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. [Figure not available: see fulltext.]
Lingua originaleEnglish
pagine (da-a)1-12
Numero di pagine12
RivistaAnalytical and Bioanalytical Chemistry
Volume408
Stato di pubblicazionePublished - 2016

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Informatics
Workflow
Cluster Analysis
Alzheimer Disease
Lipids
Metabolites
Plasmas
Information Storage and Retrieval
Biomarkers
Tandem Mass Spectrometry
Mass spectrometry
Statistical methods

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Biochemistry

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@article{c39d4e75dffc43dcac8d271d3df2a922,
title = "FragClust and TestClust, two informatics tools for chemical structure hierarchical clustering analysis applied to lipidomics. The example of Alzheimer's disease",
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. [Figure not available: see fulltext.]",
author = "Angela Aronica and {Di Gaudio}, Francesca and Cefalu', {Angelo Baldassare} and Maurizio Averna and Ornella Palesano and Altieri, {Grazia Ida} and Massimiliano Greco and Serena Indelicato and David Bongiorno and Roberto Monastero and Davide Noto and Francesca Fayer and Manuela Fontana and Sergio Indelicato",
year = "2016",
language = "English",
volume = "408",
pages = "1--12",
journal = "Fresenius Zeitschrift fur Analytische Chemie",
issn = "0016-1152",
publisher = "Springer Verlag",

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TY - JOUR

T1 - FragClust and TestClust, two informatics tools for chemical structure hierarchical clustering analysis applied to lipidomics. The example of Alzheimer's disease

AU - Aronica, Angela

AU - Di Gaudio, Francesca

AU - Cefalu', Angelo Baldassare

AU - Averna, Maurizio

AU - Palesano, Ornella

AU - Altieri, Grazia Ida

AU - Greco, Massimiliano

AU - Indelicato, Serena

AU - Bongiorno, David

AU - Monastero, Roberto

AU - Noto, Davide

AU - Fayer, Francesca

AU - Fontana, Manuela

AU - Indelicato, Sergio

PY - 2016

Y1 - 2016

N2 - 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. [Figure not available: see fulltext.]

AB - 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. [Figure not available: see fulltext.]

UR - http://hdl.handle.net/10447/183602

M3 - Article

VL - 408

SP - 1

EP - 12

JO - Fresenius Zeitschrift fur Analytische Chemie

JF - Fresenius Zeitschrift fur Analytische Chemie

SN - 0016-1152

ER -