC/MS approaches have recently emerged and provide potent methods for expanding the dimension and complexity of the metabolic networks that can be investigated. A number of software tools have been developed to process the highly complex MS data collected in such studies; however, a method to optimize the extraction of valuable isotopic data is lacking. OBJECTIVES To develop and validate a method to optimize automated data processing for untargeted MS-based isotopic tracing investigations of metabolism. METHODS The method is based on the application of a suitable reference material to rationally perform parameter optimization throughout the complete data processing workflow. It was applied in the context of 13C-labelling experiments and with two different software, namely geoRge and X13CMS. It was illustrated with the study of a E. coli mutant impaired for central metabolism. RESULTS The optimization methodology provided significant gain in the number and quality of extracted isotopic data, independently of the software considered. Pascal triangle samples are well suited for such purpose since they allow both the identification of analytical issues and optimization of data processing at the same time. CONCLUSION The proposed method maximizes the biological value of untargeted MS-based isotopic tracing investigations by revealing the full metabolic information that is encoded in the labelling patterns of metabolites.
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Affiliation(s)
- Noémie Butin
- RESTORE, CNRS ERL5311, EFS, ENVT, Inserm U1031, UPS, Université de Toulouse, Toulouse, France
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Cécilia Bergès
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Jean-Charles Portais
- RESTORE, CNRS ERL5311, EFS, ENVT, Inserm U1031, UPS, Université de Toulouse, Toulouse, France
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France
| | - Floriant Bellvert
- Toulouse Biotechnology Institute, TBI-INSA de Toulouse INSA/ CNRS 5504-UMR INSA/INRA 792, 5504, Toulouse, France.
- MetaboHUB-MetaToul, National Infrastructure of Metabolomics and Fluxomics, 31077, Toulouse, France.
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6
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Meng YM, Jiang X, Zhao X, Meng Q, Wu S, Chen Y, Kong X, Qiu X, Su L, Huang C, Wang M, Liu C, Wong PP. Hexokinase 2-driven glycolysis in pericytes activates their contractility leading to tumor blood vessel abnormalities. Nat Commun 2021; 12:6011. [PMID: 34650057 PMCID: PMC8517026 DOI: 10.1038/s41467-021-26259-y] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 09/22/2021] [Indexed: 11/09/2022] Open
Abstract
Defective pericyte-endothelial cell interaction in tumors leads to a chaotic, poorly organized and dysfunctional vasculature. However, the underlying mechanism behind this is poorly studied. Herein, we develop a method that combines magnetic beads and flow cytometry cell sorting to isolate pericytes from tumors and normal adjacent tissues from patients with non-small cell lung cancer (NSCLC) and hepatocellular carcinoma (HCC). Pericytes from tumors show defective blood vessel supporting functions when comparing to those obtained from normal tissues. Mechanistically, combined proteomics and metabolic flux analysis reveals elevated hexokinase 2(HK2)-driven glycolysis in tumor pericytes, which up-regulates their ROCK2-MLC2 mediated contractility leading to impaired blood vessel supporting function. Clinically, high percentage of HK2 positive pericytes in blood vessels correlates with poor patient overall survival in NSCLC and HCC. Administration of a HK2 inhibitor induces pericyte-MLC2 driven tumor vasculature remodeling leading to enhanced drug delivery and efficacy against tumor growth. Overall, these data suggest that glycolysis in tumor pericytes regulates their blood vessel supporting role. Pericyte-endothelial cells interaction defines tumor vasculature and has implications in tumorigenesis development and therapy efficacy. Here, the authors show that hexokinase 2- driven glycolysis activates ROCK1-MLC2 mediated contractility in pericytes leading to tumor blood vessel abnormality.
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Affiliation(s)
- Ya-Ming Meng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Department of Obstetrics and Gynecology, Center for Reproductive Medicine, Key Laboratory for Major Obstetric Diseases of Guangdong Province, The Third Affiliated Hospital of Guangzhou Medical University, 510150, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Xue Jiang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Xinbao Zhao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Department of Ultrasound, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Qiong Meng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Sangqing Wu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Yitian Chen
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Xiangzhan Kong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Xiaoyi Qiu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Liangping Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Cheng Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Minghui Wang
- Department of Thoracic surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Chao Liu
- Department of Biliary-Pancreatic Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China
| | - Ping-Pui Wong
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangdong-Hong Kong Joint Laboratory for RNA medicine, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China. .,Medical Research Center, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 510120, Guangzhou, China.
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7
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Wasito H, Hermann G, Fitz V, Troyer C, Hann S, Koellensperger G. Yeast-based reference materials for quantitative metabolomics. Anal Bioanal Chem 2021; 414:4359-4368. [PMID: 34642781 PMCID: PMC9142427 DOI: 10.1007/s00216-021-03694-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 09/15/2021] [Accepted: 09/24/2021] [Indexed: 11/28/2022]
Abstract
We introduce a new concept of yeast-derived biological matrix reference material for metabolomics research relying on in vivo synthesis of a defined biomass, standardized extraction followed by absolute quantification with isotope dilution. The yeast Pichia pastoris was grown using full control- and online monitoring fed-batch fermentations followed by fast cold methanol quenching and boiling ethanol extraction. Dried extracts served for the quantification campaign. A metabolite panel of the evolutionarily conserved primary metabolome (amino acids, nucleotides, organic acids, and metabolites of the central carbon metabolism) was absolutely quantified by isotope dilution utilizing uniformly labeled 13C-yeast-based internal standards. The study involved two independent laboratories employing complementary mass spectrometry platforms, namely hydrophilic interaction liquid chromatography-high resolution mass spectrometry (HILIC-HRMS) and gas chromatography-tandem mass spectrometry (GC–MS/MS). Homogeneity, stability tests (on a panel of >70 metabolites over a period of 6 months), and excellent biological repeatability of independent fermentations over a period of 2 years showed the feasibility of producing biological reference materials on demand. The obtained control ranges proved to be fit for purpose as they were either superior or comparable to the established reference materials in the field.
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Affiliation(s)
- Hendri Wasito
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 18, 1190, Vienna, Austria.,Department of Pharmacy, Faculty of Health Sciences, Jenderal Soedirman University, Dr. Soeparno Street, 53122, Purwokerto, Indonesia
| | - Gerrit Hermann
- Core Facility Mass Spectrometry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 11, 1190, Vienna, Austria.,ISOtopic Solutions, Waehringer Str. 38, 1090, Vienna, Austria
| | - Veronika Fitz
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Waehringer Str. 38, 1090, Vienna, Austria
| | - Christina Troyer
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - Stephan Hann
- Institute of Analytical Chemistry, Department of Chemistry, University of Natural Resources and Life Sciences (BOKU) Vienna, Muthgasse 18, 1190, Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Waehringer Str. 38, 1090, Vienna, Austria. .,Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090, Vienna, Austria.
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8
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Rampler E, Hermann G, Grabmann G, El Abiead Y, Schoeny H, Baumgartinger C, Köcher T, Koellensperger G. Benchmarking Non-Targeted Metabolomics Using Yeast-Derived Libraries. Metabolites 2021; 11:metabo11030160. [PMID: 33802096 PMCID: PMC7998801 DOI: 10.3390/metabo11030160] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 03/01/2021] [Accepted: 03/08/2021] [Indexed: 12/19/2022] Open
Abstract
Non-targeted analysis by high-resolution mass spectrometry (HRMS) is an essential discovery tool in metabolomics. To date, standardization and validation remain a challenge. Community-wide accepted cost-effective benchmark materials are lacking. In this work, we propose yeast (Pichia pastoris) extracts derived from fully controlled fermentations for this purpose. We established an open-source metabolite library of >200 identified metabolites based on compound identification by accurate mass, matching retention times, and MS/MS, as well as a comprehensive literature search. The library includes metabolites from the classes of (1) organic acids and derivatives (2) nucleosides, nucleotides, and analogs, (3) lipids and lipid-like molecules, (4) organic oxygen compounds, (5) organoheterocyclic compounds, (6) organic nitrogen compounds, and (7) benzoids at expected concentrations ranges of sub-nM to µM. As yeast is a eukaryotic organism, key regulatory elements are highly conserved between yeast and all annotated metabolites were also reported in the human metabolome database (HMDB). Orthogonal state-of-the-art reversed-phase (RP-) and hydrophilic interaction chromatography mass spectrometry (HILIC-MS) non-targeted analysis and authentic standards revealed that 104 out of the 206 confirmed metabolites were reproducibly recovered and stable over the course of three years when stored at −80 °C. Overall, 67 out of these 104 metabolites were identified with comparably stable areas over all three yeast fermentation and are the ideal starting point for benchmarking experiments. The provided yeast benchmark material enabled not only to test for the chemical space and coverage upon method implementation and developments but also allowed in-house routines for instrumental performance tests. Transferring the quality control strategy of proteomics workflows based on the number of protein identification in HeLa extracts, metabolite IDs in the yeast benchmarking material can be used as metabolomics quality control. Finally, the benchmark material opens new avenues for batch-to-batch corrections in large-scale non-targeted metabolomics studies.
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Affiliation(s)
- Evelyn Rampler
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria; (E.R.); (G.H.); (Y.E.A.); (H.S.); (C.B.)
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Gerrit Hermann
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria; (E.R.); (G.H.); (Y.E.A.); (H.S.); (C.B.)
- ISOtopic Solutions, Währinger Str. 38, 1090 Vienna, Austria
| | - Gerlinde Grabmann
- Metabolomics Core Facility, Vienna BioCenter Core Facilities, Dr.-Bohr-Gasse 3, 1030 Vienna, Austria; (G.G.); (T.K.)
| | - Yasin El Abiead
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria; (E.R.); (G.H.); (Y.E.A.); (H.S.); (C.B.)
| | - Harald Schoeny
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria; (E.R.); (G.H.); (Y.E.A.); (H.S.); (C.B.)
| | - Christoph Baumgartinger
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria; (E.R.); (G.H.); (Y.E.A.); (H.S.); (C.B.)
| | - Thomas Köcher
- Metabolomics Core Facility, Vienna BioCenter Core Facilities, Dr.-Bohr-Gasse 3, 1030 Vienna, Austria; (G.G.); (T.K.)
| | - Gunda Koellensperger
- Department of Analytical Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria; (E.R.); (G.H.); (Y.E.A.); (H.S.); (C.B.)
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- Correspondence:
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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Shi X, Xi B, Jasbi P, Turner C, Jin Y, Gu H. Comprehensive Isotopic Targeted Mass Spectrometry: Reliable Metabolic Flux Analysis with Broad Coverage. Anal Chem 2020; 92:11728-11738. [PMID: 32697570 PMCID: PMC7546585 DOI: 10.1021/acs.analchem.0c01767] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Metabolic flux analysis (MFA) is highly relevant to understanding metabolic mechanisms of various biological processes. While the pace of methodology development in MFA has been rapid, a major challenge the field continues to witness is limited metabolite coverage, often restricted to a small to moderate number of well-known compounds. In addition, isotopic peaks from an enriched metabolite tend to have low abundances, which makes liquid chromatography tandem mass spectrometry (LC-MS/MS) highly useful in MFA due to its high sensitivity and specificity. Previously we have built large-scale LC-MS/MS approaches that can be routinely used for measurement of up to ∼1,900 metabolite/feature levels [Gu et al. Anal. Chem. 2015, 87, 12355-12362. Shi et al. Anal. Chem. 2019, 91, 13737-13745.]. In this study, we aim to expand our previous studies focused on metabolite level measurements to flux analysis and establish a novel comprehensive isotopic targeted mass spectrometry (CIT-MS) method for reliable MFA analysis with broad coverage. As a proof-of-principle, we have applied CIT-MS to compare the steady-state enrichment of metabolites between Myc(oncogene)-On and Myc-Off Tet21N human neuroblastoma cells cultured with U-13C6-glucose medium. CIT-MS is operationalized using multiple reaction monitoring (MRM) mode and is able to perform MFA of 310 identified metabolites (142 reliably detected, 46 kinetically profiled) selected from >35 metabolic pathways of strong biological significance. Further, we developed a novel concept of relative flux, which eliminates the requirement of absolute quantitation in traditional MFA and thus enables comparative MFA under the pseudosteady state. As a result, CIT-MS was shown to possess the advantages of broad coverage, easy implementation, fast throughput, and more importantly, high fidelity and accuracy in MFA. In principle, CIT-MS can be easily adapted to track the flux of other labeled tracers (such as 15N-tracers) in any metabolite detectable by LC-MS/MS and in various biological models (such as mice). Therefore, CIT-MS has great potential to bring new insights to both basic and clinical metabolism research.
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Affiliation(s)
- Xiaojian Shi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, 13208 East Shea Boulevard, Scottsdale, Arizona 85259, United States
| | - Bowei Xi
- Department of Statistics, Purdue University, West Lafayette, Indiana 47907, United States
| | - Paniz Jasbi
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, 13208 East Shea Boulevard, Scottsdale, Arizona 85259, United States
| | - Cassidy Turner
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, 13208 East Shea Boulevard, Scottsdale, Arizona 85259, United States
| | - Yan Jin
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, 13208 East Shea Boulevard, Scottsdale, Arizona 85259, United States
| | - Haiwei Gu
- Arizona Metabolomics Laboratory, College of Health Solutions, Arizona State University, 13208 East Shea Boulevard, Scottsdale, Arizona 85259, United States
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Raetz M, Bonner R, Hopfgartner G. SWATH-MS for metabolomics and lipidomics: critical aspects of qualitative and quantitative analysis. Metabolomics 2020; 16:71. [PMID: 32504120 DOI: 10.1007/s11306-020-01692-0] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/29/2020] [Indexed: 12/19/2022]
Abstract
INTRODUCTION While liquid chromatography coupled to mass spectrometric detection in the selected reaction monitoring detection mode offers the best quantification sensitivity for omics, the number of target analytes is limited, must be predefined and specific methods developed. Data independent acquisition (DIA), including SWATH using quadrupole time of flight or orbitrap mass spectrometers and generic acquisition methods, has emerged as a powerful alternative technique for quantitative and qualitative analyses since it can cover a wide range of analytes without predefinition. OBJECTIVES Here we review the current state of DIA, SWATH-MS and highlight novel acquisition strategies for metabolomics and lipidomics and opportunities for data analysis tools. METHOD Different databases were searched for papers that report developments and applications of DIA and in particular SWATH-MS in metabolomics and lipidomics. RESULTS DIA methods generate digital sample records that can be mined retrospectively as further knowledge is gained and, with standardized acquisition schemes, used in multiple studies. The different chemical spaces of metabolites and lipids require different specificities, hence different acquisition and data processing approaches must be considered for their analysis. CONCLUSIONS Although the hardware and acquisition modes are well defined for SWATH-MS, a major challenge for routine use remains the lack of appropriate software tools capable of handling large datasets and large numbers of analytes.
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Affiliation(s)
- Michel Raetz
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211, Geneva, Switzerland
| | - Ron Bonner
- Ron Bonner Consulting, Newmarket, ON, L3Y 3C7, Canada
| | - Gérard Hopfgartner
- Life Sciences Mass Spectrometry, Department of Inorganic and Analytical Chemistry, University of Geneva, 24 Quai Ernest Ansermet, CH-1211, Geneva, Switzerland.
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