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End date: 2019 × Start date: 2010 × Type: Text × Subject: Classification accuracy × WGL Institute: IPHT ×

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    Raman-spectroscopy based cell identification on a microhole array chip
    (Basel : MDPI AG, 2014) Neugebauer, U.; Kurz, C.; Bocklitz, T.; Berger, T.; Velten, T.; Clement, J.H.; Krafft, C.; Popp, J.
    Circulating tumor cells (CTCs) from blood of cancer patients are valuable prognostic markers and enable monitoring responses to therapy. The extremely low number of CTCs makes their isolation and characterization a major technological challenge. For label-free cell identification a novel combination of Raman spectroscopy with a microhole array platform is described that is expected to support high-throughput and multiplex analyses. Raman spectra were registered from regularly arranged cells on the chip with low background noise from the silicon nitride chip membrane. A classification model was trained to distinguish leukocytes from myeloblasts (OCI-AML3) and breast cancer cells (MCF-7 and BT-20). The model was validated by Raman spectra of a mixed cell population. The high spectral quality, low destructivity and high classification accuracy suggests that this approach is promising for Raman activated cell sorting.
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    A manual and an automatic TERS based virus discrimination
    (Cambridge : RSC Publ., 2015) Olschewski, Konstanze; Kämmer, Evelyn; Stöckel, Stephan; Bocklitz, Thomas; Deckert-Gaudig, Tanja; Zell, Roland; Cialla-May, Dana; Weber, Karina; Deckert, Volker; Popp, Jürgen
    Rapid techniques for virus identification are more relevant today than ever. Conventional virus detection and identification strategies generally rest upon various microbiological methods and genomic approaches, which are not suited for the analysis of single virus particles. In contrast, the highly sensitive spectroscopic technique tip-enhanced Raman spectroscopy (TERS) allows the characterisation of biological nano-structures like virions on a single-particle level. In this study, the feasibility of TERS in combination with chemometrics to discriminate two pathogenic viruses, Varicella-zoster virus (VZV) and Porcine teschovirus (PTV), was investigated. In a first step, chemometric methods transformed the spectral data in such a way that a rapid visual discrimination of the two examined viruses was enabled. In a further step, these methods were utilised to perform an automatic quality rating of the measured spectra. Spectra that passed this test were eventually used to calculate a classification model, through which a successful discrimination of the two viral species based on TERS spectra of single virus particles was also realised with a classification accuracy of 91%.