The Hitchhiker’s Guide to Untargeted Lipidomics Analysis: Practical Guidelines
Abstract
:1. Introduction
2. Experimental Design
2.1. Measurements of Lipidome Composition
2.2. Study Design Considerations
2.3. Materials
2.4. Equipment
3. Procedure of Data Analysis
3.1. Data Conversion
3.2. Data Import
3.3. Peak Picking
3.4. Peak Alignment
3.5. Peak Grouping
3.6. Selection of Parameters for Peak Picking, Alignment, and Grouping
3.7. Imputation of Missing Values
3.8. Data Export
3.9. Filtering of Peaks
3.10. Normalization
3.11. Annotation
4. Results
4.1. Visualization of LC-MS Data
4.2. Applications of Untargeted Lipidomics
4.3. Future Challenges
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Simons, K.; Toomre, D. Lipid rafts and signal transduction. Nat. Rev. Mol. Cell Biol. 2000, 1, 31–39. [Google Scholar] [CrossRef]
- Han, X.; MHoltzman, D.; McKeel, D.W.; Kelley, J.; Morris, J.C. Substantial sulfatide deficiency and ceramide elevation in very early Alzheimer’s disease: Potential role in disease pathogenesis. J. Neurochem. 2002, 82, 809–818. [Google Scholar] [CrossRef] [PubMed]
- Adibhatla, R.M.; Hatcher, J.F.; Dempsey, R.J. Lipids and lipidomics in brain injury and diseases. AAPS J. 2006, 8, 314–321. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Colsch, B.; Afonso, C.; Turpin, J.C.; Portoukalian, J.; Tabet, J.C.; Baumann, N. Sulfogalactosylceramides in motor and psycho-cognitive adult metachromatic leukodystrophy: Relations between clinical, biochemical analysis and molecular aspects. Biochim. Biophys. Acta 2008, 1780, 434–440. [Google Scholar] [CrossRef] [PubMed]
- Ariga, T.; McDonald, M.P.; Yu, R.K. Role of ganglioside metabolism in the pathogenesis of Alzheimer’s disease—A review. J. Lipid Res. 2008, 49, 1157–1175. [Google Scholar] [CrossRef] [Green Version]
- Haughey, N.J.; Bandaru, V.V.R.; Bae, M.; Mattson, M.P. Roles for dysfunctional sphingolipid metabolism in Alzheimer’s disease neuropathogenesis. Biochim. Biophys. Acta 2010, 1801, 878–886. [Google Scholar] [CrossRef] [Green Version]
- Wenk, M.R. The emerging field of lipidomics. Nat. Rev. Drug Discov. 2005, 4, 594–610. [Google Scholar] [CrossRef] [PubMed]
- Lamari, F.; Mochel, F.; Sedel, F.; Saudubray, J.M. Disorders of phospholipids, sphingolipids and fatty acids biosynthesis: Toward a new category of inherited metabolic diseases. J. Inherit. Metab. Dis. 2013, 36, 411–425. [Google Scholar] [CrossRef] [PubMed]
- Want, E.J.; Masson, P.; Michopoulos, F.; Wilson, I.D.; Theodoridis, G.; Plumb, R.S.; Shockcor, J.; Loftus, N.; Holmes, E.; Nicholson, J.K. Global metabolic profiling of animal and human tissues via uplc-ms. Nat. Protoc. 2013, 8, 17–32. [Google Scholar] [CrossRef]
- Smith, C.A.; Want, E.J.; O’Maille, G.; Abagyan, R.; Siuzdak, G. XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 2006, 78, 779–787. [Google Scholar] [CrossRef]
- Tautenhahn, R.; Böttcher, C.; Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinform. 2008, 9, 504. [Google Scholar] [CrossRef] [Green Version]
- Benton, H.P.; Want, E.J.; Ebbels, T.M.D. Correction of mass calibration gaps in liquid chromatography-mass spectrometry metabolomics data. Bioinformatics 2010, 26, 2488–2489. [Google Scholar] [CrossRef] [Green Version]
- Fahy, E.; Subramaniam, S.; Murphy, R.; Nishijima, M.; Raetz, C.; Shimizu, T.; Spener, F.; van Meer, G.; Wakelam, M.; Dennis, E.A. Update of the LIPID MAPS comprehensive classification system for lipids. J. Lipid Res. 2009, 50, 9–14. [Google Scholar] [CrossRef] [Green Version]
- Wishart, D.S.; Feunang, Y.D.; Marcu, A.; Guo, A.C.; Liang, K.; Vázquez-Fresno, R.; Sajed, T.; Johnson, D.; Li, C.; Karu, N.; et al. HMDB 4.0—The Human Metabolome Database for 2018. Nucleic Acids Res. 2018, 46, D608–D617. [Google Scholar] [CrossRef]
- Fahy, E.; Alvarez-Jarreta, J.; Brasher, C.J.; Nguyen, A.; Hawksworth, J.I.; Rodrigues, P.; Meckelmann, S.; Allen, S.M.; O’Donnell, V.B. LipidFinder on LIPID MAPS: Peak filtering, MS searching and statistical analysis for lipidomics. Bioinformatics 2019, 35, 685–687. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pluskal, T.; Castillo, S.; Villar-Briones, A.; Oresic, M. MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinform. 2010, 11, 395. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Pang, Z.; Chong, J.; Zhou, G.; Morais, D.; Chang, L.; Barrette, M.; Gauthier, C.; Jacques, P.E.; Li, S.; Xia, J. MetaboAnalyst 5.0: Narrowing the gap between raw spectra and functional insights. Nucl. Acids Res. 2021, 49, W388–W396. [Google Scholar] [CrossRef]
- Davidson, R.L.; Weber, R.J.; Liu, H.; Sharma-Oates, A.; Viant, M.R. Galaxy-M: A Galaxy workflow for processing and analyzing direct infusion and liquid chromatography mass spectrometry-based metabolomics data. Gigascience 2016, 5, 10. [Google Scholar] [CrossRef] [Green Version]
- Herzog, R.; Schuhmann, K.; Schwudke, D.; Sampaio, J.L.; Bornstein, S.R.; Schroeder, M.; Shevchenko, A. LipidXplorer: A software for consensual cross-platform lipidomics. PLoS ONE 2012, 7, e29851. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Röst, H.L.; Sachsenberg, T.; Aiche, S.; Bielow, C.; Weisser, H.; Aicheler, F.; Andreotti, S.; Ehrlich, H.; Gutenbrunner, P.; Kenar, E.; et al. OpenMS: A flexible open-source software platform for mass spectrometry data analysis. Nat. Methods 2016, 13, 741–748. [Google Scholar] [CrossRef]
- Hartler, J.; Trötzmüller, M.; Chitraju, C.; Spener, F.; Köfeler, H.C.; Thallinger, G.G. Lipid Data Analyzer: Unattended identification and quantitation of lipids in LC-MS data. Bioinformatics 2011, 27, 572–577. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Ni, Z.; Angelidou, G.; Lange, M.; Hoffmann, R.; Fedorova, M. LipidHunter Identifies Phospholipids by High-Throughput Processing of LC-MS and Shotgun Lipidomics Datasets. Anal. Chem. 2017, 89, 8800–8807. [Google Scholar] [CrossRef] [PubMed]
- Lommen, A.; Kools, H.J. MetAlign 3.0: Performance enhancement by efficient use of advances in computer hardware. Metabolomics 2012, 8, 719–726. [Google Scholar] [CrossRef] [Green Version]
- Koelmel, J.P.; Kroeger, N.M.; Ulmer, C.Z.; Bowden, J.A.; Patterson, R.E.; Cochran, J.A.; Beecher, C.W.W.; Garrett, T.J.; Yost, R.A. LipidMatch: An automated workflow for rule-based lipid identification using untargeted high-resolution tandem mass spectrometry data. BMC Bioinform. 2017, 18, 331. [Google Scholar] [CrossRef]
- Alcoriza-Balaguer, M.I.; García-Cañaveras, J.C.; López, A.; Conde, I.; Oscar, J.; Carretero, J.; Lahoz, A. LipidMS: An R Package for Lipid Annotation in Untargeted Liquid Chromatography-Data Independent Acquisition-Mass Spectrometry Lipidomics. Anal. Chem. 2019, 91, 836–845. [Google Scholar] [CrossRef]
- Yamada, T.; Uchikata, T.; Sakamoto, S.; Yokoi, Y.; Fukusaki, E.; Bamba, T. Development of a lipid profiling system using reverse-phase liquid chromatography coupled to high-resolution mass spectrometry with rapid polarity switching and an automated lipid identification software. J. Chromatogr. A 2013, 1292, 211–218. [Google Scholar] [CrossRef]
- Tikunov, Y.M.; Laptenok, S.; Hall, R.D.; Bovy, A.; de Vos, R.C. MSClust: A tool for unsupervised mass spectra extraction of chromatography-mass spectrometry ion-wise aligned data. Metabolomics Off. J. Metabolomic Soc. 2012, 8, 714–718. [Google Scholar] [CrossRef] [Green Version]
- Kind, T.; Liu, K.H.; Lee, D.Y.; DeFelice, B.; Meissen, J.K.; Fiehn, O. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 2013, 10, 755–758. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Tsugawa, H.; Ikeda, K.; Takahashi, M.; Satoh, A.; Mori, Y.; Uchino, H.; Okahashi, N.; Yamada, Y.; Tada, I.; Bonini, P.; et al. A lipidome atlas in MS-DIAL 4. Nat. Biotechnol. 2020, 38, 1159–1163. [Google Scholar] [CrossRef]
- Kyle, J.E.; Crowell, K.L.; Casey, C.P.; Fujimoto, G.M.; Kim, S.; Dautel, S.E.; Smith, R.D.; Payne, S.H.; Metz, T.O. LIQUID: An-open source software for identifying lipids in LC-MS/MS-based lipidomics data. Bioinformatics 2017, 33, 1744–1746. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Mohamed, A.; Molendijk, J.; Hill, M.M. lipidr: A Software Tool for Data Mining and Analysis of Lipidomics Datasets. J. Proteom. Res. 2020, 19, 2890–2897. [Google Scholar] [CrossRef] [PubMed]
- Lipyd: A Python Module for Lipidomics LC MS/MS Data Analysis. Available online: https://saezlab.github.io/lipyd/ (accessed on 11 October 2021).
- Hutchins, P.D.; Russell, J.D.; Coon, J.J. LipiDex: An Integrated Software Package for High-Confidence Lipid Identification. Cell Syst. 2018, 6, 621–625. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Molenaar, M.R.; Jeucken, A.; Wassenaar, T.A.; van de Lest, C.H.A.; Brouwers, J.F.; Helms, J.B. LION/web: A web-based ontology enrichment tool for lipidomic data analysis. Gigascience 2019, 8, giz061. [Google Scholar] [CrossRef] [Green Version]
- Wong, G.; Chan, J.; Kingwell, B.A.; Leckie, C.; Meikle, P.J. LICRE: Unsupervised feature correlation reduction for lipidomics. Bioinformatics 2014, 30, 2832–2833. [Google Scholar] [CrossRef] [Green Version]
- Lin, W.J.; Shen, P.; Liu, H.; Cho, Y.; Hsu, M.; Lin, I.; Chen, F.; Yang, J.; Ma, W.; Cheng, W. LipidSig: A web-based tool for lipidomic data analysis. Nucleic Acids Res. 2021, 49, W336–W345. [Google Scholar] [CrossRef]
- Ni, Z.; Fedorova, M. LipidLynxX: A data transfer hub to support integration of large scale lipidomics datasets. bioRxiv 2020, 4, 033894. [Google Scholar]
- Ni, Z.; Angelidou, G.; Hoffmann, R.; Fedorova, M. LPPtiger software for lipidome-specific prediction and identification of oxidized phospholipids from LC-MS datasets. Sci. Rep. 2017, 7, 15138. [Google Scholar] [CrossRef] [Green Version]
- Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13, 2498–2504. [Google Scholar] [CrossRef]
- Acevedo, A.; Durán, C.; Ciucci, S.; Gerl, M.J.; Cannistraci, C.V. LIPEA: Lipid Pathway Enrichment Analysis. bioRxiv 2018, 274969. [Google Scholar] [CrossRef] [Green Version]
- Misra, B.B.; Fahrmann, J.F.; Grapov, D. Review of emerging metabolomic tools and resources: 2015–2016. Electrophoresis 2017, 38, 2257–2274. [Google Scholar] [CrossRef] [PubMed]
- Klåvus, A.; Kokla, M.; Noerman, S.; Koistinen, V.M.; Tuomainen, M.; Zarei, I.; Meuronen, T.; Häkkinen, M.R.; Rummukainen, S.; Farizah Babu, A.; et al. “Notame”: Workflow for Non-Targeted LC–MS Metabolic Profiling. Metabolites 2020, 10, 135. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kessner, D.; Chambers, M.; Burke, R.; Agus, D.; Mallick, P. ProteoWizard: Open source software for rapid proteomics tools development. Bioinformatics 2008, 24, 2534–2536. [Google Scholar] [CrossRef]
- Chambers, M.C.; Maclean, B.; Burke, R.; Amodei, D.; Ruderman, D.L.; Neumann, S.; Gatto, L.; Fischer, B.; Pratt, B.; Egertson, J.; et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 2012, 30, 918–920. [Google Scholar] [CrossRef] [PubMed]
- Libiseller, G.; Dvorzak, M.; Kleb, U.; Gander, E.; Eisenberg, T.; Madeo, F.; Neumann, S.; Trausinger, G.; Sinner, F.; Pieber, T.; et al. IPO: A tool for automated optimization of XCMS parameters. BMC Bioinform. 2015, 16, 118. [Google Scholar] [CrossRef] [Green Version]
- Albóniga, O.E.; González, O.; Alonso, R.M.; Xu, Y.; Goodacre, R. Optimization of XCMS parameters for LC–MS metabolomics: An assessment of automated versus manual tuning and its effect on the final results. Metabolomics 2020, 16, 14. [Google Scholar] [CrossRef] [PubMed]
- Rohart, F.; Gautier, B.; Singh, A.; Lê Cao, K.-A. mixOmics: An R package for ‘omics feature selection and multiple data integration. PLoS Comput. Biol. 2017, 13, e1005752. [Google Scholar] [CrossRef] [Green Version]
- Sysi-Aho, M.; Katajamaa, M.; Yetukuri, L.; Orešič, M. Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinform. 2007, 8, 93. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Patti, G.J.; Tautenhahn, R.; Siuzdak, G. Meta-analysis of untargeted metabolomic data from multiple profiling experiments. Nat. Protoc. 2012, 7, 508–516. [Google Scholar] [CrossRef] [Green Version]
- Prince, J.T.; Marcotte, E.M. Chromatographic alignment of ESI-LC-MS proteomics data sets by ordered bijective interpolated warping. Anal. Chem. 2006, 78, 6140–6152. [Google Scholar] [CrossRef]
- Wehrens, R.; Hageman, J.A.; van Eeuwijk, F.; Kooke, R.; Flood, P.J.; Wijnker, E.; Keurentjes, J.J.B.; Lommen, A.; van Eekelen, H.D.L.M.; Hall, R.D.; et al. Improved batch correction in untargeted MS-based metabolomics. Metabolomics 2016, 12, 88. [Google Scholar] [CrossRef] [Green Version]
- Schiffman, C.; Petrick, L.; Perttula, K.; Yano, Y.; Carlsson, H.; Whitehead, T.; Metayer, C.; Hayes, J.; Rappaport, S.; Dudoit, S. Filtering procedures for untargeted LC-MS metabolomics data. BMC Bioinform. 2019, 20, 334. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bolstad, B.M.; Irizarry, R.A.; Astrand, M.; Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 2003, 19, 185–193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Aimo, L.; Liechti, R.; Hyka-Nouspikel, N.; Niknejad, A.; Gleizes, A.; Götz, L.; Kuznetsov, D.; David, F.P.A.; van der Goot, F.G.; Riezman, H.; et al. The SwissLipids knowledgebase for lipid biology. Bioinformatics 2015, 31, 2860–2866. [Google Scholar] [CrossRef] [Green Version]
- Sumner, L.W.; Amberg, A.; Barrett, D.; Beale, M.H.; Beger, R.; Daykin, C.A.; Fan, T.W.-M.; Fiehn, O.; Goodacre, R.; Griffin, J.L.; et al. Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 2007, 3, 211–221. [Google Scholar] [CrossRef] [Green Version]
- Liebisch, G.; Vizcaíno, J.A.; Köfeler, H.; Trötzmüller, M.; Griffiths, W.J.; Schmitz, G.; Spener, F.; Wakelam, M.J.O. Shorthand Notation for Lipid Structures Derived from Mass Spectrometry. J. Lipid Res. 2013, 54, 1523–1530. [Google Scholar] [CrossRef] [Green Version]
- Jolliffe, I.T. Principal Component Analysis. In Springer Series in Statistic; Springer: New York, NY, USA, 2002. [Google Scholar]
- Ingram, L.M.; Finnerty, M.C.; Mansoura, M.; Chou, C.W.; Cummings, B.S. Identification of lipidomic profiles associated with drug-resistant prostate cancer cells. Lipids Health Dis. 2021, 20, 15. [Google Scholar] [CrossRef]
- Zhang, X.; Liu, W.; Zan, J.; Wu, C.; Tan, W. Untargeted lipidomics reveals progression of early Alzheimer’s disease in APP/PS1 transgenic mice. Sci. Rep. 2020, 10, 14509. [Google Scholar] [CrossRef]
- Xicota, L.; Ichou, F.; Lejeune, F.X.; Colsch, B.; Tenenhaus, A.; Leroy, I.; Fontaine, G.; Lhomme, M.; Bertin, H.; Habert, M.-O.; et al. Multi-omics signature of brain amyloid deposition in asymptomatic individuals at-risk for Alzheimer’s disease: The INSIGHT-preAD study. EBioMed. 2019, 47, 518–528. [Google Scholar] [CrossRef] [Green Version]
- Harshfield, E.L.; Koulman, A.; Ziemek, D.; Marney, L.; Fauman, E.B.; Paul, D.S.; Stacey, D.; Rasheed, A.; Lee, J.-J.; Shah, N.; et al. An Unbiased Lipid Phenotyping Approach To Study the Genetic Determinants of Lipids and Their Association with Coronary Heart Disease Risk Factors. J. Proteom. Res. 2019, 18, 2397–2410. [Google Scholar] [CrossRef] [PubMed]
- Wu, X.; Zhu, J.; Zhang, Y.; Li, W.; Rong, X.; Feng, Y. Lipidomics study of plasma phospholipid metabolism in early type 2 diabetes rats with ancient prescription Huang-Qi-San intervention by UPLC/Q-TOF-MS and correlation coefficient. Chem.-Biol. Interact. 2016, 256, 71–84. [Google Scholar] [CrossRef]
- Lee, S.H.; Hong, S.H.; Tang, C.H.; Ling, Y.S.; Chen, K.H.; Liang, H.J.; Lin, C.Y. Mass spectrometry-based lipidomics to explore the biochemical effects of naphthalene toxicity or tolerance in a mouse model. PLoS ONE 2018, 13, e0204829. [Google Scholar] [CrossRef]
- Dei Cas, M.; Zulueta, A.; Mingione, A.; Caretti, A.; Ghidoni, R.; Signorelli, P.; Paroni, R. An Innovative Lipidomic Workflow to Investigate the Lipid Profile in a Cystic Fibrosis Cell Line. Cells 2020, 9, 1197. [Google Scholar] [CrossRef]
- Cajka, T.; Smilowitz, J.T.; Fiehn, O. Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography–High-Resolution Mass Spectrometry Platforms. Anal. Chem. 2017, 89, 12360–12368. [Google Scholar] [CrossRef] [PubMed]
- Lê Cao, K.A.; Boitard, S.; Besse, P. Sparse PLS discriminant analysis: Biologically relevant feature selection and graphical displays for multiclass problems. J. BMC Bioinform. 2011, 12, 253. [Google Scholar] [CrossRef] [Green Version]
- Ruiz-Perez, D.; Guan, H.; Madhivanan, P.; Mathee, K.; Narasimhan, G. So you think you can PLS-DA? BMC Bioinform. 2020, 21, 2. [Google Scholar] [CrossRef] [PubMed]
- Kjeldahl, K.; Bro, R. Some common misunderstandings in chemometrics. J. Chemom. 2010, 24, 558–564. [Google Scholar] [CrossRef]
- Brereton, R.G.; Lloyd, G.R. Partial least squares discriminant analysis: Taking the magic away. J. Chemom. 2014, 28, 213–225. [Google Scholar] [CrossRef]
- Gromski, P.S.; Muhamadali, H.; Ellis, D.I.; Xu, Y.; Correa, E.; Turner, M.L.; Goodacre, R. A tutorial review: Metabolomics and partial least squares-discriminant analysis—A marriage of convenience or a shotgun wedding. Anal. Chim. Acta 2015, 879, 10–23. [Google Scholar] [CrossRef]
- Gromski, P.S.; Xu, Y.; Correa, E.; Ellis, D.I.; Turner, M.L.; Goodacre, R. A comparative investigation of modern feature selection and classification approaches for the analysis of mass spectrometry data. Anal. Chim. Acta 2014, 829, 1–8. [Google Scholar] [CrossRef]
- Want, E.J. LC-MS Untargeted Analysis. In Metabolic Profiling. Methods in Molecular Biology; Theodoridis, G., Gika, H., Wilson, I., Eds.; Humana Press: New York, NY, USA, 2018; Volume 1738. [Google Scholar]
- Subramanian, I.; Verma, S.; Kumar, S.; Jere, A.; Anamika, K. Multi-omics Data Integration, Interpretation, and Its Application. Bioinform. Biol. Insights 2020, 14, 1177932219899051. [Google Scholar] [CrossRef] [Green Version]
- Kanehisa, M.; Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 2000, 28, 27–30. [Google Scholar] [CrossRef] [PubMed]
- Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019, 28, 1947–1951. [Google Scholar] [CrossRef] [PubMed]
- Kanehisa, M.; Furumichi, M.; Sato, Y.; Ishiguro-Watanabe, M.; Tanabe, M. KEGG: Integrating viruses and cellular organisms. Nucleic Acids Res. 2021, 49, D545–D551. [Google Scholar] [CrossRef]
- Jassal, B.; Matthews, L.; Viteri, G.; Gong, C.; Lorente, P.; Fabregat, A.; Sidiropoulos, K.; Cook, J.; Gillespie, M.; Haw, R.; et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020, 48, D498–D503. [Google Scholar] [CrossRef] [PubMed]
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Smirnov, D.; Mazin, P.; Osetrova, M.; Stekolshchikova, E.; Khrameeva, E. The Hitchhiker’s Guide to Untargeted Lipidomics Analysis: Practical Guidelines. Metabolites 2021, 11, 713. https://doi.org/10.3390/metabo11110713
Smirnov D, Mazin P, Osetrova M, Stekolshchikova E, Khrameeva E. The Hitchhiker’s Guide to Untargeted Lipidomics Analysis: Practical Guidelines. Metabolites. 2021; 11(11):713. https://doi.org/10.3390/metabo11110713
Chicago/Turabian StyleSmirnov, Dmitrii, Pavel Mazin, Maria Osetrova, Elena Stekolshchikova, and Ekaterina Khrameeva. 2021. "The Hitchhiker’s Guide to Untargeted Lipidomics Analysis: Practical Guidelines" Metabolites 11, no. 11: 713. https://doi.org/10.3390/metabo11110713