@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} } @proceedings {3303, title = {Comparative analysis of spatial patterns of gene expression in Drosophila Melanogaster imaginal discs.}, year = {2007}, month = {2007 Apr 21}, abstract = {

Determining the precise spatial extent of expression of genes across different tissues, along with knowledge of the biochemical function of the genes is critical for understanding the roles of various genes in the development of metazoan organisms. To address this problem, we have developed high-throughput methods for generating images of gene expression in Drosophila melanogaster imaginal discs and for the automated analysis of these images. Our method automatically learns tissue shapes from a small number of manually segmented training examples and automatically aligns, extracts and scores new images, which are analyzed to generate gene expression maps for each gene. We have developed a reverse lookup procedure that enables us to identify genes that have spatial expression patterns most similar to a given gene of interest. Our methods enable us to cluster both the genes and the pixels that of the maps, thereby identifying sets of genes that have similar patterns, and regions of the tissues of interest that have similar gene expression profiles across a large number of genes.

}, author = {Harmon, C and Ahammad, Parvez and Hammonds, Ann S and Weiszmann, Richard and Celniker, Susan E and Sastry, S and Rubin, Gerald M} } @proceedings {3305, title = {Joint nonparametric alignment for analyzing spatial gene expression patterns of Drosophila imaginal discs.}, volume = {2}, year = {2005}, month = {2005 Jun 20}, pages = {755-60}, abstract = {

To compare spatial patterns of gene expression, one must analyze a large number of images as current methods are only able to measure a small number of genes at a time. Bringing images of corresponding tissues into alignment is a critical first step in making a meaningful comparative analysis of these spatial patterns. Significant image noise and variability in the shapes make it hard to pick a canonical shape model. In this paper, we address these problems by combining segmentation and unsupervised shape learning algorithms. We first segment images to acquire structures of interest, then jointly align the shapes of these acquired structures using an unsupervised nonparametric maximum likelihood algorithm along the lines of congealing, while simultaneously learning the underlying shape model and associated transformations. The learned transformations are applied to corresponding images to bring them into alignment in one step. We demonstrate the results for images of various classes of Drosophila imaginal discs and discuss the methodology used for a quantitative analysis of spatial gene expression patterns.

}, author = {Ahammad, Parvez and Harmon, C and Hammonds, Ann S and Sastry, S and Rubin, Gerald M} }