Correlation Queries for Mass Spectrometry Imaging
(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:
https://lup.lub.lu.se/record/2368964
- author
- Suits, Frank ; Fehniger, Thomas LU ; Végvári, Ákos LU ; Marko-Varga, György LU and Hotvatovich, Peter
- organization
- publishing date
- 2013
- 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
- 2022-04-27 20:33:37
@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}},
keywords = {{MALDI; Mass Spectrometry; Imaging; Proteomics; Biomarker; Correlation}},
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}},
}