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Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis

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Abstract

In metabolomics, the rapid identification of quantitative differences between multiple biological samples remains a major challenge. While capillary electrophoresis–mass spectrometry (CE–MS) is a powerful tool to simultaneously quantify charged metabolites, reliable and easy-to-use software that is well suited to analyze CE–MS metabolic profiles is still lacking. Optimized software tools for CE–MS are needed because of the sometimes large variation in migration time between runs and the wider variety of peak shapes in CE–MS data compared with LC–MS or GC–MS. Therefore, we implemented a stand-alone application named JDAMP (Java application for Differential Analysis of Metabolite Profiles), which allows users to identify the metabolites that vary between two groups. The main features include fast calculation modules and a file converter using an original compact file format, baseline subtraction, dataset normalization and alignment, visualization on 2D plots (m/z and time axis) with matching metabolite standards, and the detection of significant differences between metabolite profiles. Moreover, it features an easy-to-use graphical user interface that requires only a few mouse-actions to complete the analysis. The interface also enables the analyst to evaluate the semiautomatic processes and interactively tune options and parameters depending on the input datasets. The confirmation of findings is available as a list of overlaid electropherograms, which is ranked using a novel difference-evaluation function that accounts for peak size and distortion as well as statistical criteria for accurate difference-detection. Overall, the JDAMP software complements other metabolomics data processing tools and permits easy and rapid detection of significant differences between multiple complex CE–MS profiles.

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References

  • Acevedo, F. J., Jimenez, J., Maldonado, S., Dominguez, E., & Narvaez, A. (2007). Classification of wines produced in specific regions by UV-visible spectroscopy combined with support vector machines. Journal of agricultural and food, 55, 6842–6849.

    Article  CAS  Google Scholar 

  • Allard, E., Backstrom, D., Danielsson, R., Sjoberg, P. J., & Bergquist, J. (2008). Comparing capillary electrophoresis-mass spectrometry fingerprints of urine samples obtained after intake of coffee, tea, or water. Analytical chemistry, 80, 8946–8955.

    Article  CAS  PubMed  Google Scholar 

  • Axen, J., Axelsson, B. O., Jornten-Karlsson, M., Petersson, P., & Sjoberg, P. J. (2007). An investigation of peak-broadening effects arising when combining CE with MS. Electrophoresis, 28, 3207–3213.

    Article  CAS  PubMed  Google Scholar 

  • Baran, R., Kochi, H., Saito, N., et al. (2006). MathDAMP: A package for differential analysis of metabolite profiles. BMC Bioinformatics, 7, 530.

    Article  PubMed  Google Scholar 

  • Baran, R., Robert, M., Suematsu, M., Soga, T., & Tomita, M. (2007). Visualization of three-way comparisons of omics data. BMC Bioinformatics, 8, 72.

    Article  PubMed  Google Scholar 

  • Bellew, M., Coram, M., Fitzgibbon, M., et al. (2006). A suite of algorithms for the comprehensive analysis of complex protein mixtures using high-resolution LC–MS. Bioinformatics, 22, 1902–1909.

    Article  CAS  PubMed  Google Scholar 

  • Broeckling, C. D., Reddy, I. R., Duran, A. L., Zhao, X., & Sumner, L. W. (2006). MET-IDEA: Data extraction tool for mass spectrometry-based metabolomics. Analytical chemistry, 78, 4334–4341.

    Article  CAS  PubMed  Google Scholar 

  • Brown, M., Dunn, W. B., Dobson, P., et al. (2009). Mass spectrometry tools and metabolite-specific databases for molecular identification in metabolomics. Analyst, 134, 1322–1332.

    Article  CAS  PubMed  Google Scholar 

  • Bunk, B., Kucklick, M., Jonas, R., et al. (2006). MetaQuant: A tool for the automatic quantification of GC/MS-based metabolome data. Bioinformatics, 22, 2962–2965.

    Article  CAS  PubMed  Google Scholar 

  • Bylund, D., Danielsson, R., Malmquist, G., & Markides, K. E. (2002). Chromatographic alignment by warping and dynamic programming as a pre-processing tool for PARAFAC modelling of liquid chromatography-mass spectrometry data. Journal of Chromatography. A, 961, 237–244.

    Article  CAS  PubMed  Google Scholar 

  • Erny, G. L., & Cifuentes, A. (2007). Simplified 2-D CE–MS mapping: Analysis of proteolytic digests. Electrophoresis, 28, 1335–1344.

    Article  CAS  PubMed  Google Scholar 

  • Fiehn, O., Kopka, J., Dormann, P., et al. (2000). Metabolite profiling for plant functional genomics. Nature biotechnology, 18, 1157–1161.

    Article  CAS  PubMed  Google Scholar 

  • Fischer, B., Grossmann, J., Roth, V., et al. (2006). Semi-supervised LC/MS alignment for differential proteomics. Bioinformatics, 22, e132–e140.

    Article  CAS  PubMed  Google Scholar 

  • Garcia-Alvarez-Coque, M. C., Simo-Alfonso, E. F., Sanchis-Mallols, J. M., & Baeza-Baeza, J. J. (2005). A new mathematical function for describing electrophoretic peaks. Electrophoresis, 26, 2076–2085.

    Article  CAS  PubMed  Google Scholar 

  • Hack, C. A., & Benner, W. H. (2002). A simple algorithm improves mass accuracy to 50–100 ppm for delayed extraction linear matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 16, 1304–1312.

    Article  CAS  PubMed  Google Scholar 

  • Haimi, P., Uphoff, A., Hermansson, M., & Somerharju, P. (2006). Software tools for analysis of mass spectrometric lipidome data. Analytical Chemistry, 78, 8324–8331.

    Article  CAS  PubMed  Google Scholar 

  • Halket, J. M., Przyborowska, A., Stein, S. E., et al. (1999). Deconvolution gas chromatography/mass spectrometry of urinary organic acids–potential for pattern recognition and automated identification of metabolic disorders. Rapid Communications in Mass Spectrometry, 13, 279–284.

    Article  CAS  PubMed  Google Scholar 

  • Hardy, N. W., & Taylor, C. F. (2007). A roadmap for the establishment of standard data exchange structures for metabolomics. Metabolomics, 3, 1573–3890.

    Article  Google Scholar 

  • Hirayama, A., Kami, K., Sugimoto, M., et al. (2009). Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Research, 69, 4918–4925.

    Article  CAS  PubMed  Google Scholar 

  • Karpievitch, Y. V., Hill, E. G., Smolka, A. J., et al. (2007). PrepMS: TOF MS data graphical preprocessing tool. Bioinformatics, 23, 264–265.

    Article  CAS  PubMed  Google Scholar 

  • Katajamaa, M., Miettinen, J., & Oresic, M. (2006). MZmine: Toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics, 22, 634–636.

    Article  CAS  PubMed  Google Scholar 

  • Katajamaa, M., & Oresic, M. (2005). Processing methods for differential analysis of LC/MS profile data. BMC Bioinformatics, 6, 179.

    Article  PubMed  Google Scholar 

  • Katajamaa, M., & Oresic, M. (2007). Data processing for mass spectrometry-based metabolomics. Journal of Chromatography. A, 1158, 318–328.

    Article  CAS  PubMed  Google Scholar 

  • Kempka, M., Sjodahl, J., Bjork, A., & Roeraade, J. (2004). Improved method for peak picking in matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Rapid Communications in Mass Spectrometry, 18, 1208–1212.

    Article  CAS  PubMed  Google Scholar 

  • Lee, R., Ptolemy, A. S., Niewczas, L., & Britz-McKibbin, P. (2007). Integrative metabolomics for characterizing unknown low-abundance metabolites by capillary electrophoresis-mass spectrometry with computer simulations. Analytical Chemistry, 79, 403–415.

    Article  CAS  PubMed  Google Scholar 

  • Liu, B. F., Sera, Y., Matsubara, N., Otsuka, K., & Terabe, S. (2003). Signal denoising and baseline correction by discrete wavelet transform for microchip capillary electrophoresis. Electrophoresis, 24, 3260–3265.

    Article  CAS  PubMed  Google Scholar 

  • Mahadevan, S., Shah, S. L., Marrie, T. J., & Slupsky, C. M. (2008). Analysis of metabolomic data using support vector machines. Analytical Chemistry, 80, 7562–7570.

    Article  CAS  PubMed  Google Scholar 

  • Mihaleva, V., Vorst, O., Maliepaard, C., et al. (2008). Accurate mass error correction in liquid chromatography time-of-flight mass spectrometry based metabolomics. Metabolomics, 4, 171–182.

    Article  CAS  Google Scholar 

  • Monton, M. R., & Soga, T. (2007). Metabolome analysis by capillary electrophoresis-mass spectrometry. Journal of Chromatography. A, 1168, 237–246.

    Article  CAS  PubMed  Google Scholar 

  • Nicholson, J. K., & Wilson, I. D. (2003). Opinion: Understanding ‘global’ systems biology: Metabonomics and the continuum of metabolism. Nature Reviews. Drug Discovery, 2, 668–676.

    Article  CAS  PubMed  Google Scholar 

  • Nordstrom, A., O’Maille, G., Qin, C., & Siuzdak, G. (2006). Nonlinear data alignment for UPLC–MS and HPLC–MS based metabolomics: Quantitative analysis of endogenous and exogenous metabolites in human serum. Analytical Chemistry, 78, 3289–3295.

    Article  PubMed  Google Scholar 

  • Ohnesorge, J., Neususs, C., & Watzig, H. (2005). Quantitation in capillary electrophoresis-mass spectrometry. Electrophoresis, 26, 3973–3987.

    Article  CAS  PubMed  Google Scholar 

  • Pedrioli, P. G., Eng, J. K., Hubley, R., et al. (2004). A common open representation of mass spectrometry data and its application to proteomics research. Nature Biotechnology, 22, 1459–1466.

    Article  CAS  PubMed  Google Scholar 

  • Plumb, R., Granger, J., Stumpf, C., et al. (2003). Metabonomic analysis of mouse urine by liquid-chromatography-time of flight mass spectrometry (LC-TOFMS): Detection of strain, diurnal and gender differences. Analyst, 128, 819–823.

    Article  CAS  PubMed  Google Scholar 

  • Reijenga, J. C., Martens, J. H., Giuliani, A., & Chiari, M. (2002). Pherogram normalization in capillary electrophoresis and micellar electrokinetic chromatography analyses in cases of sample matrix-induced migration time shifts. Journal of Chromatography B, Analytical Technologies in the Biomedical and Life Sciences, 770, 45–51.

    Article  CAS  Google Scholar 

  • Reo, N. V. (2002). NMR-based metabolomics. Drug and Chemical Toxicology, 25, 375–382.

    Article  CAS  PubMed  Google Scholar 

  • Robinson, M. D., De Souza, D. P., Keen, W. W., et al. (2007). A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments. BMC Bioinformatics, 8, 419.

    Article  PubMed  Google Scholar 

  • Ruckstuhl, A. F., Jacobson, M. P., Field, R. W., & Dodd, J. A. (2001). Baseline subtraction using robust local regression estimation. Journal of Quantitative Spectroscopy and Radiative Transfer, 68, 179–193.

    Article  CAS  Google Scholar 

  • Saito, N., Robert, M., Kitamura, S., et al. (2006). Metabolomics approach for enzyme discovery. Journal of Proteome Research, 5, 1979–1987.

    Article  CAS  PubMed  Google Scholar 

  • Shaffer, J. P. (1995). Multiple hypothesis testing. Annual Review of Psychology, 46, 561–584.

    Article  Google Scholar 

  • Simo, C., Moreno-Arribas, M. V., & Cifuentes, A. (2008). Ion-trap versus time-of-flight mass spectrometry coupled to capillary electrophoresis to analyze biogenic amines in wine. Journal of Chromatography. A, 1195, 150–156.

    Article  CAS  PubMed  Google Scholar 

  • 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.

    Article  CAS  PubMed  Google Scholar 

  • Soga, T., Baran, R., Suematsu, M., et al. (2006). Differential metabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione consumption. Journal of Biological Chemistry, 281, 16768–16776.

    Article  CAS  PubMed  Google Scholar 

  • Soga, T., Ohashi, Y., Ueno, Y., et al. (2003). Quantitative metabolome analysis using capillary electrophoresis mass spectrometry. Journal of Proteome Research, 2, 488–494.

    Article  CAS  PubMed  Google Scholar 

  • Styczynski, M. P., Moxley, J. F., Tong, L. V., et al. (2007). Systematic identification of conserved metabolites in GC/MS data for metabolomics and biomarker discovery. Analytical Chemistry, 79, 966–973.

    Article  CAS  PubMed  Google Scholar 

  • Tautenhahn, R., Bottcher, C., & Neumann, S. (2008). Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics, 9, 504.

    Article  PubMed  Google Scholar 

  • Vivo-Truyols, G., Torres-Lapasio, J. R., van Nederkassel, A. M., Vander Heyden, Y., & Massart, D. L. (2005a). Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals part I: Peak detection. Journal of Chromatography. A, 1096, 133–145.

    Article  CAS  PubMed  Google Scholar 

  • Vivo-Truyols, G., Torres-Lapasio, J. R., van Nederkassel, A. M., Vander Heyden, Y., & Massart, D. L. (2005b). Automatic program for peak detection and deconvolution of multi-overlapped chromatographic signals part II: Peak model and deconvolution algorithms. Journal of Chromatography. A, 1096, 146–155.

    Article  CAS  PubMed  Google Scholar 

  • Wallace, W. E., Kearsley, A. J., & Guttman, C. M. (2004). An operator-independent approach to mass spectral peak identification and integration. Analytical Chemistry, 76, 2446–2452.

    Article  CAS  PubMed  Google Scholar 

  • Wang, T., Shao, K., Chu, Q., et al. (2009). Automics: An integrated platform for NMR-based metabonomics spectral processing and data analysis. BMC Bioinformatics, 10, 83.

    Article  PubMed  Google Scholar 

  • Wee, A., Grayden, D. B., Zhu, Y., Petkovic-Duran, K., & Smith, D. (2008). A continuous wavelet transform algorithm for peak detection. Electrophoresis, 29, 4215–4225.

    Article  CAS  PubMed  Google Scholar 

  • Wittke, S., Fliser, D., Haubitz, M., et al. (2003). Determination of peptides and proteins in human urine with capillary electrophoresis-mass spectrometry, a suitable tool for the establishment of new diagnostic markers. Journal of Chromatography. A, 1013, 173–181.

    Article  CAS  PubMed  Google Scholar 

  • Wong, J. W., Cagney, G., & Cartwright, H. M. (2005). SpecAlign–processing and alignment of mass spectra datasets. Bioinformatics, 21, 2088–2090.

    Article  CAS  PubMed  Google Scholar 

  • Wu, J., & McAllister, H. (2003). Exact mass measurement on an electrospray ionization time-of-flight mass spectrometer: Error distribution and selective averaging. Journal of Mass Spectrometry, 38, 1043–1053.

    Article  CAS  PubMed  Google Scholar 

  • Yoshida, S., Hashimoto, K., Tanaka-Kanai, K., Yoshimoto, H., & Kobayashi, O. (2007). Identification and characterization of amidase-homologous AMI1 genes of bottom-fermenting yeast. Yeast, 24, 1075–1084.

    Article  CAS  PubMed  Google Scholar 

  • Zhao, Q., Stoyanova, R., Du, S., Sajda, P., & Brown, T. R. (2006). HiRes–a tool for comprehensive assessment and interpretation of metabolomic data. Bioinformatics, 22, 2562–2564.

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

We thank Dr. Yusuke Tanigawara and Dr. Akito Nishimuta of the School of Medicine, Keio University, Dr. Satoshi Yoshida and Dr. Hideki Koizumi of Kirin Holdings, Dr. Akira Oikawa of Riken, and Dr. Eri Shimizu and Dr. Tadahiro Ozawa of Kao Corporation, for valuable discussions. We also thank Maki Sugawara, Hiroko Ueda, Shinobu Abe, and Kazuki Sugisaki of IAB for measurement, data analyses, and programming, and Dr. Ursula Petralia for editing the manuscript. This work was supported by research grants from the Yamagata Prefectural Government and the City of Tsuruoka.

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Authors and Affiliations

  1. Institute for Advanced Biosciences, Keio University, Tsuruoka, Yamagata, 997-0017, Japan

    Masahiro Sugimoto, Akiyoshi Hirayama, Martin Robert, Richard Baran, Katsuya Kawai, Tomoyoshi Soga & Masaru Tomita

  2. Department of Bioinformatics, Mitsubishi Space Software Co. Ltd, Amagasaki, Hyogo, 661-0001, Japan

    Masahiro Sugimoto, Keizo Uehara & Katsuya Kawai

  3. Human Metabolome Technologies Inc, Tsuruoka, Yamagata, 997-0052, Japan

    Takamasa Ishikawa, Tomoyoshi Soga & Masaru Tomita

  4. Life Sciences Division, MS: 84R0171, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA

    Richard Baran

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  1. Masahiro Sugimoto
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Correspondence to Masahiro Sugimoto.

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Supplementary material 1 (DOC 36 kb)

11306_2009_175_MOESM2_ESM.ppt

Figure S1 Screenshots of some of the main functions of JDAMP: (A) File import window. (B) Results window for background subtraction and noise filtering. (C) Alignment setup window. Alignment results windows to visualize distributed peak locations (D) and the migration time-normalization function (E).(PPT 325 kb)

11306_2009_175_MOESM3_ESM.ppt

Figure S2 Baseline and noise structure in CE–MS data. Total ion electropherogram (A) and three examples of extracted electropherograms at 122.0964 m/z (B), 193.0681 m/z (C) and 307.1765 m/z (D) from mouse serum samples used in this study. The electropherograms were obtained using Analyst QS from raw files. The last three electropherograms were noise-reduced electropherograms at 122 m/z (E), 193 m/z (F), and 308 m/z (G) (corresponding to B, C, and D, respectively). (PPT 190 kb)

11306_2009_175_MOESM4_ESM.ppt

Figure S3 Performance of the migration time normalization procedure. The vertical lines in the box indicate upper median, median and lower median, and the whiskers indicate the maximum and minimum values. Migration times for the peaks matched between two samples before/after migration time normalization are depicted in (A–D). The plots (E) show all paired peaks assigned in the DP process, corresponding to (A). (PPT 187 kb)

11306_2009_175_MOESM5_ESM.ppt

Figure S4 Typical traces of the electric current applied to the capillary during each run. The blue and red trajectories represent different analyses of the same sample on the same instrument. The figures were generated using MassHunter software controlling an Agilent TOF–MS system. (PPT 144 kb)

11306_2009_175_MOESM6_ESM.ppt

Figure S5 Overlaid electropherograms of the results ranked between 13 and 50 based on calculations performed using the datapoint-by-datapoint t-score, smoothed t-score, ABSRel, area, or Gaussian area functions. The results ranked above 12 are shown in Fig. 5. The red and blue curves represent the peaks for the samples and control datasets, respectively. (PPT 2865 kb)

11306_2009_175_MOESM7_ESM.ppt

Figure S6 Graph (A) and (B) are the association between the ranks of differences within top 50th order detected by MathDAMP and JDAMP based on ABSRel (A) and smoothed t-score (B). The missing data indicate corresponding results were not assigned within top 50 ranking by the other tool. Graphs (C), (D), and (E) were electropherograms at 112 m/z, 567 m/z, and 132 m/z detected by MathDAMP based on ABSRel (C and D) and smoothed t-score (E), respectively. The blue and red lines denote controls and samples, respectively. (PPT 375 kb)

11306_2009_175_MOESM8_ESM.ppt

Figure S7 Processing results of standard solution with additional spiking (+50%) of three metabolites using MZMine. Panel (A) shows the peak detection results around N-α-Benzenolarginine ethylester (307 m/z) in one of the datasets, and panel (B) shows the aligned peak location of six datasets. Although the two black rectangles were not detected, the intersect of the red lines was detected as a peak, as shown in the table in (A). Panels (C) and (D) depict the datasets used for (A) in a 3D representation showing the full range (C) and between 100 m/z and 1000 m/z (D), respectively. Table (E) shows the portion of the aligned matrix related to peaks for 2,4-Dimethylaniline (122.0964 m/z). The red rectangles are expected to be aligned. Figures (F) and (G) depict the extracted electropherograms binned between 122.09 m/z and 122.10 m/z. The red triangle indicates the top of the 2,4-Dimethylaniline peak (only shown in misaligned electropherograms). Panels (A), (B), (E), (F) and (G) are screen-shots of MZMine (Katajamaa, et al., 2006), and figures (C) and (D) were generated using MZMine ver.2 (Okinawa Institute of Science and Technology, http://mzmine.sourceforge.net/). (PPT 718 kb)

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Sugimoto, M., Hirayama, A., Ishikawa, T. et al. Differential metabolomics software for capillary electrophoresis-mass spectrometry data analysis. Metabolomics 6, 27–41 (2010). https://doi.org/10.1007/s11306-009-0175-1

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