Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry

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

Research output: Contribution to journalArticle

80 Citations (Scopus)

Abstract

Diagnosis of human bladder cancer in untreated tissue sections is achieved by using imaging data from desorption electrospray ionization mass spectrometry (DESI-MS) combined with multivariate statistical analysis. We use the distinctive DESI-MS glycerophospholipid (GP) mass spectral profiles to visually characterize and formally classify twenty pairs (40 tissue samples) of human cancerous and adjacent normal bladder tissue samples. The individual ion images derived from the acquired profiles correlate with standard histological hematoxylin and eosin (H&E)-stained serial sections. The profiles allow us to classify the disease status of the tissue samples with high accuracy as judged by reference histological data. To achieve this, the data from the twenty pairs were divided into a training set and a validation set. Spectra from the tumor and normal regions of each of the tissue sections in the training set were used for orthogonal projection to latent structures (O-PLS) treated partial least-square discriminate analysis (PLS-DA). This predictive model was then validated by using the validation set and showed a 5% error rate for classification and a misclassification rate of 12%. It was also used to create synthetic images of the tissue sections showing pixel-by-pixel disease classification of the tissue and these data agreed well with the independent classification that uses histological data by a certified pathologist. This represents the first application of multivariate statistical methods for classification by ambient ionization although these methods have been applied previously to other MS imaging methods. The results are encouraging in terms of the development of a method that could be utilized in a clinical setting through visualization and diagnosis of intact tissue.

Original languageEnglish
Pages (from-to)2897-2902
Number of pages6
JournalChemistry (Weinheim an der Bergstrasse, Germany)
Volume17
Issue number10
DOIs
StatePublished - Mar 1 2011

Fingerprint

Ionization
Mass spectrometry
Tissue
Imaging techniques
Electrospray ionization
Desorption
Statistical methods
Pixels
Glycerophospholipids
Hematoxylin
Eosine Yellowish-(YS)
Tumors
Visualization
Ions

Keywords

  • cancer
  • desorption electrospray ionization
  • lipidomics
  • mass spectrometry
  • molecular imaging
  • multivariate statistics

ASJC Scopus subject areas

  • Chemistry(all)

Cite this

Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry. / Dill, Allison L.; Eberlin, Livia S.; Costa, Anthony B.; Zheng, Cheng; Ifa, Demian R.; Cheng, Liang; Masterson, Timothy; Koch, Michael; Vitek, Olga; Cooks, R. Graham.

In: Chemistry (Weinheim an der Bergstrasse, Germany), Vol. 17, No. 10, 01.03.2011, p. 2897-2902.

Research output: Contribution to journalArticle

Dill, Allison L. ; Eberlin, Livia S. ; Costa, Anthony B. ; Zheng, Cheng ; Ifa, Demian R. ; Cheng, Liang ; Masterson, Timothy ; Koch, Michael ; Vitek, Olga ; Cooks, R. Graham. / Multivariate statistical identification of human bladder carcinomas using ambient ionization imaging mass spectrometry. In: Chemistry (Weinheim an der Bergstrasse, Germany). 2011 ; Vol. 17, No. 10. pp. 2897-2902.
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