Publications

Articles

  • M. A. Jones, S. H. Cho, N. H. Patterson, R. V. de Plas, J. M. Spraggins, M. R. Boothby, and R. M. Caprioli, “Discovering new lipidomic features using cell type specific fluorophore expression to provide spatial and biological specificity in a multimodal workflow with MALDI imaging mass spectrometry,” Analytical chemistry, vol. 92, iss. 10, p. 7079–7086, 2020. doi:10.1021/acs.analchem.0c00446
    [BibTeX] [Download PDF]
    @article{Jones2020,
    doi = {10.1021/acs.analchem.0c00446},
    url = {https://doi.org/10.1021/acs.analchem.0c00446},
    year = {2020},
    month = apr,
    publisher = {American Chemical Society ({ACS})},
    volume = {92},
    number = {10},
    pages = {7079--7086},
    author = {Marissa A. Jones and Sung Hoon Cho and Nathan Heath Patterson and Raf Van de Plas and Jeffrey M. Spraggins and Mark R. Boothby and Richard M. Caprioli},
    title = {Discovering New Lipidomic Features Using Cell Type Specific Fluorophore Expression to Provide Spatial and Biological Specificity in a Multimodal Workflow with {MALDI} Imaging Mass Spectrometry},
    journal = {Analytical Chemistry}
    }

  • W. J. Perry, A. Weiss, R. V. de Plas, J. M. Spraggins, R. M. Caprioli, and E. P. Skaar, “Integrated molecular imaging technologies for investigation of metals in biological systems: a brief review,” Current opinion in chemical biology, vol. 55, p. 127–135, 2020. doi:10.1016/j.cbpa.2020.01.008
    [BibTeX] [Download PDF]
    @article{Perry2020,
    doi = {10.1016/j.cbpa.2020.01.008},
    url = {https://doi.org/10.1016/j.cbpa.2020.01.008},
    year = {2020},
    month = apr,
    publisher = {Elsevier {BV}},
    volume = {55},
    pages = {127--135},
    author = {William J. Perry and Andy Weiss and Raf Van de Plas and Jeffrey M. Spraggins and Richard M. Caprioli and Eric P. Skaar},
    title = {Integrated molecular imaging technologies for investigation of metals in biological systems: A brief review},
    journal = {Current Opinion in Chemical Biology}
    }

  • N. Verbeeck, R. M. Caprioli, and R. V. de Plas, “Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry,” Mass spectrometry reviews, vol. 39, iss. 3, p. 245–291, 2020. doi:10.1002/mas.21602
    [BibTeX] [Download PDF]
    @article{Verbeeck2020,
    doi = {10.1002/mas.21602},
    url = {https://doi.org/10.1002/mas.21602},
    year = {2020},
    month = may,
    publisher = {Wiley},
    volume = {39},
    number = {3},
    pages = {245--291},
    author = {Nico Verbeeck and Richard M. Caprioli and Raf Van de Plas},
    title = {Unsupervised machine learning for exploratory data analysis in imaging mass spectrometry},
    journal = {Mass Spectrometry Reviews}
    }

  • J. M. Spraggins, K. V. Djambazova, E. S. Rivera, L. G. Migas, E. K. Neumann, A. Fuetterer, J. Suetering, N. Goedecke, A. Ly, R. V. de Plas, and R. M. Caprioli, “High-performance molecular imaging with MALDI trapped ion-mobility time-of-flight (timsTOF) mass spectrometry,” Analytical chemistry, vol. 91, iss. 22, p. 14552–14560, 2019. doi:10.1021/acs.analchem.9b03612
    [BibTeX] [Download PDF]
    @article{Spraggins2019,
    doi = {10.1021/acs.analchem.9b03612},
    url = {https://doi.org/10.1021/acs.analchem.9b03612},
    year = {2019},
    month = oct,
    publisher = {American Chemical Society ({ACS})},
    volume = {91},
    number = {22},
    pages = {14552--14560},
    author = {Jeffrey M. Spraggins and Katerina V. Djambazova and Emilio S. Rivera and Lukasz G. Migas and Elizabeth K. Neumann and Arne Fuetterer and Juergen Suetering and Niels Goedecke and Alice Ly and Raf Van de Plas and Richard M. Caprioli},
    title = {High-Performance Molecular Imaging with {MALDI} Trapped Ion-Mobility Time-of-Flight ({timsTOF}) Mass Spectrometry},
    journal = {Analytical Chemistry}
    }

  • “The human body at cellular resolution: the NIH human biomolecular atlas program,” Nature, vol. 574, iss. 7777, p. 187–192, 2019. doi:10.1038/s41586-019-1629-x
    [BibTeX] [Download PDF]
    @article{2019,
    doi = {10.1038/s41586-019-1629-x},
    url = {https://doi.org/10.1038/s41586-019-1629-x},
    year = {2019},
    month = oct,
    publisher = {Springer Science and Business Media {LLC}},
    volume = {574},
    number = {7777},
    pages = {187--192},
    title = {The human body at cellular resolution: the {NIH} Human Biomolecular Atlas Program},
    journal = {Nature}
    }

  • N. H. Patterson, M. Tuck, R. V. de Plas, and R. M. Caprioli, “Advanced registration and analysis of MALDI imaging mass spectrometry measurements through autofluorescence microscopy,” Analytical chemistry, vol. 90, iss. 21, p. 12395–12403, 2018. doi:10.1021/acs.analchem.8b02884
    [BibTeX] [Download PDF]
    @article{Patterson2018,
    doi = {10.1021/acs.analchem.8b02884},
    url = {https://doi.org/10.1021/acs.analchem.8b02884},
    year = {2018},
    month = oct,
    publisher = {American Chemical Society ({ACS})},
    volume = {90},
    number = {21},
    pages = {12395--12403},
    author = {Nathan Heath Patterson and Michael Tuck and Raf Van de Plas and Richard M. Caprioli},
    title = {Advanced Registration and Analysis of {MALDI} Imaging Mass Spectrometry Measurements through Autofluorescence Microscopy},
    journal = {Analytical Chemistry}
    }

  • N. H. Patterson, M. Tuck, A. Lewis, A. Kaushansky, J. L. Norris, R. V. de Plas, and R. M. Caprioli, “Next generation histology-directed imaging mass spectrometry driven by autofluorescence microscopy,” Analytical chemistry, vol. 90, iss. 21, p. 12404–12413, 2018. doi:10.1021/acs.analchem.8b02885
    [BibTeX] [Download PDF]
    @article{Patterson2018,
    doi = {10.1021/acs.analchem.8b02885},
    url = {https://doi.org/10.1021/acs.analchem.8b02885},
    year = {2018},
    month = oct,
    publisher = {American Chemical Society ({ACS})},
    volume = {90},
    number = {21},
    pages = {12404--12413},
    author = {Nathan Heath Patterson and Michael Tuck and Adam Lewis and Alexis Kaushansky and Jeremy L. Norris and Raf Van de Plas and Richard M. Caprioli},
    title = {Next Generation Histology-Directed Imaging Mass Spectrometry Driven by Autofluorescence Microscopy},
    journal = {Analytical Chemistry}
    }

  • J. E. Cassat, J. L. Moore, K. J. Wilson, Z. Stark, B. M. Prentice, R. V. de Plas, W. J. Perry, Y. Zhang, J. Virostko, D. C. Colvin, K. L. Rose, A. M. Judd, M. L. Reyzer, J. M. Spraggins, C. M. Grunenwald, J. C. Gore, R. M. Caprioli, and E. P. Skaar, “Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions,” Science translational medicine, vol. 10, iss. 432, p. eaan6361, 2018. doi:10.1126/scitranslmed.aan6361
    [BibTeX] [Download PDF]
    @article{Cassat2018,
    doi = {10.1126/scitranslmed.aan6361},
    url = {https://doi.org/10.1126/scitranslmed.aan6361},
    year = {2018},
    month = mar,
    publisher = {American Association for the Advancement of Science ({AAAS})},
    volume = {10},
    number = {432},
    pages = {eaan6361},
    author = {James E. Cassat and Jessica L. Moore and Kevin J. Wilson and Zach Stark and Boone M. Prentice and Raf Van de Plas and William J. Perry and Yaofang Zhang and John Virostko and Daniel C. Colvin and Kristie L. Rose and Audra M. Judd and Michelle L. Reyzer and Jeffrey M. Spraggins and Caroline M. Grunenwald and John C. Gore and Richard M. Caprioli and Eric P. Skaar},
    title = {Integrated molecular imaging reveals tissue heterogeneity driving host-pathogen interactions},
    journal = {Science Translational Medicine}
    }

  • B. M. Prentice, D. J. Ryan, R. V. de Plas, R. M. Caprioli, and J. M. Spraggins, “Enhanced ion transmission efficiency up to m/z 24 000 for MALDI protein imaging mass spectrometry,” Analytical chemistry, vol. 90, iss. 8, p. 5090–5099, 2018. doi:10.1021/acs.analchem.7b05105
    [BibTeX] [Download PDF]
    @article{Prentice2018,
    doi = {10.1021/acs.analchem.7b05105},
    url = {https://doi.org/10.1021/acs.analchem.7b05105},
    year = {2018},
    month = feb,
    publisher = {American Chemical Society ({ACS})},
    volume = {90},
    number = {8},
    pages = {5090--5099},
    author = {Boone M. Prentice and Daniel J. Ryan and Raf Van de Plas and Richard M. Caprioli and Jeffrey M. Spraggins},
    title = {Enhanced Ion Transmission Efficiency up to m/z 24 000 for {MALDI} Protein Imaging Mass Spectrometry},
    journal = {Analytical Chemistry}
    }

  • N. Verbeeck, J. M. Spraggins, M. J. M. Murphy, H. Wang, A. Y. Deutch, R. M. Caprioli, and R. V. de Plas, “Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis,” Biochimica et biophysica acta (BBA) – proteins and proteomics, vol. 1865, iss. 7, p. 967–977, 2017. doi:10.1016/j.bbapap.2017.02.016
    [BibTeX] [Download PDF]
    @article{Verbeeck2017,
    doi = {10.1016/j.bbapap.2017.02.016},
    url = {https://doi.org/10.1016/j.bbapap.2017.02.016},
    year = {2017},
    month = jul,
    publisher = {Elsevier {BV}},
    volume = {1865},
    number = {7},
    pages = {967--977},
    author = {Nico Verbeeck and Jeffrey M. Spraggins and Monika J.M. Murphy and Hui-dong Wang and Ariel Y. Deutch and Richard M. Caprioli and Raf Van de Plas},
    title = {Connecting imaging mass spectrometry and magnetic resonance imaging-based anatomical atlases for automated anatomical interpretation and differential analysis},
    journal = {Biochimica et Biophysica Acta ({BBA}) - Proteins and Proteomics}
    }

  • J. L. Norris, M. A. Farrow, D. B. Gutierrez, L. D. Palmer, N. Muszynski, S. D. Sherrod, J. C. Pino, J. L. Allen, J. M. Spraggins, A. L. R. Lubbock, A. Jordan, W. Burns, J. C. Poland, C. Romer, L. M. Manier, Y. Nei, B. M. Prentice, K. L. Rose, S. Hill, R. V. de Plas, T. Tsui, N. M. Braman, R. M. Keller, S. A. Rutherford, N. Lobdell, C. F. Lopez, B. D. Lacy, J. A. McLean, J. P. Wikswo, E. P. Skaar, and R. M. Caprioli, “Integrated, high-throughput, multiomics platform enables data-driven construction of cellular responses and reveals global drug mechanisms of action,” Journal of proteome research, vol. 16, iss. 3, p. 1364–1375, 2017. doi:10.1021/acs.jproteome.6b01004
    [BibTeX] [Download PDF]
    @article{Norris2017,
    doi = {10.1021/acs.jproteome.6b01004},
    url = {https://doi.org/10.1021/acs.jproteome.6b01004},
    year = {2017},
    month = feb,
    publisher = {American Chemical Society ({ACS})},
    volume = {16},
    number = {3},
    pages = {1364--1375},
    author = {Jeremy L. Norris and Melissa A. Farrow and Danielle B. Gutierrez and Lauren D. Palmer and Nicole Muszynski and Stacy D. Sherrod and James C. Pino and Jamie L. Allen and Jeffrey M. Spraggins and Alex L. R. Lubbock and Ashley Jordan and William Burns and James C. Poland and Carrie Romer and M. Lisa Manier and Yuan-wei Nei and Boone M. Prentice and Kristie L. Rose and Salisha Hill and Raf Van de Plas and Tina Tsui and Nathaniel M. Braman and M. Ray Keller and Stacey A. Rutherford and Nichole Lobdell and Carlos F. Lopez and D. Borden Lacy and John A. McLean and John P. Wikswo and Eric P. Skaar and Richard M. Caprioli},
    title = {Integrated, High-Throughput, Multiomics Platform Enables Data-Driven Construction of Cellular Responses and Reveals Global Drug Mechanisms of Action},
    journal = {Journal of Proteome Research}
    }

  • D. M. G. Anderson, R. V. de Plas, K. L. Rose, S. Hill, K. L. Schey, A. C. Solga, D. H. Gutmann, and R. M. Caprioli, “3-d imaging mass spectrometry of protein distributions in mouse neurofibromatosis 1 (NF1)-associated optic glioma,” Journal of proteomics, vol. 149, p. 77–84, 2016. doi:10.1016/j.jprot.2016.02.004
    [BibTeX] [Download PDF]
    @article{Anderson2016,
    doi = {10.1016/j.jprot.2016.02.004},
    url = {https://doi.org/10.1016/j.jprot.2016.02.004},
    year = {2016},
    month = oct,
    publisher = {Elsevier {BV}},
    volume = {149},
    pages = {77--84},
    author = {David M.G. Anderson and Raf Van de Plas and Kristie L. Rose and Salisha Hill and Kevin L. Schey and Anne C. Solga and David H. Gutmann and Richard M. Caprioli},
    title = {3-D imaging mass spectrometry of protein distributions in mouse Neurofibromatosis 1 ({NF}1)-associated optic glioma},
    journal = {Journal of Proteomics}
    }

  • E. Marien, M. Meister, T. Muley, T. G. del Pulgar, R. Derua, J. M. Spraggins, R. V. de Plas, F. Vanderhoydonc, J. Machiels, M. M. Binda, J. Dehairs, J. Willette-Brown, Y. Hu, H. Dienemann, M. Thomas, P. A. Schnabel, R. M. Caprioli, J. C. Lacal, E. Waelkens, and J. V. Swinnen, “Phospholipid profiling identifies acyl chain elongation as a ubiquitous trait and potential target for the treatment of lung squamous cell carcinoma,” Oncotarget, vol. 7, iss. 11, p. 12582–12597, 2016. doi:10.18632/oncotarget.7179
    [BibTeX] [Download PDF]
    @article{Marien2016,
    doi = {10.18632/oncotarget.7179},
    url = {https://doi.org/10.18632/oncotarget.7179},
    year = {2016},
    month = feb,
    publisher = {Impact Journals, {LLC}},
    volume = {7},
    number = {11},
    pages = {12582--12597},
    author = {Eyra Marien and Michael Meister and Thomas Muley and Teresa Gomez del Pulgar and Rita Derua and Jeffrey M. Spraggins and Raf Van de Plas and Frank Vanderhoydonc and Jelle Machiels and Maria Mercedes Binda and Jonas Dehairs and Jami Willette-Brown and Yinling Hu and Hendrik Dienemann and Michael Thomas and Philipp A. Schnabel and Richard M. Caprioli and Juan Carlos Lacal and Etienne Waelkens and Johannes V. Swinnen},
    title = {Phospholipid profiling identifies acyl chain elongation as a ubiquitous trait and potential target for the treatment of lung squamous cell carcinoma},
    journal = {Oncotarget}
    }

  • R. Van de Plas, J. Yang, J. Spraggins, and R. M. Caprioli, “Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping,” Nature methods, vol. 12, iss. 4, p. 366–372, 2015. doi:10.1038/nmeth.3296
    [BibTeX] [Abstract] [Download PDF]

    We describe a predictive imaging modality created by ‘fusing’ two distinct technologies: imaging mass spectrometry (IMS) and microscopy. IMS-generated molecular maps, rich in chemical information but having coarse spatial resolution, are combined with optical microscopy maps, which have relatively low chemical specificity but high spatial information. The resulting images combine the advantages of both technologies, enabling prediction of a molecular distribution both at high spatial resolution and with high chemical specificity. Multivariate regression is used to model variables in one technology, using variables from the other technology. We demonstrate the potential of image fusion through several applications: (i) ‘sharpening’ of IMS images, which uses microscopy measurements to predict ion distributions at a spatial resolution that exceeds that of measured ion images by ten times or more; (ii) prediction of ion distributions in tissue areas that were not measured by IMS; and (iii) enrichment of biological signals and attenuation of instrumental artifacts, revealing insights not easily extracted from either microscopy or IMS individually.

    @article{VandePlas2015,
    abstract = {We describe a predictive imaging modality created by 'fusing' two distinct technologies: imaging mass spectrometry (IMS) and microscopy. IMS-generated molecular maps, rich in chemical information but having coarse spatial resolution, are combined with optical microscopy maps, which have relatively low chemical specificity but high spatial information. The resulting images combine the advantages of both technologies, enabling prediction of a molecular distribution both at high spatial resolution and with high chemical specificity. Multivariate regression is used to model variables in one technology, using variables from the other technology. We demonstrate the potential of image fusion through several applications: (i) 'sharpening' of IMS images, which uses microscopy measurements to predict ion distributions at a spatial resolution that exceeds that of measured ion images by ten times or more; (ii) prediction of ion distributions in tissue areas that were not measured by IMS; and (iii) enrichment of biological signals and attenuation of instrumental artifacts, revealing insights not easily extracted from either microscopy or IMS individually.},
    author = {Van de Plas, Raf and Yang, Junhai and Spraggins, Jeffrey and Caprioli, Richard M},
    doi = {10.1038/nmeth.3296},
    issn = {1548-7091},
    journal = {Nature methods},
    keywords = {Animals,Brain,Computer-Assisted,Image Processing,Mass Spectrometry,Mice,Microscopy,instrumentation,methods,ultrastructure},
    language = {eng},
    month = apr,
    number = {4},
    pages = {366--372},
    pmid = {25707028},
    publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
    shorttitle = {Nat Meth},
    title = {Image fusion of mass spectrometry and microscopy: a multimodality paradigm for molecular tissue mapping},
    url = {http://dx.doi.org/10.1038/nmeth.3296 http://www.nature.com/nmeth/journal/v12/n4/full/nmeth.3296.html},
    volume = {12},
    year = {2015},
    month_numeric = {4},
    file = {}
    }

  • E. Marien, M. Meister, T. Muley, S. Fieuws, S. Bordel, R. Derua, J. Spraggins, R. Van de Plas, J. Dehairs, J. Wouters, M. Bagadi, H. Dienemann, M. Thomas, P. A. Schnabel, R. M. Caprioli, E. Waelkens, and J. V. Swinnen, “Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles,” International journal of cancer, vol. 137, iss. 7, pp. 1539-1548, 2015. doi:10.1002/ijc.29517
    [BibTeX] [Abstract] [Download PDF]

    Non-small cell lung cancer (NSCLC) is the leading cause of cancer death globally. To develop better diagnostics and more effective treatments, research in the past decades has focused on identification of molecular changes in the genome, transcriptome, proteome, and more recently also the metabolome. Phospholipids, which nevertheless play a central role in cell functioning, remain poorly explored. Here, using a mass spectrometry (MS)-based phospholipidomics approach, we profiled 179 phospholipid species in malignant and matched non-malignant lung tissue of 162 NSCLC patients (73 in a discovery cohort and 89 in a validation cohort). We identified 91 phospholipid species that were differentially expressed in cancer versus non-malignant tissues. Most prominent changes included a decrease in sphingomyelins (SMs) and an increase in specific phosphatidylinositols (PIs). Also a decrease in multiple phosphatidylserines (PSs) was observed, along with an increase in several phosphatidylethanolamine (PE) and phosphatidylcholine (PC) species, particularly those with 40 or 42 carbon atoms in both fatty acyl chains together. 2D-imaging MS of the most differentially expressed phospholipids confirmed their differential abundance in cancer cells. We identified lipid markers that can discriminate tumor versus normal tissue and different NSCLC subtypes with an AUC (area under the ROC curve) of 0.999 and 0.885, respectively. In conclusion, using both shotgun and 2D-imaging lipidomics analysis, we uncovered a hitherto unrecognized alteration in phospholipid profiles in NSCLC. These changes may have important biological implications and may have significant potential for biomarker development.

    @article{doi:10.1002/ijc.29517,
    author = {Marien, Eyra and Meister, Michael and Muley, Thomas and Fieuws, Steffen and Bordel, Sergio and Derua, Rita and Spraggins, Jeffrey and Van de Plas, Raf and Dehairs, Jonas and Wouters, Jens and Bagadi, Muralidhararao and Dienemann, Hendrik and Thomas, Michael and Schnabel, Philipp A. and Caprioli, Richard M. and Waelkens, Etienne and Swinnen, Johannes V.},
    title = {Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles},
    journal = {International Journal of Cancer},
    volume = {137},
    number = {7},
    pages = {1539-1548},
    keywords = {non-small cell lung cancer, lipidomics, phospholipids, mass spectrometry, 2D-imaging MS},
    doi = {10.1002/ijc.29517},
    url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ijc.29517},
    eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1002/ijc.29517},
    abstract = {Non-small cell lung cancer (NSCLC) is the leading cause of cancer death globally. To develop better diagnostics and more effective treatments, research in the past decades has focused on identification of molecular changes in the genome, transcriptome, proteome, and more recently also the metabolome. Phospholipids, which nevertheless play a central role in cell functioning, remain poorly explored. Here, using a mass spectrometry (MS)-based phospholipidomics approach, we profiled 179 phospholipid species in malignant and matched non-malignant lung tissue of 162 NSCLC patients (73 in a discovery cohort and 89 in a validation cohort). We identified 91 phospholipid species that were differentially expressed in cancer versus non-malignant tissues. Most prominent changes included a decrease in sphingomyelins (SMs) and an increase in specific phosphatidylinositols (PIs). Also a decrease in multiple phosphatidylserines (PSs) was observed, along with an increase in several phosphatidylethanolamine (PE) and phosphatidylcholine (PC) species, particularly those with 40 or 42 carbon atoms in both fatty acyl chains together. 2D-imaging MS of the most differentially expressed phospholipids confirmed their differential abundance in cancer cells. We identified lipid markers that can discriminate tumor versus normal tissue and different NSCLC subtypes with an AUC (area under the ROC curve) of 0.999 and 0.885, respectively. In conclusion, using both shotgun and 2D-imaging lipidomics analysis, we uncovered a hitherto unrecognized alteration in phospholipid profiles in NSCLC. These changes may have important biological implications and may have significant potential for biomarker development.},
    year = {2015}
    }

  • E. Marien, M. Meister, T. Muley, S. Fieuws, S. Bordel, R. Derua, J. Spraggins, R. Van de Plas, J. Dehairs, J. Wouters, M. Bagadi, H. Dienemann, M. Thomas, P. A. Schnabel, R. M. Caprioli, E. Waelkens, and J. V. Swinnen, “Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles.,” International journal of cancer. journal international du cancer, vol. 137, iss. 7, p. 1539–1548, 2015. doi:10.1002/ijc.29517
    [BibTeX] [Abstract] [Download PDF]

    Non-small cell lung cancer (NSCLC) is the leading cause of cancer death globally. To develop better diagnostics and more effective treatments, research in the past decades has focused on identification of molecular changes in the genome, transcriptome, proteome, and more recently also the metabolome. Phospholipids, which nevertheless play a central role in cell functioning, remain poorly explored. Here, using a mass spectrometry (MS)-based phospholipidomics approach, we profiled 179 phospholipid species in malignant and matched non-malignant lung tissue of 162 NSCLC patients (73 in a discovery cohort and 89 in a validation cohort). We identified 91 phospholipid species that were differentially expressed in cancer versus non-malignant tissues. Most prominent changes included a decrease in sphingomyelins (SMs) and an increase in specific phosphatidylinositols (PIs). Also a decrease in multiple phosphatidylserines (PSs) was observed, along with an increase in several phosphatidylethanolamine (PE) and phosphatidylcholine (PC) species, particularly those with 40 or 42 carbon atoms in both fatty acyl chains together. 2D-imaging MS of the most differentially expressed phospholipids confirmed their differential abundance in cancer cells. We identified lipid markers that can discriminate tumor versus normal tissue and different NSCLC subtypes with an AUC (area under the ROC curve) of 0.999 and 0.885, respectively. In conclusion, using both shotgun and 2D-imaging lipidomics analysis, we uncovered a hitherto unrecognized alteration in phospholipid profiles in NSCLC. These changes may have important biological implications and may have significant potential for biomarker development.

    @article{Marien2015a,
    abstract = {Non-small cell lung cancer (NSCLC) is the leading cause of cancer death globally. To develop better diagnostics and more effective treatments, research in the past decades has focused on identification of molecular changes in the genome, transcriptome, proteome, and more recently also the metabolome. Phospholipids, which nevertheless play a central role in cell functioning, remain poorly explored. Here, using a mass spectrometry (MS)-based phospholipidomics approach, we profiled 179 phospholipid species in malignant and matched non-malignant lung tissue of 162 NSCLC patients (73 in a discovery cohort and 89 in a validation cohort). We identified 91 phospholipid species that were differentially expressed in cancer versus non-malignant tissues. Most prominent changes included a decrease in sphingomyelins (SMs) and an increase in specific phosphatidylinositols (PIs). Also a decrease in multiple phosphatidylserines (PSs) was observed, along with an increase in several phosphatidylethanolamine (PE) and phosphatidylcholine (PC) species, particularly those with 40 or 42 carbon atoms in both fatty acyl chains together. 2D-imaging MS of the most differentially expressed phospholipids confirmed their differential abundance in cancer cells. We identified lipid markers that can discriminate tumor versus normal tissue and different NSCLC subtypes with an AUC (area under the ROC curve) of 0.999 and 0.885, respectively. In conclusion, using both shotgun and 2D-imaging lipidomics analysis, we uncovered a hitherto unrecognized alteration in phospholipid profiles in NSCLC. These changes may have important biological implications and may have significant potential for biomarker development.},
    author = {Marien, Eyra and Meister, Michael and Muley, Thomas and Fieuws, Steffen and Bordel, Sergio and Derua, Rita and Spraggins, Jeffrey and Van de Plas, Raf and Dehairs, Jonas and Wouters, Jens and Bagadi, Muralidhararao and Dienemann, Hendrik and Thomas, Michael and Schnabel, Philipp A and Caprioli, Richard M and Waelkens, Etienne and Swinnen, Johannes V},
    doi = {10.1002/ijc.29517},
    issn = {1097-0215 (Electronic)},
    journal = {International journal of cancer. Journal international du cancer},
    language = {eng},
    month = oct,
    number = {7},
    pages = {1539--1548},
    pmid = {25784292},
    title = {Non-small cell lung cancer is characterized by dramatic changes in phospholipid profiles.},
    url = {http://www.ncbi.nlm.nih.gov/pubmed/25784292},
    volume = {137},
    year = {2015},
    month_numeric = {10},
    file = {}
    }

  • P. Dittwald, V. T. Nghia, G. A. Harris, R. M. Caprioli, R. Van de Plas, K. Laukens, A. Gambin, and D. Valkenborg, “Towards automated discrimination of lipids versus peptides from full scan mass spectra.,” Eupa open proteomics, vol. 4, p. 87–100, 2014. doi:10.1016/j.euprot.2014.05.002
    [BibTeX] [Abstract] [Download PDF]

    Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium.

    @article{Dittwald2014,
    abstract = {Although physicochemical fractionation techniques play a crucial role in the analysis of complex mixtures, they are not necessarily the best solution to separate specific molecular classes, such as lipids and peptides. Any physical fractionation step such as, for example, those based on liquid chromatography, will introduce its own variation and noise. In this paper we investigate to what extent the high sensitivity and resolution of contemporary mass spectrometers offers viable opportunities for computational separation of signals in full scan spectra. We introduce an automatic method that can discriminate peptide from lipid peaks in full scan mass spectra, based on their isotopic properties. We systematically evaluate which features maximally contribute to a peptide versus lipid classification. The selected features are subsequently used to build a random forest classifier that enables almost perfect separation between lipid and peptide signals without requiring ion fragmentation and classical tandem MS-based identification approaches. The classifier is trained on in silico data, but is also capable of discriminating signals in real world experiments. We evaluate the influence of typical data inaccuracies of common classes of mass spectrometry instruments on the optimal set of discriminant features. Finally, the method is successfully extended towards the classification of individual lipid classes from full scan mass spectral features, based on input data defined by the Lipid Maps Consortium.},
    author = {Dittwald, Piotr and Nghia, Vu Trung and Harris, Glenn A and Caprioli, Richard M and Van de Plas, Raf and Laukens, Kris and Gambin, Anna and Valkenborg, Dirk},
    doi = {10.1016/j.euprot.2014.05.002},
    file = {},
    issn = {2212-9685 (Print)},
    journal = {EuPA open proteomics},
    keywords = {Bioinformatics,Lipid centrifuge,Lipid/peptide classification,Lipidomics,Machine learning,Peptidomics},
    language = {ENG},
    month = sep,
    pages = {87--100},
    pmid = {25414814},
    title = {Towards automated discrimination of lipids versus peptides from full scan mass spectra.},
    url = {http://www.sciencedirect.com/science/article/pii/S221296851400035X},
    volume = {4},
    year = {2014},
    month_numeric = {9}
    }

  • N. Verbeeck, J. Yang, B. De Moor, R. M. Caprioli, E. Waelkens, and R. Van de Plas, “Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases.,” Analytical chemistry, vol. 86, iss. 18, p. 8974–8982, 2014. doi:10.1021/ac502838t
    [BibTeX] [Abstract] [Download PDF]

    Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments.

    @article{Verbeeck2014,
    abstract = {Imaging mass spectrometry (IMS) has become a prime tool for studying the distribution of biomolecules in tissue. Although IMS data sets can become very large, computational methods have made it practically feasible to search these experiments for relevant findings. However, these methods lack access to an important source of information that many human interpretations rely upon: anatomical insight. In this work, we address this need by (1) integrating a curated anatomical data source with an empirically acquired IMS data source, establishing an algorithm-accessible link between them and (2) demonstrating the potential of such an IMS-anatomical atlas link by applying it toward automated anatomical interpretation of ion distributions in tissue. The concept is demonstrated in mouse brain tissue, using the Allen Mouse Brain Atlas as the curated anatomical data source that is linked to MALDI-based IMS experiments. We first develop a method to spatially map the anatomical atlas to the IMS data sets using nonrigid registration techniques. Once a mapping is established, a second computational method, called correlation-based querying, gives an elementary demonstration of the link by delivering basic insight into relationships between ion images and anatomical structures. Finally, a third algorithm moves further beyond both registration and correlation by providing automated anatomical interpretation of ion images. This task is approached as an optimization problem that deconstructs ion distributions as combinations of known anatomical structures. We demonstrate that establishing a link between an IMS experiment and an anatomical atlas enables automated anatomical annotation, which can serve as an important accelerator both for human and machine-guided exploration of IMS experiments.},
    author = {Verbeeck, Nico and Yang, Junhai and De Moor, Bart and Caprioli, Richard M and Waelkens, Etienne and Van de Plas, Raf},
    doi = {10.1021/ac502838t},
    file = {},
    issn = {1520-6882 (Electronic)},
    journal = {Analytical chemistry},
    keywords = {Algorithms,Animals,Automation,Brain,Brain-Computer Interfaces,Brain: anatomy \& histology,Brain: metabolism,Computer-Assisted,Humans,Image Processing,Imaging,Ions,Ions: chemistry,Ions: metabolism,Mass,Matrix-Assisted Laser Desorpti,Mice,Spectrometry,Three-Dimensional,anatomy \& histology,chemistry,metabolism},
    language = {eng},
    month = sep,
    number = {18},
    pages = {8974--8982},
    pmid = {25153352},
    publisher = {American Chemical Society},
    title = {Automated anatomical interpretation of ion distributions in tissue: linking imaging mass spectrometry to curated atlases.},
    url = {http://dx.doi.org/10.1021/ac502838t},
    volume = {86},
    year = {2014},
    month_numeric = {9}
    }

  • A. Fassbender, N. Verbeeck, D. Börnigen, C. M. Kyama, A. Bokor, A. Vodolazkaia, K. Peeraer, C. Tomassetti, C. Meuleman, O. Gevaert, R. Van de Plas, F. Ojeda, B. De Moor, Y. Moreau, E. Waelkens, T. M. D’Hooghe, D. Bornigen, C. M. Kyama, A. Bokor, A. Vodolazkaia, K. Peeraer, C. Tomassetti, C. Meuleman, O. Gevaert, R. Van de Plas, F. Ojeda, B. De Moor, Y. Moreau, E. Waelkens, and T. M. D’Hooghe, “Combined mrna microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis,” Human reproduction, vol. 27, iss. 7, p. 2020–9, 2012. doi:10.1093/humrep/des127
    [BibTeX] [Abstract] [Download PDF]

    BACKGROUND: An early semi-invasive diagnosis of endometriosis has the potential to allow early treatment and minimize disease progression but no such test is available at present. Our aim was to perform a combined mRNA microarray and proteomic analysis on the same eutopic endometrium sample obtained from patients with and without endometriosis. METHODS: mRNA and protein fractions were extracted from 49 endometrial biopsies obtained from women with laparoscopically proven presence (n= 31) or absence (n= 18) of endometriosis during the early luteal (n= 27) or menstrual phase (n= 22) and analyzed using microarray and proteomic surface enhanced laser desorption ionization-time of flight mass spectrometry, respectively. Proteomic data were analyzed using a least squares-support vector machines (LS-SVM) model built on 70\% (training set) and 30\% of the samples (test set). RESULTS: mRNA analysis of eutopic endometrium did not show any differentially expressed genes in women with endometriosis when compared with controls, regardless of endometriosis stage or cycle phase. mRNA was differentially expressed (P< 0.05) in women with (925 genes) and without endometriosis (1087 genes) during the menstrual phase when compared with the early luteal phase. Proteomic analysis based on five peptide peaks [2072 mass/charge (m/z); 2973 m/z; 3623 m/z; 3680 m/z and 21133 m/z] using an LS-SVM model applied on the luteal phase endometrium training set allowed the diagnosis of endometriosis (sensitivity, 91; 95\% confidence interval (CI): 74-98; specificity, 80; 95\% CI: 66-97 and positive predictive value, 87.9\%; negative predictive value, 84.8\%) in the test set. CONCLUSION: mRNA expression of eutopic endometrium was comparable in women with and without endometriosis but different in menstrual endometrium when compared with luteal endometrium in women with endometriosis. Proteomic analysis of luteal phase endometrium allowed the diagnosis of endometriosis with high sensitivity and specificity in training and test sets. A potential limitation of our study is the fact that our control group included women with a normal pelvis as well as women with concurrent pelvic disease (e.g. fibroids, benign ovarian cysts, hydrosalpinges), which may have contributed to the comparable mRNA expression profile in the eutopic endometrium of women with endometriosis and controls.

    @article{Fassbender2012a,
    abstract = {BACKGROUND: An early semi-invasive diagnosis of endometriosis has the potential to allow early treatment and minimize disease progression but no such test is available at present. Our aim was to perform a combined mRNA microarray and proteomic analysis on the same eutopic endometrium sample obtained from patients with and without endometriosis. METHODS: mRNA and protein fractions were extracted from 49 endometrial biopsies obtained from women with laparoscopically proven presence (n= 31) or absence (n= 18) of endometriosis during the early luteal (n= 27) or menstrual phase (n= 22) and analyzed using microarray and proteomic surface enhanced laser desorption ionization-time of flight mass spectrometry, respectively. Proteomic data were analyzed using a least squares-support vector machines (LS-SVM) model built on 70\% (training set) and 30\% of the samples (test set). RESULTS: mRNA analysis of eutopic endometrium did not show any differentially expressed genes in women with endometriosis when compared with controls, regardless of endometriosis stage or cycle phase. mRNA was differentially expressed (P< 0.05) in women with (925 genes) and without endometriosis (1087 genes) during the menstrual phase when compared with the early luteal phase. Proteomic analysis based on five peptide peaks [2072 mass/charge (m/z); 2973 m/z; 3623 m/z; 3680 m/z and 21133 m/z] using an LS-SVM model applied on the luteal phase endometrium training set allowed the diagnosis of endometriosis (sensitivity, 91; 95\% confidence interval (CI): 74-98; specificity, 80; 95\% CI: 66-97 and positive predictive value, 87.9\%; negative predictive value, 84.8\%) in the test set. CONCLUSION: mRNA expression of eutopic endometrium was comparable in women with and without endometriosis but different in menstrual endometrium when compared with luteal endometrium in women with endometriosis. Proteomic analysis of luteal phase endometrium allowed the diagnosis of endometriosis with high sensitivity and specificity in training and test sets. A potential limitation of our study is the fact that our control group included women with a normal pelvis as well as women with concurrent pelvic disease (e.g. fibroids, benign ovarian cysts, hydrosalpinges), which may have contributed to the comparable mRNA expression profile in the eutopic endometrium of women with endometriosis and controls.},
    author = {Fassbender, A and Verbeeck, N and B\"ornigen, D and Kyama, C M and Bokor, A and Vodolazkaia, A and Peeraer, K and Tomassetti, C and Meuleman, C and Gevaert, O and Van de Plas, R and Ojeda, F and De Moor, B and Moreau, Y and Waelkens, E and D'Hooghe, T M and Bornigen, D and Kyama, C M and Bokor, A and Vodolazkaia, A and Peeraer, K and Tomassetti, C and Meuleman, C and Gevaert, O and Van de Plas, R and Ojeda, F and De Moor, B and Moreau, Y and Waelkens, E and D'Hooghe, T M},
    doi = {10.1093/humrep/des127},
    issn = {1460-2350},
    journal = {Human Reproduction},
    keywords = {Adult,Biological,Biological Markers,Biological Markers: chemistry,Biological: metabolism,Biopsy,Case-Control Studies,Endometriosis,Endometriosis: diagnosis,Endometriosis: metabolism,Endometriosis: physiopathology,Endometrium,Endometrium: pathology,Female,Humans,Mass,Matrix-Assisted Laser Desorpti,Messenger,Messenger: metabolism,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Peptides,Peptides: chemistry,Predictive Value of Tests,Proteomics,Proteomics: methods,RNA,Retrospective Studies,Spectrometry,Support Vector Machines,Tumor Markers},
    month = jul,
    number = {7},
    pages = {2020--9},
    pmid = {22556377},
    title = {Combined mRNA microarray and proteomic analysis of eutopic endometrium of women with and without endometriosis},
    url = {http://www.humrep.oxfordjournals.org/cgi/doi/10.1093/humrep/des127 http://humrep.oxfordjournals.org/content/27/7/2020.short},
    volume = {27},
    year = {2012},
    month_numeric = {7},
    file = {}
    }

  • A. Fassbender, E. Waelkens, N. Verbeeck, C. M. Kyama, A. Bokor, A. Vodolazkaia, R. Van de Plas, C. Meuleman, K. Peeraer, C. Tomassetti, O. Gevaert, F. Ojeda, B. De Moor, and T. DʼHooghe, "Proteomics analysis of plasma for early diagnosis of endometriosis," Obstetrics & gynecology, vol. 119, iss. 2, Part 1, p. 276–285, 2012.
    [BibTeX] [Download PDF]
    @article{Fassbender2012,
    author = {Fassbender, Amelie and Waelkens, Etienne and Verbeeck, Nico and Kyama, Cleophas M and Bokor, Attila and Vodolazkaia, Alexandra and Van de Plas, Raf and Meuleman, Christel and Peeraer, Karen and Tomassetti, Carla and Gevaert, Olivier and Ojeda, Fabian and De Moor, Bart and DʼHooghe, Thomas},
    journal = {Obstetrics \& Gynecology},
    number = {2, Part 1},
    pages = {276--285},
    title = {Proteomics Analysis of Plasma for Early Diagnosis of Endometriosis},
    url = {http://content.wkhealth.com/linkback/openurl?sid=WKPTLP:landingpage\&an=00006250-201202000-00012},
    volume = {119},
    year = {2012},
    file = {}
    }

  • M. Signoretto, R. Van de Plas, B. De Moor, and J. A. K. Suykens, "Tensor versus matrix completion: a comparison with application to spectral data," Ieee signal processing letters, vol. 18, iss. 7, p. 403–406, 2011. doi:10.1109/LSP.2011.2151856
    [BibTeX] [Abstract] [Download PDF]

    Tensor completion recently emerged as a generalization of matrix completion for higher order arrays. This problem formulation allows one to exploit the structure of data that intrinsically have multiple dimensions. In this work, we recall a convex formulation for minimum (multilinear) ranks completion of arrays of arbitrary order. Successively we focus on completion of partially observed spectral images; the latter can be naturally represented as third order tensors and typically exhibit intraband correlations. We compare different convex formulations and assess them through case studies.

    @article{Signoretto2011,
    abstract = {Tensor completion recently emerged as a generalization of matrix completion for higher order arrays. This problem formulation allows one to exploit the structure of data that intrinsically have multiple dimensions. In this work, we recall a convex formulation for minimum (multilinear) ranks completion of arrays of arbitrary order. Successively we focus on completion of partially observed spectral images; the latter can be naturally represented as third order tensors and typically exhibit intraband correlations. We compare different convex formulations and assess them through case studies.},
    author = {Signoretto, Marco and Van de Plas, Raf and De Moor, Bart and Suykens, Johan A. K.},
    doi = {10.1109/LSP.2011.2151856},
    issn = {1070-9908},
    journal = {IEEE Signal Processing Letters},
    keywords = {Arrays,Hyperspectral imaging,Indexes,Least squares approximation,Matrix decomposition,Tensile stress,arbitrary order,convex formulation,convex programming,higher order arrays,image processing,image reconstruction,intraband correlations,matrix completion,matrix completion generalization,minimum ranks completion,multilinear ranks completion,partially observed spectral images,spectral data,tensor completion,tensors},
    month = jul,
    number = {7},
    pages = {403--406},
    shorttitle = {Signal Processing Letters, IEEE},
    title = {Tensor Versus Matrix Completion: A Comparison With Application to Spectral Data},
    url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5764499},
    volume = {18},
    year = {2011},
    month_numeric = {7},
    file = {}
    }

  • A. Fassbender, P. Simsa, C. M. Kyama, E. Waelkens, A. Mihalyi, C. Meuleman, O. Gevaert, R. Van de Plas, B. de Moor, and T. M. D'Hooghe, "Trizol treatment of secretory phase endometrium allows combined proteomic and mrna microarray analysis of the same sample in women with and without endometriosis.," Reproductive biology and endocrinology : rb&e, vol. 9, iss. 1, p. 44, 2011. doi:10.1186/1477-7827-9-44
    [BibTeX]
    @article{Fassbender2011,
    author = {Fassbender, Amelie and Simsa, Peter and Kyama, Cleophas M and Waelkens, Etienne and Mihalyi, Attila and Meuleman, Christel and Gevaert, Olivier and Van de Plas, Raf and de Moor, Bart and D'Hooghe, Thomas M},
    doi = {10.1186/1477-7827-9-44},
    issn = {1477-7827 (Electronic)},
    journal = {Reproductive biology and endocrinology : RB\&E},
    language = {eng},
    number = {1},
    pages = {44},
    pmid = {25927325},
    title = {TRIzol treatment of secretory phase endometrium allows combined proteomic and mRNA microarray analysis of the same sample in women with and without endometriosis.},
    volume = {9},
    year = {2011},
    file = {}
    }

  • C. M. Kyama, A. Mihalyi, O. Gevaert, E. Waelkens, P. Simsa, R. Van de Plas, C. Meuleman, B. De Moor, and T. M. D'Hooghe, "Evaluation of endometrial biomarkers for semi-invasive diagnosis of endometriosis.," Fertility and sterility, vol. 95, iss. 4, p. 1333–1338, 2011. doi:10.1016/j.fertnstert.2010.06.084
    [BibTeX] [Abstract] [Download PDF]

    OBJECTIVE: To test the hypothesis that specific proteins and peptides are expressed differentially in eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate, or severe) during the secretory phase. DESIGN: Patients with endometriosis were compared with controls. SETTING: University hospital. PATIENT(S): A total of 29 patients during the secretory phase were selected for this study on the basis of cycle phase and presence or absence of endometriosis. INTERVENTION(S): Endometriosis was confirmed laparoscopically and histologically in 19 patients with endometriosis of revised American Society for Reproductive Medicine stages (9 minimal-mild and 10 moderate-severe), and the presence of a normal pelvis was documented by laparoscopy in 10 controls. MAIN OUTCOME MEASURE(S): Protein expression of endometrium was evaluated with use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. The differential expression of protein mass peaks was analyzed with use of support vector machine algorithms and logistic regression models. RESULT(S): Data preprocessing resulted in differential expression of 73, 30, and 131 mass peaks between controls and patients with endometriosis (all stages), with minimal-mild endometriosis, and with moderate-severe endometriosis, respectively. Endometriosis was diagnosed with high sensitivity (89.5\%) and specificity (90\%) with use of five down-regulated mass peaks (1.949 kDa, 5.183 kDa, 8.650 kDa, 8.659 kDa, and 13.910 kDa) obtained after support vector machine ranking and logistic regression classification. With use of a similar analysis, minimal-mild endometriosis was diagnosed with four mass peaks (two up-regulated: 35.956 kDa and 90.675 kDa and two down-regulated: 1.924 kDa and 2.504 kDa) with maximal sensitivity (100\%) and specificity (100\%). The 90.675-kDa and 35.956-kDa mass peaks were identified as T-plastin and annexin V, respectively. CONCLUSION(S): Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis of secretory phase endometrium combined with bioinformatics puts forward a prospective panel of potential biomarkers with sensitivity of 100\% and specificity of 100\% for the diagnosis of minimal to mild endometriosis.

    @article{Kyama2011,
    abstract = {OBJECTIVE: To test the hypothesis that specific proteins and peptides are expressed differentially in eutopic endometrium of women with and without endometriosis and at specific stages of the disease (minimal, mild, moderate, or severe) during the secretory phase. DESIGN: Patients with endometriosis were compared with controls. SETTING: University hospital. PATIENT(S): A total of 29 patients during the secretory phase were selected for this study on the basis of cycle phase and presence or absence of endometriosis. INTERVENTION(S): Endometriosis was confirmed laparoscopically and histologically in 19 patients with endometriosis of revised American Society for Reproductive Medicine stages (9 minimal-mild and 10 moderate-severe), and the presence of a normal pelvis was documented by laparoscopy in 10 controls. MAIN OUTCOME MEASURE(S): Protein expression of endometrium was evaluated with use of surface-enhanced laser desorption/ionization time-of-flight mass spectrometry. The differential expression of protein mass peaks was analyzed with use of support vector machine algorithms and logistic regression models. RESULT(S): Data preprocessing resulted in differential expression of 73, 30, and 131 mass peaks between controls and patients with endometriosis (all stages), with minimal-mild endometriosis, and with moderate-severe endometriosis, respectively. Endometriosis was diagnosed with high sensitivity (89.5\%) and specificity (90\%) with use of five down-regulated mass peaks (1.949 kDa, 5.183 kDa, 8.650 kDa, 8.659 kDa, and 13.910 kDa) obtained after support vector machine ranking and logistic regression classification. With use of a similar analysis, minimal-mild endometriosis was diagnosed with four mass peaks (two up-regulated: 35.956 kDa and 90.675 kDa and two down-regulated: 1.924 kDa and 2.504 kDa) with maximal sensitivity (100\%) and specificity (100\%). The 90.675-kDa and 35.956-kDa mass peaks were identified as T-plastin and annexin V, respectively. CONCLUSION(S): Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry analysis of secretory phase endometrium combined with bioinformatics puts forward a prospective panel of potential biomarkers with sensitivity of 100\% and specificity of 100\% for the diagnosis of minimal to mild endometriosis.},
    author = {Kyama, Cleophas M and Mihalyi, Attila and Gevaert, Olivier and Waelkens, Etienne and Simsa, Peter and Van de Plas, Raf and Meuleman, Christel and De Moor, Bart and D'Hooghe, Thomas M},
    doi = {10.1016/j.fertnstert.2010.06.084},
    file = {},
    issn = {1556-5653},
    journal = {Fertility and sterility},
    keywords = {Adult,Biological Markers,Biological Markers: analysis,Endometriosis,Endometriosis: diagnosis,Endometriosis: metabolism,Endometriosis: pathology,Endometrium,Endometrium: metabolism,Endometrium: pathology,Female,Humans,Mass,Matrix-Assisted Laser Desorpti,Prospective Studies,Spectrometry,Young Adult,analysis,diagnosis,metabolism,methods,pathology,standards},
    language = {eng},
    month = mar,
    number = {4},
    pages = {1333--1338},
    pmid = {20800833},
    title = {Evaluation of endometrial biomarkers for semi-invasive diagnosis of endometriosis.},
    url = {http://www.sciencedirect.com/science/article/pii/S0015028210010393},
    volume = {95},
    year = {2011},
    month_numeric = {3}
    }

  • J. Luts, F. Ojeda, R. Van de Plas, B. De Moor, S. Van Huffel, and J. A. K. Suykens, "A tutorial on support vector machine-based methods for classification problems in chemometrics.," Analytica chimica acta, vol. 665, iss. 2, p. 129–45, 2010. doi:10.1016/j.aca.2010.03.030
    [BibTeX] [Abstract] [Download PDF]

    This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.

    @article{Luts2010,
    abstract = {This tutorial provides a concise overview of support vector machines and different closely related techniques for pattern classification. The tutorial starts with the formulation of support vector machines for classification. The method of least squares support vector machines is explained. Approaches to retrieve a probabilistic interpretation are covered and it is explained how the binary classification techniques can be extended to multi-class methods. Kernel logistic regression, which is closely related to iteratively weighted least squares support vector machines, is discussed. Different practical aspects of these methods are addressed: the issue of feature selection, parameter tuning, unbalanced data sets, model evaluation and statistical comparison. The different concepts are illustrated on three real-life applications in the field of metabolomics, genetics and proteomics.},
    author = {Luts, Jan and Ojeda, Fabian and Van de Plas, Raf and De Moor, Bart and Van Huffel, Sabine and Suykens, Johan A K},
    doi = {10.1016/j.aca.2010.03.030},
    issn = {1873-4324},
    journal = {Analytica chimica acta},
    keywords = {Algorithms,Artificial Intelligence,Brain Neoplasms,Brain Neoplasms: classification,Image Interpretation, Computer-Assisted,Least-Squares Analysis,Logistic Models,Pattern Recognition, Automated,Pattern Recognition, Automated: methods},
    month = apr,
    number = {2},
    pages = {129--45},
    pmid = {20417323},
    title = {A tutorial on support vector machine-based methods for classification problems in chemometrics.},
    url = {http://www.sciencedirect.com/science/article/pii/S0003267010003132},
    volume = {665},
    year = {2010},
    month_numeric = {4},
    file = {}
    }

  • I. Cadron, T. Van Gorp, F. Amant, I. Vergote, P. Moerman, E. Waelkens, A. Daemen, R. Van De Plas, B. De Moor, and R. Zeillinger, "The use of laser microdissection and seldi-tof ms in ovarian cancer tissue to identify protein profiles.," Anticancer res, vol. 29, iss. 4, p. 1039–1045, 2009.
    [BibTeX] [Abstract] [Download PDF]

    Background: There is a strong need for prognostic biomarkers in ovarian cancer patients due to the heterogeneous responses on current treatment modalities. Materials and Methods: This study investigates the feasibility of combining laser microdissection (LMD) and surface enhanced laser desorption ionization-time of flight mass spectrometry (SELDI-TOF MS) in ovarian cancer tissue to obtain protein profiles. Results: Ideal conditions for preparing a protein lysate were determined and subsequently analysed on SELDI-TOF MS. Applying these protocols on tissue of 9 ovarian cancer patients showed different protein profiles between platinum sensitive and resistant patients. Conclusion: This shows that combining optimised protocols for LMD with SELDI-TOF MS can be used to obtain discriminatory protein profiles. However, studies with large patient numbers and validation sets are essential to identify reliable biomarkers using this approach.

    @article{Cadron2009,
    abstract = {Background: There is a strong need for prognostic biomarkers in ovarian cancer patients due to the heterogeneous responses on current treatment modalities. Materials and Methods: This study investigates the feasibility of combining laser microdissection (LMD) and surface enhanced laser desorption ionization-time of flight mass spectrometry (SELDI-TOF MS) in ovarian cancer tissue to obtain protein profiles. Results: Ideal conditions for preparing a protein lysate were determined and subsequently analysed on SELDI-TOF MS. Applying these protocols on tissue of 9 ovarian cancer patients showed different protein profiles between platinum sensitive and resistant patients. Conclusion: This shows that combining optimised protocols for LMD with SELDI-TOF MS can be used to obtain discriminatory protein profiles. However, studies with large patient numbers and validation sets are essential to identify reliable biomarkers using this approach. },
    author = {Cadron, Isabelle and Van Gorp, Toon and Amant, Frederic and Vergote, Ignace and Moerman, Philippe and Waelkens, Etienne and Daemen, Anneleen and Van De Plas, Raf and De Moor, Bart and Zeillinger, Robert},
    issn = {0250-7005 (Print)},
    journal = {Anticancer Res},
    keywords = {Adult,Aged,Biological,Drug Resistance,Female,Humans,Lasers,Mass,Matrix-Assisted Laser Desorpti,Microdissection,Middle Aged,Neoplasm,Neoplasm Proteins,Organoplatinum Compounds,Ovarian Neoplasms,Protein Array Analysis,Proteome,Spectrometry,Tumor Markers,analysis,chemistry,drug therapy,methods,pathology,therapeutic use},
    language = {eng},
    month = apr,
    number = {4},
    pages = {1039--1045},
    pmid = {19414343},
    title = {The use of laser microdissection and SELDI-TOF MS in ovarian cancer tissue to identify protein profiles.},
    url = {http://ar.iiarjournals.org/content/29/4/1039.short},
    volume = {29},
    year = {2009},
    month_numeric = {4},
    file = {}
    }

  • K. Lemaire, M. A. Ravier, A. Schraenen, J. W. M. Creemers, R. Van de Plas, M. Granvik, L. Van Lommel, E. Waelkens, F. Chimienti, G. A. Rutter, P. Gilon, P. A. in't Veld, and F. C. Schuit, "Insulin crystallization depends on zinc transporter znt8 expression, but is not required for normal glucose homeostasis in mice.," Proceedings of the national academy of sciences of the united states of america, vol. 106, iss. 35, p. 14872–7, 2009. doi:10.1073/pnas.0906587106
    [BibTeX] [Abstract] [Download PDF]

    Zinc co-crystallizes with insulin in dense core secretory granules, but its role in insulin biosynthesis, storage and secretion is unknown. In this study we assessed the role of the zinc transporter ZnT8 using ZnT8-knockout (ZnT8(-/-)) mice. Absence of ZnT8 expression caused loss of zinc release upon stimulation of exocytosis, but normal rates of insulin biosynthesis, normal insulin content and preserved glucose-induced insulin release. Ultrastructurally, mature dense core insulin granules were rare in ZnT8(-/-) beta cells and were replaced by immature, pale insulin "progranules," which were larger than in ZnT8(+/+) islets. When mice were fed a control diet, glucose tolerance and insulin sensitivity were normal. However, after high-fat diet feeding, the ZnT8(-/-) mice became glucose intolerant or diabetic, and islets became less responsive to glucose. Our data show that the ZnT8 transporter is essential for the formation of insulin crystals in beta cells, contributing to the packaging efficiency of stored insulin. Interaction between the ZnT8(-/-) genotype and diet to induce diabetes is a model for further studies of the mechanism of disease of human ZNT8 gene mutations.

    @article{Lemaire2009,
    abstract = {Zinc co-crystallizes with insulin in dense core secretory granules, but its role in insulin biosynthesis, storage and secretion is unknown. In this study we assessed the role of the zinc transporter ZnT8 using ZnT8-knockout (ZnT8(-/-)) mice. Absence of ZnT8 expression caused loss of zinc release upon stimulation of exocytosis, but normal rates of insulin biosynthesis, normal insulin content and preserved glucose-induced insulin release. Ultrastructurally, mature dense core insulin granules were rare in ZnT8(-/-) beta cells and were replaced by immature, pale insulin "progranules," which were larger than in ZnT8(+/+) islets. When mice were fed a control diet, glucose tolerance and insulin sensitivity were normal. However, after high-fat diet feeding, the ZnT8(-/-) mice became glucose intolerant or diabetic, and islets became less responsive to glucose. Our data show that the ZnT8 transporter is essential for the formation of insulin crystals in beta cells, contributing to the packaging efficiency of stored insulin. Interaction between the ZnT8(-/-) genotype and diet to induce diabetes is a model for further studies of the mechanism of disease of human ZNT8 gene mutations.},
    author = {Lemaire, K and Ravier, M A and Schraenen, A and Creemers, J W M and Van de Plas, R and Granvik, M and Van Lommel, L and Waelkens, E and Chimienti, F and Rutter, G A and Gilon, P and in't Veld, P A and Schuit, F C},
    doi = {10.1073/pnas.0906587106},
    file = {},
    issn = {1091-6490 (Electronic)},
    journal = {Proceedings of the National Academy of Sciences of the United States of America},
    keywords = {Animals,Calcium,Calcium: metabolism,Cation Transport Proteins,Cation Transport Proteins: deficiency,Cation Transport Proteins: genetics,Cation Transport Proteins: metabolism,Crystallization,Electron,Glucose,Glucose Intolerance,Glucose Intolerance: chemically induced,Glucose Intolerance: genetics,Glucose Intolerance: metabolism,Glucose: administration \& dosage,Glucose: metabolism,Inbred C57BL,Insulin,Insulin: chemistry,Insulin: metabolism,Islets of Langerhans,Islets of Langerhans: metabolism,Islets of Langerhans: secretion,Islets of Langerhans: ultrastructure,Knockout,Mice,Microscopy,Transmission,Zinc,Zinc: metabolism,administration \& dosage,chemically induced,chemistry,deficiency,genetics,metabolism,secretion,ultrastructure},
    language = {eng},
    month = sep,
    number = {35},
    pages = {14872--7},
    pmid = {19706465},
    title = {Insulin crystallization depends on zinc transporter ZnT8 expression, but is not required for normal glucose homeostasis in mice.},
    url = {http://www.pnas.org/content/106/35/14872.short},
    volume = {106},
    year = {2009},
    month_numeric = {9}
    }

In Collections

  • F. Ojeda, M. Signoretto, R. de Plas, E. Waelkens, B. De Moor, and J. A. K. Suykens, "Semi-supervised learning of sparse linear models in mass spectral imaging," in Pattern recognition in bioinformatics, Springer berlin heidelberg, 2010, p. 325–334.
    [BibTeX]
    @incollection{ojeda2010semi,
    author = {Ojeda, Fabian and Signoretto, Marco and de Plas, Raf and Waelkens, Etienne and De Moor, Bart and Suykens, Johan A K},
    booktitle = {Pattern Recognition in Bioinformatics},
    pages = {325--334},
    publisher = {Springer Berlin Heidelberg},
    title = {Semi-supervised learning of sparse linear models in mass spectral imaging},
    year = {2010},
    file = {}
    }

In Proceedings

  • R. Van de Plas, B. De Moor, and E. Waelkens, "Discrete wavelet transform-based multivariate exploration of tissue via imaging mass spectrometry," in Proceedings of the 2008 acm symposium on applied computing - sac '08, New York, New York, USA, 2008, p. 1307. doi:10.1145/1363686.1363989
    [BibTeX] [Download PDF]
    @inproceedings{VandePlas2008,
    address = {New York, New York, USA},
    author = {Van de Plas, Raf and De Moor, Bart and Waelkens, Etienne},
    booktitle = {Proceedings of the 2008 ACM symposium on Applied computing - SAC '08},
    doi = {10.1145/1363686.1363989},
    file = {},
    isbn = {9781595937537},
    keywords = {bioinformatics,discrete wavelet transform,imaging,mass spectrometry,principal component analysis,proteomics},
    month = mar,
    pages = {1307},
    publisher = {ACM Press},
    title = {Discrete wavelet transform-based multivariate exploration of tissue via imaging mass spectrometry},
    url = {http://dl.acm.org/citation.cfm?id=1363686.1363989},
    year = {2008},
    month_numeric = {3}
    }

  • C. M. Kyama, A. Mihalyi, O. Gevaert, P. Simsa, E. Waelkens, R. Van de Plas, M. J.M, C. Meuleman, and T. D'Hooghe, "Endometrial biomarkers for semi-invasive diagnosis of endometriosis," in Human reproduction, 2007, p. i107.
    [BibTeX] [Download PDF]
    @inproceedings{Kyama2007,
    author = {Kyama, C.M and Mihalyi, A and Gevaert, Olivier and Simsa, P and Waelkens, Etienne and Van de Plas, Raf and J.M, Mwenda and Meuleman, Christel and D'Hooghe, Thomas},
    booktitle = {Human Reproduction},
    issn = {0268-1161},
    keywords = {SISTA},
    language = {en},
    month = jul,
    number = {1},
    pages = {i107},
    publisher = {Published for the European Society of Human Reproduction and Embryology by IRL Press},
    title = {Endometrial biomarkers for semi-invasive diagnosis of endometriosis},
    url = {https://lirias.kuleuven.be/handle/123456789/241282},
    volume = {22},
    year = {2007},
    month_numeric = {7},
    file = {}
    }

  • R. Van de Plas, F. Ojeda, M. Dewil, L. Van Den Bosch, B. De Moor, and E. Waelkens, "Prospective exploration of biochemical tissue composition via imaging mass spectrometry guided by principal component analysis," in Pacific symposium on biocomputing. pacific symposium on biocomputing, Maui, Hawaii, 2007, p. 458–69.
    [BibTeX] [Abstract] [Download PDF]

    MALDI-based Imaging Mass Spectrometry (IMS) is an analytical technique that provides the opportunity to study the spatial distribution of biomolecules including proteins and peptides in organic tissue. IMS measures a large collection of mass spectra spread out over an organic tissue section and retains the absolute spatial location of these measurements for analysis and imaging. The classical approach to IMS imaging, producing univariate ion images, is not well suited as a first step in a prospective study where no a priori molecular target mass can be formulated. The main reasons for this are the size and the multivariate nature of IMS data. In this paper we describe the use of principal component analysis as a multivariate pre-analysis tool, to identify the major spatial and mass-related trends in the data and to guide further analysis downstream. First, a conceptual overview of principal component analysis for IMS is given. Then, we demonstrate the approach on an IMS data set collected from a transversal section of the spinal cord of a standard control rat.

    @inproceedings{VandePlas2007a,
    abstract = {MALDI-based Imaging Mass Spectrometry (IMS) is an analytical technique that provides the opportunity to study the spatial distribution of biomolecules including proteins and peptides in organic tissue. IMS measures a large collection of mass spectra spread out over an organic tissue section and retains the absolute spatial location of these measurements for analysis and imaging. The classical approach to IMS imaging, producing univariate ion images, is not well suited as a first step in a prospective study where no a priori molecular target mass can be formulated. The main reasons for this are the size and the multivariate nature of IMS data. In this paper we describe the use of principal component analysis as a multivariate pre-analysis tool, to identify the major spatial and mass-related trends in the data and to guide further analysis downstream. First, a conceptual overview of principal component analysis for IMS is given. Then, we demonstrate the approach on an IMS data set collected from a transversal section of the spinal cord of a standard control rat.},
    address = {Maui, Hawaii},
    author = {Van de Plas, Raf and Ojeda, Fabian and Dewil, Maarten and Van Den Bosch, Ludo and De Moor, Bart and Waelkens, Etienne},
    booktitle = {Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing},
    file = {},
    issn = {2335-6936 (Print)},
    keywords = {Animals,Computational Biology,Computer-Assisted,Computer-Assisted: statistics \&,Image Processing,Mass,Matrix-Assisted Laser Desorpti,Nerve Tissue Proteins,Nerve Tissue Proteins: metabolism,Principal Component Analysis,Proteins,Proteins: metabolism,Rats,Spectrometry,Spinal Cord,Spinal Cord: metabolism,Tissue Distribution,metabolism,statistics \& numerical data},
    language = {en},
    month = jan,
    pages = {458--69},
    pmid = {17990510},
    title = {Prospective Exploration of Biochemical Tissue Composition via Imaging Mass Spectrometry Guided by Principal Component Analysis},
    url = {http://europepmc.org/abstract/med/17990510},
    year = {2007},
    month_numeric = {1}
    }

  • R. Van de Plas, B. De Moor, and E. Waelkens, "Imaging mass spectrometry based exploration of biochemical tissue composition using peak intensity weighted pca," in 2007 ieee/nih life science systems and applications workshop, 2007, p. 209–212. doi:10.1109/LSSA.2007.4400921
    [BibTeX] [Abstract] [Download PDF]

    Imaging mass spectrometry or mass spectral imaging (MSI) is a technology that provides us with the opportunity to study the spatial distribution of biomolecules such as proteins, peptides, and metabolites throughout organic tissue sections. MSI adds a spatial dimension to mass spectrometry and biomarker-oriented studies without the requirement for labels, as is the case with more traditional techniques such as fluorescense microscopy. It has particular merit for studies where no prior hypothesis of target molecules is available, as it can simultaneously track a wide range of molecules within its mass range. This makes MSI a potent exploratory tool for elucidating the spatiobiochemical topology in tissue. This paper elaborates on the principal component analysis (PCA)-based unsupervised decomposition of an MSI-measured organic tissue section into its underlying biochemical trends. We introduce a method to control the weight that particular peak intensity ranges are allowed to exert on the final decomposition model. The extension provides a way for peak intensity-based scaling to be incorporated directly into the decomposition process, for the purpose of denoising or contrast enhancement. The method makes use of peak height transformations that are conceptually equivalent to what is known in digital image processing as gray level transformations, but rather than aiming to enhance contrast for human interpretation they are used to influence the unsupervised decomposition process. As an example, we apply a combined denoising/contrast stretching measure to the MSI-measurement of a section of rat spinal cord.

    @inproceedings{VandePlas2007b,
    abstract = {Imaging mass spectrometry or mass spectral imaging (MSI) is a technology that provides us with the opportunity to study the spatial distribution of biomolecules such as proteins, peptides, and metabolites throughout organic tissue sections. MSI adds a spatial dimension to mass spectrometry and biomarker-oriented studies without the requirement for labels, as is the case with more traditional techniques such as fluorescense microscopy. It has particular merit for studies where no prior hypothesis of target molecules is available, as it can simultaneously track a wide range of molecules within its mass range. This makes MSI a potent exploratory tool for elucidating the spatiobiochemical topology in tissue. This paper elaborates on the principal component analysis (PCA)-based unsupervised decomposition of an MSI-measured organic tissue section into its underlying biochemical trends. We introduce a method to control the weight that particular peak intensity ranges are allowed to exert on the final decomposition model. The extension provides a way for peak intensity-based scaling to be incorporated directly into the decomposition process, for the purpose of denoising or contrast enhancement. The method makes use of peak height transformations that are conceptually equivalent to what is known in digital image processing as gray level transformations, but rather than aiming to enhance contrast for human interpretation they are used to influence the unsupervised decomposition process. As an example, we apply a combined denoising/contrast stretching measure to the MSI-measurement of a section of rat spinal cord.},
    author = {Van de Plas, Raf and De Moor, Bart and Waelkens, Etienne},
    booktitle = {2007 IEEE/NIH Life Science Systems and Applications Workshop},
    doi = {10.1109/LSSA.2007.4400921},
    isbn = {978-1-4244-1812-1},
    keywords = {Fluorescence,Mass spectroscopy,Microscopy,Molecular biophysics,Noise reduction,Peptides,Principal component analysis,Proteins,Target tracking,Topology,biochemical tissue composition,biochemistry,biological tissues,biomedical optical imaging,biomolecular patial distribution,contrast enhancement,denoising,digital image processing,fluorescence,fluorescense microscopy,gray level transformations,image denoising,image enhancement,imaging mass spectrometry,intensity-based scaling,mass spectra,mass spectral imaging,mass spectroscopic chemical analysis,medical image processing,metabolites,neurophysiology,optical microscopy,peak intensity weighted PCA,peptides,principal component analysis,proteins,rat spinal cord,spatiobiochemical topology,unsupervised decomposition},
    month = nov,
    pages = {209--212},
    publisher = {IEEE},
    shorttitle = {Life Science Systems and Applications Workshop, 20},
    title = {Imaging mass spectrometry based exploration of biochemical tissue composition using peak intensity weighted PCA},
    url = {http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=4400921},
    year = {2007},
    month_numeric = {11},
    file = {}
    }

  • R. Van de Plas, K. Pelckmans, B. De Moor, and E. Waelkens, "Spatial querying of imaging mass spectrometry data: a nonnegative least squares approach," in The nips workshop on machine learning in computational biology (nips mlcb), Whistler, B.C., 2007, p. 1–4.
    [BibTeX]
    @inproceedings{VandePlas2007,
    address = {Whistler, B.C.},
    author = {Van de Plas, Raf and Pelckmans, K. and De Moor, B. and Waelkens, E.},
    booktitle = {The NIPS workshop on Machine Learning in Computational Biology (NIPS MLCB)},
    pages = {1--4},
    title = {Spatial Querying of Imaging Mass Spectrometry Data: A Nonnegative Least Squares Approach},
    year = {2007},
    file = {}
    }