Although the concept of the cytoskeleton as a cell-shape-determining scaffold is well established, it remains enigmatic how eukaryotic organelles adopt and maintain a specific morphology. The Filamentous Temperature Sensitive Z (FtsZ) protein family, an ancient tubulin, generates complex polymer networks, with striking similarity to the cytoskeleton, in the chloroplasts of the moss Physcomitrella patens. Certain members of this protein family are essential for structural integrity and shaping of chloroplasts, while others are not, illustrating the functional diversity within the FtsZ protein family. Here, we apply a combination of confocal laser scanning microscopy and a self-developed semi-automatic computational image analysis method for the quantitative characterisation and comparison of network morphologies and connectivity features for two selected, functionally dissimilar FtsZ isoforms, FtsZ1-2 and FtsZ2-1. We show that FtsZ1-2 and FtsZ2-1 networks are significantly different for 8 out of 25 structural descriptors. Therefore, our results demonstrate that different FtsZ isoforms are capable of generating polymer networks with distinctive morphological and connectivity features which might be linked to the functional differences between the two isoforms. To our knowledge, this is the first study to employ computational algorithms in the quantitative comparison of different classes of protein networks in living cells.

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Graphical abstract

Throughout the process of aging, deterioration of bone macro-and micro-architecture, as well as material decomposition result in a loss of strength and therefore in an increased likelihood of fractures. To date, precise contributions of age-related changes in bone (re)modeling and (de)mineralization dynamics and its effect on the loss of functional integrity are not completely understood. Here, we present an image-based deep learning approach to quantitatively describe the dynamic effects of short-term aging and adaptive response to treatment in proximal mouse tibia and fibula. Our approach allowed us to perform an end-to-end age prediction based on µCT images to determine the dynamic biological process of tissue maturation during a two week period, therefore permitting a short-term bone aging prediction with 95% accuracy. In a second application, our radiomics analysis reveals that two weeks of in vivo mechanical loading are associated with an underlying rejuvenating effect of 5 days. Additionally, by quantitatively analyzing the learning process, we could, for the first time, identify the localization of the age-relevant encoded information and demonstrate 89% load-induced similarity of these locations in the loaded tibia with younger bones. These data suggest that our method enables identifying a general prognostic phenotype of a certain bone age as well as a temporal and localized loading-treatment effect on this apparent bone age. Future translational applications of this method may provide an improved decision-support method for osteoporosis treatment at low cost.