Neuroecology of alcohol risk and reward: Methanol boosts pheromones and courtship success in Drosophila melanogaster

Attraction of Drosophila melanogaster toward by-products of alcoholic fermentation, especially ethanol, has been extensively studied. Previous research has provided several interpretations of this attraction, including potential drug abuse, or a self-medicating coping strategy after mate rejection. We posit that the ecologically adaptive value of alcohol attraction has not been fully explored. Here, we assert a simple yet vital biological rationale for this alcohol preference. Flies display attraction to fruits rich in alcohol, specifically ethanol and methanol, where contact results in a rapid amplification of fatty acid-derived pheromones that enhance courtship success. We also identify olfactory sensory neurons that detect these alcohols, where we reveal roles in both attraction and aversion, and show that valence is balanced around alcohol concentration. Moreover, we demonstrate that methanol can be deadly, and adult flies must therefore accurately weigh the trade-off between benefits and costs for exposure within their naturally fermented and alcohol-rich environments.

Large-volume fully automated cell reconstruction generates a cell atlas of plant tissues

The geometric shape and arrangement of individual cells play a role in shaping organ functions. However, analyzing multicellular features and exploring their connectomes in centimeter-scale plant organs remain challenging. Here, we established a set of frameworks named Large-Volume Fully Automated Cell Reconstruction (LVACR), enabling the exploration of three-dimensional (3D) cytological features and cellular connectivity in plant tissues. Through benchmark testing, our framework demonstrated superior efficiency in cell segmentation and aggregation, successfully addressing the inherent challenges posed by light sheet fluorescence microscopy (LSFM) imaging. Using LVACR, we successfully established a cell atlas of different plant tissues. Cellular morphology analysis revealed differences of cell clusters and shapes in between different poplar (P. simonii Carr. and P. canadensis Moench.) seeds, whereas topological analysis revealed that they maintained conserved cellular connectivity. Furthermore, LVACR spatiotemporally demonstrated an initial burst of cell proliferation, accompanied by morphological transformations at an early stage in developing the shoot apical meristem. During subsequent development, cell differentiation produced anisotropic features, thereby resulting in various cell shapes. Overall, our findings provided valuable insights into the precise spatial arrangement and cellular behavior of multicellular organisms, thus enhancing our understanding of the complex processes underlying plant growth and differentiation.

Neurobiology of egg-laying behavior in Drosophila: neural control of the female reproductive system

Egg-laying is one of the key aspects of female reproductive behavior in insects. Egg-laying has been studied since the dawn of Drosophila melanogaster as a model organism. The female's internal state, hormones, and external factors, such as nutrition, light, and social environment, affect egg-laying output. However, only recently, neurobiological features of egg-laying behavior have been studied in detail. fruitless and doublesex, two key players in the sex determination pathway, have become focal points in identifying neurons of reproductive significance in both central and peripheral nervous systems. The reproductive tract and external terminalia house sensory neurons that carry the sensory information of egg maturation, mating and egg-laying. These sensory signals include the presence of male accessory gland products and mechanical stimuli. The abdominal neuromere houses neurons that receive information from the reproductive tract, including sex peptide abdominal ganglion neurons (SAGs), and send their information to the brain. In the brain, neuronal groups like aDNs and pC1 clusters modulate egg-laying decision-making, and other neurons like oviINs and oviDNs are necessary for egg-laying itself. Lastly, motor neurons involved in egg-laying, which are mostly octopaminergic, reside in the abdominal neuromere and orchestrate the muscle movements required for laying the egg. Egg-laying neuronal control is important in various evolutionary processes like cryptic female choice, and using different Drosophila species can provide intriguing avenues for the future of the field.

A Visual Interface for Exploring Hypotheses About Neural Circuits

One of the fundamental problems in neurobiological research is to understand how neural circuits generate behaviors in response to sensory stimuli. Elucidating such neural circuits requires anatomical and functional information about the neurons that are active during the processing of the sensory information and generation of the respective response, as well as an identification of the connections between these neurons. With modern imaging techniques, both morphological properties of individual neurons as well as functional information related to sensory processing, information integration and behavior can be obtained. Given the resulting information, neurobiologists are faced with the task of identifying the anatomical structures down to individual neurons that are linked to the studied behavior and the processing of the respective sensory stimuli. Here, we present a novel interactive tool that assists neurobiologists in the aforementioned tasks by allowing them to extract hypothetical neural circuits constrained by anatomical and functional data. Our approach is based on two types of structural data: brain regions that are anatomically or functionally defined, and morphologies of individual neurons. Both types of structural data are interlinked and augmented with additional information. The presented tool allows the expert user to identify neurons using Boolean queries. The interactive formulation of these queries is supported by linked views, using, among other things, two novel 2D abstractions of neural circuits. The approach was validated in two case studies investigating the neural basis of vision-based behavioral responses in zebrafish larvae. Despite this particular application, we believe that the presented tool will be of general interest for exploring hypotheses about neural circuits in other species, genera and taxa.

NeuronBridge: an intuitive web application for neuronal morphology search across large data sets

BACKGROUND

Neuroscience research in Drosophila is benefiting from large-scale connectomics efforts using electron microscopy (EM) to reveal all the neurons in a brain and their connections. To exploit this knowledge base, researchers relate a connectome's structure to neuronal function, often by studying individual neuron cell types. Vast libraries of fly driver lines expressing fluorescent reporter genes in sets of neurons have been created and imaged using confocal light microscopy (LM), enabling the targeting of neurons for experimentation. However, creating a fly line for driving gene expression within a single neuron found in an EM connectome remains a challenge, as it typically requires identifying a pair of driver lines where only the neuron of interest is expressed in both. This task and other emerging scientific workflows require finding similar neurons across large data sets imaged using different modalities.

RESULTS

Here, we present NeuronBridge, a web application for easily and rapidly finding putative morphological matches between large data sets of neurons imaged using different modalities. We describe the functionality and construction of the NeuronBridge service, including its user-friendly graphical user interface (GUI), extensible data model, serverless cloud architecture, and massively parallel image search engine.

CONCLUSIONS

NeuronBridge fills a critical gap in the Drosophila research workflow and is used by hundreds of neuroscience researchers around the world. We offer our software code, open APIs, and processed data sets for integration and reuse, and provide the application as a service at http://neuronbridge.janelia.org .

Molecular logic of synaptic diversity between Drosophila tonic and phasic motoneurons

Although neuronal subtypes display unique synaptic organization and function, the underlying transcriptional differences that establish these features are poorly understood. To identify molecular pathways that contribute to synaptic diversity, single-neuron Patch-seq RNA profiling was performed on Drosophila tonic and phasic glutamatergic motoneurons. Tonic motoneurons form weaker facilitating synapses onto single muscles, while phasic motoneurons form stronger depressing synapses onto multiple muscles. Super-resolution microscopy and in vivo imaging demonstrated that synaptic active zones in phasic motoneurons are more compact and display enhanced Ca2+ influx compared with their tonic counterparts. Genetic analysis identified unique synaptic properties that mapped onto gene expression differences for several cellular pathways, including distinct signaling ligands, post-translational modifications, and intracellular Ca2+ buffers. These findings provide insights into how unique transcriptomes drive functional and morphological differences between neuronal subtypes.

Semantic representation of neural circuit knowledge in Caenorhabditis elegans

In modern biology, new knowledge is generated quickly, making it challenging for researchers to efficiently acquire and synthesise new information from the large volume of primary publications. To address this problem, computational approaches that generate machine-readable representations of scientific findings in the form of knowledge graphs have been developed. These representations can integrate different types of experimental data from multiple papers and biological knowledge bases in a unifying data model, providing a complementary method to manual review for interacting with published knowledge. The Gene Ontology Consortium (GOC) has created a semantic modelling framework that extends individual functional gene annotations to structured descriptions of causal networks representing biological processes (Gene Ontology-Causal Activity Modelling, or GO-CAM). In this study, we explored whether the GO-CAM framework could represent knowledge of the causal relationships between environmental inputs, neural circuits and behavior in the model nematode C. elegans [C. elegans Neural-Circuit Causal Activity Modelling (CeN-CAM)]. We found that, given extensions to several relevant ontologies, a wide variety of author statements from the literature about the neural circuit basis of egg-laying and carbon dioxide (CO2) avoidance behaviors could be faithfully represented with CeN-CAM. Through this process, we were able to generate generic data models for several categories of experimental results. We also discuss how semantic modelling may be used to functionally annotate the C. elegans connectome. Thus, Gene Ontology-based semantic modelling has the potential to support various machine-readable representations of neurobiological knowledge.

Threat gates visual aversion via theta activity in Tachykinergic neurons

Animals must adapt sensory responses to an ever-changing environment for survival. Such sensory modulation is especially critical in a threatening situation, in which animals often promote aversive responses to, among others, visual stimuli. Recently, threatened Drosophila has been shown to exhibit a defensive internal state. Whether and how threatened Drosophila promotes visual aversion, however, remains elusive. Here we report that mechanical threats to Drosophila transiently gate aversion from an otherwise neutral visual object. We further identified the neuropeptide tachykinin, and a single cluster of neurons expressing it ("Tk-GAL42 ∩ Vglut neurons"), that are responsible for gating visual aversion. Calcium imaging analysis revealed that mechanical threats are encoded in Tk-GAL42 ∩ Vglut neurons as elevated activity. Remarkably, we also discovered that a visual object is encoded in Tk-GAL42 ∩ Vglut neurons as θ oscillation, which is causally linked to visual aversion. Our data reveal how a single cluster of neurons adapt organismal sensory response to a threatening situation through a neuropeptide and a combination of rate/temporal coding schemes.

The Drivers of Diversity: Integrated genetic and hormonal cues regulate neural diversity

Proper functioning of the nervous system relies not only on the generation of a vast repertoire of distinct neural cell types but also on the precise neural circuitry within them. How the generation of highly diverse neural populations is regulated during development remains a topic of interest. Landmark studies in Drosophila have identified the genetic and temporal cues regulating neural diversity and thus have provided valuable insights into our understanding of temporal patterning of the central nervous system. The development of the Drosophila central complex, which is mostly derived from type II neural stem cell (NSC) lineages, showcases how a small pool of NSCs can give rise to vast and distinct progeny. Similar to the human outer subventricular zone (OSVZ) neural progenitors, type II NSCs generate intermediate neural progenitors (INPs) to expand and diversify lineages that populate higher brain centers. Each type II NSC has a distinct spatial identity and timely regulated expression of many transcription factors and mRNA binding proteins. Additionally, INPs derived from them show differential expression of genes depending on their birth order. Together type II NSCs and INPs display a combinatorial temporal patterning that expands neural diversity of the central brain lineages. We cover advances in current understanding of type II NSC temporal patterning and discuss similarities and differences in temporal patterning mechanisms of various NSCs with a focus on how cell-intrinsic and extrinsic hormonal cues regulate temporal transitions in NSCs during larval development. Cell extrinsic ligands activate conserved signaling pathways and extrinsic hormonal cues act as a temporal switch that regulate temporal progression of the NSCs. We conclude by elaborating on how a progenitor's temporal code regulates the fate specification and identity of distinct neural types. At the end, we also discuss open questions in linking developmental cues to neural identity, circuits, and underlying behaviors in the adult fly.

A searchable image resource of Drosophila GAL4 driver expression patterns with single neuron resolution

Precise, repeatable genetic access to specific neurons via GAL4/UAS and related methods is a key advantage of Drosophila neuroscience. Neuronal targeting is typically documented using light microscopy of full GAL4 expression patterns, which generally lack the single-cell resolution required for reliable cell type identification. Here, we use stochastic GAL4 labeling with the MultiColor FlpOut approach to generate cellular resolution confocal images at large scale. We are releasing aligned images of 74,000 such adult central nervous systems. An anticipated use of this resource is to bridge the gap between neurons identified by electron or light microscopy. Identifying individual neurons that make up each GAL4 expression pattern improves the prediction of split-GAL4 combinations targeting particular neurons. To this end, we have made the images searchable on the NeuronBridge website. We demonstrate the potential of NeuronBridge to rapidly and effectively identify neuron matches based on morphology across imaging modalities and datasets.

A single-cell resolution gene expression atlas of the larval zebrafish brain

The advent of multimodal brain atlases promises to accelerate progress in neuroscience by allowing in silico queries of neuron morphology, connectivity, and gene expression. We used multiplexed fluorescent in situ RNA hybridization chain reaction (HCR) technology to generate expression maps across the larval zebrafish brain for a growing set of marker genes. The data were registered to the Max Planck Zebrafish Brain (mapzebrain) atlas, thus allowing covisualization of gene expression, single-neuron tracings, and expertly curated anatomical segmentations. Using post hoc HCR labeling of the immediate early gene cfos, we mapped responses to prey stimuli and food ingestion across the brain of freely swimming larvae. This unbiased approach revealed, in addition to previously described visual and motor areas, a cluster of neurons in the secondary gustatory nucleus, which express the marker calb2a, as well as a specific neuropeptide Y receptor, and project to the hypothalamus. This discovery exemplifies the power of this new atlas resource for zebrafish neurobiology.

Virtual Fly Brain-An interactive atlas of the Drosophila nervous system

As a model organism, Drosophila is uniquely placed to contribute to our understanding of how brains control complex behavior. Not only does it have complex adaptive behaviors, but also a uniquely powerful genetic toolkit, increasingly complete dense connectomic maps of the central nervous system and a rapidly growing set of transcriptomic profiles of cell types. But this also poses a challenge: Given the massive amounts of available data, how are researchers to Find, Access, Integrate and Reuse (FAIR) relevant data in order to develop an integrated anatomical and molecular picture of circuits, inform hypothesis generation, and find reagents for experiments to test these hypotheses? The Virtual Fly Brain (virtualflybrain.org) web application & API provide a solution to this problem, using FAIR principles to integrate 3D images of neurons and brain regions, connectomics, transcriptomics and reagent expression data covering the whole CNS in both larva and adult. Users can search for neurons, neuroanatomy and reagents by name, location, or connectivity, via text search, clicking on 3D images, search-by-image, and queries by type (e.g., dopaminergic neuron) or properties (e.g., synaptic input in the antennal lobe). Returned results include cross-registered 3D images that can be explored in linked 2D and 3D browsers or downloaded under open licenses, and extensive descriptions of cell types and regions curated from the literature. These solutions are potentially extensible to cover similar atlasing and data integration challenges in vertebrates.

Brain Data Standards - A method for building data-driven cell-type ontologies

Large-scale single-cell 'omics profiling is being used to define a complete catalogue of brain cell types, something that traditional methods struggle with due to the diversity and complexity of the brain. But this poses a problem: How do we organise such a catalogue - providing a standard way to refer to the cell types discovered, linking their classification and properties to supporting data? Cell ontologies provide a partial solution to these problems, but no existing ontology schemas support the definition of cell types by direct reference to supporting data, classification of cell types using classifications derived directly from data, or links from cell types to marker sets along with confidence scores. Here we describe a generally applicable schema that solves these problems and its application in a semi-automated pipeline to build a data-linked extension to the Cell Ontology representing cell types in the Primary Motor Cortex of humans, mice and marmosets. The methods and resulting ontology are designed to be scalable and applicable to similar whole-brain atlases currently in preparation.

Molecular Logic of Synaptic Diversity Between Drosophila Tonic and Phasic Motoneurons

Although neuronal subtypes display unique synaptic organization and function, the underlying transcriptional differences that establish these features is poorly understood. To identify molecular pathways that contribute to synaptic diversity, single neuron PatchSeq RNA profiling was performed on Drosophila tonic and phasic glutamatergic motoneurons. Tonic motoneurons form weaker facilitating synapses onto single muscles, while phasic motoneurons form stronger depressing synapses onto multiple muscles. Super-resolution microscopy and in vivo imaging demonstrated synaptic active zones in phasic motoneurons are more compact and display enhanced Ca 2+ influx compared to their tonic counterparts. Genetic analysis identified unique synaptic properties that mapped onto gene expression differences for several cellular pathways, including distinct signaling ligands, post-translational modifications and intracellular Ca 2+ buffers. These findings provide insights into how unique transcriptomes drive functional and morphological differences between neuronal subtypes.

Orthopedia expression during Drosophila melanogaster nervous system development and its regulation by microRNA-252

During brain development of Drosophila melanogaster many transcription factors are involved in regulating neural fate and morphogenesis. In our study we show that the transcription factor Orthopedia (Otp), a member of the 57B homeobox gene cluster, plays an important role in this process. Otp is expressed in a stable pattern in defined lineages from mid-embryonic stages into the adult brain and therefore a very stable marker for these lineages. We determined the abundance of the two different otp transcripts in the brain and hindgut during development using qPCR. CRISPR/Cas9 generated otp mutants of the longer protein form significantly affect the expression of Otp in specific areas. We generated an otp enhancer trap strain by gene targeting and reintegration of Gal4, which mimics the complete expression of otp during development except the embryonic hindgut expression. Since in the embryo, the expression of Otp is posttranscriptionally regulated, we looked for putative miRNAs interacting with the otp 3'UTR, and identified microRNA-252 as a candidate. Further analyses with mutated and deleted forms of the microRNA-252 interacting sequence in the otp 3'UTR demonstrate an in vivo interaction of microRNA-252 with the otp 3'UTR. An effect of this interaction is seen in the adult brain, where Otp expression is partially abolished in a knockout strain of microRNA-252. Our results show that Otp is another important factor for brain development in Drosophila melanogaster.

Internal state affects local neuron function in an early sensory processing center to shape olfactory behavior in Drosophila larvae

Crawling insects, when starved, tend to have fewer head wavings and travel in straighter tracks in search of food. We used the Drosophila melanogaster larva to investigate whether this flexibility in the insect's navigation strategy arises during early olfactory processing and, if so, how. We demonstrate a critical role for Keystone-LN, an inhibitory local neuron in the antennal lobe, in implementing head-sweep behavior. Keystone-LN responds to odor stimuli, and its inhibitory output is required for a larva to successfully navigate attractive and aversive odor gradients. We show that insulin signaling in Keystone-LN likely mediates the starvation-dependent changes in head-sweep magnitude, shaping the larva's odor-guided movement. Our findings demonstrate how flexibility in an insect's navigation strategy can arise from context-dependent modulation of inhibitory neurons in an early sensory processing center. They raise new questions about modulating a circuit's inhibitory output to implement changes in a goal-directed movement.

The sloppy relationship between neural circuit structure and function

Investigating and describing the relationships between the structure of a circuit and its function has a long tradition in neuroscience. Since neural circuits acquire their structure through sophisticated developmental programmes, and memories and experiences are maintained through synaptic modification, it is to be expected that structure is closely linked to function. Recent findings challenge this hypothesis from three different angles: Function does not strongly constrain circuit parameters, many parameters in neural circuits are irrelevant and contribute little to function, and circuit parameters are unstable and subject to constant random drift. At the same time however, recent work also showed that dynamics in neural circuit activity that is related to function are robust over time and across individuals. Here this apparent contradiction is addressed by considering the properties of neural manifolds that restrict circuit activity to functionally relevant subspaces, and it will be suggested that degenerate, anisotropic and unstable parameter spaces are a closely related to the structure and implementation of functionally relevant neural manifolds. Abstract figure legend What are the relationships between noisy and highly variable microscopic neural circuit variables on the one hand and the generation of behaviour on the other? Here it is proposed that an intermediate level of description exists where this relationship can be understood in terms of low-dimensional dynamics. Recordings of neural activity during unconstrained behaviour and the development of new machine learning methods will help to uncover these links. This article is protected by copyright. All rights reserved.

neuPrint: An open access tool for EM connectomics

Due to advances in electron microscopy and deep learning, it is now practical to reconstruct a connectome, a description of neurons and the chemical synapses between them, for significant volumes of neural tissue. Smaller past reconstructions were primarily used by domain experts, could be handled by downloading data, and performance was not a serious problem. But new and much larger reconstructions upend these assumptions. These networks now contain tens of thousands of neurons and tens of millions of connections, with yet larger reconstructions pending, and are of interest to a large community of non-specialists. Allowing other scientists to make use of this data needs more than publication—it requires new tools that are publicly available, easy to use, and efficiently handle large data. We introduce neuPrint to address these data analysis challenges. Neuprint contains two major components—a web interface and programmer APIs. The web interface is designed to allow any scientist worldwide, using only a browser, to quickly ask and answer typical biological queries about a connectome. The neuPrint APIs allow more computer-savvy scientists to make more complex or higher volume queries. NeuPrint also provides features for assessing reconstruction quality. Internally, neuPrint organizes connectome data as a graph stored in a neo4j database. This gives high performance for typical queries, provides access though a public and well documented query language Cypher, and will extend well to future larger connectomics databases. Our experience is also an experiment in open science. We find a significant fraction of the readers of the article proceed to examine the data directly. In our case preprints worked exactly as intended, with data inquiries and PDF downloads starting immediately after pre-print publication, and little affected by formal publication later. From this we deduce that many readers are more interested in our data than in our analysis of our data, suggesting that data-only papers can be well appreciated and that public data release can speed up the propagation of scientific results by many months. We also find that providing, and keeping, the data available for online access imposes substantial additional costs to connectomics research.

An incentive circuit for memory dynamics in the mushroom body of Drosophila melanogaster

Insects adapt their response to stimuli, such as odours, according to their pairing with positive or negative reinforcements, such as sugar or shock. Recent electrophysiological and imaging findings in Drosophila melanogaster allow detailed examination of the neural mechanisms supporting the acquisition, forgetting, and assimilation of memories. We propose that this data can be explained by the combination of a dopaminergic plasticity rule that supports a variety of synaptic strength change phenomena, and a circuit structure (derived from neuroanatomy) between dopaminergic and output neurons that creates different roles for specific neurons. Computational modelling shows that this circuit allows for rapid memory acquisition, transfer from short term to long term, and exploration/exploitation trade-off. The model can reproduce the observed changes in the activity of each of the identified neurons in conditioning paradigms and can be used for flexible behavioural control.

Fly Cell Atlas: A single-nucleus transcriptomic atlas of the adult fruit fly

For more than 100 years, the fruit fly Drosophila melanogaster has been one of the most studied model organisms. Here, we present a single-cell atlas of the adult fly, Tabula Drosophilae, that includes 580,000 nuclei from 15 individually dissected sexed tissues as well as the entire head and body, annotated to >250 distinct cell types. We provide an in-depth analysis of cell type-related gene signatures and transcription factor markers, as well as sexual dimorphism, across the whole animal. Analysis of common cell types between tissues, such as blood and muscle cells, reveals rare cell types and tissue-specific subtypes. This atlas provides a valuable resource for the Drosophila community and serves as a reference to study genetic perturbations and disease models at single-cell resolution.

Cell type ontologies of the Human Cell Atlas

Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body while at the same time raising the need to formalize this new knowledge. Here, we discuss current efforts to harmonize and integrate different sources of annotations of cell types and states into a reference cell ontology. We illustrate with examples how a unified ontology can consolidate and advance our understanding of cell types across scientific communities and biological domains.

Identification of Neuronal Polarity by Node-Based Machine Learning

Identifying the direction of signal flows in neural networks is important for understanding the intricate information dynamics of a living brain. Using a dataset of 213 projection neurons distributed in more than 15 neuropils of a Drosophila brain, we develop a powerful machine learning algorithm: node-based polarity identifier of neurons (NPIN). The proposed model is trained only by information specific to nodes, the branch points on the skeleton, and includes both Soma Features (which contain spatial information from a given node to a soma) and Local Features (which contain morphological information of a given node). After including the spatial correlations between nodal polarities, our NPIN provided extremely high accuracy (>96.0%) for the classification of neuronal polarity, even for complex neurons with more than two dendrite/axon clusters. Finally, we further apply NPIN to classify the neuronal polarity of neurons in other species (Blowfly and Moth), which have much less neuronal data available. Our results demonstrate the potential of NPIN as a powerful tool to identify the neuronal polarity of insects and to map out the signal flows in the brain's neural networks if more training data become available in the future.

A protocol to detect neurodegeneration in Drosophila melanogaster whole-brain mounts using advanced microscopy

Drosophila melanogaster is an excellent model organism to study neurodegeneration. Assessing evident neurodegeneration within the fly brain involves the laborious preparation of thin-sectioned H&E-stained heads to visualize brain vacuole degeneration. Here, we present an advanced microscopy-based protocol, without the need for sectioning, to detect vacuole degeneration within whole fly brains by applying commonly used stains to reveal the brain parenchyma. This approach preserves the whole-brain architecture and enables rapid, reproducible, and quantitative analyses of vacuole-like degeneration associated with specific brain regions. For complete details on the use and execution of this protocol, please refer to Behnke et al. (2021).

A unified mechanism for innate and learned visual landmark guidance in the insect central complex

Insects can navigate efficiently in both novel and familiar environments, and this requires flexiblity in how they are guided by sensory cues. A prominent landmark, for example, can elicit strong innate behaviours (attraction or menotaxis) but can also be used, after learning, as a specific directional cue as part of a navigation memory. However, the mechanisms that allow both pathways to co-exist, interact or override each other are largely unknown. Here we propose a model for the behavioural integration of innate and learned guidance based on the neuroanatomy of the central complex (CX), adapted to control landmark guided behaviours. We consider a reward signal provided either by an innate attraction to landmarks or a long-term visual memory in the mushroom bodies (MB) that modulates the formation of a local vector memory in the CX. Using an operant strategy for a simulated agent exploring a simple world containing a single visual cue, we show how the generated short-term memory can support both innate and learned steering behaviour. In addition, we show how this architecture is consistent with the observed effects of unilateral MB lesions in ants that cause a reversion to innate behaviour. We suggest the formation of a directional memory in the CX can be interpreted as transforming rewarding (positive or negative) sensory signals into a mapping of the environment that describes the geometrical attractiveness (or repulsion). We discuss how this scheme might represent an ideal way to combine multisensory information gathered during the exploration of an environment and support optimal cue integration.

Whole-body integration of gene expression and single-cell morphology

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

•

A cellular atlas integrates gene expression and ultrastructure for an entire annelid

•

Morphometry of all segmented cells, nuclei, and chromatin categorizes cell classes

•

Molecular anatomy and projectome of head ganglionic nuclei and mushroom bodies

•

An open-source browser for multimodal big image data exploration and analysis

Planarian Anatomy Ontology: a resource to connect data within and across experimental platforms

As the planarian research community expands, the need for an interoperable data organization framework for tool building has become increasingly apparent. Such software would streamline data annotation and enhance cross-platform and cross-species searchability. We created the Planarian Anatomy Ontology (PLANA), an extendable relational framework of defined Schmidtea mediterranea (Smed) anatomical terms used in the field. At publication, PLANA contains over 850 terms describing Smed anatomy from subcellular to system levels across all life cycle stages, in intact animals and regenerating body fragments. Terms from other anatomy ontologies were imported into PLANA to promote interoperability and comparative anatomy studies. To demonstrate the utility of PLANA as a tool for data curation, we created resources for planarian embryogenesis, including a staging series and molecular fate-mapping atlas, and the Planarian Anatomy Gene Expression database, which allows retrieval of a variety of published transcript/gene expression data associated with PLANA terms. As an open-source tool built using FAIR (findable, accessible, interoperable, reproducible) principles, our strategy for continued curation and versioning of PLANA also provides a platform for community-led growth and evolution of this resource.

Summary: Description of the construction of an anatomy ontology tool for planaria with examples of its potential use to curate and mine data across multiple experimental platforms.

SNT: a unifying toolbox for quantification of neuronal anatomy

SNT is an end-to-end framework for neuronal morphometry and whole-brain connectomics that supports tracing, proof-editing, visualization, quantification and modeling of neuroanatomy. With an open architecture, a large user base, community-based documentation, support for complex imagery and several model organisms, SNT is a flexible resource for the broad neuroscience community. SNT is both a desktop application and multi-language scripting library, and it is available through the Fiji distribution of ImageJ.

Accelerating with FlyBrainLab the discovery of the functional logic of the Drosophila brain in the connectomic and synaptomic era

In recent years, a wealth of Drosophila neuroscience data have become available including cell type and connectome/synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fruit fly brain, we have developed FlyBrainLab, a unique open-source computing platform that integrates 3D exploration and visualization of diverse datasets with interactive exploration of the functional logic of modeled executable brain circuits. FlyBrainLab's User Interface, Utilities Libraries and Circuit Libraries bring together neuroanatomical, neurogenetic and electrophysiological datasets with computational models of different researchers for validation and comparison within the same platform. Seeking to transcend the limitations of the connectome/synaptome, FlyBrainLab also provides libraries for molecular transduction arising in sensory coding in vision/olfaction. Together with sensory neuron activity data, these libraries serve as entry points for the exploration, analysis, comparison, and evaluation of circuit functions of the fruit fly brain.

From whole-mount to single-cell spatial assessment of gene expression in 3D

Unravelling spatio-temporal patterns of gene expression is crucial to understanding core biological principles from embryogenesis to disease. Here we review emerging technologies, providing automated, high-throughput, spatially resolved quantitative gene expression data. Novel techniques expand on current benchmark protocols, expediting their incorporation into ongoing research. These approaches digitally reconstruct patterns of embryonic expression in three dimensions, and have successfully identified novel domains of expression, cell types, and tissue features. Such technologies pave the way for unbiased and exhaustive recapitulation of gene expression levels in spatial and quantitative terms, promoting understanding of the molecular origin of developmental defects, and improving medical diagnostics.

The Wiring Logic of an Identified Serotonergic Neuron That Spans Sensory Networks

Serotonergic neurons project widely throughout the brain to modulate diverse physiological and behavioral processes. However, a single-cell resolution understanding of the connectivity of serotonergic neurons is currently lacking. Using a whole-brain EM dataset of a female Drosophila, we comprehensively determine the wiring logic of a broadly projecting serotonergic neuron (the CSDn) that spans several olfactory regions. Within the antennal lobe, the CSDn differentially innervates each glomerulus, yet surprisingly, this variability reflects a diverse set of presynaptic partners, rather than glomerulus-specific differences in synaptic output, which is predominately to local interneurons. Moreover, the CSDn has distinct connectivity relationships with specific local interneuron subtypes, suggesting that the CSDn influences distinct aspects of local network processing. Across olfactory regions, the CSDn has different patterns of connectivity, even having different connectivity with individual projection neurons that also span these regions. Whereas the CSDn targets inhibitory local neurons in the antennal lobe, the CSDn has more distributed connectivity in the LH, preferentially synapsing with principal neuron types based on transmitter content. Last, we identify individual novel synaptic partners associated with other sensory domains that provide strong, top-down input to the CSDn. Together, our study reveals the complex connectivity of serotonergic neurons, which combine the integration of local and extrinsic synaptic input in a nuanced, region-specific manner.SIGNIFICANCE STATEMENT All sensory systems receive serotonergic modulatory input. However, a comprehensive understanding of the synaptic connectivity of individual serotonergic neurons is lacking. In this study, we use a whole-brain EM microscopy dataset to comprehensively determine the wiring logic of a broadly projecting serotonergic neuron in the olfactory system of Drosophila Collectively, our study demonstrates, at a single-cell level, the complex connectivity of serotonergic neurons within their target networks, identifies specific cell classes heavily targeted for serotonergic modulation in the olfactory system, and reveals novel extrinsic neurons that provide strong input to this serotonergic system outside of the context of olfaction. Elucidating the connectivity logic of individual modulatory neurons provides a ground plan for the seemingly heterogeneous effects of modulatory systems.

Dietary sugar inhibits satiation by decreasing the central processing of sweet taste

From humans to vinegar flies, exposure to diets rich in sugar and fat lowers taste sensation, changes food choices, and promotes feeding. However, how these peripheral alterations influence eating is unknown. Here we used the genetically tractable organism D. melanogaster to define the neural mechanisms through which this occurs. We characterized a population of protocerebral anterior medial dopaminergic neurons (PAM DANs) that innervates the β’2 compartment of the mushroom body and responds to sweet taste. In animals fed a high sugar diet, the response of PAM-β’2 to sweet stimuli was reduced and delayed, and sensitive to the strength of the signal transmission out of the sensory neurons. We found that PAM-β’2 DANs activity controls feeding rate and satiation: closed-loop optogenetic activation of β’2 DANs restored normal eating in animals fed high sucrose. These data argue that diet-dependent alterations in taste weaken satiation by impairing the central processing of sensory signals.

Obesity is a major health problem affecting over 650 million adults worldwide. It is typically caused by overeating high-energy foods, which often contain a lot of sugar. Consuming sugary foods triggers the production of a reward signal called dopamine in the brains of insects and mammals, which reinforces sugar-consuming behavior. The brain balances this with a process called ‘sensory-enhanced satiety’, which makes foods that provide a stronger sensation of sweetness better at reducing hunger and further eating.

High-energy food was scarce for most of human evolution, but over the past century sugar has become readily available in our diet leading to an increase in obesity. Last year, a study in fruit flies reported that a sugary diet reduces the sensitivity to sweet flavors, which leads to overeating and weight gain. It appears that this sensitivity is linked to the effectiveness of sensory-enhanced satiety. However, the mechanism linking diets high in sugar and overeating is still poorly understood. One hypothesis is that fruit flies estimate the energy content of food based on the degree of dopamine released in response to the sugar.

May et al. compared the responses of neurons in fruit flies fed a normal diet to those in flies fed a diet high in sugar. As expected, both groups activated the neurons involved in the dopamine reward response when they tasted sugar. However, when the flies were on a sugar-heavy diet, these neurons were less active. This was because the neurons responsible for tasting sweetness were activated less in flies fed a high-sugar diet, leading to a lowered response by the neurons that produce dopamine. The flies in these experiments were genetically engineered so that the dopamine-producing neurons could be artificially activated in response to light, a technique called optogenetics. When May et al. applied this technique to the flies on a sugar-heavy diet, they were able to stop these flies from overeating.

These findings provide further evidence to support the idea that a sugary diet reduces the brain’s sensitivity to overeating. Given the significant healthcare cost of obesity to society, this improved understanding could help public health initiatives focusing on manufacturing food that is lower in sugar.

An Adult Brain Atlas Reveals Broad Neuroanatomical Changes in Independently Evolved Populations of Mexican Cavefish

A shift in environmental conditions impacts the evolution of complex developmental and behavioral traits. The Mexican cavefish, Astyanax mexicanus, is a powerful model for examining the evolution of development, physiology, and behavior because multiple cavefish populations can be compared to an extant, ancestral-like surface population of the same species. Many behaviors have diverged in cave populations of A. mexicanus, and previous studies have shown that cavefish have a loss of sleep, reduced stress, an absence of social behaviors, and hyperphagia. Despite these findings, surprisingly little is known about the changes in neuroanatomy that underlie these behavioral phenotypes. Here, we use serial sectioning to generate brain atlases of surface fish and three independent cavefish populations. Volumetric reconstruction of serial-sectioned brains confirms convergent evolution on reduced optic tectum volume in all cavefish populations tested. In addition, we quantified volumes of specific neuroanatomical loci within several brain regions that have previously been implicated in behavioral regulation, including the hypothalamus, thalamus, and habenula. These analyses reveal an enlargement of the hypothalamus in all cavefish populations relative to surface fish, as well as subnuclei-specific differences within the thalamus and prethalamus. Taken together, these analyses support the notion that changes in environmental conditions are accompanied by neuroanatomical changes in brain structures associated with behavior. This atlas provides a resource for comparative neuroanatomy of additional brain regions and the opportunity to associate brain anatomy with evolved changes in behavior.

A subset of brain neurons controls regurgitation in adult Drosophila melanogaster

Taste is essential for animals to evaluate food quality and make important decisions about food choice and intake. How complex brains process sensory information to produce behavior is an essential question in the field of sensory neurobiology. Currently, little is known about higher-order taste circuits in the brain as compared with those of other sensory systems. Here, we used the common vinegar fly, Drosophila melanogaster, to screen for candidate neurons labeled by different transgenic GAL4 lines in controlling feeding behaviors. We found that activation of one line (VT041723-GAL4) produces 'proboscis holding' behavior (extrusion of the mouthpart without withdrawal). Further analysis showed that the proboscis holding phenotype indicates an aversive response, as flies pre-fed with either sucrose or water prior to neuronal activation exhibited regurgitation. Anatomical characterization of VT041723-GAL4-labeled neurons suggests that they receive sensory input from peripheral taste neurons. Overall, our study identifies a subset of brain neurons labeled by VT041723-GAL4 that may be involved in a taste circuit that controls regurgitation.

A Genetic Toolkit for Dissecting Dopamine Circuit Function in Drosophila

The neuromodulator dopamine (DA) plays a key role in motor control, motivated behaviors, and higher-order cognitive processes. Dissecting how these DA neural networks tune the activity of local neural circuits to regulate behavior requires tools for manipulating small groups of DA neurons. To address this need, we assembled a genetic toolkit that allows for an exquisite level of control over the DA neural network in Drosophila. To further refine targeting of specific DA neurons, we also created reagents that allow for the conversion of any existing GAL4 line into Split GAL4 or GAL80 lines. We demonstrated how this toolkit can be used with recently developed computational methods to rapidly generate additional reagents for manipulating small subsets or individual DA neurons. Finally, we used the toolkit to reveal a dynamic interaction between a small subset of DA neurons and rearing conditions in a social space behavioral assay.

Visualization of insect metamorphosis

Metamorphosis and, in particular, holometaboly, the development of organisms through a series of discrete stages (egg, larva, pupa, adult) that hardly resemble one another but are finely adapted to specific roles in the life cycle of the organism, has fascinated and mystified humans throughout history. However, it can be difficult to visualize the dramatic changes that occur during holometaboly without destructive sampling, traditionally through histology. However, advances in imaging technologies developed mainly for medical sciences have been applied to studies of insect metamorphosis over the past couple of decades. These include micro-computed tomography, magnetic resonance imaging and optical coherence tomography. A major advantage of these techniques is that they are rapid and non-destructive, enabling virtual dissection of an organism in any plane by anyone who has access to the image files and the necessary software. They can also be applied in some cases to visualize metamorphosis in vivo, including the periods of most rapid and dramatic morphological change. This review focusses on visualizing the intra-puparial holometabolous metamorphosis of cyclorraphous flies (Diptera), including the primary model organism for all genetic investigations, Drosophila melanogaster, and the blow flies of medical, veterinary and forensic importance, but also discusses similar studies on other insect orders. This article is part of the theme issue 'The evolution of complete metamorphosis'.

The Jewel Wasp Standard Brain: Average shape atlas and morphology of the female Nasonia vitripennis brain

Nasonia, a genus of parasitoid wasps, is a promising model system in the study of developmental and evolutionary genetics, as well as complex traits such as learning. Of these "jewel wasps", the species Nasonia vitripennis is widely spread and widely studied. To accelerate neuroscientific research in this model species, fundamental knowledge of its nervous system is needed. To this end, we present an average standard brain of recently eclosed naïve female N. vitripennis wasps obtained by the iterative shape averaging method. This "Jewel Wasp Standard Brain" includes the optic lobe (excluding the lamina), the anterior optic tubercle, the antennal lobe, the lateral horn, the mushroom body, the central complex, and the remaining unclassified neuropils in the central brain. Furthermore, we briefly describe these well-defined neuropils and their subregions in the N. vitripennis brain. A volumetric analysis of these neuropils is discussed in the context of brains of other insect species. The Jewel Wasp Standard Brain will provide a framework to integrate and consolidate the results of future neurobiological studies in N. vitripennis. In addition, the volumetric analysis provides a baseline for future work on age- and experience-dependent brain plasticity.

Functional and anatomical specificity in a higher olfactory centre

Most sensory systems are organized into parallel neuronal pathways that process distinct aspects of incoming stimuli. In the insect olfactory system, second order projection neurons target both the mushroom body, required for learning, and the lateral horn (LH), proposed to mediate innate olfactory behavior. Mushroom body neurons form a sparse olfactory population code, which is not stereotyped across animals. In contrast, odor coding in the LH remains poorly understood. We combine genetic driver lines, anatomical and functional criteria to show that the Drosophila LH has ~1400 neurons and >165 cell types. Genetically labeled LHNs have stereotyped odor responses across animals and on average respond to three times more odors than single projection neurons. LHNs are better odor categorizers than projection neurons, likely due to stereotyped pooling of related inputs. Our results reveal some of the principles by which a higher processing area can extract innate behavioral significance from sensory stimuli.

Developmental organization of central neurons in the adult Drosophila ventral nervous system

We have used MARCM to reveal the adult morphology of the post embryonically produced neurons in the thoracic neuromeres of the Drosophila VNS. The work builds on previous studies of the origins of the adult VNS neurons to describe the clonal organization of the adult VNS. We present data for 58 of 66 postembryonic thoracic lineages, excluding the motor neuron producing lineages (15 and 24) which have been described elsewhere. MARCM labels entire lineages but where both A and B hemilineages survive (e.g., lineages 19, 12, 13, 6, 1, 3, 8, and 11), the two hemilineages can be discriminated and we have described each hemilineage separately. Hemilineage morphology is described in relation to the known functional domains of the VNS neuropil and based on the anatomy we are able to assign broad functional roles for each hemilineage. The data show that in a thoracic hemineuromere, 16 hemilineages are primarily involved in controlling leg movements and walking, 9 are involved in the control of wing movements, and 10 interface between both leg and wing control. The data provide a baseline of understanding of the functional organization of the adult Drosophila VNS. By understanding the morphological organization of these neurons, we can begin to define and test the rules by which neuronal circuits are assembled during development and understand the functional logic and evolution of neuronal networks.

Neurocalcin regulates nighttime sleep and arousal in Drosophila

Sleep-like states in diverse organisms can be separated into distinct stages, each with a characteristic arousal threshold. However, the molecular pathways underlying different sleep stages remain unclear. The fruit fly, Drosophila melanogaster, exhibits consolidated sleep during both day and night, with night sleep associated with higher arousal thresholds compared to day sleep. Here we identify a role for the neuronal calcium sensor protein Neurocalcin (NCA) in promoting sleep during the night but not the day by suppressing nocturnal arousal and hyperactivity. We show that both circadian and light-sensing pathways define the temporal window in which NCA promotes sleep. Furthermore, we find that NCA promotes sleep by suppressing synaptic release from a dispersed wake-promoting neural network and demonstrate that the mushroom bodies, a sleep-regulatory center, are a module within this network. Our results advance the understanding of how sleep stages are genetically defined.

The development and assembly of the Drosophila adult ventral nerve cord

In order to generate complex motor outputs, the nervous system integrates multiple sources of sensory information that ultimately controls motor neurons to generate coordinated movements. The neural circuits that integrate higher order commands from the brain and generate motor outputs are located in the nerve cord of the central nervous system. Recently, genetic access to distinct functional subtypes that make up the Drosophila adult ventral nerve cord has significantly begun to advance our understanding of the structural organization and functions of the neural circuits coordinating motor outputs. Moreover, lineage-tracing and genetic intersection tools have been instrumental in deciphering the developmental mechanisms that generate and assemble the functional units of the adult nerve cord. Together, the Drosophila adult ventral nerve cord is emerging as a powerful system to understand the development and function of neural circuits that are responsible for coordinating complex motor outputs.

Fast near-whole-brain imaging in adult Drosophila during responses to stimuli and behavior

Whole-brain recordings give us a global perspective of the brain in action. In this study, we describe a method using light field microscopy to record near-whole brain calcium and voltage activity at high speed in behaving adult flies. We first obtained global activity maps for various stimuli and behaviors. Notably, we found that brain activity increased on a global scale when the fly walked but not when it groomed. This global increase with walking was particularly strong in dopamine neurons. Second, we extracted maps of spatially distinct sources of activity as well as their time series using principal component analysis and independent component analysis. The characteristic shapes in the maps matched the anatomy of subneuropil regions and, in some cases, a specific neuron type. Brain structures that responded to light and odor were consistent with previous reports, confirming the new technique's validity. We also observed previously uncharacterized behavior-related activity as well as patterns of spontaneous voltage activity.

Insights from intoxicated Drosophila

Our understanding of alcohol use disorder (AUD), particularly alcohol's effects on the nervous system, has unquestionably benefited from the use of model systems such as Drosophila melanogaster. Here, we briefly introduce the use of flies in alcohol research, and highlight the genetic accessibility and neurobiological contribution that flies have made to our understanding of AUD. Future fly research offers unique opportunities for addressing unresolved questions in the alcohol field, such as the neuromolecular and circuit basis for cravings and alcohol-induced neuroimmune dysfunction. This review strongly advocates for interdisciplinary approaches and translational collaborations with the united goal of confronting the major health problems associated with alcohol abuse and addiction.

OpenWorm: overview and recent advances in integrative biological simulation of Caenorhabditis elegans

The adoption of powerful software tools and computational methods from the software industry by the scientific research community has resulted in a renewed interest in integrative, large-scale biological simulations. These typically involve the development of computational platforms to combine diverse, process-specific models into a coherent whole. The OpenWorm Foundation is an independent research organization working towards an integrative simulation of the nematode Caenorhabditis elegans, with the aim of providing a powerful new tool to understand how the organism's behaviour arises from its fundamental biology. In this perspective, we give an overview of the history and philosophy of OpenWorm, descriptions of the constituent sub-projects and corresponding open-science management practices, and discuss current achievements of the project and future directions.This article is part of a discussion meeting issue 'Connectome to behaviour: modelling C. elegans at cellular resolution'.

Drosophila Courtship: Love Is Not Blind

Animals rely on sensory cues to help them find suitable mates. Visual cues are particularly useful for locating mates during the day. A new study has revealed key visual neurons in male Drosophila used to identify and pursue potential mates.

A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster

Drosophila melanogaster has a rich repertoire of innate and learned behaviors. Its 100,000-neuron brain is a large but tractable target for comprehensive neural circuit mapping. Only electron microscopy (EM) enables complete, unbiased mapping of synaptic connectivity; however, the fly brain is too large for conventional EM. We developed a custom high-throughput EM platform and imaged the entire brain of an adult female fly at synaptic resolution. To validate the dataset, we traced brain-spanning circuitry involving the mushroom body (MB), which has been extensively studied for its role in learning. All inputs to Kenyon cells (KCs), the intrinsic neurons of the MB, were mapped, revealing a previously unknown cell type, postsynaptic partners of KC dendrites, and unexpected clustering of olfactory projection neurons. These reconstructions show that this freely available EM volume supports mapping of brain-spanning circuits, which will significantly accelerate Drosophila neuroscience. VIDEO ABSTRACT.

A Fly's Eye View of Natural and Drug Reward

Animals encounter multiple stimuli each day. Some of these stimuli are innately appetitive or aversive, while others are assigned valence based on experience. Drugs like ethanol can elicit aversion in the short term and attraction in the long term. The reward system encodes the predictive value for different stimuli, mediating anticipation for attractive or punishing stimuli and driving animal behavior to approach or avoid conditioned stimuli. The neurochemistry and neurocircuitry of the reward system is partly evolutionarily conserved. In both vertebrates and invertebrates, including Drosophila melanogaster, dopamine is at the center of a network of neurotransmitters and neuromodulators acting in concert to encode rewards. Behavioral assays in D. melanogaster have become increasingly sophisticated, allowing more direct comparison with mammalian research. Moreover, recent evidence has established the functional modularity of the reward neural circuits in Drosophila. This functional modularity resembles the organization of reward circuits in mammals. The powerful genetic and molecular tools for D. melanogaster allow characterization and manipulation at the single-cell level. These tools are being used to construct a detailed map of the neural circuits mediating specific rewarding stimuli and have allowed for the identification of multiple genes and molecular pathways that mediate the effects of reinforcing stimuli, including their rewarding effects. This report provides an overview of the research on natural and drug reward in D. melanogaster, including natural rewards such as sugar and other food nutrients, and drug rewards including ethanol, cocaine, amphetamine, methamphetamine, and nicotine. We focused mainly on the known genetic and neural mechanisms underlying appetitive reward for sugar and reward for ethanol. We also include genes, molecular pathways, and neural circuits that have been identified using assays that test the palatability of the rewarding stimulus, the preference for the rewarding stimulus, or other effects of the stimulus that indicate how it can modify behavior. Commonalities between mechanisms of natural and drug reward are highlighted and future directions are presented, putting forward questions best suited for research using D. melanogaster as a model organism.

A Statistically Representative Atlas for Mapping Neuronal Circuits in the Drosophila Adult Brain

Imaging the expression patterns of reporter constructs is a powerful tool to dissect the neuronal circuits of perception and behavior in the adult brain of Drosophila, one of the major models for studying brain functions. To date, several Drosophila brain templates and digital atlases have been built to automatically analyze and compare collections of expression pattern images. However, there has been no systematic comparison of performances between alternative atlasing strategies and registration algorithms. Here, we objectively evaluated the performance of different strategies for building adult Drosophila brain templates and atlases. In addition, we used state-of-the-art registration algorithms to generate a new group-wise inter-sex atlas. Our results highlight the benefit of statistical atlases over individual ones and show that the newly proposed inter-sex atlas outperformed existing solutions for automated registration and annotation of expression patterns. Over 3,000 images from the Janelia Farm FlyLight collection were registered using the proposed strategy. These registered expression patterns can be searched and compared with a new version of the BrainBaseWeb system and BrainGazer software. We illustrate the validity of our methodology and brain atlas with registration-based predictions of expression patterns in a subset of clock neurons. The described registration framework should benefit to brain studies in Drosophila and other insect species.

Kaleido: Visualizing Big Brain Data with Automatic Color Assignment for Single-Neuron Images

Effective 3D visualization is essential for connectomics analysis, where the number of neural images easily reaches over tens of thousands. A formidable challenge is to simultaneously visualize a large number of distinguishable single-neuron images, with reasonable processing time and memory for file management and 3D rendering. In the present study, we proposed an algorithm named "Kaleido" that can visualize up to at least ten thousand single neurons from the Drosophila brain using only a fraction of the memory traditionally required, without increasing computing time. Adding more brain neurons increases memory only nominally. Importantly, Kaleido maximizes color contrast between neighboring neurons so that individual neurons can be easily distinguished. Colors can also be assigned to neurons based on biological relevance, such as gene expression, neurotransmitters, and/or development history. For cross-lab examination, the identity of every neuron is retrievable from the displayed image. To demonstrate the effectiveness and tractability of the method, we applied Kaleido to visualize the 10,000 Drosophila brain neurons obtained from the FlyCircuit database ( http://www.flycircuit.tw/modules.php?name=kaleido ). Thus, Kaleido visualization requires only sensible computer memory for manual examination of big connectomics data.

Cell ontology in an age of data-driven cell classification

Background

Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessible to biologists in general? How can they be related back to the rich classical knowledge of cell-types, anatomy and development? How will data from the various types of single cell analysis be made cross-searchable? Structured annotation with ontology terms provides a potential solution to these problems. In turn, there is great potential for using the outputs of data-driven cell classification to structure ontologies and integrate them with data-driven cell query systems.

Results

Focusing on examples from the mouse retina and Drosophila olfactory system, I present worked examples illustrating how formalization of cell ontologies can enhance querying of data-driven cell-classifications and how ontologies can be extended by integrating the outputs of data-driven cell classifications.

Conclusions

Annotation with ontology terms can play an important role in making data driven classifications searchable and query-able, but fulfilling this potential requires standardized formal patterns for structuring ontologies and annotations and for linking ontologies to the outputs of data-driven classification.