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Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry

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Dill, Allison L., Eberlin, Livia S., Zheng, Cheng, Costa, Anthony B., Ifa, Demian R., Cheng, Liang, Masterson, Timothy A., Koch, Michael O., Vitek, Olga, Cooks, R. Graham·2010·Analytical and Bioanalytical Chemistry

Abstract

AbstractDesorption electrospray ionization (DESI) mass spectrometry (MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of 11 sample pairs of human papillary renal cell carcinoma (RCC) and adjacent normal tissue and nine sample pairs of clear cell RCC and adjacent normal tissue. DESI-MS images showing the spatial distributions of particular glycerophospholipids (GPs) and free fatty acids in the negative ion mode were compared to serial tissue sections stained with hematoxylin and eosin (H&E). Increased absolute intensities as well as changes in relative abundance were seen for particular compounds in the tumor regions of the samples. Multivariate statistical analysis using orthogonal projection to latent structures treated partial least square discriminate analysis (PLS-DA) was used for visualization and classification of the tissue pairs using the full mass spectra as predictors. PLS-DA successfully distinguished tumor from normal tissue for both papillary and clear cell RCC with misclassification rates obtained from the validation set of 14.3% and 7.8%, respectively. It was also used to distinguish papillary and clear cell RCC from each other and from the combined normal tissues with a reasonable misclassification rate of 23%, as determined from the validation set. Overall DESI-MS imaging combined with multivariate statistical analysis shows promise as a molecular pathology technique for diagnosing cancerous and normal tissue on the basis of GP profiles. FigureMolecular disease diagnostics by DESI without sample preparation. a Good information is obtained by mapping the distribution of individual compounds in the tissue (e.g., PI(18:0/20:4). b Even better discrimination between tumor and healthy tissue is achieved using PLS-DA to consider all the data after having established through a training set of samples the features that correlate with disease as recognized by standard H&E stain pathological examination

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Metadata

Authors
Dill, A. L., Eberlin, L. S., Zheng, C., Costa, A. B., Ifa, D. R., Cheng, L., Masterson, T. A., Koch, M. O., Vitek, O., & Cooks, R. G.
Venue
Analytical and Bioanalytical Chemistry
Published
2010-10-15
Volume
398
Issue
7-8
Pages
2969-2978
ISSN
  • 1618-2642 (ISSN-L)
  • 1618-2650
OpenAlex
W2148430691
Cited by
162
References
1
FWCI
4.01
Type
journal article
Language
en
Open access
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CoChat. (2026). Citation check: Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry. https://app.cochat.ai/papers/10.1007/s00216-010-4259-6
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Dill, A. L., Eberlin, L. S., Zheng, C., Costa, A. B., Ifa, D. R., Cheng, L., Masterson, T. A., Koch, M. O., Vitek, O., & Cooks, R. G. (2010). Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry. Analytical and Bioanalytical Chemistry. https://doi.org/10.1007/s00216-010-4259-6

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