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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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Title: Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer
Authors: Inglese, P
McKenzie, JS
Mroz, A
Kinross, J
Veselkov, K
Holmes, E
Takats, Z
Nicholson, JK
Glen, RC
Item Type: Journal Article
Abstract: Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.
Issue Date: 21-Feb-2017
Date of Acceptance: 18-Feb-2017
URI: http://hdl.handle.net/10044/1/45054
DOI: https://dx.doi.org/10.1039/c6sc03738k
ISSN: 2041-6539
Publisher: Royal Society of Chemistry
Start Page: 3500
End Page: 3511
Journal / Book Title: Chemical Science
Volume: 8
Replaces: 10044/1/44821
http://hdl.handle.net/10044/1/44821
Copyright Statement: © The Royal Society of Chemistry 2017. This is an open access article licensed under a Creative Commons Attribution -NonComercial 3.0 (https://creativecommons.org/licenses/by-nc/3.0/)
Sponsor/Funder: Commission of the European Communities
Commission of the European Communities
Medical Research Council (MRC)
Bowel & Cancer Research
Commission of the European Communities
Funder's Grant Number: 305259
617896
MR/L01632X/1
N/A
634402
Keywords: Science & Technology
Physical Sciences
Chemistry, Multidisciplinary
Chemistry
MASS-SPECTROMETRY
IONIZATION
EXPRESSION
DISCOVERY
HISTOLOGY
APOPTOSIS
NETWORKS
MODELS
TISSUE
CELLS
Publication Status: Published
Open Access location: https://dx.doi.org/10.1039/C6SC03738K
Appears in Collections:Division of Surgery
Faculty of Medicine



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