Quantitative image analysis is an advancing field in systems biology since it provides the data needed for modeling spatio-temporal changes caused by signalling cascades which include protein movements and transformations of the cellular 3D architecture. Signalling networks such as the canonical Wnt-pathway regulate cellular differentiation steps controlled by TCF-dependent transcription. The activation of transcription depends on β-catenin translocation into the nucleus. We quantified the translocation of proteins of the wnt cascade between different cellular compartments during the early differentiation phase of human neural progenitor cells VM 197 by semi-automatic quantitative analysis of confocal image stacks using Imaris™ software (Bitplane). To investigate the influence of Wnt-signaling on both, the cellular fate and β-catenin localization, the cells were treated with either activators or inhibitors of the Wnt signalling cascade: SB216763 an inhibitor of glycogen synthase kinase-(GSK)-3β, WIF-I (Wnt inhibitory factor-I) and the canonical Wnt-antagonist Dickkopf-1 (Dkk1). Wnt3a, a stimulator of the canonical Wnt-pathway, was used to investigate the upstream effects of this pathway. The results show that the nuclear β-catenin concentration increases pari passu with differentiation into neuronal and glial phenotypes. Treatment with SB216763 and Wnt3a enhanced this effect, whereas Dkk1 and WIF-I treatment delayed it. One major aspect of this work was the comparison of Wnt pathway effects between neurons and glia cells. The continuous quantification of the neuronal phenotype during the first days supported the evidence that the differentiation in these cells is indeed controlled by the Wnt-pathway since it is accelerated or delayed by Wnt-agonists and antagonists, respectively. This precise quantification of fluorescence signals in 3D volumes correlates with neuronal phenotype development and, therefore, provides a means to investigate also other protein translocation processes between cellular compartments. Since the method is applicable to quantitatively describe different phenotypes the obtained data are well-suited for modeling cellular differentiation mechanisms which are correlated with and possibly caused by spatio-temporal changes in signal protein distribution.

DOI: 10.1002/cyto.b.20462, talk, chosen from abstracts