Abstract
Mass spectrometry (MS) is currently the most utilized analytical instrument for evaluating the metabolite composition of a biological sample at both the qualitative and quantitative level. The exponential growth of raw data generated through increasingly versatile mass spectrometers requires sophisticated algorithms to process and visualize the raw data to address biological questions. The structural and quantitative diversity of a single species’ metabolome (e.g. all metabolite species) under different experimental conditions itself forms a very large and complex dataset to analyze. We have developed a free, Java-based metabolomics application “Metabolite Imager” (www.metaboliteimager.com) that enables customized analysis and visualization of the metabolite distributions in tissues acquired through MS-based imaging approaches. Metabolite Imager algorithms perform customized targeted searching of metabolites through user-defined and publicly-available databases enabling the analysis of spatial distributions of large metabolite numbers in tissue sections. Metabolite Imager’s automated, two-dimensional image generator has several customizable features for producing high-resolution images. Additional Metabolite Imager algorithms support identifying targeted and unknown detected metabolites in selected tissue regions using spatially-based enrichment analysis that could impact metabolic engineering strategies. Co-localization algorithms of metabolites and selected ions by m/z enable analysis of precursor-product relationships in situ that will be important for expanding the biological context of metabolic pathways.
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References
Adams, R., Geissman, T. A., & Edwards, J. D. (1960). Gossypol, a pigment of cottonseed. Chemical Reviews, 60, 555–574.
Boccard, J., Veuthey, J.-L., & Rudaz, S. (2010). Knowledge discovery in metabolomics: An overview of MS data handling. Journal of Separation Science, 33, 290–304.
Canelas, A. B., ten Pierick, A., Ras, C., Seifar, R. M., van Dam, J. C., van Gulik, W. M., et al. (2009). quantitative evaluation of intracellular metabolite extraction techniques for yeast metabolomics. Analytical Chemistry, 81, 7379–7389.
Caprioli, R.M. (1998). Method and apparatus for imaging biological samples with MALDI MS (Google Patents).
Caspi, R., Altman, T., Dale, J. M., Dreher, K., Fulcher, C. A., Gilham, F., et al. (2010). The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases. Nucleic Acids Research, 38, 473–479.
Chae, L., Lee, I., Shin, J., & Rhee, S. Y. (2012). Towards understanding how molecular networks evolve in plants. Current Opinion in Plant Biology, 15, 177–184.
Chaurand, P., Schwartz, S. A., Reyzer, M. L., & Caprioli, R. M. (2005). Imaging mass spectrometry: Principles and potentials. Toxicologic Pathology, 33, 92–101.
Dettmer, K., Aronov, P. A., & Hammock, B. D. (2007). Mass spectrometry-based metabolomics. Mass Spectrometry Reviews, 26, 51–78.
Dietmair, S., Timmins, N. E., Gray, P. P., Nielsen, L. K., & Krömer, J. O. (2010). Towards quantitative metabolomics of mammalian cells: Development of a metabolite extraction protocol. Analytical Biochemistry, 404, 155–164.
Dodou, K. (2005). Investigations on gossypol: Past and present developments. Expert Opinion on Investigational Drugs, 14, 1419–1434.
Fiehn, O. (2002). Metabolomics—the link between genotypes and phenotypes. Plant Molecular Biology, 48, 155–171.
Goodacre, R., Vaidyanathan, S., Dunn, W. B., Harrigan, G. G., & Kell, D. B. (2004). Metabolomics by numbers: Acquiring and understanding global metabolite data. Trends in Biotechnology, 22, 245–252.
Guenther, S., Koestler, M., Schulz, O., & Spengler, B. (2010). Laser spot size and laser power dependence of ion formation in high resolution MALDI imaging. International Journal of Mass Spectrometry, 294, 7–15.
Gustafsson, J. O. R., Oehler, M. K., Ruszkiewicz, A., McColl, S. R., & Hoffmann, P. (2011). MALDI imaging mass spectrometry (MALDI-IMS)—application of spatial proteomics for ovarian cancer classification and diagnosis. International Journal of Molecular Sciences, 12, 773–794.
Han, X., & Gross, R. W. (2005). Shotgun lipidomics: Electrospray ionization mass spectrometric analysis and quantitation of cellular lipidomes directly from crude extracts of biological samples. Mass Spectrometry Reviews, 24, 367–412.
Hattori, M., Okuno, Y., Goto, S., & Kanehisa, M. (2003). Development of a chemical structure comparison method for integrated analysis of chemical and genomic information in the metabolic pathways. Journal of the American Chemical Society, 125, 11853–11865.
Hedin, P. A., Parrott, W. L., & Jenkins, J. N. (1992). Relationships of glands, cotton square terpenoid aldehydes, and other allelochemicals to larval growth of Heliothis virescens (Lepidoptera: Noctuidae). Journal of Economic Entomology, 85, 359–364.
Horn, P. J., & Chapman, K. D. (2012). Lipidomics in tissues, cells and subcellular compartments. The Plant Journal, 70, 69–80.
Horn, P. J., James, C. N., Gidda, S., Kilaru, A., Dyer, J. M., Mullen, R. T., et al. (2013a). Identification of a new class of lipid droplet-associated proteins in plants. Plant Physiology, 162(4), 1926–1936.
Horn, P. J., Korte, A. R., Neogi, P. B., Love, E., Fuchs, J., Strupat, K., et al. (2012). Spatial mapping of lipids at cellular resolution in embryos of cotton. Plant Cell, 24, 622–636.
Horn, P. J., Ledbetter, N. R., James, C. N., Hoffman, W. D., Case, C. R., Verbeck, G. F., et al. (2011). Visualization of lipid droplet composition by direct organelle mass spectrometry. Journal of Biological Chemistry, 286, 3298–3306.
Horn, P.J., Silva, J.E., Anderson, D., Fuchs, J., Borisjuk, L., Nazarenus, T.J., Shulaev, V., Cahoon, E.B., and Chapman, K.D. (2013). Imaging Heterogeneity of Membrane and Storage Lipids in Transgenic Camelina sativa Seeds with Altered Fatty Acid Profiles. The Plant Journal. (In Press).
Hu, Q., Noll, R. J., Li, H., Makarov, A., Hardman, M., & Graham Cooks, R. (2005). The Orbitrap: A new mass spectrometer. Journal of Mass Spectrometry, 40, 430–443.
James, C. N., Horn, P. J., Case, C. R., Gidda, S. K., Zhang, D., Mullen, R. T., et al. (2010). Disruption of the Arabidopsis CGI-58 homologue produces Chanarin–Dorfman-like lipid droplet accumulation in plants. Proceedings of the National Academy of Sciences, 107, 17833–17838.
Källback, P., Shariatgorji, M., Nilsson, A., & Andrén, P. E. (2012). Novel mass spectrometry imaging software assisting labeled normalization and quantitation of drugs and neuropeptides directly in tissue sections. Journal of Proteomics, 75, 4941–4951.
Kaspar, S., Peukert, M., Svatos, A., Matros, A., & Mock, H.-P. (2011). MALDI-imaging mass spectrometry—An emerging technique in plant biology. Proteomics, 11, 1840–1850.
Kind, T., & Fiehn, O. (2006). Metabolomic database annotations via query of elemental compositions: Mass accuracy is insufficient even at less than 1 ppm. BMC Bioinformatics, 7, 234.
Lee, Y. J., Perdian, D. C., Song, Z., Yeung, E. S., & Nikolau, B. J. (2012). Use of mass spectrometry for imaging metabolites in plants. The Plant Journal, 70, 81–95.
Lindstrom, P.J., and Mallard, W.G. (2003). NIST Chemistry WebBook, NIST Standard Reference Database Number 69, National Institute of Standards and Technology, Gaithersburg MD, 20899. Retrieved July 15, 2013, from http://webbook.nist.gov.
Mao, Y., Lu, S., Wang, L., & Chen, X. (2006). Biosynthesis of gossypol in cotton. CAB Reviews: Perspective in Agriculture, Veterinary Science, Nutrition and Natural Resources, 49, 1–12.
McDonnell, L. A., van Remoortere, A., de Velde, N., van Zeijl, R. J. M., & Deelder, A. M. (2010). Imaging mass spectrometry data reduction: Automated feature identification and extraction. Journal of the American Society for Mass Spectrometry, 21, 1969–1978.
Mueller, L. A., Zhang, P., & Rhee, S. Y. (2003). AraCyc: A biochemical pathway database for arabidopsis. Plant Physiology, 132, 453–460.
Murphy, R. C., Hankin, J. A., & Barkley, R. M. (2009). Imaging of lipid species by MALDI mass spectrometry. Journal of Lipid Research, 50, S317–S322.
Pedrioli, P. G. A., Eng, J. K., Hubley, R., Vogelzang, M., Deutsch, E. W., Raught, B., et al. (2004). A common open representation of mass spectrometry data and its application to proteomics research. Nature Biotechnology, 22, 1459–1466.
Perdian, D. C., & Lee, Y. J. (2010). Imaging MS methodology for more chemical information in less data acquisition time utilizing a hybrid linear ion trap—Orbitrap mass spectrometer. Analytical Chemistry, 82, 9393–9400.
Scalbert, A., Brennan, L., Fiehn, O., Hankemeier, T., Kristal, B., Ommen, B., et al. (2009). Mass-spectrometry-based metabolomics: Limitations and recommendations for future progress with particular focus on nutrition research. Metabolomics, 5, 435–458.
Schramm, T., Hester, A., Klinkert, I., Both, J.-P., Heeren, R. M. A., Brunelle, A., et al. (2012). imzML—A common data format for the flexible exchange and processing of mass spectrometry imaging data. Journal of Proteomics, 75, 5106–5110.
Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R., & Siuzdak, G. (2006). XCMS: Processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Analytical Chemistry, 78, 779–787.
Stipanovic, R. D., Bell, A. A., & Benedict, C. R. (1999). Cotton pest resistance: The role of pigment gland constituent. In H. G. Cutler & S. J. Cutler (Eds.), Biologically active natural products: Agrochemicals (pp. 211–220). Boca Raton: CRC Press LLC.
Sturm, M., Bertsch, A., Gropl, C., Hildebrandt, A., Hussong, R., Lange, E., et al. (2008). OpenMS—an open-source software framework for mass spectrometry. BMC Bioinformatics, 9, 163.
Sud, M., Fahy, E., Cotter, D., Brown, A., Dennis, E. A., Glass, C. K., et al. (2007). LMSD: LIPID MAPS structure database. Nucleic Acids Research, 35, 527–532.
Sugimoto, M., Kawakami, M., Robert, M., Soga, T., & Tomita, M. (2012). Bioinformatics tools for mass spectroscopy-based metabolomic data processing and analysis. Current Bioinformatics, 7, 96–108.
Tohge, T., & Fernie, A. R. (2009). Web-based resources for mass-spectrometry-based metabolomics: A user’s guide. Phytochemistry, 70, 450–456.
Verhaert, P. E. M., Pinkse, M. H., Strupat, K., & Conaway, M. P. (2010). Imaging of similar mass neuropeptides in neuronal tissue by enhanced resolution MALDI MS with an ion trap—OrbitrapTM hybrid instrument. In S. S. Rubakhin & J. V. Sweedler (Eds.), Mass spectrometry imaging (pp. 433–449). New York: Humana Press.
Wagner, T. A., Liu, J., Stipanovic, R. D., Puckhaber, L. S., & Bell, A. A. (2012). Hemigossypol, a constituent in developing glanded cottonseed (Gossypium hirsutum). Journal of Agricultural and Food Chemistry, 60, 2594–2598.
Walch, A., Rauser, S., Deininger, S.-O., & Höfler, H. (2008). MALDI imaging mass spectrometry for direct tissue analysis: A new frontier for molecular histology. Histochemistry and Cell Biology, 130, 421–434.
Welti, R., Shah, J., Li, W., Li, M., Chen, J., Burke, J. J., et al. (2007). Plant lipidomics: Discerning biological function by profiling plant complex lipids using mass spectrometry. Front Biosci, 12, 2494–2506.
Whittern, C., Miller, E., & Pratt, D. (1984). Cottonseed flavonoids as lipid antioxidants. Journal of the American Oil Chemists Society, 61, 1075–1078.
Wishart, D. S. (2008). Quantitative metabolomics using NMR. TrAC, Trends in Analytical Chemistry, 27, 228–237.
Wishart, D. S., Tzur, D., Knox, C., Eisner, R., Guo, A. C., et al. (2007). HMDB: The human metabolome database. Nucleic Acids Research, 35, D521–D526.
Wu, H., Southam, A. D., Hines, A., & Viant, M. R. (2008). High-throughput tissue extraction protocol for NMR- and MS-based metabolomics. Analytical Biochemistry, 372, 204–212.
Xiong, X., Xu, W., Eberlin, L., Wiseman, J., Fang, X., Jiang, Y., et al. (2012). Data processing for 3D mass spectrometry imaging. Journal of the American Society for Mass Spectrometry, 23, 1147–1156.
Zhou, Z., Marepally, S., Nune, D., Pallakollu, P., Ragan, G., Roth, M., et al. (2011). LipidomeDB data calculation environment: Online processing of direct-infusion mass spectral data for lipid profiles. Lipids, 46, 879–884.
Acknowledgements
Application development is supported in part by grants from the US Department of Energy, BER Division, DE-FG02-09ER64812 and Cotton Incorporated (Agreement# 08-395) to KDC. The MSI facilities are supported by the Hoblitzelle Foundation. We thank Kerstin Strupat and Mari Prieto Conaway of Thermo-Fisher Scientific for technical support in MSI experiments. Mr. Patrick Horn was supported through the UNT Doctoral Fellowship program. We thank Dr. Markus Lange, Washington State University, and Dr. Vladimir Shulaev, University of North Texas, for their helpful comments on the manuscript. We also thank Drew Sturtevant, Danielle Anderson, Clayton Rowe, and Dr. Sarah Holt for testing and providing feedback on application.
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11306_2013_575_MOESM1_ESM.pdf
Online Resource 1. Generalized processing schematic mapping the entities necessary to generate a 2D imaging in Metabolite Imager (PDF 636 kb)
11306_2013_575_MOESM2_ESM.txt
Online Resource 2. Example tab-delimited, text file representing a single raw spectral scan converted using Metabolite Imager (TXT 60 kb)
Online Resource 3. Example imaging setup file for Metabolite Imager 2D image processing (XLSX 17 kb)
11306_2013_575_MOESM4_ESM.txt
Online Resource 4. Example list of all ions to search for in a particular 2D imaging process with conflicts representing peaks that might not be resolved due to the selected searching tolerances (TXT 98 kb)
11306_2013_575_MOESM8_ESM.xls
Online Resource 8. Example results from searching and annotating all peaks within a scan from an cottonseed section (XLS 848 kb)
11306_2013_575_MOESM9_ESM.txt
Online Resource 9. Example seed imaging filter file with values (1 or 0) designating either the inclusion or exclusion of this spot from additional analysis (TXT 82 kb)
11306_2013_575_MOESM10_ESM.pdf
Online Resource 10. Analysis of standard, free gossypol for detection of in-source fragments. (a) MALDI-MS full scan, negative mode acquisition of gossypol standard. Peaks with the selected red box are amplified in part (b) showing low amounts of hemigossypol likely produced through in-source fragmentation and the absence of desoxyhemigossypol (PDF 264 kb)
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Horn, P.J., Chapman, K.D. Metabolite Imager: customized spatial analysis of metabolite distributions in mass spectrometry imaging. Metabolomics 10, 337–348 (2014). https://doi.org/10.1007/s11306-013-0575-0
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DOI: https://doi.org/10.1007/s11306-013-0575-0