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Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

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Title: Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer
Authors: Veselkov, KA
Mirnezami, R
Strittmatter, N
Goldin, RD
Kinross, J
Speller, AVM
Abramov, T
Jones, EA
Darzi, A
Holmes, E
Nicholson, JK
Takats, Z
Item Type: Journal Article
Abstract: Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.
Issue Date: 21-Jan-2014
Date of Acceptance: 9-Dec-2013
URI: http://hdl.handle.net/10044/1/53861
DOI: https://dx.doi.org/10.1073/pnas.1310524111
ISSN: 0027-8424
Publisher: National Academy of Sciences
Start Page: 1216
End Page: 1221
Journal / Book Title: Proceedings of the National Academy of Sciences of the United States of America
Volume: 111
Issue: 3
Copyright Statement: Freely available online through the PNAS open access option.
Sponsor/Funder: National Institute for Health Research
Funder's Grant Number: NF-SI-0510-10186
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
MULTIDISCIPLINARY SCIENCES
PARTIAL LEAST-SQUARES
TISSUE-SECTIONS
PROTEINS
NORMALIZATION
VALIDATION
PEPTIDES
Algorithms
Biomarkers
Colorectal Neoplasms
Computational Biology
Humans
Image Processing, Computer-Assisted
Lipids
Multivariate Analysis
Reproducibility of Results
Signal Processing, Computer-Assisted
Software
Spectrometry, Mass, Electrospray Ionization
Biological Markers
MD Multidisciplinary
Publication Status: Published
Appears in Collections:Division of Surgery
Department of Medicine
Faculty of Medicine



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