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Issue 5, 2017
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Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

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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.

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

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Publication details

The article was received on 19 Aug 2016, accepted on 18 Feb 2017 and first published on 21 Feb 2017


Article type: Edge Article
DOI: 10.1039/C6SC03738K
Citation: Chem. Sci., 2017,8, 3500-3511
  • Open access: Creative Commons BY-NC license
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    Deep learning and 3D-DESI imaging reveal the hidden metabolic heterogeneity of cancer

    P. Inglese, J. S. McKenzie, A. Mroz, J. Kinross, K. Veselkov, E. Holmes, Z. Takats, J. K. Nicholson and R. C. Glen, Chem. Sci., 2017, 8, 3500
    DOI: 10.1039/C6SC03738K

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