Unsupervised unmixing of Raman microspectroscopic images for morphochemical analysis of non-dried brain tumor specimens

Research output: Contribution to journalResearch articleContributedpeer-review

Contributors

  • Norbert Bergner - , Leibniz Institute of Photonic Technology (Author)
  • Christoph Krafft - , Leibniz Institute of Photonic Technology (Author)
  • Kathrin D. Geiger - , University Hospital Carl Gustav Carus Dresden, Institute of Pathology (Author)
  • Matthias Kirsch - , University Hospital Carl Gustav Carus Dresden, Department of Neurosurgery (Author)
  • Gabriele Schackert - , University Hospital Carl Gustav Carus Dresden, Department of Neurosurgery (Author)
  • Jürgen Popp - , Leibniz Institute of Photonic Technology, Friedrich Schiller University Jena (Author)

Abstract

Raman microspectroscopic imaging provides molecular contrast in a label-free manner with subcellular spatial resolution. These properties might complement clinical tools for diagnosis of tissue and cells in the future. Eight Raman spectroscopic images were collected with 785 nm excitation from five non-dried brain specimens immersed in aqueous buffer. The specimens were assigned to molecular and granular layers of cerebellum, cerebrum with and without scattered tumor cells of astrocytoma WHO grade III, ependymoma WHO grade II, astrocytomaWHO grade III, and glioblastoma multiforme WHO grade IV with subnecrotic and necrotic regions. In contrast with dried tissue section, these samples were not affected by drying effects such as crystallization of lipids or denaturation of proteins and nucleic acids. The combined data sets were processed by use of the hyperspectral unmixing algorithms N-FINDR and VCA. Both unsupervised approaches calculated seven endmembers that reveal the abundance plots and spectral signatures of cholesterol, cholesterol ester, nucleic acids, carotene, proteins, lipids, and buffer. The endmembers were correlated with Raman spectra of reference materials. The focus of the single mode laser near 1 μm and the step size of 2 μm were sufficiently small to resolve morphological details, for example cholesterol ester islets and cell nuclei. The results are compared for both unmixing algorithms and with previously reported supervised spectral decomposition techniques.

Details

Original languageEnglish
Pages (from-to)719-725
Number of pages7
JournalAnalytical and Bioanalytical Chemistry
Volume403
Issue number3
Publication statusPublished - May 2012
Peer-reviewedYes

External IDs

PubMed 22367289

Keywords

ASJC Scopus subject areas

Keywords

  • Brain tumors, Hyperspectral unmixing, Non-dried specimens, Raman imaging