by Keyword: Multivariate analysis

By year:[ 2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 ]

Mas, S., Torro, A., Fernández, L., Bec, N., Gongora, C., Larroque, C., Martineau, P., de Juan, A., Marco, S., (2020). MALDI imaging mass spectrometry and chemometric tools to discriminate highly similar colorectal cancer tissues Talanta 208, 120455

Intratumour heterogeneity due to cancer cell clonal evolution and microenvironment composition and tumor differences due to genetic variations between patients suffering of the same cancer pathology play a crucial role in patient response to therapies. This study is oriented to show that matrix-assisted laser-desorption ionization-Mass spectrometry imaging (MALDI-MSI), combined with an advanced multivariate data processing pipeline can be used to discriminate subtle variations between highly similar colorectal tumors. To this aim, experimental tumors reproducing the emergence of drug-resistant clones were generated in athymic mice using subcutaneous injection of different mixes of two isogenic cell lines, the irinotecan-resistant HCT116-SN50 (R) and its sibling human colon adenocarcinoma sensitive cell line HCT116 (S). Because irinotecan-resistant and irinotecan-sensitive are derived from the same original parental HCT116 cell line, their genetic characteristics and molecular compositions are closely related. The multivariate data processing pipeline proposed relies on three steps: (a) multiset multivariate curve resolution (MCR) to separate biological contributions from background; (b) multiset K-means segmentation using MCR scores of the biological contributions to separate between tumor and necrotic parts of the tissues; and (c) partial-least squares discriminant analysis (PLS-DA) applied to tumor pixel spectra to discriminate between R and S tumor populations. High levels of correct classification rates (0.85), sensitivity (0.92) and specificity (0.77) for the PLS-DA classification model were obtained. If previously labelled tissue is available, the multistep modeling strategy proposed constitutes a good approach for the identification and characterization of highly similar phenotypic tumor subpopulations that could be potentially applicable to any kind of cancer tissue that exhibits substantial heterogeneity. © 2019 Elsevier B.V.

Keywords: Chemometrics, Colorectal cancer, MALDI imaging, Multivariate analysis, Tumor heterogeneity

Marco, S., Pomareda, V., Pardo, A., Kessler, M., Goebel, J., Mueller, G., (2009). Blind source separation for ion mobility spectra Olfaction and Electronic Nose: Proceedings of the 13th International Symposium on Olfaction and Electronic Nose 13th International Symposium on Olfaction and the Electronic Nose (ed. Pardo, M., Sberveglieri, G.), Amer Inst Physics (Brescia, Italy) 1137, 551-553

Miniaturization is a powerful trend for smart chemical instrumentation in a diversity of applications.. It is know that miniaturization in IMS leads to a degradation of the system characteristics. For the present work, we are interested in signal processing solutions to mitigate limitations introduced by limited drift tube length that basically involve a loss of chemical selectivity. While blind source separation techniques (BSS) are popular in other domains, their application for smart chemical instrumentation is limited. However, in some conditions, basically linearity, BSS may fully recover the concentration time evolution and the pure spectra with few underlying hypothesis. This is extremely helpful in conditions where non-expected chemical interferents may appear, or unwanted perturbations may pollute the spectra. SIMPLISMA has been advocated by Harrington et al. in several papers. However, more modem methods of BSS for bilinear decomposition with the restriction of positiveness have appeared in the last decade. In order to explore and compare the performances of those methods a series of experiments were performed.

Keywords: Ion Mobility Spectrometry (IMS), Blind Source Separation (BSS), Multivariate Analysis, SIMPLISMA, MCR, Non-Negative Matrix Factorization (NMF)