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Abstract: The hallmarks of Alzheimer’s disease (AD) are characterized by cognitive decline and behavioral changes. The most prominent brain region affected by the progression of AD is the hippocampal formation. The pathogenesis involves a successive loss of hippocampal neurons accompanied by a decline in learning and memory consolidation mainly attributed to an accumulation of senile plaques. The amyloid precursor protein (APP) has been identified as precursor of Aβ-peptides, the main constituents of senile plaques. Until now, little is known about the physiological function of APP within the central nervous system. The allocation of APP to the proteome of the highly dynamic presynaptic active zone (PAZ) highlights APP as a yet unknown player in neuronal communication and signaling. In this study, we analyze the impact of APP deletion on the hippocampal PAZ proteome. The native hippocampal PAZ derived from APP mouse mutants (APP-KOs and NexCreAPP/APLP2-cDKOs) was isolated by subcellular fractionation and immunopurification. Subsequently, an isobaric labeling was performed using TMT6 for protein identification and quantification by high-resolution mass spectrometry. We combine bioinformatics tools and biochemical approaches to address the proteomics dataset and to understand the role of individual proteins. The impact of APP deletion on the hippocampal PAZ proteome was visualized by creating protein-protein interaction (PPI) networks that incorporated APP into the synaptic vesicle cycle, cytoskeletal organization, and calcium-homeostasis. The combination of subcellular fractionation, immunopurification, proteomic analysis, and bioinformatics allowed us to identify APP as structural and functional regulator in a context-sensitive manner within the hippocampal active zone network.
Author Summary: More than 20 years ago, the amyloid precursor protein (APP) was identified as the precursor protein of the Aβ peptide, the main component of senile plaques in brains affected by Alzheimer’s disease. However, little is known about the physiological function of amyloid precursor protein. Allocating APP to the proteome of the structurally and functionally dynamic presynaptic active zone highlights APP as a hitherto unknown player within the presynaptic network. The hippocampus is the most prominent brain region for learning and memory consolidation, and a vulnerable target for neurodegenerative disease, e. g. Alzheimer’s disease. Therefore, our experimental design is focused on the hippocampal neurotransmitter release site. Currently, the underlying mechanism of how APP acts within presynaptic networks is still elusive. Within the scope of this research article, we constructed a network of APP within the presynaptic active zone and how deletion of APP affects these individual networks. We combine bioinformatics tools and biochemical approaches to address the dataset provided by proteomics. Furthermore, we could unravel that APP executes regulatory functions within the synaptic vesicle cycle, cytoskeletal rearrangements and Ca2+-homeostasis. Taken together, our findings offer a new perspective on the physiological function of APP in the central nervous system and may provide a molecular link to the pathogenesis of Alzheimer’s disease.
Background: Due to the large amount of data produced by advanced microscopy, automated image analysis is crucial in modern biology. Most applications require reliable cell nuclei segmentation. However, in many biological specimens cell nuclei are densely packed and appear to touch one another in the images. Therefore, a major difficulty of three-dimensional cell nuclei segmentation is the decomposition of cell nuclei that apparently touch each other. Current methods are highly adapted to a certain biological specimen or a specific microscope. They do not ensure similarly accurate segmentation performance, i.e. their robustness for different datasets is not guaranteed. Hence, these methods require elaborate adjustments to each dataset.
Results: We present an advanced three-dimensional cell nuclei segmentation algorithm that is accurate and robust. Our approach combines local adaptive pre-processing with decomposition based on Lines-of-Sight (LoS) to separate apparently touching cell nuclei into approximately convex parts. We demonstrate the superior performance of our algorithm using data from different specimens recorded with different microscopes. The three-dimensional images were recorded with confocal and light sheet-based fluorescence microscopes. The specimens are an early mouse embryo and two different cellular spheroids. We compared the segmentation accuracy of our algorithm with ground truth data for the test images and results from state-of-the-art methods. The analysis shows that our method is accurate throughout all test datasets (mean F-measure: 91%) whereas the other methods each failed for at least one dataset (F-measure≤69%). Furthermore, nuclei volume measurements are improved for LoS decomposition. The state-of-the-art methods required laborious adjustments of parameter values to achieve these results. Our LoS algorithm did not require parameter value adjustments. The accurate performance was achieved with one fixed set of parameter values.
Conclusion: We developed a novel and fully automated three-dimensional cell nuclei segmentation method incorporating LoS decomposition. LoS are easily accessible features that ensure correct splitting of apparently touching cell nuclei independent of their shape, size or intensity. Our method showed superior performance compared to state-of-the-art methods, performing accurately for a variety of test images. Hence, our LoS approach can be readily applied to quantitative evaluation in drug testing, developmental and cell biology.
Membranes are central for cells as borders to the environment or intracellular organelle definition. They are composed of and harbor different molecules like various lipid species and sterols, and they are generally crowded with proteins. The membrane system is very dynamic and components show lateral, rotational and translational diffusion. The consequence of the latter is that phase separation can occur in membranes in vivo and in vitro. It was documented that molecular dynamics simulations of an idealized plasma membrane model result in formation of membrane areas where either saturated lipids and cholesterol (liquid-ordered character, Lo) or unsaturated lipids (liquid-disordered character, Ld) were enriched. Furthermore, current discussions favor the idea that proteins are sorted into the liquid-disordered phase of model membranes, but experimental support for the behavior of isolated proteins in native membranes is sparse. To gain insight into the protein behavior we built a model of the red blood cell membrane with integrated glycophorin A dimer. The sorting and the dynamics of the dimer were subsequently explored by coarse-grained molecular dynamics simulations. In addition, we inspected the impact of lipid head groups and the presence of cholesterol within the membrane on the dynamics of the dimer within the membrane. We observed that cholesterol is important for the formation of membrane areas with Lo and Ld character. Moreover, it is an important factor for the reproduction of the dynamic behavior of the protein found in its native environment. The protein dimer was exclusively sorted into the domain of Ld character in the model red blood cell plasma membrane. Therefore, we present structural information on the glycophorin A dimer distribution in the plasma membrane in the absence of other factors like e.g. lipid anchors in a coarse grain resolution.