Correlation Queries for Mass Spectrometry Imaging
Author
Summary, in English
Mass spectrometry imaging (MSI) generates large volumetric data sets consisting of mass to charge
ratio (m/z), ion current, and x,y coordinate location. A typical analysis can acquire a dataset comprising
tens of thousands of ion signals at each of thousands of sampling locations. This dataset volume often
serves the limited purpose of measuring the distribution of a small set of ions with known m/z, but those
m/z values represent only a fraction of the full mass spectrum present in the volume of data. There are
few tools to assist the exploration of the remaining volume of unknown data in terms of demonstrating
similarities of associations in tissue compartment distributions of singular ions or in groupings of ions.
To address this problem we have devised several methods to query the full volume of scanned data to
find new m/z values of potential interest based on similarity to biological structures, or to the spatial
distribution of known ions. We present a novel approach to extract information from MSI data that
relies on pre-calculated data structures to allow interactive queries of large data sets with a typical
laptop. These queries are based on different forms of correlation, and the output consists of a ranked list
of m/z values, from most correlated to most anti-correlated with the query, each with an associated
image. We describe these query methods in detail and provide examples demonstrating the power of the
methods to “discover” m/z values of ions that have potentially interesting correlations with known
histological structures. Such “discovered“ ions may be further correlated with either positional locations
or the coincident distribution of other ions, using successive queries. The ability to discover new ions of
interest in the unknown bulk of an MSI dataset offers the potential to further our understanding of
biological and physiological processes related to health and disease.
ratio (m/z), ion current, and x,y coordinate location. A typical analysis can acquire a dataset comprising
tens of thousands of ion signals at each of thousands of sampling locations. This dataset volume often
serves the limited purpose of measuring the distribution of a small set of ions with known m/z, but those
m/z values represent only a fraction of the full mass spectrum present in the volume of data. There are
few tools to assist the exploration of the remaining volume of unknown data in terms of demonstrating
similarities of associations in tissue compartment distributions of singular ions or in groupings of ions.
To address this problem we have devised several methods to query the full volume of scanned data to
find new m/z values of potential interest based on similarity to biological structures, or to the spatial
distribution of known ions. We present a novel approach to extract information from MSI data that
relies on pre-calculated data structures to allow interactive queries of large data sets with a typical
laptop. These queries are based on different forms of correlation, and the output consists of a ranked list
of m/z values, from most correlated to most anti-correlated with the query, each with an associated
image. We describe these query methods in detail and provide examples demonstrating the power of the
methods to “discover” m/z values of ions that have potentially interesting correlations with known
histological structures. Such “discovered“ ions may be further correlated with either positional locations
or the coincident distribution of other ions, using successive queries. The ability to discover new ions of
interest in the unknown bulk of an MSI dataset offers the potential to further our understanding of
biological and physiological processes related to health and disease.
Department/s
Publishing year
2013
Language
English
Pages
4398-4404
Publication/Series
Analytical Chemistry
Volume
85
Issue
9
Document type
Journal article
Publisher
The American Chemical Society (ACS)
Topic
- Analytical Chemistry
Keywords
- MALDI
- Mass Spectrometry
- Imaging
- Proteomics
- Biomarker
- Correlation
Status
Published
ISBN/ISSN/Other
- ISSN: 1520-6882