Klinisch-Spektroskopische Diagnostik

In this contribution we review our recent results on Raman approaches to address important biomedical questions. Raman based methods are ideal tools for medical and life science research: Raman measures without contact, providing information on processes within living cells without disturbing them. Raman measures quickly, often overcoming the need for complex and time-consuming laboratory analyses. Raman measures precisely, providing the ultra-sensitive detection capabilities needed for life science research. However, in order to achieve ultra-sensitive detection limits, Raman signal enhancing techniques like e. g. surface enhanced Raman scattering (SERS) must be employed. Raman spectroscopy enables the investigation of biological samples of different sizes i. e. organs via tissue sections, cells, viruses towards DNA/RNA. The combination of SERS with an atomic force microscope enables a molecular specific analysis of single viruses, which is hard to accomplish by conventional microbiological methods like e. g. PCR. Raman-spectroscopic technologies can be coupled with a microscope to investigate structures down to a submicron range. This approach can be used for an online / on-site identification of microorganisms (e. g. pathogens) based on their characteristic Raman fingerprint and is therefore of great relevance for an efficient medical diagnosis, air- and soil monitoring or food analysis. The implementation of Raman spectroscopy in a microfluidic chip allows for an automated classification of cells like e. g. circulating tumor cells. Besides single cells whole tissue sections like biopsy specimens can be characterized by means of Raman-microspectroscopy. The processing of the chemically specific Ramanmaps via mathematical approaches for subsequent spectral analysis and classification enables an objective evaluation of the tissue samples for an early disease diagnosis like e. g. cancer. The low Raman scattering cross section results in long acquisition times limiting the recording of Raman images of large tissue areas and thus, clinical applications. The acquisition times can be reduced by utilizing non-linear Raman approaches like CARS (coherent anti-Stokes Raman scattering) and allows recording Raman images of single characteristic Raman bands in real time.