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FragClust and TestClust, two informatics tools for chemical structure hierarchical clustering analysis applied to lipidomics. The example of Alzheimer's disease

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An Erratum to this article was published on 19 February 2016

Abstract

Lipidomic analysis is able to measure simultaneously thousands of compounds belonging to a few lipid classes. In each lipid class, compounds differ only by the acyl radical, ranging between C10:0 (capric acid) and C24:0 (lignoceric acid). Although some metabolites have a peculiar pathological role, more often compounds belonging to a single lipid class exert the same biological effect. Here, we present a lipidomics workflow that extracts the tandem mass spectrometry data from individual files and uses them to group compounds into structurally homogeneous clusters by chemical structure hierarchical clustering analysis (CHCA). The case-to-control peak area ratios of the metabolites are then analyzed within clusters. We created two freely available applications to assist the workflow: FragClust to generate the tables to be subjected to CHCA, and TestClust to perform statistical analysis on clustered data. We used the lipidomics data from the plasma of Alzheimer's disease (AD) patients in comparison with healthy controls to test the workflow. To date, the search for plasma biomarkers in AD has not provided reliable results. This article shows that the workflow is helpful to understand the behavior of whole lipid classes in plasma of AD patients.

Chemical Hierarchical Cluster Analysis applied to Lipidomics. Software assisted workflow.

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Acknowledgments

The authors thank Leopoldo Ceraulo from the University of Palermo for his precious contribution in the writing and proofreading of the manuscript.

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    Authors and Affiliations

    1. Department of Biopatologia e Biotecnologie Mediche e Forensi (DiBiMEF), AOUP ‘‘Paolo Giaccone’’ University of Palermo, Palermo, Italy

      Francesca Di Gaudio

    2. Mass Spectrometry Laboratory for Clinical Risk and Quality Control, A.O.U.P. “P. Giaccone”, University of Palermo, Palermo, Italy

      Sergio Indelicato & Manuela Fontana

    3. Department of Experimental Biomedicine and Clinical Neurosciences, University of Palermo, Palermo, Italy

      Roberto Monastero & Angela Aronica

    4. Department of Biomedicine Internal Medicine and Medical Specialties, Division of Internal Medicine and Genetic Dyslipidemias, University of Palermo, Palermo, Italy

      Grazia Ida Altieri, Francesca Fayer, Ornella Palesano, Angelo B. Cefalù, Massimiliano Greco, Davide Noto & Maurizio R. Averna

    5. Department of Scienze e Tecnologie Biologiche Chimiche e Farmaceutiche (STEBICEF), University of Palermo, Palermo, Italy

      David Bongiorno & Serena Indelicato

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    1. Francesca Di Gaudio
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    Corresponding author

    Correspondence to Maurizio R. Averna.

    Ethics declarations

    The study was approved by the local ethics committee and written informed consent was obtained from all participants or their legal representatives. The procedures adopted were in accordance with the Helsinki Declaration of 1975, as revised in 1983, and were approved by the Ethics Committee of the University of Palermo.

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    The authors declare that they have no conflict of interest.

    Additional information

    Francesca Di Gaudio, Sergio Indelicato contributed equally to this work.

    Davide Noto and Maurizio R. Averna contributed equally to this work.

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    Di Gaudio, F., Indelicato, S., Monastero, R. et al. FragClust and TestClust, two informatics tools for chemical structure hierarchical clustering analysis applied to lipidomics. The example of Alzheimer's disease. Anal Bioanal Chem 408, 2215–2226 (2016). https://doi.org/10.1007/s00216-015-9229-6

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    • DOI: https://doi.org/10.1007/s00216-015-9229-6

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