Automatic generic registration of mass spectrometry imaging data to histology using nonlinear stochastic embedding.

TitleAutomatic generic registration of mass spectrometry imaging data to histology using nonlinear stochastic embedding.
Publication TypeJournal Article
Year of Publication2014
AuthorsAbdelmoula, W.M., Škrášková K., Balluff B., Carreira R.J., Tolner E.A., Lelieveldt B.P.F., van der Maaten L., Morreau H., van den Maagdenberg A.M.J.M., Heeren R.M.A., McDonnell L.A., Dijkstra J.
JournalAnal Chem
Volume86
Pagination9204-11
Date Published2014 Sep 16
ISSN1520-6882
Abstract

The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types.

DOI10.1021/ac502170f
Alternate JournalAnal. Chem.
PubMed ID25133861
06/03/2015