@article {6884, title = {A systematic nomenclature for the insect brain.}, journal = {Neuron}, volume = {81}, year = {2014}, month = {2014 Feb 19}, pages = {755-65}, abstract = {

Despite the importance of the insect nervous system for functional and developmental neuroscience, descriptions of insect brains have suffered from a lack of uniform nomenclature. Ambiguous definitions of brain regions and fiber bundles have contributed to the variation of names used to describe the same structure. The lack of clearly determined neuropil boundaries has made it difficult to document precise locations of neuronal projections for connectomics study. To address such issues, a consortium of\ neurobiologists studying arthropod brains, the Insect Brain Name Working Group, has established the present hierarchical nomenclature system, using the brain of Drosophila melanogaster as the reference framework, while taking the brains of other taxa into careful consideration for maximum consistency and expandability. The following summarizes the consortium{\textquoteright}s nomenclature system and highlights examples of existing ambiguities and remedies for them. This nomenclature is intended to serve as a\ standard of reference for the study of the brain of Drosophila and other insects.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2013.12.017}, author = {Ito, Kei and Shinomiya, Kazunori and Ito, Masayoshi and Armstrong, J Douglas and Boyan, George and Hartenstein, Volker and Harzsch, Steffen and Heisenberg, Martin and Homberg, Uwe and Jenett, Arnim and Keshishian, Haig and Restifo, Linda L and R{\"o}ssler, Wolfgang and Simpson, Julie H and Strausfeld, Nicholas J and Strauss, Roland and Vosshall, Leslie B} } @article {5423, title = {A GAL4-driver line resource for Drosophila neurobiology.}, journal = {Cell Reports}, volume = {2}, year = {2012}, month = {2012 Oct 25}, pages = {991-1001}, abstract = {

We established a collection of 7,000 transgenic lines of Drosophila melanogaster. Expression of GAL4 in each line is controlled by a different, defined fragment of genomic DNA that serves as a transcriptional enhancer. We used confocal microscopy of dissected nervous systems to determine the expression patterns driven by each fragment in the adult brain and ventral nerve cord. We present image data on 6,650 lines. Using both manual and machine-assisted annotation, we describe the expression patterns in the most useful lines. We illustrate the utility of these data for identifying novel neuronal cell types, revealing brain asymmetry, and describing the nature and extent of neuronal shape stereotypy. The GAL4 lines allow expression of exogenous genes in distinct, small subsets of the adult nervous system. The set of DNA fragments, each driving a documented expression pattern, will facilitate the generation of additional constructs for manipulating neuronal function. synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For the overall strategy and methods used to produce the GAL4 lines:
Pfeiffer, B.D., Jenett, A., Hammonds, A.S., Ngo, T.T., Misra, S., Murphy, C., Scully, A., Carlson, J.W., Wan, K.H., Laverty, T.R., Mungall, C., Svirskas, R., Kadonaga, J.T., Doe, C.Q., Eisen, M.B., Celniker, S.E., Rubin, G.M. (2008). Tools for neuroanatomy and neurogenetics in Drosophila. Proc. Natl. Acad. Sci. USA 105, 9715-9720. http://www.pnas.org/content/105/28/9715.full.pdf+html synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For data on expression in the embryo:
Manning, L., Purice, M.D., Roberts, J., Pollard, J.L., Bennett, A.L., Kroll, J.R., Dyukareva, A.V., Doan, P.N., Lupton, J.R., Strader, M.E., Tanner, S., Bauer, D., Wilbur, A., Tran, K.D., Laverty, T.R., Pearson, J.C., Crews, S.T., Rubin, G.M., and Doe, C.Q. (2012) Annotated embryonic CNS expression patterns of 5000 GMR GAL4 lines: a resource for manipulating gene expression and analyzing cis-regulatory motifs. Cell Reports (2012) Doi: 10.1016/j.celrep.2012.09.009 http://www.cell.com/cell-reports/fulltext/S2211-1247(12)00290-2 synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For data on expression in imaginal discs:
Jory, A., Estella, C., Giorgianni, M.W., Slattery, M., Laverty, T.R., Rubin, G.M., and Mann, R.S. (2012) A survey of 6300 genomic fragments for cis-regulatory activity in the imaginal discs of Drosophila melanogaster. Cell Reports (2012) Doi: 10.1016/j.celrep.2012.09.010 http://www.cell.com/cell-reports/fulltext/S2211-1247(12)00291-4 synapse was substantially elevated, at the endocytic zone there was no enhanced polymerization activity. We conclude that actin subserves spatially diverse, independently regulated processes throughout spines. Perisynaptic actin forms a uniquely dynamic structure well suited for direct, active regulation of the synapse.

For data on expression in the larval nervous system:
Li, H.-H., Kroll, J.R., Lennox, S., Ogundeyi, O., Jeter, J., Depasquale, G., and Truman, J.W. (2013) A GAL4 driver resource for developmental and behavioral studies on the larval CNS of Drosophila. Cell Reports (submitted).

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2012.09.011}, author = {Jenett, Arnim and Rubin, Gerald M and Ngo, Teri-T B and Shepherd, David and Murphy, Christine and Dionne, Heather and Pfeiffer, Barret D and Cavallaro, Amanda and Hall, Donald and Jeter, Jennifer and Iyer, Nirmala and Fetter, Dona and Hausenfluck, Joanna H and Peng, Hanchuan and Trautman, Eric T and Svirskas, Robert R and Myers, Eugene W and Iwinski, Zbigniew R and Aso, Yoshinori and Depasquale, Gina M and Enos, Adrianne and Hulamm, Phuson and Lam, Shing Chun Benny and Li, Hsing-Hsi and Laverty, Todd R and Long, Fuhui and Qu, Lei and Murphy, Sean D and Rokicki, Konrad and Safford, Todd and Shaw, Kshiti and Simpson, Julie H and Sowell, Allison and Tae, Susana and Yu, Yang and Zugates, Christopher T} } @article {4157, title = {BrainAligner: 3D registration atlases of Drosophila brains.}, journal = {Nature Methods}, volume = {8}, year = {2011}, month = {2011 Jun}, pages = {493-500}, abstract = {

Analyzing Drosophila melanogaster neural expression patterns in thousands of three-dimensional image stacks of individual brains requires registering them into a canonical framework based on a fiducial reference of neuropil morphology. Given a target brain labeled with predefined landmarks, the BrainAligner program automatically finds the corresponding landmarks in a subject brain and maps it to the coordinate system of the target brain via a deformable warp. Using a neuropil marker (the antibody nc82) as a reference of the brain morphology and a target brain that is itself a statistical average of data for 295 brains, we achieved a registration accuracy of 2 μm on average, permitting assessment of stereotypy, potential connectivity and functional mapping of the adult fruit fly brain. We used BrainAligner to generate an image pattern atlas of 2954 registered brains containing 470 different expression patterns that cover all the major compartments of the fly brain.

}, keywords = {Algorithms, Animals, Brain, Computer-Assisted, Drosophila melanogaster, Drosophila Proteins, Gene Expression, Genetically Modified, Green Fluorescent Proteins, Image Processing, Neuropil, Recombinant Proteins, Software, Transcription Factors}, issn = {1548-7105}, doi = {10.1038/nmeth.1602}, author = {Peng, Hanchuan and Chung, Phuong and Long, Fuhui and Qu, Lei and Jenett, Arnim and Seeds, Andrew M and Myers, Eugene W and Simpson, Julie H} } @article {987, title = {Refinement of tools for targeted gene expression in Drosophila.}, journal = {Genetics}, volume = {186}, year = {2010}, month = {2010 Oct}, pages = {735-55}, abstract = {

A wide variety of biological experiments rely on the ability to express an exogenous gene in a transgenic animal at a defined level and in a spatially and temporally controlled pattern. We describe major improvements of the methods available for achieving this objective in Drosophila melanogaster. We have systematically varied core promoters, UTRs, operator sequences, and transcriptional activating domains used to direct gene expression with the GAL4, LexA, and Split GAL4 transcription factors and the GAL80 transcriptional repressor. The use of site-specific integration allowed us to make quantitative comparisons between different constructs inserted at the same genomic location. We also characterized a set of PhiC31 integration sites for their ability to support transgene expression of both drivers and responders in the nervous system. The increased strength and reliability of these optimized reagents overcome many of the previous limitations of these methods and will facilitate genetic manipulations of greater complexity and sophistication.

}, issn = {1943-2631}, doi = {10.1534/genetics.110.119917}, author = {Pfeiffer, Barret D and Ngo, Teri-T B and Hibbard, Karen L and Murphy, Christine and Jenett, Arnim and Truman, James W and Rubin, Gerald M} } @article {2696, title = {Segmentation of center brains and optic lobes in 3D confocal images of adult fruit fly brains.}, journal = {Methods}, volume = {50}, year = {2010}, month = {2010 Feb}, pages = {63-9}, abstract = {

Automatic alignment (registration) of 3D images of adult fruit fly brains is often influenced by the significant displacement of the relative locations of the two optic lobes (OLs) and the center brain (CB). In one of our ongoing efforts to produce a better image alignment pipeline of adult fruit fly brains, we consider separating CB and OLs and align them independently. This paper reports our automatic method to segregate CB and OLs, in particular under conditions where the signal to noise ratio (SNR) is low, the variation of the image intensity is big, and the relative displacement of OLs and CB is substantial. We design an algorithm to find a minimum-cost 3D surface in a 3D image stack to best separate an OL (of one side, either left or right) from CB. This surface is defined as an aggregation of the respective minimum-cost curves detected in each individual 2D image slice. Each curve is defined by a list of control points that best segregate OL and CB. To obtain the locations of these control points, we derive an energy function that includes an image energy term defined by local pixel intensities and two internal energy terms that constrain the curve{\textquoteright}s smoothness and length. Gradient descent method is used to optimize this energy function. To improve both the speed and robustness of the method, for each stack, the locations of optimized control points in a slice are taken as the initialization prior for the next slice. We have tested this approach on simulated and real 3D fly brain image stacks and demonstrated that this method can reasonably segregate OLs from CBs despite the aforementioned difficulties.

}, keywords = {Algorithms, Animals, Automation, Brain, Brain Mapping, Computer Graphics, Computer Simulation, Computer-Assisted, Confocal, Drosophila melanogaster, Image Enhancement, Image Interpretation, Imaging, Microscopy, Models, Nonmammalian, Optic Lobe, Reproducibility of Results, Statistical, Three-Dimensional}, issn = {1095-9130}, doi = {10.1016/j.ymeth.2009.08.004}, author = {Lam, Shing Chun Benny and Ruan, Zongcai and Zhao, Ting and Long, Fuhui and Jenett, Arnim and Simpson, Julie and Myers, Eugene W and Peng, Hanchuan} } @article {4980, title = {Standardized atlas of the brain of the desert locust, Schistocerca gregaria.}, journal = {Cell and Tissue Research}, volume = {333}, year = {2008}, month = {2008 Jul 27}, pages = {125-45}, abstract = {

In order to understand the connectivity of neuronal networks, their constituent neurons should ideally be studied in a common framework. Since morphological data from physiologically characterized and stained neurons usually arise from different individual brains, this can only be performed in a virtual standardized brain that compensates for interindividual variability. The desert locust, Schistocerca gregaria, is an insect species used widely for the analysis of olfactory and visual signal processing, endocrine functions, and neural networks controlling motor output. To provide a common multi-user platform for neural circuit analysis in the brain of this species, we have generated a standardized three-dimensional brain of this locust. Serial confocal images from whole-mount locust brains were used to reconstruct 34 neuropil areas in ten brains. For standardization, we compared two different methods: an iterative shape-averaging (ISA) procedure by using affine transformations followed by iterative nonrigid registrations, and the Virtual Insect Brain (VIB) protocol by using global and local rigid transformations followed by local nonrigid transformations. Both methods generated a standard brain, but for different applications. Whereas the VIB technique was designed to visualize anatomical variability between the input brains, the purpose of the ISA method was the opposite, i.e., to remove this variability. A novel individually labeled neuron, connecting the lobula to the midbrain and deutocerebrum, has been registered into the ISA atlas and demonstrates its usefulness and accuracy for future analysis of neural networks. The locust standard brain is accessible at http://www.3d-insectbrain.com .

}, keywords = {Anatomic, Anatomy, Animals, Artistic, Atlases as Topic, Brain, Brain Mapping, Grasshoppers, Imaging, Models, Neuroanatomy, Neurological, Neurons, Three-Dimensional}, issn = {0302-766X}, doi = {10.1007/s00441-008-0620-x}, author = {Kurylas, Angela E and Rohlfing, Torsten and Krofczik, Sabine and Jenett, Arnim and Homberg, Uwe} } @article {988, title = {Tools for neuroanatomy and neurogenetics in Drosophila.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {105}, year = {2008}, month = {2008 Jul 15}, pages = {9715-20}, abstract = {

We demonstrate the feasibility of generating thousands of transgenic Drosophila melanogaster lines in which the expression of an exogenous gene is reproducibly directed to distinct small subsets of cells in the adult brain. We expect the expression patterns produced by the collection of 5,000 lines that we are currently generating to encompass all neurons in the brain in a variety of intersecting patterns. Overlapping 3-kb DNA fragments from the flanking noncoding and intronic regions of genes thought to have patterned expression in the adult brain were inserted into a defined genomic location by site-specific recombination. These fragments were then assayed for their ability to function as transcriptional enhancers in conjunction with a synthetic core promoter designed to work with a wide variety of enhancer types. An analysis of 44 fragments from four genes found that \>80\% drive expression patterns in the brain; the observed patterns were, on average, comprised of \<100 cells. Our results suggest that the D. melanogaster genome contains \>50,000 enhancers and that multiple enhancers drive distinct subsets of expression of a gene in each tissue and developmental stage. We expect that these lines will be valuable tools for neuroanatomy as well as for the elucidation of neuronal circuits and information flow in the fly brain.

}, keywords = {Animals, Biomedical Research, Brain, Developmental, Drosophila melanogaster, Enhancer Elements, Gene Expression Regulation, Genes, Genetic, Genetically Modified, Insect, Neurons, Neurosciences, Recombination}, issn = {1091-6490}, doi = {https://doi.org/10.1073/pnas.0803697105}, author = {Pfeiffer, Barret D and Jenett, Arnim and Hammonds, Ann S and Ngo, Teri-T B and Misra, Sima and Murphy, Christine and Scully, Audra and Carlson, Joseph W and Wan, Kenneth H and Laverty, Todd R and Mungall, Chris and Svirskas, Rob and Kadonaga, James T and Doe, Chris Q and Eisen, Michael B and Celniker, Susan E and Rubin, Gerald M} } @article {4979, title = {Multiple memory traces for olfactory reward learning in Drosophila.}, journal = {The Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {27}, year = {2007}, month = {2007 Oct 10}, pages = {11132-8}, abstract = {

Physical traces underlying simple memories can be confined to a single group of cells in the brain. In the fly Drosophila melanogaster, the Kenyon cells of the mushroom bodies house traces for both appetitive and aversive odor memories. The adenylate cyclase protein, Rutabaga, has been shown to mediate both traces. Here, we show that, for appetitive learning, another group of cells can additionally accommodate a Rutabaga-dependent memory trace. Localized expression of rutabaga in either projection neurons, the first-order olfactory interneurons, or in Kenyon cells, the second-order interneurons, is sufficient for rescuing the mutant defect in appetitive short-term memory. Thus, appetitive learning may induce multiple memory traces in the first- and second-order olfactory interneurons using the same plasticity mechanism. In contrast, aversive odor memory of rutabaga is rescued selectively in the Kenyon cells, but not in the projection neurons. This difference in the organization of memory traces is consistent with the internal representation of reward and punishment.

}, keywords = {Animals, Drosophila melanogaster, Female, Learning, Male, Nerve Net, Olfactory Pathways, Reward, Smell}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.2712-07.2007}, author = {Thum, Andreas S and Jenett, Arnim and Ito, Kei and Heisenberg, Martin and Tanimoto, Hiromu} } @article {4981, title = {The virtual insect brain protocol: creating and comparing standardized neuroanatomy.}, journal = {BMC Bioinformatics}, volume = {7}, year = {2006}, month = {2006 Dec 29}, pages = {544}, abstract = {

In the fly Drosophila melanogaster, new genetic, physiological, molecular and behavioral techniques for the functional analysis of the brain are rapidly accumulating. These diverse investigations on the function of the insect brain use gene expression patterns that can be visualized and provide the means for manipulating groups of neurons as a common ground. To take advantage of these patterns one needs to know their typical anatomy.

}, keywords = {Anatomic, Animals, Brain, Computer Graphics, Computer Simulation, Drosophila melanogaster, Imaging, Models, Neuroanatomy, Neurological, Software, Subtraction Technique, Three-Dimensional, User-Computer Interface}, issn = {1471-2105}, doi = {10.1186/1471-2105-7-544}, author = {Jenett, Arnim and Schindelin, Johannes E and Heisenberg, Martin} }