Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

Walid M. Abdelmoula, Karolina Skraskova, Benjamin Balluff, Ricardo J. Carreira, Else A. Tolner, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Hans Morreau, Arn M. J. M. van den Maagdenberg, Ron M. A. Heeren, Liam A. McDonnell*, Jouke Dijkstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

42 Citations (Web of Science)

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.
Original languageEnglish
Pages (from-to)9204-9211
JournalAnalytical Chemistry
Volume86
Issue number18
DOIs
Publication statusPublished - 16 Sep 2014

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