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Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging

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Abstract

A detailed comparison of six multivariate algorithms is presented to analyze and generate Raman microscopic images that consist of a large number of individual spectra. This includes the segmentation algorithms for hierarchical cluster analysis, fuzzy C-means cluster analysis, and k-means cluster analysis and the spectral unmixing techniques for principal component analysis and vertex component analysis (VCA). All algorithms are reviewed and compared. Furthermore, comparisons are made to the new approach N-FINDR. In contrast to the related VCA approach, the used implementation of N-FINDR searches for the original input spectrum from the non-dimension reduced input matrix and sets it as the endmember signature. The algorithms were applied to hyperspectral data from a Raman image of a single cell. This data set was acquired by collecting individual spectra in a raster pattern using a 0.5-μm step size via a commercial Raman microspectrometer. The results were also compared with a fluorescence staining of the cell including its mitochondrial distribution. The ability of each algorithm to extract chemical and spatial information of subcellular components in the cell is discussed together with advantages and disadvantages.

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Acknowledgments

CK and JP acknowledge financial support of the European Union via the Europäischer Fonds für Regionale Entwicklung (EFRE) and the “Thüringer Ministerium für Bildung, Wissenschaft und Kultur” (Project: B714-07037).

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Authors and Affiliations

  1. Institute of Technology and Innovation, University of Southern Denmark, Odense, Denmark

    Martin Hedegaard & Søren Hassing

  2. Institute of Photonic Technology, Jena, Germany

    Christian Matthäus, Christoph Krafft & Jürgen Popp

  3. Department of Chemistry and Chemical Biology, Northeastern University, Boston, MA, USA

    Max Diem

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

    Jürgen Popp

Authors
  1. Martin Hedegaard
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  2. Christian Matthäus
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  3. Søren Hassing
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  4. Christoph Krafft
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  5. Max Diem
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  6. Jürgen Popp
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Corresponding author

Correspondence to Christoph Krafft.

Additional information

Dedicated to Professor Akira Imamura on the occasion of his 77th birthday and published as part of the Imamura Festschrift Issue.

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Hedegaard, M., Matthäus, C., Hassing, S. et al. Spectral unmixing and clustering algorithms for assessment of single cells by Raman microscopic imaging. Theor Chem Acc 130, 1249–1260 (2011). https://doi.org/10.1007/s00214-011-0957-1

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  • DOI: https://doi.org/10.1007/s00214-011-0957-1

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