Goals and results

In this project, we developed an image processing algorithm to optimize the quantitative output of 3D microscopy data (together with Robert F. Murphy (Carnegie Mellon University, Pittsburgh, PA, USA)). For this, we use MATLAB. MATLAB is a high-level, interactive programming environment for numerical computation, with more than 1000 built-in mathematical, statistical and engineering functions, including statistical and image analysis tools. Our program uses the Image Processing Toolbox to distinguish between neurons and non-neurons (glial cells, astrocytes, undifferentiated cells). For both cell types the fraction of the protein of interest (e.g. β-catenin) in nucleus and cytoplasm, respectively, can be determined. Note, that without this method only the nucleus was quantified correctly. To use the data for mathematical modeling, the protein distribution within both, nucleus and cytoplasm, has to be quantified necessarily, especially to make a statement about compartment specific protein accumulation. Using variable thresholds, artifacts within the microscopic pictures can be excluded from the calculations.

Interdisciplinarity

For this project the mutual effort of experts from the field of biology who acquired qualitative image-data and the field of automatic data analysis was necessary. The cooperation led to a better understanding of the necessity of automatic and quantitative data acquisition and analysis in biology and a better insight into the complexity of the measured image-data and the biological system under investigation.