Statistical analysis of the intermediate filament network in cells of mesenchymal lineage by greyvalue-oriented image segmentation

摘要

Intermediate filament networks are part of the cytoskeleton and protect cellular integrity during large deformations. In cells from mesenchymal lineage the cytoskeleton is centrally involved in signal transduction, thereby influencing differentiation. We study the ultrastructure of IF networks in three human mesenchymal cell types, namely undifferentiated mesenchymal stem cells, chondrocytes, and osteoblasts. In order to capture the high morphological variability of IF networks we apply techniques from image analysis to extract the network graph from 2D scanning electron microscopy (SEM) images in a fully automatic way, which allows for a high-throughput analysis of SEM data. The extracted network graphs are analyzed by techniques from spatial statistics to detect differences in network morphology between different cell types and infer strategies of network remodeling used by the cells to adapt their mechanical properties during migration and differentiation.