Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.

Détails

ID Serval
serval:BIB_4154037D19C8
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Histology-driven data mining of lipid signatures from multiple imaging mass spectrometry analyses: application to human colorectal cancer liver metastasis biopsies.
Périodique
Analytical Chemistry
Auteur(s)
Thomas A., Patterson N.H., Marcinkiewicz M.M., Lazaris A., Metrakos P., Chaurand P.
ISSN
1520-6882 (Electronic)
ISSN-L
0003-2700
Statut éditorial
Publié
Date de publication
2013
Volume
85
Numéro
5
Pages
2860-2866
Langue
anglais
Notes
Publication types: Journal Article ; Research Support, Non-U.S. Gov'tPublication Status: ppublish
Résumé
Imaging mass spectrometry (IMS) represents an innovative tool in the cancer research pipeline, which is increasingly being used in clinical and pharmaceutical applications. The unique properties of the technique, especially the amount of data generated, make the handling of data from multiple IMS acquisitions challenging. This work presents a histology-driven IMS approach aiming to identify discriminant lipid signatures from the simultaneous mining of IMS data sets from multiple samples. The feasibility of the developed workflow is evaluated on a set of three human colorectal cancer liver metastasis (CRCLM) tissue sections. Lipid IMS on tissue sections was performed using MALDI-TOF/TOF MS in both negative and positive ionization modes after 1,5-diaminonaphthalene matrix deposition by sublimation. The combination of both positive and negative acquisition results was performed during data mining to simplify the process and interrogate a larger lipidome into a single analysis. To reduce the complexity of the IMS data sets, a sub data set was generated by randomly selecting a fixed number of spectra from a histologically defined region of interest, resulting in a 10-fold data reduction. Principal component analysis confirmed that the molecular selectivity of the regions of interest is maintained after data reduction. Partial least-squares and heat map analyses demonstrated a selective signature of the CRCLM, revealing lipids that are significantly up- and down-regulated in the tumor region. This comprehensive approach is thus of interest for defining disease signatures directly from IMS data sets by the use of combinatory data mining, opening novel routes of investigation for addressing the demands of the clinical setting.
Mots-clé
2-Naphthylamine/analogs & derivatives, 2-Naphthylamine/chemistry, Aged, Biopsy, Colorectal Neoplasms/pathology, Data Mining, Discriminant Analysis, Feasibility Studies, Female, Histological Techniques, Humans, Lipid Metabolism, Liver Neoplasms/pathology, Liver Neoplasms/secondary, Middle Aged, Molecular Imaging/methods, Spectrometry, Mass, Matrix-Assisted Laser Desorption-Ionization
Pubmed
Web of science
Création de la notice
21/01/2015 17:51
Dernière modification de la notice
03/03/2018 16:32
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