Label-free tissue classification by FTIR- and QCL-based IR-imaging (Prof. K. Gerwert)

  • Talk has been cancelled!
  • Datum: 10.04.2018
  • Uhrzeit: 14:30 - 15:30
  • Vortragende(r): Prof. Dr. Klaus Gerwert
  • Lehrstuhl für Biophysik, Ruhr-Universität Bochum
  • Raum: New Lecture Hall, Room B 0.32
  • Gastgeber: MPQ
Infrared imaging in combination with bioinformatics is an emerging tool for label-free, non-invasive annotation of tissue, cells, and body fluids.

1This approach is applied to fresh frozen but also to paraffin embedded tissue samples usually used in pathology. For the entities colon, bladder, and lung, classifiers are established to annotate cancerous tissue in an automated workflow with sensitivity and specificity of over 95%.2 Recently, deep learning methods are applied to improve the classifier performance. However, the therapeutic decision of the clinician requires a differential diagnosis. Therefore, in the next step a predictive differential diagnostics is established. We were able to differentiate between the predictive subtypes in lung 3, colon 4and recently bladder cancer. An important milestone was than the combination with–omics technologies providing in addition to spatial also molecular resolution. The label-free classified tissue is cut out by laser microdissection and analyzed by proteomics. This is successfully applied to the mesothelioma subtypes.5The currently used biomarker panels in pathology are identified in a validation approach. Recently, we identified in a discovery approach novel biomarker, which differentiate between aggressive forms of bladder cancer, which are difficult to distinguish by pathology today.

A break through is obtained now by a QCL-based microscope. The images show the same quality as the FTIR-images before. Especially disturbing coherence effects are largely reduced now. The most advantage is the reduction of measuring-time. This is now drastically reduced from about 20 hours down to few minutes. The QCL based approach opens now new avenues for clinical applications of tissue classification by IR-imaging.

References:

1 Gerwert K., Großerüschkamp F., Ollesch J. (2014), SPIE Newsroom, DOI: 10.1117/2.1201-312.005297.
2 Kallenbach-Thieltges A., Großerüschkamp F., Mosig A., Diem M., Tannapfel A., Gerwert K. (2013), J. Biophotonics, 6(1), 88-100.
3 Großerüschkamp F., Kallenbach-Thieltges A., Behrens T., Brüning T., Altmayer M., Stamatis G., Theegarten D., Gerwert, K. (2015), Analyst, 140(7), 2114-20.
4 Kuepper C., Großerueschkamp F., Kallenbach‐Thieltges A., Mosig A., Tannapfel A. and Gerwert K. (2016), Faraday Discussion, DOI 10.1039/C5FD00157A.
5 Großerueschkamp, F., Bracht, T., Diehl, H. C., Kuepper, C., Ahrens, M., Kallenbach-Thieltges, A., Mosig, A., Eisenacher, M., Marcus, K., Behrens, Th., Brüning, Th., Theegarten, D., Sitek, B., Gerwert, K. (2017), Scientific Reports, 7, 44829, DOI:10.1038/srep44829.

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