Connectome-driven neural inventory of a complete visual system.

Vision provides animals with detailed information about their surroundings, conveying diverse features such as color, form, and movement across the visual scene. Computing these parallel spatial features requires a large and diverse network of neurons, such that in animals as distant as flies and humans, visual regions comprise half the brain's volume. These visual brain regions often reveal remarkable structure-function relationships, with neurons organized along spatial maps with shapes that directly relate to their roles in visual processing. To unravel the stunning diversity of a complex visual system, a careful mapping of the neural architecture matched to tools for targeted exploration of that circuitry is essential. Here, we report a new connectome of the right optic lobe from a male Drosophila central nervous system FIB-SEM volume and a comprehensive inventory of the fly's visual neurons. We developed a computational framework to quantify the anatomy of visual neurons, establishing a basis for interpreting how their shapes relate to spatial vision. By integrating this analysis with connectivity information, neurotransmitter identity, and expert curation, we classified the ~53,000 neurons into 727 types, about half of which are systematically described and named for the first time. Finally, we share an extensive collection of split-GAL4 lines matched to our neuron type catalog. Together, this comprehensive set of tools and data unlock new possibilities for systematic investigations of vision in Drosophila, a foundation for a deeper understanding of sensory processing.

The cell-type-specific spatial organization of the anterior thalamic nuclei of the mouse brain.

Understanding the cell-type composition and spatial organization of brain regions is crucial for interpreting brain computation and function. In the thalamus, the anterior thalamic nuclei (ATN) are involved in a wide variety of functions, yet the cell-type composition of the ATN remains unmapped at a single-cell and spatial resolution. Combining single-cell RNA sequencing, spatial transcriptomics, and multiplexed fluorescent in situ hybridization, we identify three discrete excitatory cell-type clusters that correspond to the known nuclei of the ATN and uncover marker genes, molecular pathways, and putative functions of these cell types. We further illustrate graded spatial variation along the dorsomedial-ventrolateral axis for all individual nuclei of the ATN and additionally demonstrate that the anteroventral nucleus exhibits spatially covarying protein products and long-range inputs. Collectively, our study reveals discrete and continuous cell-type organizational principles of the ATN, which will help to guide and interpret experiments on ATN computation and function.

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 .

Sharp cell-type-identity changes differentiate the retrosplenial cortex from the neocortex.

The laminae of the neocortex are fundamental processing layers of the mammalian brain. Notably, such laminae are believed to be relatively stereotyped across short spatial scales such that shared laminae between nearby brain regions exhibit similar constituent cells. Here, we consider a potential exception to this rule by studying the retrosplenial cortex (RSC), a brain region known for sharp cytoarchitectonic differences across its granular-dysgranular border. Using a variety of transcriptomics techniques, we identify, spatially map, and interpret the excitatory cell-type landscape of the mouse RSC. In doing so, we uncover that RSC gene expression and cell types change sharply at the granular-dysgranular border. Additionally, supposedly homologous laminae between the RSC and the neocortex are effectively wholly distinct in their cell-type composition. In collection, the RSC exhibits a variety of intrinsic cell-type specializations and embodies an organizational principle wherein cell-type identities can vary sharply within and between brain regions.

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.

Multidisciplinary prosthetic rehabilitation of an adult patient with cleidocranial dysplasia by using a rapid external distraction device: A clinical report.

The multidisciplinary treatment of a 41-year-old man with cleidocranial dysplasia is described. A rapid external distraction device was used to reposition the maxilla before the prosthodontic rehabilitation.

Neuronal cell types, projections, and spatial organization of the central amygdala.

The central amygdala (CEA) has been richly studied for interpreting function and behavior according to specific cell types and circuits. Such work has typically defined molecular cell types by classical inhibitory marker genes; consequently, whether marker-gene-defined cell types exhaustively cover the CEA and co-vary with connectivity remains unresolved. Here, we combined single-cell RNA sequencing, multiplexed fluorescent in situ hybridization, immunohistochemistry, and long-range projection mapping to derive a "bottom-up" understanding of CEA cell types. In doing so, we identify two major cell types, encompassing one-third of all CEA neurons, that have gone unresolved in previous studies. In spatially mapping these novel types, we identify a non-canonical CEA subdomain associated with Nr2f2 expression and uncover an Isl1-expressing medial cell type that accounts for many long-range CEA projections. Our results reveal new CEA organizational principles across cell types and spatial scales and provide a framework for future work examining cell-type-specific behavior and function.

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.

Whole-cell organelle segmentation in volume electron microscopy.

Cells contain hundreds of organelles and macromolecular assemblies. Obtaining a complete understanding of their intricate organization requires the nanometre-level, three-dimensional reconstruction of whole cells, which is only feasible with robust and scalable automatic methods. Here, to support the development of such methods, we annotated up to 35 different cellular organelle classes-ranging from endoplasmic reticulum to microtubules to ribosomes-in diverse sample volumes from multiple cell types imaged at a near-isotropic resolution of 4 nm per voxel with focused ion beam scanning electron microscopy (FIB-SEM)1. We trained deep learning architectures to segment these structures in 4 nm and 8 nm per voxel FIB-SEM volumes, validated their performance and showed that automatic reconstructions can be used to directly quantify previously inaccessible metrics including spatial interactions between cellular components. We also show that such reconstructions can be used to automatically register light and electron microscopy images for correlative studies. We have created an open data and open-source web repository, 'OpenOrganelle', to share the data, computer code and trained models, which will enable scientists everywhere to query and further improve automatic reconstruction of these datasets.

Spatially patterned excitatory neuron subtypes and projections of the claustrum.

The claustrum is a functionally and structurally complex brain region, whose very spatial extent remains debated. Histochemical-based approaches typically treat the claustrum as a relatively narrow anatomical region that primarily projects to the neocortex, whereas circuit-based approaches can suggest a broader claustrum region containing projections to the neocortex and other regions. Here, in the mouse, we took a bottom-up and cell-type-specific approach to complement and possibly unite these seemingly disparate conclusions. Using single-cell RNA-sequencing, we found that the claustrum comprises two excitatory neuron subtypes that are differentiable from the surrounding cortex. Multicolor retrograde tracing in conjunction with 12-channel multiplexed in situ hybridization revealed a core-shell spatial arrangement of these subtypes, as well as differential downstream targets. Thus, the claustrum comprises excitatory neuron subtypes with distinct molecular and projection properties, whose spatial patterns reflect the narrower and broader claustral extents debated in previous research. This subtype-specific heterogeneity likely shapes the functional complexity of the claustrum.

A connectome and analysis of the adult Drosophila central brain.

The neural circuits responsible for animal behavior remain largely unknown. We summarize new methods and present the circuitry of a large fraction of the brain of the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses in, and proofread such large data sets. We define cell types, refine computational compartments, and provide an exhaustive atlas of cell examples and types, many of them novel. We provide detailed circuits consisting of neurons and their chemical synapses for most of the central brain. We make the data public and simplify access, reducing the effort needed to answer circuit questions, and provide procedures linking the neurons defined by our analysis with genetic reagents. Biologically, we examine distributions of connection strengths, neural motifs on different scales, electrical consequences of compartmentalization, and evidence that maximizing packing density is an important criterion in the evolution of the fly's brain.

Animal brains of all sizes, from the smallest to the largest, work in broadly similar ways. Studying the brain of any one animal in depth can thus reveal the general principles behind the workings of all brains. The fruit fly Drosophila is a popular choice for such research. With about 100,000 neurons ­ compared to some 86 billion in humans ­ the fly brain is small enough to study at the level of individual cells. But it nevertheless supports a range of complex behaviors, including navigation, courtship and learning. Thanks to decades of research, scientists now have a good understanding of which parts of the fruit fly brain support particular behaviors. But exactly how they do this is often unclear. This is because previous studies showing the connections between cells only covered small areas of the brain. This is like trying to understand a novel when all you can see is a few isolated paragraphs. To solve this problem, Scheffer, Xu, Januszewski, Lu, Takemura, Hayworth, Huang, Shinomiya et al. prepared the first complete map of the entire central region of the fruit fly brain. The central brain consists of approximately 25,000 neurons and around 20 million connections. To prepare the map ­ or connectome ­ the brain was cut into very thin 8nm slices and photographed with an electron microscope. A three-dimensional map of the neurons and connections in the brain was then reconstructed from these images using machine learning algorithms. Finally, Scheffer et al. used the new connectome to obtain further insights into the circuits that support specific fruit fly behaviors. The central brain connectome is freely available online for anyone to access. When used in combination with existing methods, the map will make it easier to understand how the fly brain works, and how and why it can fail to work correctly. Many of these findings will likely apply to larger brains, including our own. In the long run, studying the fly connectome may therefore lead to a better understanding of the human brain and its disorders. Performing a similar analysis on the brain of a small mammal, by scaling up the methods here, will be a likely next step along this path.

Extensive and spatially variable within-cell-type heterogeneity across the basolateral amygdala.

The basolateral amygdala complex (BLA), extensively connected with both local amygdalar nuclei as well as long-range circuits, is involved in a diverse array of functional roles. Understanding the mechanisms of such functional diversity will be greatly informed by understanding the cell-type-specific landscape of the BLA. Here, beginning with single-cell RNA sequencing, we identified both discrete and graded continuous gene-expression differences within the mouse BLA. Via in situ hybridization, we next mapped this discrete transcriptomic heterogeneity onto a sharp spatial border between the basal and lateral amygdala nuclei, and identified continuous spatial gene-expression gradients within each of these regions. These discrete and continuous spatial transformations of transcriptomic cell-type identity were recapitulated by local morphology as well as long-range connectivity. Thus, BLA excitatory neurons are a highly heterogenous collection of neurons that spatially covary in molecular, cellular, and circuit properties. This heterogeneity likely drives pronounced spatial variation in BLA computation and function.

Estimating the power of sequence covariation for detecting conserved RNA structure.

Pairwise sequence covariations are a signal of conserved RNA secondary structure. We describe a method for distinguishing when lack of covariation signal can be taken as evidence against a conserved RNA structure, as opposed to when a sequence alignment merely has insufficient variation to detect covariations. We find that alignments for several long non-coding RNAs previously shown to lack covariation support do have adequate covariation detection power, providing additional evidence against their proposed conserved structures. AVAILABILITY AND IMPLEMENTATION: The R-scape web server is at eddylab.org/R-scape, with a link to download the source code. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The subiculum is a patchwork of discrete subregions.

In the hippocampus, the classical pyramidal cell type of the subiculum acts as a primary output, conveying hippocampal signals to a diverse suite of downstream regions. Accumulating evidence suggests that the subiculum pyramidal cell population may actually be comprised of discrete subclasses. Here, we investigated the extent and organizational principles governing pyramidal cell heterogeneity throughout the mouse subiculum. Using single-cell RNA-seq, we find that the subiculum pyramidal cell population can be deconstructed into eight separable subclasses. These subclasses were mapped onto abutting spatial domains, ultimately producing a complex laminar and columnar organization with heterogeneity across classical dorsal-ventral, proximal-distal, and superficial-deep axes. We further show that these transcriptomically defined subclasses correspond to differential protein products and can be associated with specific projection targets. This work deconstructs the complex landscape of subiculum pyramidal cells into spatially segregated subclasses that may be observed, controlled, and interpreted in future experiments.

Do denture processing techniques affect the mechanical properties of denture teeth?

STATEMENT OF PROBLEM: Denture tooth fracture may limit the longevity of dental prostheses. Whether the strength of the denture tooth material is affected by the denture processing technique is unclear. PURPOSE: The purpose of this in vitro study was to investigate whether the denture processing technique affects the mechanical properties of denture tooth materials. MATERIAL AND METHODS: Two denture processing techniques, injection and compression molding, were tested for 3 types of denture teeth: nanohybrid composite (NHC), interpenetrating network (IPN), and microfiller-reinforced polyacrylic (MRP). Denture teeth were processed by using an injection-molded resin or a compression-molded resin. Unprocessed denture teeth served as the control. After teeth were processed, they were sectioned into rectangular beams for 3-point bend testing (n=20 to 24). Elastic moduli were determined from load deflection and maximum stress from maximum bending load. The results were statistically analyzed by using 2-way ANOVA and multiple comparisons (α=.05). RESULTS: The processing technique and the type of denture tooth affected both the elastic modulus and the maximum stress. The injection-molded technique resulted in significantly higher (24% to 26%) elastic modulus for NHC and IPN (12% higher in MRP, but not statistically significant) and higher (12% to 17%) maximum stresses for IPN and MRP (3% lower in NHC, but not statistically significant). Compression-molded technique increased the elastic modulus of IPN and NHC by 10% to 17% (3% lower in MRP but not statistically significant), but maximum stresses were not statistically significantly different in any of the tested teeth. Regardless of processing, MRP teeth had the highest elastic modulus (8.0 to 9.2 GPa) but the lowest maximum stresses (97 to 124 MPa), whereas IPN teeth had the lowest elastic modulus (5.5 GPa) but high or highest maximum stress (171 to 192 MPa). CONCLUSIONS: The injection-molded technique significantly increased the elastic modulus of NHC and IPN teeth and significantly increased the maximum stress of IPN teeth. The compression-molded technique did not significantly affect mechanical properties of denture teeth.

A statistical test for conserved RNA structure shows lack of evidence for structure in lncRNAs.

Many functional RNAs have an evolutionarily conserved secondary structure. Conservation of RNA base pairing induces pairwise covariations in sequence alignments. We developed a computational method, R-scape (RNA Structural Covariation Above Phylogenetic Expectation), that quantitatively tests whether covariation analysis supports the presence of a conserved RNA secondary structure. R-scape analysis finds no statistically significant support for proposed secondary structures of the long noncoding RNAs HOTAIR, SRA, and Xist.

The Dfam database of repetitive DNA families.

Repetitive DNA, especially that due to transposable elements (TEs), makes up a large fraction of many genomes. Dfam is an open access database of families of repetitive DNA elements, in which each family is represented by a multiple sequence alignment and a profile hidden Markov model (HMM). The initial release of Dfam, featured in the 2013 NAR Database Issue, contained 1143 families of repetitive elements found in humans, and was used to produce more than 100 Mb of additional annotation of TE-derived regions in the human genome, with improved speed. Here, we describe recent advances, most notably expansion to 4150 total families including a comprehensive set of known repeat families from four new organisms (mouse, zebrafish, fly and nematode). We describe improvements to coverage, and to our methods for identifying and reducing false annotation. We also describe updates to the website interface. The Dfam website has moved to http://dfam.org. Seed alignments, profile HMMs, hit lists and other underlying data are available for download.

HMMER web server: 2015 update.

The HMMER website, available at http://www.ebi.ac.uk/Tools/hmmer/, provides access to the protein homology search algorithms found in the HMMER software suite. Since the first release of the website in 2011, the search repertoire has been expanded to include the iterative search algorithm, jackhmmer. The continued growth of the target sequence databases means that traditional tabular representations of significant sequence hits can be overwhelming to the user. Consequently, additional ways of presenting homology search results have been developed, allowing them to be summarised according to taxonomic distribution or domain architecture. The taxonomy and domain architecture representations can be used in combination to filter the results according to the needs of a user. Searches can also be restricted prior to submission using a new taxonomic filter, which not only ensures that the results are specific to the requested taxonomic group, but also improves search performance. The repertoire of profile hidden Markov model libraries, which are used for annotation of query sequences with protein families and domains, has been expanded to include the libraries from CATH-Gene3D, PIRSF, Superfamily and TIGRFAMs. Finally, we discuss the relocation of the HMMER webserver to the European Bioinformatics Institute and the potential impact that this will have.

Skylign: a tool for creating informative, interactive logos representing sequence alignments and profile hidden Markov models.

BACKGROUND: Logos are commonly used in molecular biology to provide a compact graphical representation of the conservation pattern of a set of sequences. They render the information contained in sequence alignments or profile hidden Markov models by drawing a stack of letters for each position, where the height of the stack corresponds to the conservation at that position, and the height of each letter within a stack depends on the frequency of that letter at that position. RESULTS: We present a new tool and web server, called Skylign, which provides a unified framework for creating logos for both sequence alignments and profile hidden Markov models. In addition to static image files, Skylign creates a novel interactive logo plot for inclusion in web pages. These interactive logos enable scrolling, zooming, and inspection of underlying values. Skylign can avoid sampling bias in sequence alignments by down-weighting redundant sequences and by combining observed counts with informed priors. It also simplifies the representation of gap parameters, and can optionally scale letter heights based on alternate calculations of the conservation of a position. CONCLUSION: Skylign is available as a website, a scriptable web service with a RESTful interface, and as a software package for download. Skylign's interactive logos are easily incorporated into a web page with just a few lines of HTML markup. Skylign may be found at http://skylign.org.