|since 2012||Ph. D. student, Bioinformatics|
Buchmann Institute for Molecular Life Sciences
Johann-Wolfgang-Goethe-Universität Frankfurt am Main (Goethe University Frankfurt am Main, Germany)
The techniques developed in fluorescence microscopy, such as light-sheet-based fluorescence microscopy (LSFM) allow scientists in biology to acquire three dimensional biological specimen in high spatial resolution. In LSFM the sample is illuminated from the side by a sheet of light. An azimuthal arrangement is used such that the detection system is perpendicular to the illumination system. Using this configuration, only the focus plane is illuminated, which results in a reduced amount of photo-toxic damage or phot-bleaching enabling the possibility to observe the specimen for relatively long periods of time. The sample is translated and rotated along the optical axis of the detection system to achieve three dimensional imaging.
The acquisition technique in LSFM contributes to an increase of information about the specimen being recorded, but also results in a considerable amount of new data generated during the experiment. With this increase in the amount of data it is often a complicated task to extract the relevant information. Quantitative analysis provides the basis to derive profound models from the observed data and enables the possibility to compare different samples or experiments with each other. If a quantitative analysis is performed by hand, there may be differences in the application and sequence of steps performed to obtain quantitative results. Therefore, the outcome of quantitative analysis strongly depends on the analyst and the software and algorithms used.
As a bioinformatician, I develop software intended to support the quantitative analysis of data coming from LSFM and various other microscopes and to offer powerful tools to help with the analysis and interpretation of biological data. I especially focus my work on the application of image segmentation methods for object identification and tracking purposes.
- Schäfer, T., Schäfer, H. & Schmitz, A., Image database analysis of Hodgkin lymphoma. Comput. Biol. Chem. 46, 1–7 (2013).