Analytical and Bioanalytical Chemistry

, Volume 398, Issue 7–8, pp 2969–2978

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 A. Masterson
  • Michael O. Koch
  • Olga Vitek
  • R. Graham Cooks
Original Paper

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.

Figure

Molecular 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

Keywords

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

Supplementary material

216_2010_4259_MOESM1_ESM.pdf (870 kb)
ESM 1(PDF 869 kb)

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Allison L. Dill
    • 1
  • Livia S. Eberlin
    • 1
  • Cheng Zheng
    • 2
  • Anthony B. Costa
    • 1
  • Demian R. Ifa
    • 1
  • Liang Cheng
    • 3
  • Timothy A. Masterson
    • 4
  • Michael O. Koch
    • 4
  • Olga Vitek
    • 2
  • R. Graham Cooks
    • 1
    • 5
  1. 1.Department of Chemistry and Center for Analytical Instrumentation DevelopmentPurdue UniversityWest LafayetteUSA
  2. 2.Department of StatisticsPurdue UniversityWest LafayetteUSA
  3. 3.Department of Pathology and Laboratory MedicineIndiana University School of MedicineIndianapolisUSA
  4. 4.Department of UrologyIndiana University School of MedicineIndianapolisUSA
  5. 5.Department of ChemistryPurdue UniversityWest LafayetteUSA

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