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

Allison L. Dill, Livia S. Eberlin, Cheng Zheng, Anthony B. Costa, Demian R. Ifa, Liang Cheng, Timothy Masterson, Michael Koch, Olga Vitek, R. Graham Cooks

Research output: Contribution to journalArticle

95 Citations (Scopus)

Abstract

Desorption 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.

Original languageEnglish
Pages (from-to)2969-2978
Number of pages10
JournalAnalytical and Bioanalytical Chemistry
Volume398
Issue number7-8
DOIs
StatePublished - Dec 2010

Fingerprint

Renal Cell Carcinoma
Ionization
Mass spectrometry
Mass Spectrometry
Cells
Tissue
Imaging techniques
Electrospray ionization
Electrospray Ionization Mass Spectrometry
Glycerophospholipids
Desorption
Least-Squares Analysis
Tumors
Statistical methods
Multivariate Analysis
Molecular Pathology
Pathology
Hematoxylin
Eosine Yellowish-(YS)
Nonesterified Fatty Acids

Keywords

  • Ambient ionization
  • Kidney cancer
  • Lipidomics
  • Mass spectrometry
  • Molecular imaging
  • Phospholipids
  • Tissue analysis

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry

Cite this

Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry. / Dill, Allison L.; Eberlin, Livia S.; Zheng, Cheng; Costa, Anthony B.; Ifa, Demian R.; Cheng, Liang; Masterson, Timothy; Koch, Michael; Vitek, Olga; Cooks, R. Graham.

In: Analytical and Bioanalytical Chemistry, Vol. 398, No. 7-8, 12.2010, p. 2969-2978.

Research output: Contribution to journalArticle

Dill, Allison L. ; Eberlin, Livia S. ; Zheng, Cheng ; Costa, Anthony B. ; Ifa, Demian R. ; Cheng, Liang ; Masterson, Timothy ; Koch, Michael ; Vitek, Olga ; Cooks, R. Graham. / Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry. In: Analytical and Bioanalytical Chemistry. 2010 ; Vol. 398, No. 7-8. pp. 2969-2978.
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