Imperial College London

Emeritus ProfessorJeremyNicholson

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Emeritus Professor of Biological Chemistry
 
 
 
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Contact

 

+44 (0)20 7594 3195j.nicholson Website

 
 
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Assistant

 

Ms Wendy Torto +44 (0)20 7594 3225

 
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Location

 

Office no. 665Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Veselkov:2014:10.1073/pnas.1310524111,
author = {Veselkov, KA and Mirnezami, R and Strittmatter, N and Goldin, RD and Kinross, J and Speller, AVM and Abramov, T and Jones, EA and Darzi, A and Holmes, E and Nicholson, JK and Takats, Z},
doi = {10.1073/pnas.1310524111},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
pages = {1216--1221},
title = {Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer},
url = {http://dx.doi.org/10.1073/pnas.1310524111},
volume = {111},
year = {2014}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - Mass spectrometry imaging (MSI) provides the opportunity toinvestigate tumor biology from an entirely novel biochemicalperspective and could lead to the identification of a new pool ofcancer biomarkers. Effective clinical translation of histology-drivenMSI in systems oncology requires precise colocalization of morphologicaland biochemical features as well as advanced methodsfor data treatment and interrogation. Currently proposed MSIworkflows are subject to several limitations, including nonoptimizedraw data preprocessing, imprecise image coregistration,and limited pattern recognition capabilities. Here we outline acomprehensive strategy for histology-driven MSI, using desorptionelectrospray ionization that covers (i) optimized data preprocessingfor improved information recovery; (ii) precise imagecoregistration; and (iii) efficient extraction of tissue-specific molecularion signatures for enhanced biochemical distinction of differenttissue types. The proposed workflow has been used to investigateregion-specific lipid signatures in colorectal cancer tissue. Uniquelipid patterns were observed using this approach according totissue type, and a tissue recognition system using multivariatemolecular ion patterns allowed highly accurate (>98%) identificationof pixels according to morphology (cancer, healthy mucosa,smooth muscle, and microvasculature). This strategy offers uniqueinsights into tumor microenvironmental biochemistry and shouldfacilitate compilation of a large-scale tissue morphology-specificMSI spectral database with which to pursue next-generation, fullyautomated histological approaches.
AU - Veselkov,KA
AU - Mirnezami,R
AU - Strittmatter,N
AU - Goldin,RD
AU - Kinross,J
AU - Speller,AVM
AU - Abramov,T
AU - Jones,EA
AU - Darzi,A
AU - Holmes,E
AU - Nicholson,JK
AU - Takats,Z
DO - 10.1073/pnas.1310524111
EP - 1221
PY - 2014///
SN - 0027-8424
SP - 1216
TI - Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer
T2 - Proceedings of the National Academy of Sciences of the United States of America
UR - http://dx.doi.org/10.1073/pnas.1310524111
UR - http://hdl.handle.net/10044/1/53861
VL - 111
ER -