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Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry
Open accessAbstract
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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Assessment of PLSDA Cross Validation
Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J. J., van Duijnhoven, J. P. M., & van Dorsten, F. A. (2008) · Metabolomics · doi:10.1007/s11306-007-0099-6 · Cited by 1,471
Cited by
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Mass spectrometry imaging under ambient conditions
Wu, C., Dill, A. L., Eberlin, L. S., Cooks, R. G., & Ifa, D. R. (2013) · Mass Spectrometry Reviews · doi:10.1002/mas.21360 · Cited by 488
Self-citationOverview of Mass Spectrometry-Based Metabolomics: Opportunities and Challenges
Gowda, G. A. N., & Djukovic, D. (2014) · Methods in Molecular Biology · doi:10.1007/978-1-4939-1258-2_1 · Cited by 334
Classifying Human Brain Tumors by Lipid Imaging with Mass Spectrometry
Eberlin, L. S., Norton, I., Dill, A. L., Golby, A. J., Ligon, K. L., Santagata, S., Cooks, R. G., & Agar, N. Y. R. (2012) · Cancer Research · doi:10.1158/0008-5472.can-11-2465 · Cited by 294
Self-citationAmbient mass spectrometry for the intraoperative molecular diagnosis of human brain tumors
Eberlin, L. S., Norton, I., Orringer, D., Dunn, I. F., Liu, X., Ide, J. L., Jarmusch, A. K., Ligon, K. L., Jolesz, F. A., Golby, A. J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1215687110 · Cited by 288
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- 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
- green
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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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