Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts

From bristle to brain: embryonic development of topographic projections from basiconic sensilla in the antennal nervous system of the locust Schistocerca gregaria

The antennal flagellum of the locust S. gregaria is an articulated structure bearing a spectrum of sensilla that responds to sensory stimuli. In this study, we focus on the basiconic-type bristles as a model for sensory system development in the antenna. At the end of embryogenesis, these bristles are found at fixed locations and then on only the most distal six articulations of the antenna. They are innervated by a dendrite from a sensory cell cluster in the underlying epithelium, with each cluster directing fused axons topographically to an antennal tract running to the brain. We employ confocal imaging and immunolabeling to (a) identify mitotically active sense organ precursors for sensory cell clusters in the most distal annuli of the early embryonic antenna; (b) observe the subsequent spatial appearance of their neuronal progeny; and (c) map the spatial and temporal organization of axon projections from such clusters into the antennal tracts. We show that early in embryogenesis, proliferative precursors are localized circumferentially within discrete epithelial domains of the flagellum. Progeny first appear distally at the antennal tip and then sequentially in a proximal direction so that sensory neuron populations are distributed in an age-dependent manner along the antenna. Autotracing reveals that axon fasciculation with a tract is also sequential and reflects the location and age of the cell cluster along the most distal annuli. Cell cluster location and bristle location are therefore represented topographically and temporally within the axon profile of the tract and its projection to the brain.

ImageJ in Computational Fractal-Based Neuroscience: Pattern Extraction and Translational Research

To explore questions asked in neuroscience, neuroscientists rely heavily on the tools available. One such toolset is ImageJ, open-source, free, biological digital image analysis software. Open-source software has matured alongside of fractal analysis in neuroscience, and today ImageJ is not a niche but a foundation relied on by a substantial number of neuroscientists for work in diverse fields including fractal analysis. This is largely owing to two features of open-source software leveraged in ImageJ and vital to vigorous neuroscience: customizability and collaboration. With those notions in mind, this chapter's aim is threefold: (1) it introduces ImageJ, (2) it outlines ways this software tool has influenced fractal analysis in neuroscience and shaped the questions researchers devote time to, and (3) it reviews a few examples of ways investigators have developed and used ImageJ for pattern extraction in fractal analysis. Throughout this chapter, the focus is on fostering a collaborative and creative mindset for translating knowledge of the fractal geometry of the brain into clinical reality.

Whole-brain annotation and multi-connectome cell typing quantifies circuit stereotypy in Drosophila

The fruit fly Drosophila melanogaster combines surprisingly sophisticated behaviour with a highly tractable nervous system. A large part of the fly's success as a model organism in modern neuroscience stems from the concentration of collaboratively generated molecular genetic and digital resources. As presented in our FlyWire companion paper 1 , this now includes the first full brain connectome of an adult animal. Here we report the systematic and hierarchical annotation of this ~130,000-neuron connectome including neuronal classes, cell types and developmental units (hemilineages). This enables any researcher to navigate this huge dataset and find systems and neurons of interest, linked to the literature through the Virtual Fly Brain database 2 . Crucially, this resource includes 4,552 cell types. 3,094 are rigorous consensus validations of cell types previously proposed in the hemibrain connectome 3 . In addition, we propose 1,458 new cell types, arising mostly from the fact that the FlyWire connectome spans the whole brain, whereas the hemibrain derives from a subvolume. Comparison of FlyWire and the hemibrain showed that cell type counts and strong connections were largely stable, but connection weights were surprisingly variable within and across animals. Further analysis defined simple heuristics for connectome interpretation: connections stronger than 10 unitary synapses or providing >1% of the input to a target cell are highly conserved. Some cell types showed increased variability across connectomes: the most common cell type in the mushroom body, required for learning and memory, is almost twice as numerous in FlyWire as the hemibrain. We find evidence for functional homeostasis through adjustments of the absolute amount of excitatory input while maintaining the excitation-inhibition ratio. Finally, and surprisingly, about one third of the cell types proposed in the hemibrain connectome could not yet be reliably identified in the FlyWire connectome. We therefore suggest that cell types should be defined to be robust to inter-individual variation, namely as groups of cells that are quantitatively more similar to cells in a different brain than to any other cell in the same brain. Joint analysis of the FlyWire and hemibrain connectomes demonstrates the viability and utility of this new definition. Our work defines a consensus cell type atlas for the fly brain and provides both an intellectual framework and open source toolchain for brain-scale comparative connectomics.

The role of cell lineage in the development of neuronal circuitry and function

Complex nervous systems have a modular architecture, whereby reiterative groups of neurons ("modules") that share certain structural and functional properties are integrated into large neural circuits. Neurons develop from proliferating progenitor cells that, based on their location and time of appearance, are defined by certain genetic programs. Given that genes expressed by a given progenitor play a fundamental role in determining the properties of its lineage (i.e., the neurons descended from that progenitor), one efficient developmental strategy would be to have lineages give rise to the structural modules of the mature nervous system. It is clear that this strategy plays an important role in neural development of many invertebrate animals, notably insects, where the availability of genetic techniques has made it possible to analyze the precise relationship between neuronal origin and differentiation since several decades. Similar techniques, developed more recently in the vertebrate field, reveal that functional modules of the mammalian cerebral cortex are also likely products of developmentally defined lineages. We will review studies that relate cell lineage to circuitry and function from a comparative developmental perspective, aiming at enhancing our understanding of neural progenitors and their lineages, and translating findings acquired in different model systems into a common conceptual framework.

Neuronal cell types in the fly: single-cell anatomy meets single-cell genomics

At around 150 000 neurons, the adult Drosophila melanogaster central nervous system is one of the largest species, for which a complete cellular catalogue is imminent. While numerically much simpler than mammalian brains, its complexity is still difficult to parse without grouping neurons into consistent types, which can number 1-1000 cells per hemisphere. We review how neuroanatomical and gene expression data are being used to discover neuronal types at scale. The correlation among multiple co-varying neuronal properties, including lineage, gene expression, morphology, connectivity, response properties and shared behavioral significance is essential to the definition of neuronal cell type. Initial studies comparing morphological and transcriptomic definitions of neuronal type suggest that these are highly consistent, but there is much to do to match these approaches brain-wide. Matched single-cell transcriptomic and morphological data provide an effective reference point to integrate other data types, including connectomics data. This will significantly enhance our ability to make functional predictions from brain wiring diagrams as well facilitating molecular genetic manipulation of neuronal types.

Structure and development of the subesophageal zone of the Drosophila brain. I. Segmental architecture, compartmentalization, and lineage anatomy

The subesophageal zone (SEZ) of the Drosophila brain houses the circuitry underlying feeding behavior and is involved in many other aspects of sensory processing and locomotor control. Formed by the merging of four neuromeres, the internal architecture of the SEZ can be best understood by identifying segmentally reiterated landmarks emerging in the embryo and larva, and following the gradual changes by which these landmarks become integrated into the mature SEZ during metamorphosis. In previous works, the system of longitudinal fibers (connectives) and transverse axons (commissures) has been used as a scaffold that provides internal landmarks for the neuromeres of the larval ventral nerve cord. We have extended the analysis of this scaffold to the SEZ and, in addition, reconstructed the tracts formed by lineages and nerves in relationship to the connectives and commissures. As a result, we establish reliable criteria that define boundaries between the four neuromeres (tritocerebrum, mandibular neuromere, maxillary neuromere, labial neuromere) of the SEZ at all stages of development. Fascicles and lineage tracts also demarcate seven columnar neuropil domains (ventromedial, ventro-lateral, centromedial, central, centrolateral, dorsomedial, dorsolateral) identifiable throughout development. These anatomical subdivisions, presented in the form of an atlas including confocal sections and 3D digital models for the larval, pupal and adult stage, allowed us to describe the morphogenetic changes shaping the adult SEZ. Finally, we mapped MARCM-labeled clones of all secondary lineages of the SEZ to the newly established neuropil subdivisions. Our work will facilitate future studies of function and comparative anatomy of the SEZ.

Learning from connectomics on the fly

Parallels between invertebrates and vertebrates in nervous system development, organisation and circuits are powerful reasons to use insects to study the mechanistic basis of behaviour. The last few years have seen the generation in Drosophila melanogaster of very large light microscopy data sets, genetic driver lines and tools to report or manipulate neural activity. These resources in conjunction with computational tools are enabling large scale characterisation of neuronal types and their functional properties. These are complemented by 3D electron microscopy, providing synaptic resolution data. A whole brain connectome of the fly larva is approaching completion based on manual reconstruction of electron-microscopy data. An adult whole brain dataset is already publicly available and focussed reconstruction is under way, but its 40× greater volume would require ∼500-5000 person-years of manual labour. Nevertheless rapid technical improvements in imaging and especially automated segmentation will likely deliver a complete adult connectome in the next 5 years. To enhance our understanding of the circuit basis of behaviour, light and electron microscopy outputs must be integrated with functional and physiological information into comprehensive databases. We review presently available data, tools and opportunities in Drosophila. We then consider the limits and potential of future progress and how this may impact neuroscience in rich model systems provided by larger insects and vertebrates.

A conserved plan for wiring up the fan-shaped body in the grasshopper and Drosophila

The central complex comprises an elaborate system of modular neuropils which mediate spatial orientation and sensory-motor integration in insects such as the grasshopper and Drosophila. The neuroarchitecture of the largest of these modules, the fan-shaped body, is characterized by its stereotypic set of decussating fiber bundles. These are generated during development by axons from four homologous protocerebral lineages which enter the commissural system and subsequently decussate at stereotypic locations across the brain midline. Since the commissural organization prior to fan-shaped body formation has not been previously analyzed in either species, it was not clear how the decussating bundles relate to individual lineages, or if the projection pattern is conserved across species. In this study, we trace the axonal projections from the homologous central complex lineages into the commissural system of the embryonic and larval brains of both the grasshopper and Drosophila. Projections into the primordial commissures of both species are found to be lineage-specific and allow putatively equivalent fascicles to be identified. Comparison of the projection pattern before and after the commencement of axon decussation in both species reveals that equivalent commissural fascicles are involved in generating the columnar neuroarchitecture of the fan-shaped body. Further, the tract-specific columns in both the grasshopper and Drosophila can be shown to contain axons from identical combinations of central complex lineages, suggesting that this columnar neuroarchitecture is also conserved.

FluoRender: joint freehand segmentation and visualization for many-channel fluorescence data analysis

Background

Image segmentation and registration techniques have enabled biologists to place large amounts of volume data from fluorescence microscopy, morphed three-dimensionally, onto a common spatial frame. Existing tools built on volume visualization pipelines for single channel or red-green-blue (RGB) channels have become inadequate for the new challenges of fluorescence microscopy. For a three-dimensional atlas of the insect nervous system, hundreds of volume channels are rendered simultaneously, whereas fluorescence intensity values from each channel need to be preserved for versatile adjustment and analysis. Although several existing tools have incorporated support of multichannel data using various strategies, the lack of a flexible design has made true many-channel visualization and analysis unavailable. The most common practice for many-channel volume data presentation is still converting and rendering pseudosurfaces, which are inaccurate for both qualitative and quantitative evaluations.

Results

Here, we present an alternative design strategy that accommodates the visualization and analysis of about 100 volume channels, each of which can be interactively adjusted, selected, and segmented using freehand tools. Our multichannel visualization includes a multilevel streaming pipeline plus a triple-buffer compositing technique. Our method also preserves original fluorescence intensity values on graphics hardware, a crucial feature that allows graphics-processing-unit (GPU)-based processing for interactive data analysis, such as freehand segmentation. We have implemented the design strategies as a thorough restructuring of our original tool, FluoRender.

Conclusion

The redesign of FluoRender not only maintains the existing multichannel capabilities for a greatly extended number of volume channels, but also enables new analysis functions for many-channel data from emerging biomedical-imaging techniques.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1694-9) contains supplementary material, which is available to authorized users.

Shedding light on cell compartmentation in the candidate phylum Poribacteria by high resolution visualisation and transcriptional profiling

Assigning functions to uncultivated environmental microorganisms continues to be a challenging endeavour. Here, we present a new microscopy protocol for fluorescence in situ hybridisation-correlative light and electron microscopy (FISH-CLEM) that enabled, to our knowledge for the first time, the identification of single cells within their complex microenvironment at electron microscopy resolution. Members of the candidate phylum Poribacteria, common and uncultivated symbionts of marine sponges, were used towards this goal. Cellular 3D reconstructions revealed bipolar, spherical granules of low electron density, which likely represent carbon reserves. Poribacterial activity profiles were retrieved from prokaryotic enriched sponge metatranscriptomes using simulation-based optimised mapping. We observed high transcriptional activity for proteins related to bacterial microcompartments (BMC) and we resolved their subcellular localisation by combining FISH-CLEM with immunohistochemistry (IHC) on ultra-thin sponge tissue sections. In terms of functional relevance, we propose that the BMC-A region may be involved in 1,2-propanediol degradation. The FISH-IHC-CLEM approach was proven an effective toolkit to combine -omics approaches with functional studies and it should be widely applicable in environmental microbiology.

BlastNeuron for Automated Comparison, Retrieval and Clustering of 3D Neuron Morphologies

Characterizing the identity and types of neurons in the brain, as well as their associated function, requires a means of quantifying and comparing 3D neuron morphology. Presently, neuron comparison methods are based on statistics from neuronal morphology such as size and number of branches, which are not fully suitable for detecting local similarities and differences in the detailed structure. We developed BlastNeuron to compare neurons in terms of their global appearance, detailed arborization patterns, and topological similarity. BlastNeuron first compares and clusters 3D neuron reconstructions based on global morphology features and moment invariants, independent of their orientations, sizes, level of reconstruction and other variations. Subsequently, BlastNeuron performs local alignment between any pair of retrieved neurons via a tree-topology driven dynamic programming method. A 3D correspondence map can thus be generated at the resolution of single reconstruction nodes. We applied BlastNeuron to three datasets: (1) 10,000+ neuron reconstructions from a public morphology database, (2) 681 newly and manually reconstructed neurons, and (3) neurons reconstructions produced using several independent reconstruction methods. Our approach was able to accurately and efficiently retrieve morphologically and functionally similar neuron structures from large morphology database, identify the local common structures, and find clusters of neurons that share similarities in both morphology and molecular profiles.

Developmental analysis of the dopamine-containing neurons of the Drosophila brain

The Drosophila dopaminergic (DAergic) system consists of a relatively small number of neurons clustered throughout the brain and ventral nerve cord. Previous work shows that clusters of DA neurons innervate different brain compartments, which in part accounts for functional diversity of the DA system. We analyzed the association between DA neuron clusters and specific brain lineages, developmental and structural units of the Drosophila brain that provide a framework of connections that can be followed throughout development. The hatching larval brain contains six groups of primary DA neurons (born in the embryo), which we assign to six distinct lineages. We can show that all larval DA clusters persist into the adult brain. Some clusters increase in cell number during late larval stages, whereas others do not become DA positive until early pupa. Ablating neuroblasts with hydroxyurea (HU) prior to onset of larval proliferation (generates secondary neurons) confirms that these added DA clusters are primary neurons born in the embryo, rather than secondary neurons. A single cluster that becomes DA positive in the late pupa, PAM1/lineage DALcm1/2, forms part of a secondary lineage that can be ablated by larval HU application. By supplying lineage information for each DA cluster, our analysis promotes further developmental and functional analyses of this important system of neurons. J. Comp. Neurol. 525:363-379, 2017. © 2016 Wiley Periodicals, Inc.

NBLAST: Rapid, Sensitive Comparison of Neuronal Structure and Construction of Neuron Family Databases

Neural circuit mapping is generating datasets of tens of thousands of labeled neurons. New computational tools are needed to search and organize these data. We present NBLAST, a sensitive and rapid algorithm, for measuring pairwise neuronal similarity. NBLAST considers both position and local geometry, decomposing neurons into short segments; matched segments are scored using a probabilistic scoring matrix defined by statistics of matches and non-matches. We validated NBLAST on a published dataset of 16,129 single Drosophila neurons. NBLAST can distinguish neuronal types down to the finest level (single identified neurons) without a priori information. Cluster analysis of extensively studied neuronal classes identified new types and unreported topographical features. Fully automated clustering organized the validation dataset into 1,052 clusters, many of which map onto previously described neuronal types. NBLAST supports additional query types, including searching neurons against transgene expression patterns. Finally, we show that NBLAST is effective with data from other invertebrates and zebrafish.

Video Abstract

•

NBLAST is a fast and sensitive algorithm to measure pairwise neuronal similarity

•

NBLAST can distinguish neuronal types at the finest level without training

•

Automated clustering of 16,129 Drosophila neurons identifies 1,052 classes

•

Online search tool for databases of single neurons or genetic driver lines

Synaptic circuitry of identified neurons in the antennal lobe of Drosophila melanogaster

In Drosophila melanogaster olfactory sensory neurons (OSNs) establish synapses with projection neurons (PNs) and local interneurons within antennal lobe (AL) glomeruli. Substantial knowledge regarding this circuitry has been obtained by functional studies, whereas ultrastructural evidence of synaptic contacts is scarce. To fill this gap, we studied serial sections of three glomeruli using electron microscopy. Ectopic expression of a membrane-bound peroxidase allowed us to map synaptic sites along PN dendrites. Our data prove for the first time that each of the three major types of AL neurons is both pre- and postsynaptic to the other two types, as previously indicated by functional studies. PN dendrites carry a large proportion of output synapses, with approximately one output per every three input synapses. Detailed reconstructions of PN dendrites showed that these synapses are distributed unevenly, with input and output sites partially segregated along a proximal-distal gradient and the thinnest branches carrying solely input synapses. Moreover, our data indicate synapse clustering, as we found evidence of dendritic tiling of PN dendrites. PN output synapses exhibited T-shaped presynaptic densities, mostly arranged as tetrads. In contrast, output synapses from putative OSNs showed elongated presynaptic densities in which the T-bar platform was supported by several pedestals and contacted as many as 20 postsynaptic profiles. We also discovered synaptic contacts between the putative OSNs. The average synaptic density in the glomerular neuropil was about two synapses/µm(3) . These results are discussed with regard to current models of olfactory glomerular microcircuits across species.

Quantitative neuroanatomy for connectomics in Drosophila

Neuronal circuit mapping using electron microscopy demands laborious proofreading or reconciliation of multiple independent reconstructions. Here, we describe new methods to apply quantitative arbor and network context to iteratively proofread and reconstruct circuits and create anatomically enriched wiring diagrams. We measured the morphological underpinnings of connectivity in new and existing reconstructions of Drosophila sensorimotor (larva) and visual (adult) systems. Synaptic inputs were preferentially located on numerous small, microtubule-free 'twigs' which branch off a single microtubule-containing 'backbone'. Omission of individual twigs accounted for 96% of errors. However, the synapses of highly connected neurons were distributed across multiple twigs. Thus, the robustness of a strong connection to detailed twig anatomy was associated with robustness to reconstruction error. By comparing iterative reconstruction to the consensus of multiple reconstructions, we show that our method overcomes the need for redundant effort through the discovery and application of relationships between cellular neuroanatomy and synaptic connectivity.

DOI:http://dx.doi.org/10.7554/eLife.12059.001

The nervous system contains cells called neurons, which connect to each other to form circuits that send and process information. Each neuron receives and transmits signals to other neurons via very small junctions called synapses. Neurons are shaped a bit like trees, and most input synapses are located in the tiniest branches. Understanding the architecture of a neuron’s branches is important to understand the role that a particular neuron plays in processing information. Therefore, neuroscientists strive to reconstruct the architecture of these branches and how they connect to one another using imaging techniques.

One imaging technique known as serial electron microscopy generates highly detailed images of neural circuits. However, reconstructing neural circuits from such images is notoriously time consuming and error prone. These errors could result in the reconstructed circuit being very different than the real-life circuit. For example, an error that leads to missing out a large branch could result in researchers failing to notice many important connections in the circuit. On the other hand, some errors may not matter much because the neurons share other synapses that are included in the reconstruction.

To understand what effect errors have on the reconstructed circuits, neuroscientists need to have a more detailed understanding of the relationship between the shape of a neuron, its synaptic connections to other neurons, and where errors commonly occur. Here, Schneider-Mizell, Gerhard et al. study this relationship in detail and then devise a faster reconstruction method that uses the shape and other properties of neurons without sacrificing accuracy. The method includes a way to include data from the shape of neurons in the circuit wiring diagrams, revealing circuit patterns that would otherwise go unnoticed.

The experiments use serial electron microscopy images of neurons from fruit flies and show that, from the tiniest larva to the adult fly, neurons form synapses with each other in a similar way. Most errors in the reconstruction only affect the tips of the smallest branches, which generally only host a single synapse. Such omissions do not have a big effect on the reconstructed circuit because strongly connected neurons make multiple synapses onto each other.

Schneider-Mizell, Gerhard et al.'s approach will help researchers to reconstruct neural circuits and analyze them more effectively than was possible before. The algorithms and tools developed in this study are available in an open source software package so that they can be used by other researchers in the future.

DOI:http://dx.doi.org/10.7554/eLife.12059.002

A circuit mechanism for the propagation of waves of muscle contraction in Drosophila

Animals move by adaptively coordinating the sequential activation of muscles. The circuit mechanisms underlying coordinated locomotion are poorly understood. Here, we report on a novel circuit for the propagation of waves of muscle contraction, using the peristaltic locomotion of Drosophila larvae as a model system. We found an intersegmental chain of synaptically connected neurons, alternating excitatory and inhibitory, necessary for wave propagation and active in phase with the wave. The excitatory neurons (A27h) are premotor and necessary only for forward locomotion, and are modulated by stretch receptors and descending inputs. The inhibitory neurons (GDL) are necessary for both forward and backward locomotion, suggestive of different yet coupled central pattern generators, and its inhibition is necessary for wave propagation. The circuit structure and functional imaging indicated that the commands to contract one segment promote the relaxation of the next segment, revealing a mechanism for wave propagation in peristaltic locomotion.

DOI:http://dx.doi.org/10.7554/eLife.13253.001

Rhythmic movements such as walking and swimming require the coordinated contraction of many different muscles. Throughout the animal kingdom, from insects to mammals, animals possess specialized circuits of neurons that are responsible for producing these patterns of muscle contraction. These circuits are known as ‘central pattern generators’.

Central pattern generators are made up of multiple types of neurons that exchange information. However, it is unclear how neurons controlling the movement of one part of the body relay information to neurons controlling the movement of other parts. To answer this question, Fushiki et al. used larvae from the fruit fly Drosophila melanogaster as a model, and combined techniques such as electrophysiology and electron microscopy with measures of the insect’s behavior.

Fruit fly larvae have bodies that are made of segments, and they can contract and relax these segments in a sequence to propel themselves forwards or backwards. The contraction of one segment is accompanied by relaxation of the segment immediately in front. Fushiki et al. found that each body segment contains a copy of the same basic neuronal circuit. This circuit is made up of excitatory and inhibitory neurons. Both types of neurons regulate movement, but the inhibitory neurons must be suppressed for movement to occur.

The experiments also showed that each circuit receives both long-range input from the brain and local sensory feedback. This combination of inputs ensures that the segments contract and relax in the correct order. Future challenges are to determine how the brain controls larval movement via its long-range projections to the body. A key step will be to map these circuits at the level of the individual neurons and the connections between them.

DOI:http://dx.doi.org/10.7554/eLife.13253.002

Adaptive and Background-Aware GAL4 Expression Enhancement of Co-registered Confocal Microscopy Images

GAL4 gene expression imaging using confocal microscopy is a common and powerful technique used to study the nervous system of a model organism such as Drosophila melanogaster. Recent research projects focused on high throughput screenings of thousands of different driver lines, resulting in large image databases. The amount of data generated makes manual assessment tedious or even impossible. The first and most important step in any automatic image processing and data extraction pipeline is to enhance areas with relevant signal. However, data acquired via high throughput imaging tends to be less then ideal for this task, often showing high amounts of background signal. Furthermore, neuronal structures and in particular thin and elongated projections with a weak staining signal are easily lost. In this paper we present a method for enhancing the relevant signal by utilizing a Hessian-based filter to augment thin and weak tube-like structures in the image. To get optimal results, we present a novel adaptive background-aware enhancement filter parametrized with the local background intensity, which is estimated based on a common background model. We also integrate recent research on adaptive image enhancement into our approach, allowing us to propose an effective solution for known problems present in confocal microscopy images. We provide an evaluation based on annotated image data and compare our results against current state-of-the-art algorithms. The results show that our algorithm clearly outperforms the existing solutions.

Patterns of growth and tract formation during the early development of secondary lineages in the Drosophila larval brain

The Drosophila brain consists of a relatively small number of invariant, genetically determined lineages which provide a model to study the relationship between gene function and neuronal architecture. In following this long-term goal, we reconstruct the morphology (projection pattern and connectivity) and gene expression patterns of brain lineages throughout development. In this article, we focus on the secondary phase of lineage morphogenesis, from the reactivation of neuroblast proliferation in the first larval instar to the time when proliferation ends and secondary axon tracts have fully extended in the late third larval instar. We have reconstructed the location and projection of secondary lineages at close (4 h) intervals and produced a detailed map in the form of confocal z-projections and digital three-dimensional models of all lineages at successive larval stages. Based on these reconstructions, we could compare the spatio-temporal pattern of axon formation and morphogenetic movements of different lineages in normal brain development. In addition to wild type, we reconstructed lineage morphology in two mutant conditions. (1) Expressing the construct UAS-p35 which rescues programmed cell death we could systematically determine which lineages normally lose hemilineages to apoptosis. (2) so-Gal4-driven expression of dominant-negative EGFR ablated the optic lobe, which allowed us to conclude that the global centrifugal movement normally affecting the cell bodies of lateral lineages in the late larva is causally related to the expansion of the optic lobe, and that the central pattern of axonal projections of these lineages is independent of the presence or absence of the optic lobe.

Topological characterization of neuronal arbor morphology via sequence representation: II--global alignment

BACKGROUND

The increasing abundance of neuromorphological data provides both the opportunity and the challenge to compare massive numbers of neurons from a wide diversity of sources efficiently and effectively. We implemented a modified global alignment algorithm representing axonal and dendritic bifurcations as strings of characters. Sequence alignment quantifies neuronal similarity by identifying branch-level correspondences between trees.

RESULTS

The space generated from pairwise similarities is capable of classifying neuronal arbor types as well as, or better than, traditional topological metrics. Unsupervised cluster analysis produces groups that significantly correspond with known cell classes for axons, dendrites, and pyramidal apical dendrites. Furthermore, the distinguishing consensus topology generated by multiple sequence alignment of a group of neurons reveals their shared branching blueprint. Interestingly, the axons of dendritic-targeting interneurons in the rodent cortex associates with pyramidal axons but apart from the (more topologically symmetric) axons of perisomatic-targeting interneurons.

CONCLUSIONS

Global pairwise and multiple sequence alignment of neurite topologies enables detailed comparison of neurites and identification of conserved topological features in alignment-defined clusters. The methods presented also provide a framework for incorporation of additional branch-level morphological features. Moreover, comparison of multiple alignment with motif analysis shows that the two techniques provide complementary information respectively revealing global and local features.

Lineage-associated tracts defining the anatomy of the Drosophila first instar larval brain

Fixed lineages derived from unique, genetically specified neuroblasts form the anatomical building blocks of the Drosophila brain. Neurons belonging to the same lineage project their axons in a common tract, which is labeled by neuronal markers. In this paper, we present a detailed atlas of the lineage-associated tracts forming the brain of the early Drosophila larva, based on the use of global markers (anti-Neuroglian, anti-Neurotactin, inscuteable-Gal4>UAS-chRFP-Tub) and lineage-specific reporters. We describe 68 discrete fiber bundles that contain axons of one lineage or pairs/small sets of adjacent lineages. Bundles enter the neuropil at invariant locations, the lineage tract entry portals. Within the neuropil, these fiber bundles form larger fascicles that can be classified, by their main orientation, into longitudinal, transverse, and vertical (ascending/descending) fascicles. We present 3D digital models of lineage tract entry portals and neuropil fascicles, set into relationship to commonly used, easily recognizable reference structures such as the mushroom body, the antennal lobe, the optic lobe, and the Fasciclin II-positive fiber bundles that connect the brain and ventral nerve cord. Correspondences and differences between early larval tract anatomy and the previously described late larval and adult lineage patterns are highlighted. Our L1 neuro-anatomical atlas of lineages constitutes an essential step towards following morphologically defined lineages to the neuroblasts of the early embryo, which will ultimately make it possible to link the structure and connectivity of a lineage to the expression of genes in the particular neuroblast that gives rise to that lineage. Furthermore, the L1 atlas will be important for a host of ongoing work that attempts to reconstruct neuronal connectivity at the level of resolution of single neurons and their synapses.

Hydroxyurea-mediated neuroblast ablation establishes birth dates of secondary lineages and addresses neuronal interactions in the developing Drosophila brain

The Drosophila brain is comprised of neurons formed by approximately 100 lineages, each of which is derived from a stereotyped, asymmetrically dividing neuroblast. Lineages serve as structural and developmental units of Drosophila brain anatomy and reconstruction of lineage projection patterns represents a suitable map of Drosophila brain circuitry at the level of neuron populations ("macro-circuitry"). Two phases of neuroblast proliferation, the first in the embryo and the second during the larval phase (following a period of mitotic quiescence), produce primary and secondary lineages, respectively. Using temporally controlled pulses of hydroxyurea (HU) to ablate neuroblasts and their corresponding secondary lineages during the larval phase, we analyzed the effect on development of primary and secondary lineages in the late larval and adult brain. Our findings indicate that timing of neuroblast re-activation is highly stereotyped, allowing us to establish "birth dates" for all secondary lineages. Furthermore, our results demonstrate that, whereas the trajectory and projection pattern of primary and secondary lineages is established in a largely independent manner, the final branching pattern of secondary neurons is dependent upon the presence of appropriate neuronal targets. Taken together, our data provide new insights into the degree of neuronal plasticity during Drosophila brain development.

Genetic transformation of structural and functional circuitry rewires the Drosophila brain

Acquisition of distinct neuronal identities during development is critical for the assembly of diverse functional neural circuits in the brain. In both vertebrates and invertebrates, intrinsic determinants are thought to act in neural progenitors to specify their identity and the identity of their neuronal progeny. However, the extent to which individual factors can contribute to this is poorly understood. We investigate the role of orthodenticle in the specification of an identified neuroblast (neuronal progenitor) lineage in the Drosophila brain. Loss of orthodenticle from this neuroblast affects molecular properties, neuroanatomical features, and functional inputs of progeny neurons, such that an entire central complex lineage transforms into a functional olfactory projection neuron lineage. This ability to change functional macrocircuitry of the brain through changes in gene expression in a single neuroblast reveals a surprising capacity for novel circuit formation in the brain and provides a paradigm for large-scale evolutionary modification of circuitry.

DOI:http://dx.doi.org/10.7554/eLife.04407.001

The cells in the brain—including the neurons that transmit information—work together in groups called neural circuits. These cells develop from precursor cells called neuroblasts. Each neuroblast can produce many cells, and it is likely that cells that develop from the same neuroblast work together in the adult brain in the same neural circuit. How the adult cells develop into their final form plays an important role in creating a neural circuit, but this process is not fully understood.

In many animals, the complexity of their brain makes it difficult to follow how each individual neuroblast develops. However, all of the neuroblasts in the relatively simple brain of the fruit fly Drosophila have been identified. Furthermore, the genes responsible for establishing the initial identity of each neuroblast in the Drosophila brain are known. These genes may also determine which adult neurons develop from the neuroblast, and when each type of neuron is produced. However, the extent to which a single gene can influence the identity of neurons is unclear.

Sen et al. focused on two types of neuroblasts, each of which, although found next to each other in the developing Drosophila brain, produces neurons for different neural circuits. One of the neuroblasts generates the olfactory neurons responsible for detecting smells; the other innervates the ‘central complex’ that has a number of roles, including controlling the fly's movements. A gene called orthodenticle is expressed by the central complex neuroblast, but not by the olfactory neuroblast, and helps to separate the two neural circuits into different regions of the fly brain.

Sen et al. found that deleting the orthodenticle gene from the central complex neuroblast causes it to develop into olfactory neurons instead of central complex neurons. Tests showed that the modified neurons are completely transformed; they not only work like olfactory neurons, but they also have the same structure and molecular properties. Sen et al. have therefore demonstrated that it is possible to drastically alter the circuitry of the fruit fly brain by changing how one gene is expressed in one neuroblast. This suggests that new neural circuits can form relatively easily, and so could help us to understand how different brain structures and neural circuits evolved.

DOI:http://dx.doi.org/10.7554/eLife.04407.002

Bioimage Informatics in the context of Drosophila research

Modern biological research relies heavily on microscopic imaging. The advanced genetic toolkit of Drosophila makes it possible to label molecular and cellular components with unprecedented level of specificity necessitating the application of the most sophisticated imaging technologies. Imaging in Drosophila spans all scales from single molecules to the entire populations of adult organisms, from electron microscopy to live imaging of developmental processes. As the imaging approaches become more complex and ambitious, there is an increasing need for quantitative, computer-mediated image processing and analysis to make sense of the imagery. Bioimage Informatics is an emerging research field that covers all aspects of biological image analysis from data handling, through processing, to quantitative measurements, analysis and data presentation. Some of the most advanced, large scale projects, combining cutting edge imaging with complex bioimage informatics pipelines, are realized in the Drosophila research community. In this review, we discuss the current research in biological image analysis specifically relevant to the type of systems level image datasets that are uniquely available for the Drosophila model system. We focus on how state-of-the-art computer vision algorithms are impacting the ability of Drosophila researchers to analyze biological systems in space and time. We pay particular attention to how these algorithmic advances from computer science are made usable to practicing biologists through open source platforms and how biologists can themselves participate in their further development.

Postembryonic lineages of the Drosophila brain: II. Identification of lineage projection patterns based on MARCM clones

The Drosophila central brain is largely composed of lineages, units of sibling neurons derived from a single progenitor cell or neuroblast. During the early embryonic period, neuroblasts generate the primary neurons that constitute the larval brain. Neuroblasts reactivate in the larva, adding to their lineages a large number of secondary neurons which, according to previous studies in which selected lineages were labeled by stably expressed markers, differentiate during metamorphosis, sending terminal axonal and dendritic branches into defined volumes of the brain neuropil. We call the overall projection pattern of neurons forming a given lineage the "projection envelope" of that lineage. By inducing MARCM clones at the early larval stage, we labeled the secondary progeny of each neuroblast. For the supraesophageal ganglion excluding mushroom body (the part of the brain investigated in the present work) we obtained 81 different types of clones. Based on the trajectory of their secondary axon tracts (described in the accompanying paper, Lovick et al., 2013), we assigned these clones to specific lineages defined in the larva. Since a labeled clone reveals all aspects (cell bodies, axon tracts, terminal arborization) of a lineage, we were able to describe projection envelopes for all secondary lineages of the supraesophageal ganglion. This work provides a framework by which the secondary neurons (forming the vast majority of adult brain neurons) can be assigned to genetically and developmentally defined groups. It also represents a step towards the goal to establish, for each lineage, the link between its mature anatomical and functional phenotype, and the genetic make-up of the neuroblast it descends from.

The intrinsic microglial molecular clock controls synaptic strength via the circadian expression of cathepsin S

Microglia are thought to play important roles in the maintenance of neuronal circuitry and the regulation of behavior. We found that the cortical microglia contain an intrinsic molecular clock and exhibit a circadian expression of cathepsin S (CatS), a microglia-specific lysosomal cysteine protease in the brain. The genetic deletion of CatS causes mice to exhibit hyperlocomotor activity and removes diurnal variations in the synaptic activity and spine density of the cortical neurons, which are significantly higher during the dark (waking) phase than the light (sleeping) phase. Furthermore, incubation with recombinant CatS significantly reduced the synaptic activity of the cortical neurons. These results suggest that CatS secreted by microglia during the dark-phase decreases the spine density of the cortical neurons by modifying the perisynaptic environment, leading to downscaling of the synaptic strength during the subsequent light-phase. Disruption of CatS therefore induces hyperlocomotor activity due to failure to downscale the synaptic strength.

Postembryonic lineages of the Drosophila brain: I. Development of the lineage-associated fiber tracts

Neurons of the Drosophila central brain fall into approximately 100 paired groups, termed lineages. Each lineage is derived from a single asymmetrically-dividing neuroblast. Embryonic neuroblasts produce 1,500 primary neurons (per hemisphere) that make up the larval CNS followed by a second mitotic period in the larva that generates approximately 10,000 secondary, adult-specific neurons. Clonal analyses based on previous works using lineage-specific Gal4 drivers have established that such lineages form highly invariant morphological units. All neurons of a lineage project as one or a few axon tracts (secondary axon tracts, SATs) with characteristic trajectories, thereby representing unique hallmarks. In the neuropil, SATs assemble into larger fiber bundles (fascicles) which interconnect different neuropil compartments. We have analyzed the SATs and fascicles formed by lineages during larval, pupal, and adult stages using antibodies against membrane molecules (Neurotactin/Neuroglian) and synaptic proteins (Bruchpilot/N-Cadherin). The use of these markers allows one to identify fiber bundles of the adult brain and associate them with SATs and fascicles of the larval brain. This work lays the foundation for assigning the lineage identity of GFP-labeled MARCM clones on the basis of their close association with specific SATs and neuropil fascicles, as described in the accompanying paper (Wong et al., 2013. Postembryonic lineages of the Drosophila brain: II. Identification of lineage projection patterns based on MARCM clones. Submitted.).

A supplementary circuit rule-set for the neuronal wiring

Limitations of known anatomical circuit rules necessitate the identification of supplementary rules. This is essential for explaining how associative sensory stimuli induce nervous system changes that generate internal sensations of memory, concurrent with triggering specific motor activities in response to specific cue stimuli. A candidate mechanism is rapidly reversible, yet stabilizable membrane hemi-fusion formed between the closely apposed postsynaptic membranes of different neurons at locations of convergence of sensory inputs during associative learning. The lateral entry of activity from the cue stimulus-activated postsynapse re-activates the opposite postsynapse through the hemi-fused area and induces the basic units of internal sensation (namely, semblions) as a systems property. Working, short-term and long-term memories can be viewed as functions of the number of re-activatible hemi-fusions present at the time of memory retrieval. Blocking membrane hemi-fusion either by the insertion of the herpes simplex virus (HSV) glycoproteins or by the deposition of insoluble intermediates of amyloid protein in the inter-postsynaptic extracellular matrix (ECM) space leads to cognitive impairments, supporting this mechanism. The introduction of membrane fusion blockers into the postsynaptic cell cytoplasm that attenuates long-term potentiation (LTP), a correlate of behavioral motor activities in response to memory retrieval, provides further support. The lateral spread of activity through the inter-postsynaptic membrane is capable of contributing to oscillating neuronal activity at certain neuronal orders. At the resting state these oscillations provide sub-threshold activation to many neurons at higher orders, including motor neurons maintaining them at a low initiation threshold for motor activity.

Automated analysis of a diverse synapse population

Synapses of the mammalian central nervous system are highly diverse in function and molecular composition. Synapse diversity per se may be critical to brain function, since memory and homeostatic mechanisms are thought to be rooted primarily in activity-dependent plastic changes in specific subsets of individual synapses. Unfortunately, the measurement of synapse diversity has been restricted by the limitations of methods capable of measuring synapse properties at the level of individual synapses. Array tomography is a new high-resolution, high-throughput proteomic imaging method that has the potential to advance the measurement of unit-level synapse diversity across large and diverse synapse populations. Here we present an automated feature extraction and classification algorithm designed to quantify synapses from high-dimensional array tomographic data too voluminous for manual analysis. We demonstrate the use of this method to quantify laminar distributions of synapses in mouse somatosensory cortex and validate the classification process by detecting the presence of known but uncommon proteomic profiles. Such classification and quantification will be highly useful in identifying specific subpopulations of synapses exhibiting plasticity in response to perturbations from the environment or the sensory periphery.

Synaptic connections are fundamental to every aspect of brain function. There is growing recognition that individual synapses are the key sites of the functional plasticity that allows brain circuits to store and retrieve memories and to adapt to changing demands and environments. There is also a growing consensus that many neurological, psychiatric, neurodevelopmental and neurodegenerative disorders may be best understood at the level of specific, proteomically-defined synapse subsets. Here, we introduce and validate computational analysis tools designed to complement array tomography, a new high-resolution proteomic imaging method, to enable the analysis of diverse synapse populations of unprecedentedly large size at the single-synapse level. We expect these new single-synapse classification and analysis tools to substantially advance the search for the specific physical traces, or engrams, of specific memories in the brains synaptic circuits. We also expect these same tools to be useful for identifying the specific subsets of synapses that are impacted by the various synaptically-rooted afflictions of the brain.

Neuroblast lineage-specific origin of the neurons of the Drosophila larval olfactory system

The complete neuronal repertoire of the central brain of Drosophila originates from only approximately 100 pairs of neural stem cells, or neuroblasts. Each neuroblast produces a highly stereotyped lineage of neurons which innervate specific compartments of the brain. Neuroblasts undergo two rounds of mitotic activity: embryonic divisions produce lineages of primary neurons that build the larval nervous system; after a brief quiescence, the neuroblasts go through a second round of divisions in larval stage to produce secondary neurons which are integrated into the adult nervous system. Here we investigate the lineages that are associated with the larval antennal lobe, one of the most widely studied neuronal systems in fly. We find that the same five neuroblasts responsible for the adult antennal lobe also produce the antennal lobe of the larval brain. However, there are notable differences in the composition of larval (primary) lineages and their adult (secondary) counterparts. Significantly, in the adult, two lineages (lNB/BAlc and adNB/BAmv3) produce uniglomerular projection neurons connecting the antennal lobe with the mushroom body and lateral horn; another lineage, vNB/BAla1, generates multiglomerular neurons reaching the lateral horn directly. lNB/BAlc, as well as a fourth lineage, vlNB/BAla2, generate a diversity of local interneurons. We describe a fifth, previously unknown lineage, BAlp4, which connects the posterior part of the antennal lobe and the neighboring tritocerebrum (gustatory center) with a higher brain center located adjacent to the mushroom body. In the larva, only one of these lineages, adNB/BAmv3, generates all uniglomerular projection neurons. Also as in the adult, lNB/BAlc and vlNB/BAla2 produce local interneurons which, in terms of diversity in architecture and transmitter expression, resemble their adult counterparts. In addition, lineages lNB/BAlc and vNB/BAla1, as well as the newly described BAlp4, form numerous types of projection neurons which along the same major axon pathways (antennal tracts) used by the antennal projection neurons, but which form connections that include regions outside the "classical" olfactory circuit triad antennal lobe-mushroom body-lateral horn. Our work will benefit functional studies of the larval olfactory circuit, and shed light on the relationship between larval and adult neurons.

Microglial cathepsin B contributes to the initiation of peripheral inflammation-induced chronic pain

Interleukin (IL)-1β and IL-18 play critical roles in the induction of chronic pain hypersensitivity. Their inactive forms are activated by caspase-1. However, little is known about the mechanism underlying the activation of pro-caspase-1. There is increasing evidence that cathepsin B (CatB), a typical lysosomal cysteine protease, is involved in the pro-caspase-1 activation and the subsequent maturation of IL-1β and IL-18. In this context, CatB is considered to be an important molecular target to control chronic pain. However, no information is currently available about the role of CatB in chronic pain hypersensitivity. We herein show that CatB deficiency or the intrathecal administration of CA-074Me, a specific CatB inhibitor, significantly inhibited the induction of complete Freund's adjuvant-induced tactile allodynia in mice without affecting peripheral inflammation. In contrast, CatB deficiency did not affect the nerve injury-induced tactile allodynia. Furthermore, CatB deficiency or CA-074Me treatment significantly inhibited the maturation and secretion of IL-1β and IL-18 by cultured microglia following treatment with the neuroactive glycoprotein chromogranin A (CGA), but not with ATP. Moreover, the IL-1β expression in spinal microglia and the induction of tactile allodynia following the intrathecal administration of CGA depended on CatB, whereas those induced by the intrathecal administration of ATP or lysophosphatidic acid were CatB independent. These results strongly suggest that CatB is an essential enzyme for the induction of chronic inflammatory pain through its activation of pro-caspase-1, which subsequently induces the maturation and secretion of IL-1β and IL-18 by spinal microglia. Therefore, CatB-specific inhibitors may represent a useful new strategy for treating inflammation-associated pain.

Origin of Drosophila mushroom body neuroblasts and generation of divergent embryonic lineages

Key to understanding the mechanisms that underlie the specification of divergent cell types in the brain is knowledge about the neurectodermal origin and lineages of their stem cells. Here, we focus on the origin and embryonic development of the four neuroblasts (NBs) per hemisphere in Drosophila that give rise to the mushroom bodies (MBs), which are central brain structures essential for olfactory learning and memory. We show that these MBNBs originate from a single field of proneural gene expression within a specific mitotic domain of procephalic neuroectoderm, and that Notch signaling is not needed for their formation. Subsequently, each MBNB occupies a distinct position in the developing MB cortex and expresses a specific combination of transcription factors by which they are individually identifiable in the brain NB map. During embryonic development each MBNB generates an individual cell lineage comprising different numbers of neurons, including intrinsic γ-neurons and various types of non-intrinsic neurons that do not contribute to the MB neuropil. This contrasts with the postembryonic phase of MBNB development during which they have been shown to produce identical populations of intrinsic neurons. We show that different neuron types are produced in a lineage-specific temporal order and that neuron numbers are regulated by differential mitotic activity of the MBNBs. Finally, we demonstrate that γ-neuron axonal outgrowth and spatiotemporal innervation of the MB lobes follows a lineage-specific mode. The MBNBs are the first stem cells of the Drosophila CNS for which the origin and complete cell lineages have been determined.

Fiji: an open-source platform for biological-image analysis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Fiji: an open-source platform for biological-image analysis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

Fiji: an open-source platform for biological-image analysis

Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.

TrakEM2 software for neural circuit reconstruction

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

Elastic volume reconstruction from series of ultra-thin microscopy sections

Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.

The DIADEM metric: comparing multiple reconstructions of the same neuron

Digital reconstructions of neuronal morphology are used to study neuron function, development, and responses to various conditions. Although many measures exist to analyze differences between neurons, none is particularly suitable to compare the same arborizing structure over time (morphological change) or reconstructed by different people and/or software (morphological error). The metric introduced for the DIADEM (DIgital reconstruction of Axonal and DEndritic Morphology) Challenge quantifies the similarity between two reconstructions of the same neuron by matching the locations of bifurcations and terminations as well as their topology between the two reconstructed arbors. The DIADEM metric was specifically designed to capture the most critical aspects in automating neuronal reconstructions, and can function in feedback loops during algorithm development. During the Challenge, the metric scored the automated reconstructions of best-performing algorithms against manually traced gold standards over a representative data set collection. The metric was compared with direct quality assessments by neuronal reconstruction experts and with clocked human tracing time saved by automation. The results indicate that relevant morphological features were properly quantified in spite of subjectivity in the underlying image data and varying research goals. The DIADEM metric is freely released open source ( http://diademchallenge.org ) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.

Serial two-photon tomography for automated ex vivo mouse brain imaging

Here we describe an automated method, which we call serial two-photon (STP) tomography, that achieves high-throughput fluorescence imaging of mouse brains by integrating two-photon microscopy and tissue sectioning. STP tomography generates high-resolution datasets that are free of distortions and can be readily warped in 3D, for example, for comparing multiple anatomical tracings. This method opens the door to routine systematic studies of neuroanatomy in mouse models of human brain disorders.

Computational methods and challenges for large-scale circuit mapping

The connectivity architecture of neuronal circuits is essential to understand how brains work, yet our knowledge about the neuronal wiring diagrams remains limited and partial. Technical breakthroughs in labeling and imaging methods starting more than a century ago have advanced knowledge in the field. However, the volume of data associated with imaging a whole brain or a significant fraction thereof, with electron or light microscopy, has only recently become amenable to digital storage and analysis. A mouse brain imaged at light-microscopic resolution is about a terabyte of data, and 1mm(3) of the brain at EM resolution is about half a petabyte. This has given rise to a new field of research, computational analysis of large-scale neuroanatomical data sets, with goals that include reconstructions of the morphology of individual neurons as well as entire circuits. The problems encountered include large data management, segmentation and 3D reconstruction, computational geometry and workflow management allowing for hybrid approaches combining manual and algorithmic processing. Here we review this growing field of neuronal data analysis with emphasis on reconstructing neurons from EM data cubes.

Asymmetric cell division and Notch signaling specify dopaminergic neurons in Drosophila

In Drosophila, dopaminergic (DA) neurons can be found from mid embryonic stages of development till adulthood. Despite their functional involvement in learning and memory, not much is known about the developmental as well as molecular mechanisms involved in the events of DA neuronal specification, differentiation and maturation. In this report we demonstrate that most larval DA neurons are generated during embryonic development. Furthermore, we show that loss of function (l-o-f) mutations of genes of the apical complex proteins in the asymmetric cell division (ACD) machinery, such as inscuteable and bazooka result in supernumerary DA neurons, whereas l-o-f mutations of genes of the basal complex proteins such as numb result in loss or reduction of DA neurons. In addition, when Notch signaling is reduced or abolished, additional DA neurons are formed and conversely, when Notch signaling is activated, less DA neurons are generated. Our data demonstrate that both ACD and Notch signaling are crucial mechanisms for DA neuronal specification. We propose a model in which ACD results in differential Notch activation in direct siblings and in this context Notch acts as a repressor for DA neuronal specification in the sibling that receives active Notch signaling. Our study provides the first link of ACD and Notch signaling in the specification of a neurotransmitter phenotype in Drosophila. Given the high degree of conservation between Drosophila and vertebrate systems, this study could be of significance to mechanisms of DA neuronal differentiation not limited to flies.

Biologically inspired EM image alignment and neural reconstruction

MOTIVATION

Three-dimensional reconstruction of consecutive serial-section transmission electron microscopy (ssTEM) images of neural tissue currently requires many hours of manual tracing and annotation. Several computational techniques have already been applied to ssTEM images to facilitate 3D reconstruction and ease this burden.

RESULTS

Here, we present an alternative computational approach for ssTEM image analysis. We have used biologically inspired receptive fields as a basis for a ridge detection algorithm to identify cell membranes, synaptic contacts and mitochondria. Detected line segments are used to improve alignment between consecutive images and we have joined small segments of membrane into cell surfaces using a dynamic programming algorithm similar to the Needleman-Wunsch and Smith-Waterman DNA sequence alignment procedures. A shortest path-based approach has been used to close edges and achieve image segmentation. Partial reconstructions were automatically generated and used as a basis for semi-automatic reconstruction of neural tissue. The accuracy of partial reconstructions was evaluated and 96% of membrane could be identified at the cost of 13% false positive detections.

AVAILABILITY

An open-source reference implementation is available in the Supplementary information.

CONTACT

seymour.kb@ed.ac.uk; douglas.armstrong@ed.ac.uk

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.