Phosphodiesterase 2 inhibitors promote axonal outgrowth in organotypic slice co-cultures.

The development of appropriate models assessing the potential of substances for regeneration of neuronal circuits is of great importance. Here, we present procedures to analyze effects of substances on fiber outgrowth based on organotypic slice co-cultures of the nigrostriatal dopaminergic system in combination with biocytin tracing and tyrosine hydroxylase labeling and subsequent automated image quantification. Selected phosphodiesterase inhibitors (PDE-Is) were studied to identify their potential growth-promoting capacities. Immunohistochemical methods were used to visualize developing fibers in the border region between ventral tegmental area/substantia nigra co-cultivated with the striatum as well as the cellular expression of PDE2A and PDE10. The quantification shows a significant increase of fiber density in the border region induced by PDE2-Is (BAY60-7550; ND7001), comparable with the potential of the nerve growth factor and in contrast to PDE10-I (MP-10). Analysis of tyrosine hydroxylase-positive fibers indicated a significant increase after treatment with BAY60-7550 and nerve growth factor in relation to dimethyl sulfoxide. Additionally, a dose-dependent increase of intracellular cGMP levels in response to the applied PDE2-Is in PDE2-transfected HEK293 cells was found. In summary, our findings show that PDE2-Is are able to significantly promote axonal outgrowth in organotypic slice co-cultures, which are a suitable model to assess growth-related effects in neuro(re)generation.

Imaging, quantification and visualization of spatio-temporal patterning in mESC colonies under different culture conditions.

MOTIVATION: Mouse embryonic stem cells (mESCs) have developed into a prime system to study the regulation of pluripotency in stable cell lines. It is well recognized that different, established protocols for the maintenance of mESC pluripotency support morphologically and functionally different cell cultures. However, it is unclear how characteristic properties of cell colonies develop over time and how they are re-established after cell passage depending on the culture conditions. Furthermore, it appears that cell colonies have an internal structure with respect to cell size, marker expression or biomechanical properties, which is not sufficiently understood. The analysis of these phenotypic properties is essential for a comprehensive understanding of mESC development and ultimately requires a bioinformatics approach to guarantee reproducibility and high-throughput data analysis. RESULTS: We developed an automated image analysis and colony tracking framework to obtain an objective and reproducible quantification of structural properties of cell colonies as they evolve in space and time. In particular, we established a method that quantifies changes in colony shape and (internal) motion using fluid image registration and image segmentation. The methodology also allows to robustly track motion, splitting and merging of colonies over a sequence of images. Our results provide a first quantitative assessment of temporal mESC colony formation and estimates of structural differences between colony growth under different culture conditions. Furthermore, we provide a stream-based visualization of structural features of individual colonies over time for the whole experiment, facilitating visual comprehension of differences between experimental conditions. Thus, the presented method establishes the basis for the model-based analysis of mESC colony development. It can be easily extended to integrate further functional information using fluorescence signals and differentiation markers. AVAILABILITY: The analysis tool is implemented C++ and Mathematica 8.0 (Wolfram Research Inc., Champaign, IL, USA). The tool is freely available from the authors. We will also provide the source code upon request. CONTACT: nico.scherf@tu-dresden.de.

Large histological serial sections for computational tissue volume reconstruction.

OBJECTIVES: A proof of principle study was conducted for microscopic tissue volume reconstructions using a new image processing chain operating on alternately stained large histological serial sections. METHODS: Digital histological images were obtained from conventional brightfield transmitted light microscopy. A powerful nonparametric nonlinear optical flow-based registration approach was used. In order to apply a simple but computationally feasible sum-of-squared-differences similarity measure even in case of differing histological stainings, a new consistent tissue segmentation procedure was placed upstream. RESULTS: Two reconstructions from uterine cervix carcinoma specimen were accomplished, one alternately stained with p16(INK4a) (surrogate tumor marker) and H&E (routine reference), and another with three different alternate stainings, H&E, p16(INK4a), and CD3 (a T-lymphocyte marker). For both cases, due to our segmentation-based reference-free nonlinear registration procedure, resulting tissue reconstructions exhibit utmost smooth image-to-image transitions without impairing warpings. CONCLUSIONS: Our combination of modern nonparametric nonlinear registration and consistent tissue segmentation has turned out to provide a superior tissue reconstruction quality.