Proteins, anatomy and networks of the fruit fly brain

Our understanding of the complexity of the brain is limited by the data we can collect and analyze. Because of experimental limitations and a desire for greater detail, most investigations focus on just one aspect of the brain. For example, brain function can be studied at many levels of abstraction including, but not limited to, gene expression, protein interactions, anatomical regions, neuronal connectivity, synaptic plasticity, and the electrical activity of neurons. By focusing on each of these levels, neuroscience has built up a detailed picture of how the brain works, but each level is understood mostly in isolation from the others. It is likely that interaction between all these levels is just as important. Therefore, a key hypothesis is that functional units spanning multiple levels of biological organization exist in the brain. This project attempted to combine neuronal circuitry analysis with functional proteomics and anatomical regions of the brain to explore this hypothesis, and took an evolutionary view of the results obtained. During the process we had to solve a number of technical challenges as the tools to undertake this type of research did not exist. Two informatics challenges for this research were to develop ways to analyze neurobiological data, such as brain protein expression patterns, to extract useful information, and how to share and present this data in a way that is fast and easy for anyone to access. This project contributes towards a more wholistic understanding of the fruit fly brain in three ways. Firstly, a screen was conducted to record the expression of proteins in the brain of the fruit fly, Drosophila melanogaster. Protein expression patterns in the fruit fly brain were recorded from 535 protein trap lines using confocal microscopy. A total of 884 3D images were annotated and made available on an easy to use website database, BrainTrap, available at fruitfly.inf.ed.ac.uk/braintrap. The website allows 3D images of the protein expression to be viewed interactively in the web browser, and an ontology-based search tool allows users to search for protein expression patterns in specific areas of interest. Different expression patterns mapped to a common template can be viewed simultaneously in multiple colours. This data bridges the gap between anatomical and biomolecular levels of understanding. Secondly, protein trap expression patterns were used to investigate the properties of the fruit fly brain. Thousands of protein-protein interactions have been recorded by methods such as yeast two-hybrid, however many of these protein pairs do not express in the same regions of the fruit fly brain. Using 535 protein expression patterns it was possible to rule out 149 protein-protein interactions. Also, protein expression patterns registered against a common template brain were used to produce new anatomical breakdowns of the fruit fly brain. Clustering techniques were able to naturally segment brain regions based only on the protein expression data. This is just one example of how, by combining proteomics with anatomy, we were able to learn more about both levels of understanding. Results are analysed further in combination with networks such as genetic homology networks, and connectivity networks. We show how the wealth of biological and neuroscience data now available in public databases can be combined with the Brain- Trap data to reveal similarities between areas of the fruit fly and mammalian brain. The BrainTrap data also informs us on the process of evolution and we show that genes found in fruit fly, yeast and mouse are more likely to be generally expressed throughout the brain, whereas genes found only in fruit fly and mouse, but not yeast, are more likely to have a specific expression pattern in the fruit fly brain. Thus, by combining data from multiple sources we can gain further insight into the complexity of the brain. Neural connectivity data is also analyzed and a new technique for enhanced motifs is developed for the combined analysis of connectivity data with other information such as neuron type data and potentially protein expression data. Thirdly, I investigated techniques for imaging the protein trap lines at higher resolution using electron microscopy (EM) and developed new informatics techniques for the automated analysis of neural connectivity data collected from serial section transmission electron microscopy (ssTEM). Measurement of the connectivity between neurons requires high resolution imaging techniques, such as electron microscopy, and images produced by this method are currently annotated manually to produce very detailed maps of cell morphology and connectivity. This is an extremely time consuming process and the volume of tissue and number of neurons that can be reconstructed is severely limited by the annotation step. I developed a set of computer vision algorithms to improve the alignment between consecutive images, and to perform partial annotation automatically by detecting membrane, synapses and mitochondria present in the images. Accuracy of the automatic annotation was evaluated on a small dataset and 96% of membrane could be identified at the cost of 13% false positives. This research demonstrates that informatics technology can help us to automatically analyze biological images and bring together genetic, anatomical, and connectivity data in a meaningful way. This combination of multiple data sources reveals more detail about each individual level of understanding, and gives us a more wholistic view of the fruit fly brain

Large-Scale Automatic Reconstruction of Neuronal Processes from Electron Microscopy Images

Automated sample preparation and electron microscopy enables acquisition of very large image data sets. These technical advances are of special importance to the field of neuroanatomy, as 3D reconstructions of neuronal processes at the nm scale can provide new insight into the fine grained structure of the brain. Segmentation of large-scale electron microscopy data is the main bottleneck in the analysis of these data sets. In this paper we present a pipeline that provides state-of-the art reconstruction performance while scaling to data sets in the GB-TB range. First, we train a random forest classifier on interactive sparse user annotations. The classifier output is combined with an anisotropic smoothing prior in a Conditional Random Field framework to generate multiple segmentation hypotheses per image. These segmentations are then combined into geometrically consistent 3D objects by segmentation fusion. We provide qualitative and quantitative evaluation of the automatic segmentation and demonstrate large-scale 3D reconstructions of neuronal processes from a $\mathbf{27,000}$ $\mathbf{\mu m^3}$ volume of brain tissue over a cube of $\mathbf{30 \; \mu m}$ in each dimension corresponding to 1000 consecutive image sections. We also introduce Mojo, a proofreading tool including semi-automated correction of merge errors based on sparse user scribbles

BrainTrap: a database of 3D protein expression patterns in the Drosophila brain

Protein-trap strains of Drosophila melanogaster provide a very useful tool for examining the 3D-expression patterns of proteins and purification of protein complexes. Here we present BrainTrap, available at http://fruitfly.inf.ed.ac.uk/braintrap, an online database of 3D confocal datasets showing reporter gene expression and protein localization in the adult brain of Drosophila. Full size images throughout the volume of the entire brain can be viewed interactively in a web browser. The database includes searchable annotations linked to the FlyBase Drosophila anatomy ontology. Anatomical search criteria can be specified using automatic completion and a hierarchical browser for the ontology. The provenance of all annotation is retained and the location where the annotator made the conclusion can be highlighted

Circadian control of mouse heart rate and blood pressure by the suprachiasmatic nuclei:behavioral effects are more significant than direct outputs

Diurnal variations in the incidence of events such as heart attack and stroke suggest a role for circadian rhythms in the etiology of cardiovascular disease. The aim of this study was to assess the influence of the suprachiasmatic nucleus (SCN) circadian clock on cardiovascular function. mice and WT. However, there was also a modest circadian rhythm of resting HR and BP that was independent of LA.If appropriate methods of analysis are used that take into account sleep and locomotor activity level, mice are a good model for understanding the contribution of circadian timing to cardiovascular function. Future studies of the influence of sleep and wakefulness on cardiovascular physiology may help to explain accumulating evidence linking disrupted sleep with cardiovascular disease in man

Analysis of the expression patterns, subcellular localisations and interaction partners of Drosophila proteins using a pigP protein trap library.

Although we now have a wealth of information on the transcription patterns of all the genes in the Drosophila genome, much less is known about the properties of the encoded proteins. To provide information on the expression patterns and subcellular localisations of many proteins in parallel, we have performed a large-scale protein trap screen using a hybrid piggyBac vector carrying an artificial exon encoding yellow fluorescent protein (YFP) and protein affinity tags. From screening 41 million embryos, we recovered 616 verified independent YFP-positive lines representing protein traps in 374 genes, two-thirds of which had not been tagged in previous P element protein trap screens. Over 20 different research groups then characterized the expression patterns of the tagged proteins in a variety of tissues and at several developmental stages. In parallel, we purified many of the tagged proteins from embryos using the affinity tags and identified co-purifying proteins by mass spectrometry. The fly stocks are publicly available through the Kyoto Drosophila Genetics Resource Center. All our data are available via an open access database (Flannotator), which provides comprehensive information on the expression patterns, subcellular localisations and in vivo interaction partners of the trapped proteins. Our resource substantially increases the number of available protein traps in Drosophila and identifies new markers for cellular organelles and structures.This work was supported by a project grant from the Wellcome Trust [076739], by a Wellcome Trust Principal Research Fellowship to D.StJ. [049818 and 080007], and by core support from the Wellcome Trust [092096] and Cancer Research UK [A14492].This is the final version of the article. It was first available from The Company of Biologists via http://dx.doi.org/10.1242/dev.11105

Dietary Modulation of Drosophila Sleep-Wake Behaviour

Background A complex relationship exists between diet and sleep but despite its impact on human health, this relationship remains uncharacterized and poorly understood. Drosophila melanogaster is an important model for the study of metabolism and behaviour, however the effect of diet upon Drosophila sleep remains largely unaddressed. Methodology/Principal Findings Using automated behavioural monitoring, a capillary feeding assay and pharmacological treatments, we examined the effect of dietary yeast and sucrose upon Drosophila sleep-wake behaviour for three consecutive days. We found that dietary yeast deconsolidated the sleep-wake behaviour of flies by promoting arousal from sleep in males and shortening periods of locomotor activity in females. We also demonstrate that arousal from nocturnal sleep exhibits a significant ultradian rhythmicity with a periodicity of 85 minutes. Increasing the dietary sucrose concentration from 5% to 35% had no effect on total sucrose ingestion per day nor any affect on arousal, however it did lengthen the time that males and females remained active. Higher dietary sucrose led to reduced total sleep by male but not female flies. Locomotor activity was reduced by feeding flies Metformin, a drug that inhibits oxidative phosphorylation, however Metformin did not affect any aspects of sleep. Conclusions We conclude that arousal from sleep is under ultradian control and regulated in a sex-dependent manner by dietary yeast and that dietary sucrose regulates the length of time that flies sustain periods of wakefulness. These findings highlight Drosophila as an important model with which to understand how diet impacts upon sleep and wakefulness in mammals and humans

Proteins, anatomy and networks of the fruit fly brain

Our understanding of the complexity of the brain is limited by the data we can collect and analyze. Because of experimental limitations and a desire for greater detail, most investigations focus on just one aspect of the brain. For example, brain function can be studied at many levels of abstraction including, but not limited to, gene expression, protein interactions, anatomical regions, neuronal connectivity, synaptic plasticity, and the electrical activity of neurons. By focusing on each of these levels, neuroscience has built up a detailed picture of how the brain works, but each level is understood mostly in isolation from the others. It is likely that interaction between all these levels is just as important. Therefore, a key hypothesis is that functional units spanning multiple levels of biological organization exist in the brain. This project attempted to combine neuronal circuitry analysis with functional proteomics and anatomical regions of the brain to explore this hypothesis, and took an evolutionary view of the results obtained. During the process we had to solve a number of technical challenges as the tools to undertake this type of research did not exist. Two informatics challenges for this research were to develop ways to analyze neurobiological data, such as brain protein expression patterns, to extract useful information, and how to share and present this data in a way that is fast and easy for anyone to access. This project contributes towards a more wholistic understanding of the fruit fly brain in three ways. Firstly, a screen was conducted to record the expression of proteins in the brain of the fruit fly, Drosophila melanogaster. Protein expression patterns in the fruit fly brain were recorded from 535 protein trap lines using confocal microscopy. A total of 884 3D images were annotated and made available on an easy to use website database, BrainTrap, available at fruitfly.inf.ed.ac.uk/braintrap. The website allows 3D images of the protein expression to be viewed interactively in the web browser, and an ontology-based search tool allows users to search for protein expression patterns in specific areas of interest. Different expression patterns mapped to a common template can be viewed simultaneously in multiple colours. This data bridges the gap between anatomical and biomolecular levels of understanding. Secondly, protein trap expression patterns were used to investigate the properties of the fruit fly brain. Thousands of protein-protein interactions have been recorded by methods such as yeast two-hybrid, however many of these protein pairs do not express in the same regions of the fruit fly brain. Using 535 protein expression patterns it was possible to rule out 149 protein-protein interactions. Also, protein expression patterns registered against a common template brain were used to produce new anatomical breakdowns of the fruit fly brain. Clustering techniques were able to naturally segment brain regions based only on the protein expression data. This is just one example of how, by combining proteomics with anatomy, we were able to learn more about both levels of understanding. Results are analysed further in combination with networks such as genetic homology networks, and connectivity networks. We show how the wealth of biological and neuroscience data now available in public databases can be combined with the Brain- Trap data to reveal similarities between areas of the fruit fly and mammalian brain. The BrainTrap data also informs us on the process of evolution and we show that genes found in fruit fly, yeast and mouse are more likely to be generally expressed throughout the brain, whereas genes found only in fruit fly and mouse, but not yeast, are more likely to have a specific expression pattern in the fruit fly brain. Thus, by combining data from multiple sources we can gain further insight into the complexity of the brain. Neural connectivity data is also analyzed and a new technique for enhanced motifs is developed for the combined analysis of connectivity data with other information such as neuron type data and potentially protein expression data. Thirdly, I investigated techniques for imaging the protein trap lines at higher resolution using electron microscopy (EM) and developed new informatics techniques for the automated analysis of neural connectivity data collected from serial section transmission electron microscopy (ssTEM). Measurement of the connectivity between neurons requires high resolution imaging techniques, such as electron microscopy, and images produced by this method are currently annotated manually to produce very detailed maps of cell morphology and connectivity. This is an extremely time consuming process and the volume of tissue and number of neurons that can be reconstructed is severely limited by the annotation step. I developed a set of computer vision algorithms to improve the alignment between consecutive images, and to perform partial annotation automatically by detecting membrane, synapses and mitochondria present in the images. Accuracy of the automatic annotation was evaluated on a small dataset and 96% of membrane could be identified at the cost of 13% false positives. This research demonstrates that informatics technology can help us to automatically analyze biological images and bring together genetic, anatomical, and connectivity data in a meaningful way. This combination of multiple data sources reveals more detail about each individual level of understanding, and gives us a more wholistic view of the fruit fly brain.EThOS - Electronic Theses Online ServiceGBUnited Kingdo