2 research outputs found
Automatic Registration of Mass Spectrometry Imaging Data Sets to the Allen Brain Atlas
Mass
spectrometry imaging holds great potential for understanding
the molecular basis of neurological disease. Several key studies have
demonstrated its ability to uncover disease-related biomolecular changes
in rodent models of disease, even if highly localized or invisible
to established histological methods. The high analytical reproducibility
necessary for the biomedical application of mass spectrometry imaging
means it is widely developed in mass spectrometry laboratories. However,
many lack the expertise to correctly annotate the complex anatomy
of brain tissue, or have the capacity to analyze the number of animals
required in preclinical studies, especially considering the significant
variability in sizes of brain regions. To address this issue, we have
developed a pipeline to automatically map mass spectrometry imaging
data sets of mouse brains to the Allen Brain Reference Atlas, which
contains publically available data combining gene expression with
brain anatomical locations. Our pipeline enables facile and rapid
interanimal comparisons by first testing if each animal’s tissue
section was sampled at a similar location and enabling the extraction
of the biomolecular signatures from specific brain regions
Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding
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