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Correlation Queries for Mass Spectrometry Imaging

Suits, Frank ; Fehniger, Thomas LU ; Végvári, Ákos LU ; Marko-Varga, György LU and Hotvatovich, Peter (2013) In Analytical Chemistry 85(9). p.4398-4404
Abstract
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... (More)
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. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
MALDI, Mass Spectrometry, Imaging, Proteomics, Biomarker, Correlation
in
Analytical Chemistry
volume
85
issue
9
pages
4398 - 4404
publisher
The American Chemical Society (ACS)
external identifiers
  • wos:000318756100031
  • scopus:84877343123
  • pmid:23537055
ISSN
1520-6882
DOI
10.1021/ac303658t
language
English
LU publication?
yes
id
684077a4-ce59-420b-b588-39c1a415045d (old id 2368964)
date added to LUP
2016-04-01 10:16:46
date last changed
2020-01-05 05:34:18
@article{684077a4-ce59-420b-b588-39c1a415045d,
  abstract     = {Mass spectrometry imaging (MSI) generates large volumetric data sets consisting of mass to charge<br/><br>
ratio (m/z), ion current, and x,y coordinate location. A typical analysis can acquire a dataset comprising<br/><br>
tens of thousands of ion signals at each of thousands of sampling locations. This dataset volume often<br/><br>
serves the limited purpose of measuring the distribution of a small set of ions with known m/z, but those<br/><br>
m/z values represent only a fraction of the full mass spectrum present in the volume of data. There are<br/><br>
few tools to assist the exploration of the remaining volume of unknown data in terms of demonstrating<br/><br>
similarities of associations in tissue compartment distributions of singular ions or in groupings of ions.<br/><br>
To address this problem we have devised several methods to query the full volume of scanned data to<br/><br>
find new m/z values of potential interest based on similarity to biological structures, or to the spatial<br/><br>
distribution of known ions. We present a novel approach to extract information from MSI data that<br/><br>
relies on pre-calculated data structures to allow interactive queries of large data sets with a typical<br/><br>
laptop. These queries are based on different forms of correlation, and the output consists of a ranked list<br/><br>
of m/z values, from most correlated to most anti-correlated with the query, each with an associated<br/><br>
image. We describe these query methods in detail and provide examples demonstrating the power of the<br/><br>
methods to “discover” m/z values of ions that have potentially interesting correlations with known<br/><br>
histological structures. Such “discovered“ ions may be further correlated with either positional locations<br/><br>
or the coincident distribution of other ions, using successive queries. The ability to discover new ions of<br/><br>
interest in the unknown bulk of an MSI dataset offers the potential to further our understanding of<br/><br>
biological and physiological processes related to health and disease.},
  author       = {Suits, Frank and Fehniger, Thomas and Végvári, Ákos and Marko-Varga, György and Hotvatovich, Peter},
  issn         = {1520-6882},
  language     = {eng},
  number       = {9},
  pages        = {4398--4404},
  publisher    = {The American Chemical Society (ACS)},
  series       = {Analytical Chemistry},
  title        = {Correlation Queries for Mass Spectrometry Imaging},
  url          = {http://dx.doi.org/10.1021/ac303658t},
  doi          = {10.1021/ac303658t},
  volume       = {85},
  year         = {2013},
}