Rainer Pielot

Dopaminergic modulation of the hippocampal neuropil proteome identified by bioorthogonal noncanonical amino acid tagging (BONCAT)

Local protein synthesis and its activity‐dependent modulation via dopamine receptor stimulation play an important role in synaptic plasticity – allowing synapses to respond dynamically to changes in their activity patterns. We describe here the metabolic labeling, enrichment, and MS‐based identification of candidate proteins specifically translated in intact hippocampal neuropil sections upon treatment with the selective D1/D5 receptor agonist SKF81297. Using the noncanonical amino acid azidohomoalanine and click chemistry, we identified over 300 newly synthesized proteins specific to dendrites and axons. Candidates specific for the SKF81297‐treated samples were predominantly involved in protein synthesis and synapse‐specific functions. Furthermore, we demonstrate a dendrite‐specific increase in proteins synthesis upon application of SKF81297. This study provides the first snapshot in the dynamics of the dopaminergic hippocampal neuropil proteome.

SynProt: a database for proteins of detergent-resistant synaptic protein preparations

Chemical synapses are highly specialized cell–cell contacts for communication between neurons in the CNS characterized by complex and dynamic protein networks at both synaptic membranes. The cytomatrix at the active zone (CAZ) organizes the apparatus for the regulated release of transmitters from the presynapse. At the postsynaptic side, the postsynaptic density constitutes the machinery for detection, integration, and transduction of the transmitter signal. Both pre- and postsynaptic protein networks represent the molecular substrates for synaptic plasticity. Their function can be altered both by regulating their composition and by post-translational modification of their components. For a comprehensive understanding of synaptic networks the entire ensemble of synaptic proteins has to be considered. To support this, we established a comprehensive database for synaptic junction proteins (SynProt database) primarily based on proteomics data obtained from biochemical preparations of detergent-resistant synaptic junctions. The database currently contains 2,788 non-redundant entries of rat, mouse, and some human proteins, which mainly have been manually extracted from 12 proteomic studies and annotated for synaptic subcellular localization. Each dataset is completed with manually added information including protein classifiers as well as automatically retrieved and updated information from public databases (UniProt and PubMed). We intend that the database will be used to support modeling of synaptic protein networks and rational experimental design.

Altered Neuronal Activity Patterns in the Visual Cortex of the Adult Rat after Partial Optic Nerve Crush—A Single-Cell Resolution Metabolic Mapping Study

Thallium autometallography (TIAMG) is a novel method for high-resolution mapping of neuronal activity. With this method, we found that a general depression of neuronal activity occurs in response to optic nerve crush (ONC) within the first 2 weeks postinjury in the contralateral dorsal lateral geniculate nucleus (dLGN) as well as in the contralateral primary visual cortex (V1). Interestingly, the neuronal activity recovered thereafter in both brain regions and reached a plateau in the tenth week postinjury in layers IV and V of V1, monocular area (V1m). Several clusters of highly active neurons in V1m were found 6 weeks after ONC in layers IV and V on the side contralateral to the lesion. We reasoned that these clusters appeared due to a reorganization of the corticocolliucular projections. Employing a combination of biotinylated dextran amine retrograde tract tracing from the superior colliculus (SC) with TIAMG in the same animal, we indeed found that the clusters of neurons with high Tl(+) uptake in V1m are spatially in register with those neuronal subpopulations that project to the SC. These data suggest that extensive reorganization plasticity exists in the adult rat visual cortex following ONC.

Proteome rearrangements after auditory learning: high-resolution profiling of synapse-enriched protein fractions from mouse brain

Learning and memory processes are accompanied by rearrangements of synaptic protein networks. While various studies have demonstrated the regulation of individual synaptic proteins during these processes, much less is known about the complex regulation of synaptic proteomes. Recently, we reported that auditory discrimination learning in mice is associated with a relative down‐regulation of proteins involved in the structural organization of synapses in various brain regions. Aiming at the identification of biological processes and signaling pathways involved in auditory memory formation, here, a label‐free quantification approach was utilized to identify regulated synaptic junctional proteins and phosphoproteins in the auditory cortex, frontal cortex, hippocampus, and striatum of mice 24 h after the learning experiment. Twenty proteins, including postsynaptic scaffolds, actin‐remodeling proteins, and RNA‐binding proteins, were regulated in at least three brain regions pointing to common, cross‐regional mechanisms. Most of the detected synaptic proteome changes were, however, restricted to individual brain regions. For example, several members of the Septin family of cytoskeletal proteins were up‐regulated only in the hippocampus, while Septin‐9 was down‐regulated in the hippocampus, the frontal cortex, and the striatum. Meta analyses utilizing several databases were employed to identify underlying cellular functions and biological pathways. Data are available via ProteomeExchange with identifier PXD003089.

Fast multi-core based multimodal registration of 2D cross-sections and 3D datasets

BackgroundSolving bioinformatics tasks often requires extensive computational power. Recent trends in processor architecture combine multiple cores into a single chip to improve overall performance. The Cell Broadband Engine (CBE), a heterogeneous multi-core processor, provides power-efficient and cost-effective high-performance computing. One application area is image analysis and visualisation, in particular registration of 2D cross-sections into 3D image datasets. Such techniques can be used to put different image modalities into spatial correspondence, for example, 2D images of histological cuts into morphological 3D frameworks.ResultsWe evaluate the CBE-driven PlayStation 3 as a high performance, cost-effective computing platform by adapting a multimodal alignment procedure to several characteristic hardware properties. The optimisations are based on partitioning, vectorisation, branch reducing and loop unrolling techniques with special attention to 32-bit multiplies and limited local storage on the computing units. We show how a typical image analysis and visualisation problem, the multimodal registration of 2D cross-sections and 3D datasets, benefits from the multi-core based implementation of the alignment algorithm. We discuss several CBE-based optimisation methods and compare our results to standard solutions. More information and the source code are available from http://cbe.ipk-gatersleben.de.ConclusionsThe results demonstrate that the CBE processor in a PlayStation 3 accelerates computational intensive multimodal registration, which is of great importance in biological/medical image processing. The PlayStation 3 as a low cost CBE-based platform offers an efficient option to conventional hardware to solve computational problems in image processing and bioinformatics.

Optimiertes Warping durch gewichtete Summen von Verschiebungsvektoren - eine neue Methode zur Reduktion von interindividuellen Variabilitäten von Hirndaten

Warping ist eine Klasse von Bildverarbeitungsverfahren, die durch Neudefinition raumlicher Beziehungen einzelner Bildpunkte zwei Bilder nicht-affin geometrisch transformieren. In dieser Arbeit definieren homologe Stutzpunkte jeweils Verschiebungsvektoren. Die Verschiebung jedes Voxel wird durch die gewichtete Summe aller dieser Verschiebungsvektoren berechnet. Das jeweilige Gewicht wird durch den Abstand des Voxel zu einem Stutzpunkt sowie dem Stutzpunktspezifischen Gewichtungsfaktor bestimmt. Um diese Gewichtungsfaktoren zu optimieren, wird eine Evolutionstrategie angewendet. Die Fitness entspricht dem Kreuzkorrelationskoeffizienten zwischen Quelle und Ziel. Diese Methode wurde an artifiziellen dreidimensionalen Daten und an 3D-Rekonstruktionen von Autoradiographien von Nagergehirnen getestet. Die erzielte Optimierung fuhrte dabei zu einer verbesserten Qualitat des Warpings.

Hormone-mediated growth dynamics of the barley pericarp as revealed by magnetic resonance imaging and transcript profiling

Highlight Magnetic resonance imaging provides an understanding of dynamics of barley pericarp growth and development, while transcript and hormone profiling unravels a role for auxin and gibberellins in spatial–temporal regulation.

A new point-based warping method for enhanced and simplified analysis of functional brain image data☆

Comparison of brain imaging data requires the exact matching of data sets from different individuals. Warping methods, used to optimize matching of data sets, can exploit either local gray value distribution or identifiable reference points within the images to be compared. Gray value-based warping, which is more comfortable, cannot be used if gray values include functional information that should be compared between images. A major drawback in the use of point-based warping methods is the lack of methods for efficient and precise definition of reference points (landmarks) within comparable data sets. Here, we present a novel approach to automatically detect sufficient numbers of landmarks, which is based on 3D differential operators. In addition, we have developed a new distance-weighted warping method, which optimizes individual local weighting factors of displacement vectors. The quality of the methods was evaluated using a set of autoradiographs documenting the metabolic activity of gerbil brains after acoustic stimulation. The new warping method was compared with known methods of landmark-based warping, i.e., warping with radial basis functions and with distance-weighted methods. For the data sets presented in this study our new optimized warping method produced an increase in linear cross correlation of 4.44%, an increase in volume overlap index of 1.55%, and a decrease in the registration error of 36.2%. In addition, the detection of functional differences was improved after warping. Therefore, the new method is a powerful tool, which enhances the comparison of complex biological structures and the quantitative evaluation of functional imaging data.

A New Approach to Define Landmarks for Point-Based Warping in Brain Imaging

An accurate comparison of inter-individual 3D image datasets of brains requires warping techniques to reduce geometric variations. In this study we use a point-based method of warping with weighted sums of displacement vectors, which is extended by an optimization process. To improve the practicability of 3D warping, we investigate fast automatic procedures for determining landmarks. The combined approach was tested on 3D autoradiographs of brains of Mongolian gerbils. The landmark-generator is based on Monte-Carlo-techniques to detect corresponding reference points at edges of anatomical structures. The warping function is distance-weighted with landmark-specific weighting factors. These weighting factors are optimized by a computational evolution strategy. Within this optimization process the quality of warping is quantified by the sum of spatial differences of manually predefined registration points (registration error). The described approach combines a highly suitable procedure to detect landmarks in brain images and a point-based warping technique, which optimizes local weighting factors. The optimization of the weighting factors improves the similarity between the warped and the target image.

Point-based warping with optimized weighting factors of displacement vectors

The accurate comparison of inter-individual 3D image brain datasets requires non-affine transformation techniques (warping) to reduce geometric variations. Constrained by the biological prerequisites we use in this study a landmark-based warping method with weighted sums of displacement vectors, which is enhanced by an optimization process. Furthermore, we investigate fast automatic procedures for determining landmarks to improve the practicability of 3D warping. This combined approach was tested on 3D autoradiographs of Gerbil brains. The autoradiographs were obtained after injecting a non-metabolized radioactive glucose derivative into the Gerbil thereby visualizing neuronal activity in the brain. Afterwards the brain was processed with standard autoradiographical methods. The landmark-generator computes corresponding reference points simultaneously within a given number of datasets by Monte-Carlo-techniques. The warping function is a distance weighted exponential function with a landmark- specific weighting factor. These weighting factors are optimized by a computational evolution strategy. The warping quality is quantified by several coefficients (correlation coefficient, overlap-index, and registration error). The described approach combines a highly suitable procedure to automatically detect landmarks in autoradiographical brain images and an enhanced point-based warping technique, optimizing the local weighting factors. This optimization process significantly improves the similarity between the warped and the target dataset.© (2000) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.