Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application... Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric – number of objects and total surface – information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of The American Society for Mass Spectrometry Springer Journals

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

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Publisher
Springer US
Copyright
Copyright © 2017 by American Society for Mass Spectrometry
Subject
Chemistry; Analytical Chemistry; Biotechnology; Organic Chemistry; Proteomics; Bioinformatics
ISSN
1044-0305
eISSN
1879-1123
D.O.I.
10.1007/s13361-017-1784-y
Publisher site
See Article on Publisher Site

Abstract

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric – number of objects and total surface – information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information.

Journal

Journal of The American Society for Mass SpectrometrySpringer Journals

Published: Sep 14, 2017

References

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