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Hyperspectral unmixing of Raman micro-images for assessment of morphological and chemical parameters in non-dried brain tumor specimens

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Abstract

Hyperspectral unmixing is an unsupervised algorithm to calculate a bilinear model of spectral endmembers and abundances of components from Raman images. Thirty-nine Raman images were collected from six glioma brain tumor specimens. The tumor grades ranged from astrocytoma WHO II to glioblastoma multiforme WHO IV. The abundance plots of the cell nuclei were processed by an image segmentation procedure to determine the average nuclei size, the number of nuclei, and the fraction of nuclei area. The latter two morphological parameters correlated with the malignancy. A combination of spectral unmixing and non-negativity constrained linear least squares fitting is introduced to assess chemical parameters. First, endmembers of the most abundant and most dissimilar components were defined that represent all data sets. Second, the content of the obtained components’ proteins, nucleic acids, lipids, and lipid to protein ratios were determined in all Raman images. Except for the protein content, all chemical parameters correlated with the malignancy. We conclude that the morphological and chemical information offer new ways to develop Raman-based classification approaches that can complement diagnosis of brain tumors. The role of non-linear Raman modalities to speed-up image acquisition is discussed.

Raman images provide morphological details about cell nuclei that are automatically processed by image segmentation procedures.

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Acknowledgments

Financial support from European Union via the “Europäischer Fonds für Regionale Entwicklung” (EFRE) and the “Thüringer Ministerium für Bildung Wissenschaft und Kultur” (TMBWK) (JenZIG project: B714-07037) and the “Bundesministerium für Bildung und Forschung” (BMBF) (MediCARS, FKZ: 13 N10774) is gratefully acknowledged.

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Authors and Affiliations

  1. Institute of Photonic Technology, Albert Einstein Straße 9, 07745, Jena, Germany

    Norbert Bergner, Anna Medyukhina, Christoph Krafft & Jürgen Popp

  2. Institute of Pathology, University Hospital Carl Gustav Carus Dresden, Fetscherstraße 74, 01307, Dresden, Germany

    Kathrin D. Geiger

  3. Clinic of Neurosurgery, University Hospital Carl Gustav Carus Dresden, Fetscherstraße 74, 01307, Dresden, Germany

    Matthias Kirsch & Gabriele Schackert

  4. Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07743, Jena, Germany

    Jürgen Popp

Authors
  1. Norbert Bergner
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  2. Anna Medyukhina
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  3. Kathrin D. Geiger
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  4. Matthias Kirsch
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  5. Gabriele Schackert
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  6. Christoph Krafft
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  7. Jürgen Popp
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Correspondence to Christoph Krafft.

Additional information

Published in the topical collection Morpho-Spectral Imaging with guest editors Cyril Petibois and Yeukuang Hwu.

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Bergner, N., Medyukhina, A., Geiger, K.D. et al. Hyperspectral unmixing of Raman micro-images for assessment of morphological and chemical parameters in non-dried brain tumor specimens. Anal Bioanal Chem 405, 8719–8728 (2013). https://doi.org/10.1007/s00216-013-7257-7

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  • DOI: https://doi.org/10.1007/s00216-013-7257-7

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