Novel molecular tumour classification using MALDI-mass spectrometry imaging of tissue micro-array

DJIDJA, M. C., CLAUDE, E., SNEL, M. F., FRANCESE, S., SCRIVEN, P., CAROLAN, V. and CLENCH, M. R. (2010). Novel molecular tumour classification using MALDI-mass spectrometry imaging of tissue micro-array. Analytical and Bioanalytical Chemistry, 397 (2), 587-601.

Full text not available from this repository.
Link to published version:: 10.1007/s00216-010-3554-6

Abstract

The development of tissue micro-array (TMA) technologies provides insights into high-throughput analysis of proteomics patterns from a large number of archived tumour samples. In the work reported here, matrix-assisted laser desorption/ionisation-ion mobility separation-mass spectrometry (MALDI-IMS-MS) profiling and imaging methodology has been used to visualise the distribution of several peptides and identify them directly from TMA sections after on-tissue tryptic digestion. A novel approach that combines MALDI-IMS-MSI and principal component analysis-discriminant analysis (PCA-DA) is described, which has the aim of generating tumour classification models based on protein profile patterns. The molecular classification models obtained by PCA-DA have been validated by applying the same statistical analysis to other tissue cores and patient samples. The ability to correlate proteomic information obtained from samples with known and/or unknown clinical outcome by statistical analysis is of great importance, since it may lead to a better understanding of tumour progression and aggressiveness and hence improve diagnosis, prognosis as well as therapeutic treatments. The selectivity, robustness and current limitations of the methodology are discussed.

Item Type: Article
Research Institute, Centre or Group: Biomedical Research Centre
Identification Number: 10.1007/s00216-010-3554-6
Depositing User: Sarah Ward
Date Deposited: 19 Jan 2011 16:07
Last Modified: 21 Sep 2012 13:29
URI: http://shura.shu.ac.uk/id/eprint/2983

Actions (login required)

View Item

Downloads

Downloads per month over past year

View more statistics