Evaluation of the activation of brass apertures in proton therapy using gamma-ray spectrometry and Monte Carlo simulations.

Collimating apertures are used in proton therapy to laterally conform treatment fields to the target volume. While this is a standard technique in passive spreading treatment heads, patient-specific apertures can supplement pencil-beam scanning (PBS) techniques to sharpen the lateral dose fall-off. A radiation protection issue is that proton-induced nuclear reactions can lead to the formation of radionuclides in the apertures. In the experiments of the current study, cylindrical, thick brass targets were irradiated with quasi-monoenergetic proton fields of 100.0 MeV and of 226.7 MeV in PBS mode. The radioactivation of these two brass samples was characterised with a low-level gamma-ray spectrometer. The activation products were scored in a Monte Carlo simulation, too, and compared with the experimental activities. For the high-energy field, 63Zn, 60Cu, and 61Cu were the most important short-lived isotopes regarding the measured specific activity. After irradiation with the 100.0 MeV field, 62Cu, 63Zn, and 60Cu had the highest activity. Regarding long-lived isotopes, which determine the storage time of the used apertures, the isotopes 57Co, 65Zn, 54Mn, 56Co had the largest contribution to the activity. The relative difference of activities between simulation and experiment was typically between 10%-20% for short-lived nuclides and were up to a factor of five larger for long-lived nuclides. Summarising experiments and simulations for both incident proton energies, 62Cu was the most important detected residual nucleus regardless if specific activity or equivalent dose is considered.

APP Is a Context-Sensitive Regulator of the Hippocampal Presynaptic Active Zone.

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 Aß-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.

The synaptic proteome.

Synapses are focal hot spots for signal transduction and plasticity in the brain. A synapse comprises an axon terminus, the presynapse, the synaptic cleft containing extracellular matrix proteins as well as adhesion molecules, and the postsynaptic density as target structure for chemical signaling. The proteomes of the presynaptic and postsynaptic active zones control neurotransmitter release and perception. These tasks demand short- and long-term structural and functional dynamics of the synapse mediated by its proteinaceous inventory. This review addresses subcellular fractionation protocols and the related proteomic approaches to the various synaptic subcompartments with an emphasis on the presynaptic active zone (PAZ). Furthermore, it discusses major constituents of the PAZ including the amyloid precursor protein family members. Numerous proteins regulating the rearrangement of the cytoskeleton are indicative of the functional and structural dynamics of the pre- and postsynapse. The identification of protein candidates of the synapse provides the basis for further analyzing the interaction of synaptic proteins with their targets, and the effect of their deletion opens novel insights into the functional role of these proteins in neuronal communication. The knowledge of the molecular interactome is also a prerequisite for understanding numerous neurodegenerative diseases.

The proteome of the presynaptic active zone from mouse brain.

Neurotransmitter release as well as the structural and functional dynamics of the presynaptic active zone is controlled by proteinaceous components. Here we describe for the first time an experimental approach for the isolation of the presynaptic active zone from individual mouse brains, a prerequisite for understanding the functional inventory of the presynaptic protein network and for the later analysis of changes occurring in mutant mice. Using a monoclonal antibody against the ubiquitous synaptic vesicle protein SV2 we immunopurified synaptic vesicles docked to the presynaptic plasma membrane. Enrichment studies by means of Western blot analysis and mass spectrometry identified 485 proteins belonging to an impressive variety of functional categories. Our data suggest that presynaptic active zones represent focal hot spots that are not only involved in the regulation of neurotransmitter release but also in multiple structural and functional alterations the adult nerve terminal undergoes during neural activity in adult CNS. They furthermore open new avenues for characterizing alterations in the active zone proteome of mutant mice and their corresponding controls, including the various mouse models of neurological diseases.

Amyloid precursor proteins are constituents of the presynaptic active zone.

The amyloid precursor protein (APP) and its mammalian homologs, APLP1, APLP2, have been allocated to an organellar pool residing in the Golgi apparatus and in endosomal compartments, and in its mature form to a cell surface-localized pool. In the brain, all APPs are restricted to neurons; however, their precise localization at the plasma membrane remained enigmatic. Employing a variety of subcellular fractionation steps, we isolated two synaptic vesicle (SV) pools from rat and mouse brain, a pool consisting of synaptic vesicles only and a pool comprising SV docked to the presynaptic plasma membrane. Immunopurification of these two pools using a monoclonal antibody directed against the 12 membrane span synaptic vesicle protein2 (SV2) demonstrated unambiguously that APP, APLP1 and APLP2 are constituents of the active zone of murine brain but essentially absent from free synaptic vesicles. The specificity of immunodetection was confirmed by analyzing the respective knock-out animals. The fractionation experiments further revealed that APP is accumulated in the fraction containing docked synaptic vesicles. These data present novel insights into the subsynaptic localization of APPs and are a prerequisite for unraveling the physiological role of all mature APP proteins in synaptic physiology.

Deep TL: progress of a machine learning aided personal dose monitoring system.

Personal dosemeters using thermoluminescence detectors can provide information about the irradiation event beyond the pure dose estimation, which is valuable for improving radiation protection measures. In the presented study, the glow curves of the novel TL-DOS dosemeters developed by the Materialprüfungsamt NRW in cooperation with the TU Dortmund University are analysed using deep learning approaches to predict the irradiation date of a single-dose irradiation of 10 mGy within a monitoring interval of 41 d. In contrast of previous work, the glow curves are measured using the current routine read-out process by pre-heating the detectors before the read-out. The irradiation dates are predicted with an accuracy of 2-5 d by the deep learning algorithm. Furthermore, the importance of the input features is evaluated using Shapley values to increase the interpretability of the neural network.

Accurate and Fast Deep Learning Dose Prediction for a Preclinical Microbeam Radiation Therapy Study Using Low-Statistics Monte Carlo Simulations.

Microbeam radiation therapy (MRT) utilizes coplanar synchrotron radiation beamlets and is a proposed treatment approach for several tumor diagnoses that currently have poor clinical treatment outcomes, such as gliosarcomas. Monte Carlo (MC) simulations are one of the most used methods at the Imaging and Medical Beamline, Australian Synchrotron to calculate the dose in MRT preclinical studies. The steep dose gradients associated with the 50µm-wide coplanar beamlets present a significant challenge for precise MC simulation of the dose deposition of an MRT irradiation treatment field in a short time frame. The long computation times inhibit the ability to perform dose optimization in treatment planning or apply online image-adaptive radiotherapy techniques to MRT. Much research has been conducted on fast dose estimation methods for clinically available treatments. However, such methods, including GPU Monte Carlo implementations and machine learning (ML) models, are unavailable for novel and emerging cancer radiotherapy options such as MRT. In this work, the successful application of a fast and accurate ML dose prediction model for a preclinical MRT rodent study is presented for the first time. The ML model predicts the peak doses in the path of the microbeams and the valley doses between them, delivered to the tumor target in rat patients. A CT imaging dataset is used to generate digital phantoms for each patient. Augmented variations of the digital phantoms are used to simulate with Geant4 the energy depositions of an MRT beam inside the phantoms with 15% (high-noise) and 2% (low-noise) statistical uncertainty. The high-noise MC simulation data are used to train the ML model to predict the energy depositions in the digital phantoms. The low-noise MC simulations data are used to test the predictive power of the ML model. The predictions of the ML model show an agreement within 3% with low-noise MC simulations for at least 77.6% of all predicted voxels (at least 95.9% of voxels containing tumor) in the case of the valley dose prediction and for at least 93.9% of all predicted voxels (100.0% of voxels containing tumor) in the case of the peak dose prediction. The successful use of high-noise MC simulations for the training, which are much faster to produce, accelerates the production of the training data of the ML model and encourages transfer of the ML model to different treatment modalities for other future applications in novel radiation cancer therapies.

Fast and accurate dose predictions for novel radiotherapy treatments in heterogeneous phantoms using conditional 3D-UNet generative adversarial networks.

PURPOSE: Novel radiotherapy techniques like synchrotron X-ray microbeam radiation therapy (MRT) require fast dose distribution predictions that are accurate at the sub-mm level, especially close to tissue/bone/air interfaces. Monte Carlo (MC) physics simulations are recognized to be one of the most accurate tools to predict the dose delivered in a target tissue but can be very time consuming and therefore prohibitive for treatment planning. Faster dose prediction algorithms are usually developed for clinically deployed treatments only. In this work, we explore a new approach for fast and accurate dose estimations suitable for novel treatments using digital phantoms used in preclinical development and modern machine learning techniques. We develop a generative adversarial network (GAN) model, which is able to emulate the equivalent Geant4 MC simulation with adequate accuracy and use it to predict the radiation dose delivered by a broad synchrotron beam to various phantoms. METHODS: The energy depositions used for the training of the GAN are obtained using full Geant4 MC simulations of a synchrotron radiation broad beam passing through the phantoms. The energy deposition is scored and predicted in voxel matrices of size 140 × 18 × 18 with a voxel edge length of 1 mm. The GAN model consists of two competing 3D convolutional neural networks, which are conditioned on the photon beam and phantom properties. The generator network has a U-Net structure and is designed to predict the energy depositions of the photon beam inside three phantoms of variable geometry with increasing complexity. The critic network is a relatively simple convolutional network, which is trained to distinguish energy depositions predicted by the generator from the ones obtained with the full MC simulation. RESULTS: The energy deposition predictions inside all phantom geometries under investigation show deviations of less than 3% of the maximum deposited energy from the simulation for roughly 99% of the voxels in the field of the beam. Inside the most realistic phantom, a simple pediatric head, the model predictions deviate by less than 1% of the maximal energy deposition from the simulations in more than 96% of the in-field voxels. For all three phantoms, the model generalizes the energy deposition predictions well to phantom geometries, which have not been used for training the model but are interpolations of the training data in multiple dimensions. The computing time for a single prediction is reduced from several hundred hours using Geant4 simulation to less than a second using the GAN model. CONCLUSIONS: The proposed GAN model predicts dose distributions inside unknown phantoms with only small deviations from the full MC simulation with computations times of less than a second. It demonstrates good interpolation ability to unseen but similar phantom geometries and is flexible enough to be trained on data with different radiation scenarios without the need for optimization of the model parameter. This proof-of-concept encourages to apply and further develop the model for the use in MRT treatment planning, which requires fast and accurate predictions with sub-mm resolutions.

APP-A Novel Player within the Presynaptic Active Zone Proteome.

The amyloid precursor protein (APP) was discovered in the 1980s as the precursor protein of the amyloid A4 peptide. The amyloid A4 peptide, also known as A-beta (Aß), is the main constituent of senile plaques implicated in Alzheimer's disease (AD). In association with the amyloid deposits, increasing impairments in learning and memory as well as the degeneration of neurons especially in the hippocampus formation are hallmarks of the pathogenesis of AD. Within the last decades much effort has been expended into understanding the pathogenesis of AD. However, little is known about the physiological role of APP within the central nervous system (CNS). Allocating APP to the proteome of the highly dynamic presynaptic active zone (PAZ) identified APP as a novel player within this neuronal communication and signaling network. The analysis of the hippocampal PAZ proteome derived from APP-mutant mice demonstrates that APP is tightly embedded in the underlying protein network. Strikingly, APP deletion accounts for major dysregulation within the PAZ proteome network. Ca2+-homeostasis, neurotransmitter release and mitochondrial function are affected and resemble the outcome during the pathogenesis of AD. The observed changes in protein abundance that occur in the absence of APP as well as in AD suggest that APP is a structural and functional regulator within the hippocampal PAZ proteome. Within this review article, we intend to introduce APP as an important player within the hippocampal PAZ proteome and to outline the impact of APP deletion on individual PAZ proteome subcommunities.

APP Deletion Accounts for Age-Dependent Changes in the Bioenergetic Metabolism and in Hyperphosphorylated CaMKII at Stimulated Hippocampal Presynaptic Active Zones.

Synaptic release sites are characterized by exocytosis-competent synaptic vesicles tightly anchored to the presynaptic active zone (PAZ) whose proteome orchestrates the fast signaling events involved in synaptic vesicle cycle and plasticity. Allocation of the amyloid precursor protein (APP) to the PAZ proteome implicated a functional impact of APP in neuronal communication. In this study, we combined state-of-the-art proteomics, electrophysiology and bioinformatics to address protein abundance and functional changes at the native hippocampal PAZ in young and old APP-KO mice. We evaluated if APP deletion has an impact on the metabolic activity of presynaptic mitochondria. Furthermore, we quantified differences in the phosphorylation status after long-term-potentiation (LTP) induction at the purified native PAZ. We observed an increase in the phosphorylation of the signaling enzyme calmodulin-dependent kinase II (CaMKII) only in old APP-KO mice. During aging APP deletion is accompanied by a severe decrease in metabolic activity and hyperphosphorylation of CaMKII. This attributes an essential functional role to APP at hippocampal PAZ and putative molecular mechanisms underlying the age-dependent impairments in learning and memory in APP-KO mice.

The Amyloid Precursor Protein-A Novel Player within the Molecular Array of Presynaptic Nanomachines.

More than 20 years ago the amyloid precursor protein (APP) was identified as the precursor protein of the Aß peptide, the main component of senile plaques in brains affected by Alzheimer's disease (AD). The pathophysiology of AD, characterized by a massive loss of synapses, cognitive decline, and behavioral changes was in principle attributed to the accumulation of Aß. Within the last decades, much effort has gone into understanding the molecular basis of the progression of AD. However, little is known about the actual physiological function of APPs. Allocating APP to the proteome of the structurally and functionally dynamic presynaptic active zone (PAZ) highlights APP as a hitherto unknown player within the setting of the presynapse. The molecular array of presynaptic nanomachines comprising the life cycle of synaptic vesicles, exo- and endocytosis, cytoskeletal rearrangements, and mitochondrial activity provides a balance between structural and functional maintenance and diversity. The generation of genetically designed mouse models further deciphered APP as an essential player in synapse formation and plasticity. Deletion of APP causes an age-dependent phenotype: while younger mice revealed almost no physiological impairments, this condition was changed in the elderly mice. Interestingly, the proteomic composition of neurotransmitter release sites already revealed substantial changes at young age. These changes point to a network that incorporates APP into a cluster of nanomachines. Currently, the underlying mechanism of how APP acts within these machines is still elusive. Within the scope of this review, we shall construct a network of APP interaction partners within the PAZ. Furthermore, we intend to outline how deletion of APP affects this network during space and time leading to impairments in learning and memory. These alterations may provide a molecular link to the pathogenesis of AD and the physiological function of APP in the central nervous system.

Regional Specializations of the PAZ Proteomes Derived from Mouse Hippocampus, Olfactory Bulb and Cerebellum.

Neurotransmitter release as well as structural and functional dynamics at the presynaptic active zone (PAZ) comprising synaptic vesicles attached to the presynaptic plasma membrane are mediated and controlled by its proteinaceous components. Here we describe a novel experimental design to immunopurify the native PAZ-complex from individual mouse brain regions such as olfactory bulb, hippocampus, and cerebellum with high purity that is essential for comparing their proteome composition. Interestingly, quantitative immunodetection demonstrates significant differences in the abundance of prominent calcium-dependent PAZ constituents. Furthermore, we characterized the proteomes of the immunoisolated PAZ derived from the three brain regions by mass spectrometry. The proteomes of the release sites from the respective regions exhibited remarkable differences in the abundance of a large variety of PAZ constituents involved in various functional aspects of the release sites such as calcium homeostasis, synaptic plasticity and neurogenesis. On the one hand, our data support an identical core architecture of the PAZ for all brain regions and, on the other hand, demonstrate that the proteinaceous composition of their presynaptic active zones vary, suggesting that changes in abundance of individual proteins strengthen the ability of the release sites to adapt to specific functional requirements.

The Proteome of the Murine Presynaptic Active Zone.

The proteome of the presynaptic active zone controls neurotransmitter release and the short- and long-term structural and functional dynamics of the nerve terminal. The proteinaceous inventory of the presynaptic active zone has recently been reported. This review will evaluate the subcellular fractionation protocols and the proteomic approaches employed. A breakthrough for the identification of the proteome of the presynaptic active zone was the successful employment of antibodies directed against a cytosolic epitope of membrane integral synaptic vesicle proteins for the immunopurification of synaptic vesicles docked to the presynaptic plasma membrane. Combining immunopurification and subsequent analytical mass spectrometry, hundreds of proteins, including synaptic vesicle proteins, components of the presynaptic fusion and retrieval machinery, proteins involved in intracellular and extracellular signaling and a large variety of adhesion molecules, were identified. Numerous proteins regulating the rearrangement of the cytoskeleton are indicative of the functional and structural dynamics of the presynapse. This review will critically discuss both the experimental approaches and prominent protein candidates identified. Many proteins have not previously been assigned to the presynaptic release sites and may be directly involved in the short- and long-term structural modulation of the presynaptic compartment. The identification of proteinaceous constituents of the presynaptic active zone provides the basis for further analyzing the interaction of presynaptic proteins with their targets and opens novel insights into the functional role of these proteins in neuronal communication.

Amyloid precursor protein knockout diminishes synaptic vesicle proteins at the presynaptic active zone in mouse brain.

The amyloid precursor protein (APP) has previously been allocated to an organellar pool residing in the Golgi apparatus and in endosomal compartments, and in its mature form to a presynaptic active zone-localized pool. By analyzing homozygous APP knockout mice we evaluated the impact of APP on synaptic vesicle protein abundance at synaptic release sites. Following immunopurification of synaptic vesicles and the attached presynaptic plasma membrane, individual proteins were subjected to quantitative Western blot analysis. We demonstrate that APP deletion in knockout animals reduces the abundance of the synaptic vesicle proteins synaptophysin, synaptotagmin-1, and SV2A at the presynaptic active zone. Conversely, deletion of the additional APP family members, APLP1 and APLP2 resulted in an increase in synaptophysin, synaptogamin-1, and SV2A abundance. When transmembrane APP is lacking in APPsα-KI/APLP2-KO mice synaptic vesicle protein abundance corresponds to that in APP -KO mice. Deletion of the synaptic vesicle protein 2 (SV2) A and B had no effect on APP and synaptophysin abundance but decreased synaptotagmin-1. Our data suggest that APP controls the abundance of synaptic vesicle proteins at the presynaptic release sites and thus impacts synaptic transmission.