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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., eLife digest 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.

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.

The α1-adrenergic receptors (α1ARs) have been implicated in numerous actions of the brain, including attention and wakefulness. Additionally, they have been identified as contributing to disorders of the brain, such as drug addiction, and recent work has shown a role of these receptors in relapse to psychostimulants. While some functionality is known, the actual subcellular localization of the subtypes of the α1ARs remains to be elucidated. Further, their anatomical relationship to receptors for other neurotransmitters, such as dopamine (DA), remains unclear. Therefore, using immunohistochemistry and electron microscopy techniques, this study describes the subcellular localization of the α1b-adrenergic receptor (α1bAR), the subtype most tied to relapse behaviors, as well as its relationship to the D1-dopamine receptor (D1R) in both the shell and core of the rat nucleus accumbens (NAc). Overall, α1bARs were found in unmyelinated axons and axon terminals with some labeling in dendrites. In accordance with other studies of the striatum, the D1R was found mainly in dendrites and spines; therefore, colocalization of the D1R with the α1bAR was rare postsynaptically. However, in the NAc shell, when the receptors were co-expressed in the same neuronal elements there was a trend for both receptors to be found on the plasma membrane, as opposed to the intracellular compartment. This study provides valuable anatomical information about the α1bAR and its relationship to the D1R and the regulation of DA and norepinephrine (NE) neurotransmission in the brain which have been examined previously.

The neural circuits responsible for behavior remain largely unknown. Previous efforts have reconstructed the complete circuits of small animals, with hundreds of neurons, and selected circuits for larger animals. Here we (the FlyEM project at Janelia and collaborators at Google) summarize new methods and present the complete circuitry of a large fraction of the brain of a much more complex animal, the fruit fly Drosophila melanogaster. Improved methods include new procedures to prepare, image, align, segment, find synapses, and proofread such large data sets; new methods that define cell types based on connectivity in addition to morphology; and new methods to simplify access to a large and evolving data set. From the resulting data we derive a better definition of computational compartments and their connections; an exhaustive atlas of cell examples and types, many of them novel; detailed circuits for most of the central brain; and exploration of the statistics and structure of different brain compartments, and the brain as a whole. We make the data public, with a web site and resources specifically designed to make it easy to explore, for all levels of expertise from the expert to the merely curious. The public availability of these data, and the simplified means to access it, dramatically reduces the effort needed to answer typical circuit questions, such as the identity of upstream and downstream neural partners, the circuitry of brain regions, and to link the neurons defined by our analysis with genetic reagents that can be used to study their functions.Note: In the next few weeks, we will release a series of papers with more involved discussions. One paper will detail the hemibrain reconstruction with more extensive analysis and interpretation made possible by this dense connectome. Another paper will explore the central complex, a brain region involved in navigation, motor control, and sleep. A final paper will present insights from the mushroom body, a center of multimodal associative learning in the fly brain.

We report a freely available software program, Pyromaker, which generates simulated traces for pyrosequencing results based on user inputs. Simulated pyrograms can aid in the analysis of complex pyrosequencing results in which various hypothesized mutations can be tested, and the resultant pyrograms can be matched with the actual pyrogram. We validated the software using the actual pyrograms for common KRAS gene mutations as well as several mutations in the BRAF, GNAS, and p53 genes. We demonstrate that all 18 possible single-base mutations within codons 12 and 13 of KRAS generate unique pyrosequencing traces and highlight the distinctions between them. We further show that all reported codon 12 and 13 complex mutations produce unique pyrograms. However, some complex mutations are indistinguishable from single-base mutations. For complicated pyrograms, Pyromaker was used in two modes, one in which hypothesis-based simulated pyrograms were pattern-matched with the actual pyrograms. In a second strategy with only the pyrogram, Pyromaker was used to identify the underlying mutation by iteratively reconstructing the mutant pyrogram. Either strategy was able to successfully identify the complex mutations, which were confirmed by cloning and sequencing. Using two examples of KRAS codon 12 mutations (specifically GGT→TTT, G12F and GGT→GAG, G12E), we report which combinations of five approaches permit unambiguous mutation identification. The most efficient approach was found to be pyrosequencing with Pyromaker.

We report a freely available software program, Pyromaker, which generates simulated pyrosequencing traces based on user inputs and aids in analysis of complex pyrosequencing results. To achieve this goal, simulated pyrograms are generated based on the hypothesized mutations and matched with the actual pyrogram. We validated the software testing common KRAS gene mutations and several mutations in the BRAF, GNAS and, p53 genes. We demonstrate that all eighteen possible single base mutations within codons 12 and 13 of KRAS gene generate unique simulated pyrosequencing traces and highlight the distinctions between them. We further verify that all reported codon 12 and 13 complex mutations produce unique pyrograms. However, some complex pyrograms are indistinguishable from single base mutations, using a standard dispensation order. Pyromaker was used in two strategies, one in which hypothesis-based simulated pyrograms were pattern-matched with the actual pyrograms. In a second mode, with only the pyrogram, Pyromaker was used to identify the underlying mutation by iteratively reconstructing the mutant pyrogram. Either strategy was able to successfully identify the complex mutations, which were confirmed by cloning and subsequent sequencing. With two examples of KRAS codon 12 mutations, (GGTαTTT, G12F and GGTαGAG, G12E), we report optimal combinations of the five analysis approaches that permit unambiguous mutation identification. The most efficient one is pyrosequencing with Pyromaker. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 103rd Annual Meeting of the American Association for Cancer Research; 2012 Mar 31-Apr 4; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2012;72(8 Suppl):Abstract nr 2095. doi:1538-7445.AM2012-2095