Imperial College London
Dr James McKenzie

DrJamesMcKenzie

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Research Associate
 
 
 
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Contact

 

j.mckenzie

 
 
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Location

 

E311Burlington DanesHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Mason:2021:10.1097/SLA.0000000000005164,
author = {Mason, SE and Manoli, E and Alexander, JL and Poynter, L and Ford, L and Paizs, P and Adebesin, A and McKenzie, JS and Rosini, F and Goldin, R and Darzi, A and Takats, Z and Kinross, JM},
doi = {10.1097/SLA.0000000000005164},
journal = {Annals of Surgery},
title = {Lipidomic profiling of colorectal lesions for real-time tissue recognition and risk-stratification using rapid evaporative ionisation mass spectrometry.},
url = {http://dx.doi.org/10.1097/SLA.0000000000005164},
volume = {00},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - OBJECTIVE: Rapid Evaporative Ionisation Mass Spectrometry (REIMS) is a metabolomic technique analysing tissue metabolites, which can be applied intra-operatively in real-time. The objective of this study was to profile the lipid composition of colorectal tissues using REIMS, assessing its accuracy for real-time tissue recognition and risk-stratification. SUMMARY BACKGROUND DATA: Metabolic dysregulation is a hallmark feature of carcinogenesis, however it remains unknown if this can be leveraged for real-time clinical applications in colorectal disease. METHODS: Patients undergoing colorectal resection were included, with carcinoma, adenoma and paired-normal mucosa sampled. Ex vivo analysis with REIMS was conducted using monopolar diathermy, with the aerosol aspirated into a Xevo G2S QToF mass spectrometer. Negatively charged ions over 600-1000m/z were used for univariate and multivariate functions including linear discriminant analysis. RESULTS: 161 patients were included, generating 1013 spectra. Unique lipidomic profiles exist for each tissue type, with REIMS differentiating samples of carcinoma, adenoma and normal mucosa with 93 1% accuracy and 96 1% negative predictive value for carcinoma. Neoplasia (carcinoma or adenoma) could be predicted with 96 0% accuracy and 91 8% negative predictive value. Adenomas can be risk-stratified by grade of dysplasia with 93 5% accuracy, but not histological subtype. The structure of 61 lipid metabolites was identified, revealing that during colorectal carcinogenesis there is progressive increase in relative abundance of phosphatidylglycerols, sphingomyelins and mono-unsaturated fatty acid containing phospholipids. CONCLUSIONS: The colorectal lipidome can be sampled by REIMS and leveraged for accurate real-time tissue recognition, in addition to risk-stratification of colorectal adenomas. Unique lipidomic features associated with carcinogenesis are described.
AU  - Mason,SE
AU  - Manoli,E
AU  - Alexander,JL
AU  - Poynter,L
AU  - Ford,L
AU  - Paizs,P
AU  - Adebesin,A
AU  - McKenzie,JS
AU  - Rosini,F
AU  - Goldin,R
AU  - Darzi,A
AU  - Takats,Z
AU  - Kinross,JM
DO  - 10.1097/SLA.0000000000005164
PY  - 2021///
SN  - 0003-4932
TI  - Lipidomic profiling of colorectal lesions for real-time tissue recognition and risk-stratification using rapid evaporative ionisation mass spectrometry.
T2  - Annals of Surgery
UR  - http://dx.doi.org/10.1097/SLA.0000000000005164
UR  - https://www.ncbi.nlm.nih.gov/pubmed/34387206
UR  - https://journals.lww.com/annalsofsurgery/Abstract/9000/Lipidomic_Profiling_of_Colorectal_Lesions_for.93333.aspx
UR  - http://hdl.handle.net/10044/1/91405
VL  - 00
ER  -
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