@article {67815, title = {EASI-FISH for thick tissue defines lateral hypothalamus spatio-molecular organization.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 Dec 01}, pages = {6361}, abstract = {

Determining the spatial organization and morphological characteristics of molecularly defined cell types is a major bottleneck for characterizing the architecture underpinning brain function. We developed Expansion-Assisted Iterative Fluorescence In Situ Hybridization (EASI-FISH) to survey gene expression in brain tissue, as well as a turnkey computational pipeline to rapidly process large EASI-FISH image datasets. EASI-FISH was optimized for thick brain sections (300\ μm) to facilitate reconstruction of spatio-molecular domains that generalize across brains. Using the EASI-FISH pipeline, we investigated the spatial distribution of dozens of molecularly defined cell types in the lateral hypothalamic area (LHA), a brain region with poorly defined anatomical organization. Mapping cell types in the LHA revealed nine spatially and molecularly defined subregions. EASI-FISH also facilitates iterative reanalysis of scRNA-seq datasets to determine marker-genes that further dissociated spatial and morphological heterogeneity. The EASI-FISH pipeline democratizes mapping molecularly defined cell types, enabling discoveries about brain organization.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.11.024}, author = {Wang, Yuhan and Eddison, Mark and Fleishman, Greg and Weigert, Martin and Xu, Shengjin and Wang, Tim and Rokicki, Konrad and Goina, Cristian and Henry, Fredrick E and Lemire, Andrew L and Schmidt, Uwe and Yang, Hui and Svoboda, Karel and Myers, Eugene W and Saalfeld, Stephan and Korff, Wyatt and Sternson, Scott M and Tillberg, Paul W} } @article {48960, title = {Cell dynamics underlying oriented growth of the Drosophila wing imaginal disc.}, journal = {Development (Cambridge, England)}, volume = {144}, year = {2017}, month = {2017 Dec 01}, pages = {4406-4421}, abstract = {

Quantitative analysis of the dynamic cellular mechanisms shaping the Drosophila wing during its larval growth phase has been limited, impeding our ability to understand how morphogen patterns regulate tissue shape. Such analysis requires explants to be imaged under conditions that maintain both growth and patterning, as well as methods to quantify how much cellular behaviors change tissue shape. Here, we demonstrate a key requirement for the steroid hormone 20-hydroxyecdysone (20E) in the maintenance of numerous patterning systems in vivo and in explant culture. We find that low concentrations of 20E support prolonged proliferation in explanted wing discs in the absence of insulin, incidentally providing novel insight into the hormonal regulation of imaginal growth. We use 20E-containing media to observe growth directly and to apply recently developed methods for quantitatively decomposing tissue shape changes into cellular contributions. We discover that whereas cell divisions drive tissue expansion along one axis, their contribution to expansion along the orthogonal axis is cancelled by cell rearrangements and cell shape changes. This finding raises the possibility that anisotropic mechanical constraints contribute to growth orientation in the wing disc.

}, keywords = {Animals, Animals, Genetically Modified, Body Patterning, Cell Proliferation, Drosophila melanogaster, Ecdysterone, Gene Expression Profiling, Gene Expression Regulation, Developmental, Genes, Insect, Imaginal Discs, Insulin, Morphogenesis, Signal Transduction, Transcription, Genetic, Wings, Animal}, issn = {1477-9129}, doi = {10.1242/dev.155069}, author = {Dye, Natalie A and Popovi{\'c}, Marko and Spannl, Stephanie and Etournay, Rapha{\"e}l and Kainm{\"u}ller, Dagmar and Ghosh, Suhrid and Myers, Eugene W and J{\"u}licher, Frank and Eaton, Suzanne} } @article {48418, title = {PreMosa: Extracting 2D surfaces from 3D microscopy mosaics.}, journal = {Bioinformatics (Oxford, England)}, volume = {33}, year = {2017}, month = {2017 Apr 05}, pages = {2563-9}, abstract = {

Motivation: A significant focus of biological research is to understand the development, organization and function of tissues. A particularly productive area of study is on single layer epithelial tissues in which the adherence junctions of cells form a 2D manifold that is fluorescently labeled. Given the size of the tissue, a microscope must collect a mosaic of overlapping 3D stacks encompassing the stained surface. Downstream interpretation is greatly simplified by preprocessing such a dataset as follows: (a) extracting and mapping the stained manifold in each stack into a single 2D projection plane, (b) correcting uneven illumination artifacts, (c) stitching the mosaic planes into a single, large 2D image, and (d) adjusting the contrast.

Results: We have developed PreMosa, an efficient, fully automatic pipeline to perform the four preprocessing tasks above resulting in a single 2D image of the stained manifold across which contrast is optimized and illumination is even. Notable features are as follows. First, the 2D projection step employs a specially developed algorithm that actually finds the manifold in the stack based on maximizing contrast, intensity and smoothness. Second, the projection step comes first, implying all subsequent tasks are more rapidly solved in 2D. And last, the mosaic melding employs an algorithm that globally adjusts contrasts amongst the 2D tiles so as to produce a seamless, high-contrast image. We conclude with an evaluation using ground-truth datasets and present results on datasets from Drosophila melanogaster wings and Schmidtae mediterranea ciliary components.

Availability: PreMosa is available under https://cblasse.github.io/premosa.

Contact: blasse@mpi-cbg.de, myers@mpi-cbg.de.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx195}, author = {Blasse, Corinna and Saalfeld, Stephan and Etournay, Rapha{\"e}l and Sagner, Andreas and Eaton, Suzanne and Myers, Eugene W} } @article {47987, title = {Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms.}, journal = {Nature Biotechnology}, volume = {34}, year = {2016}, month = {2016 Oct 31}, pages = {1267-78}, abstract = {

Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.

}, issn = {1546-1696}, doi = {10.1038/nbt.3708}, author = {Royer, Lo{\"\i}c A and Lemon, William C and Chhetri, Raghav K and Wan, Yinan and Coleman, Michael and Myers, Eugene W and Keller, Philipp J} } @article {48163, title = {Automated detection and quantification of single RNAs at cellular resolution in zebrafish embryos.}, journal = {Development (Cambridge, England)}, volume = {143}, year = {2016}, month = {2016 Feb 01}, pages = {540-6}, abstract = {

Analysis of differential gene expression is crucial for the study of cell fate and behavior during embryonic development. However, automated methods for the sensitive detection and quantification of RNAs at cellular resolution in embryos are lacking. With the advent of single-molecule fluorescence in situ hybridization (smFISH), gene expression can be analyzed at single-molecule resolution. However, the limited availability of protocols for smFISH in embryos and the lack of efficient image analysis pipelines have hampered quantification at the (sub)cellular level in complex samples such as tissues and embryos. Here, we present a protocol for smFISH on zebrafish embryo sections in combination with an image analysis pipeline for automated transcript detection and cell segmentation. We use this strategy to quantify gene expression differences between different cell types and identify differences in subcellular transcript localization between genes. The combination of our smFISH protocol and custom-made, freely available, analysis pipeline will enable researchers to fully exploit the benefits of quantitative transcript analysis at cellular and subcellular resolution in tissues and embryos.

}, keywords = {Animals, Automation, Cell Membrane, Embryo, Nonmammalian, Gene Expression Regulation, Developmental, In Situ Hybridization, Fluorescence, RNA, RNA, Messenger, Subcellular Fractions, Transcription, Genetic, Zebrafish}, issn = {1477-9129}, doi = {10.1242/dev.128918}, author = {Stapel, L Carine and Lombardot, Benoit and Broaddus, Coleman and Kainmueller, Dagmar and Jug, Florian and Myers, Eugene W and Vastenhouw, Nadine L} } @conference {48178, title = {Mapping auto-context decision forests to deep ConvNets for semantic segmentation.}, booktitle = {British Machine Vision Conference}, year = {2016}, month = {2016 Sep 19}, address = {York}, abstract = {

In this paper, we propose a mapping from the Auto-context model to a deep Convolutional Neural Network (ConvNet), bridging the gap be- tween these two models, and helping address the challenge of training ConvNets with limited training data.\ 

}, url = {http://www.bmva.org/bmvc/2016/abstracts.pdf}, author = {Richmond, David L and Kainmueller, Dagmar and Yang, Michael Y and Myers, Eugene W and Rother, Carsten} } @article {47274, title = {A platform for brain-wide imaging and reconstruction of individual neurons.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Jan 20}, abstract = {

The structure of axonal arbors controls how signals from individual neurons are routed within the mammalian brain. However, the arbors of very few long-range projection neurons have been reconstructed in their entirety, as axons with diameters as small as 100 nm arborize in target regions dispersed over many millimeters of tissue. We introduce a platform for high-resolution, three-dimensional fluorescence imaging of complete tissue volumes that enables the visualization and reconstruction of long-range axonal arbors. This platform relies on a high-speed two-photon microscope integrated with a tissue vibratome and a suite of computational tools for large-scale image data. We demonstrate the power of this approach by reconstructing the axonal arbors of multiple neurons in the motor cortex across a single mouse brain.

}, issn = {2050-084X}, doi = {10.7554/eLife.10566}, author = {Economo, Michael N and Clack, Nathan G and Lavis, Luke D and Gerfen, Charles R and Svoboda, Karel and Myers, Eugene W and Chandrashekar, Jayaram} } @article {46813, title = {BlastNeuron for automated comparison, retrieval and clustering of 3D neuron morphologies.}, journal = {Neuroinformatics}, volume = {13}, year = {2015}, month = {2015 Jun 3}, pages = {487-99}, abstract = {

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.

}, issn = {1559-0089}, doi = {10.1007/s12021-015-9272-7}, author = {Wan, Yinan and Long, Fuhui and Qu, Lei and Xiao, Hang and Hawrylycz, Michael and Myers, Eugene W and Peng, Hanchuan} } @article {45058, title = {Fast, accurate reconstruction of cell lineages from large-scale fluorescence microscopy data.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 20}, pages = {951-8}, abstract = {

The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for the segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (i) generality by reconstructing cell lineages in four-dimensional, terabyte-sized image data sets of fruit fly, zebrafish and mouse embryos acquired with three types of fluorescence microscopes, (ii) scalability by analyzing advanced stages of development with up to 20,000 cells per time point at 26,000 cells min(-1) on a single computer workstation and (iii) ease of use by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. Our approach achieves on average 97.0\% linkage accuracy across all species and imaging modalities. Using our system, we performed the first cell lineage reconstruction of early Drosophila melanogaster nervous system development, revealing neuroblast dynamics throughout an entire embryo.

}, issn = {1548-7105}, doi = {10.1038/nmeth.3036}, author = {Amat, Fernando and Lemon, William and Mossing, Daniel P and McDole, Katie and Wan, Yinan and Branson, Kristin and Myers, Eugene W and Keller, Philipp J} } @conference {48180, title = {Tracking by assignment facilitates data curation.}, booktitle = {Medical Image Computing and Computer-Assisted Intervention {\textendash} MICCAI Workshop 2014}, year = {2014}, month = {2014 Sep 14}, address = {Boston, MA}, abstract = {

Object tracking is essential for a multitude of biomedical re- search projects. Automated methods are desired in order to avoid im- possible amounts of manual tracking efforts. However, automatically found solutions are not free of errors, and these errors again have to be identified and resolved manually. We propose six innovative ways for semi-automatic curation of automatically found tracking solutions. Respective user interactions are six simple operations: Inclusion and ex- clusion of objects and tracking decisions, specification of the number of objects, and one-click altering of object segmentations. We show how all proposed interactions can be elegantly incorporated into {\textquotedblleft}assignment models{\textquotedblright} [1,2,3,4,5,6], an innovative and increasingly popular tracking paradigm. Given some user interaction, the tracking engine is capable of computing the respective globally optimal tracking solution efficiently, even benefitting from {\textquotedblleft}warm start{\textquotedblright}-capabilities. We show that after in- teractively pointing at a single mistake, multiple segmentation and track- ing errors can be fixed automatically in one single re-evaluation, provably leading to the new, feedback-conscious global optimum.\ 

}, url = {http://interactivemedical.org/imic2014/CameraReadyPapers/Paper\%203/Jug_Interactive_IMIC2014.pdf}, author = {Jug, Florian and Tobias Pietzsch and Kainmueller, Dagmar and Myers, Eugene W} } @article {5536, title = {Fast and robust optical flow for time-lapse microscopy using super-voxels.}, journal = {Bioinformatics}, volume = {29}, year = {2013}, month = {2013 Feb}, pages = {373-80}, abstract = {

Optical flow is a key method used for quantitative motion estimation of biological structures in light microscopy. It has also been used as a key module in segmentation and tracking systems and is considered a mature technology in the field of computer vision. However, most of the research focused on 2D natural images, which are small in size and rich in edges and texture information. In contrast, 3D time-lapse recordings of biological specimens comprise up to several terabytes of image data and often exhibit complex object dynamics as well as blurring due to the point-spread-function of the microscope. Thus, new approaches to optical flow are required to improve performance for such data. We solve optical flow in large 3D time-lapse microscopy datasets by defining a Markov random field (MRF) over super-voxels in the foreground and applying motion smoothness constraints between super-voxels instead of voxel-wise. This model is tailored to the specific characteristics of light microscopy datasets: super-voxels help registration in textureless areas, the MRF over super-voxels efficiently propagates motion information between neighboring cells and the background subtraction and super-voxels reduce the dimensionality of the problem by an order of magnitude. We validate our approach on large 3D time-lapse datasets of Drosophila and zebrafish development by analyzing cell motion patterns. We show that our approach is, on average, 10x faster than commonly used optical flow implementations in the Insight Tool-Kit (ITK) and reduces the average flow end point error by 50\% in regions with complex dynamic processes, such as cell divisions.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/bts706}, author = {Amat, Fernando and Myers, Eugene W and Keller, Philipp J} } @article {6158, title = {The mechanical variables underlying object localization along the axis of the whisker.}, journal = {The Journal of Neuroscience}, volume = {33}, year = {2013}, month = {2013 Apr 17}, pages = {6726-41}, abstract = {

Rodents move their whiskers to locate objects in space. Here we used psychophysical methods to show that head-fixed mice can localize objects along the axis of a single whisker, the radial dimension, with one-millimeter precision. High-speed videography allowed us to estimate the forces and bending moments at the base of the whisker, which underlie radial distance measurement. Mice judged radial object location based on multiple touches. Both the number of touches (1-17) and the forces exerted by the pole on the whisker (up to 573 μN; typical peak amplitude, 100 μN) varied greatly across trials. We manipulated the bending moment and lateral force pressing the whisker against the sides of the follicle and the axial force pushing the whisker into the follicle by varying the compliance of the object during behavior. The behavioral responses suggest that mice use multiple variables (bending moment, axial force, lateral force) to extract radial object localization. Characterization of whisker mechanics revealed that whisker bending stiffness decreases gradually with distance from the face over five orders of magnitude. As a result, the relative amplitudes of different stress variables change dramatically with radial object distance. Our data suggest that mice use distance-dependent whisker mechanics to estimate radial object location using an algorithm that does not rely on precise control of whisking, is robust to variability in whisker forces, and is independent of object compliance and object movement. More generally, our data imply that mice can measure the amplitudes of forces in the sensory follicles for tactile sensation.

}, keywords = {Algorithms, Animals, Biological, Computer Simulation, Corneal Topography, Cues, Decision Making, Electron, Exploratory Behavior, Functional Laterality, Inbred C57BL, Male, Mice, Microscopy, Models, Physical Stimulation, Psychophysics, Scanning, Time Factors, Touch, Vibrissae}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.4316-12.2013}, author = {Pammer, Lorenz and O{\textquoteright}Connor, Daniel H and Hires, S Andrew and Clack, Nathan G and Huber, Daniel and Myers, Eugene W and Svoboda, Karel} } @article {6674, title = {Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography.}, journal = {Frontiers in Neural Circuits}, volume = {7}, year = {2013}, month = {2013}, pages = {177}, abstract = {

The subcellular locations of synapses on pyramidal neurons strongly influences dendritic integration and synaptic plasticity. Despite this, there is little quantitative data on spatial distributions of specific types of synaptic input. Here we use array tomography (AT), a high-resolution optical microscopy method, to examine thalamocortical (TC) input onto layer 5 pyramidal neurons. We first verified the ability of AT to identify synapses using parallel electron microscopic analysis of TC synapses in layer 4. We then use large-scale array tomography (LSAT) to measure TC synapse distribution on L5 pyramidal neurons in a 1.00 {\texttimes} 0.83 {\texttimes} 0.21 mm(3) volume of mouse somatosensory cortex. We found that TC synapses primarily target basal dendrites in layer 5, but also make a considerable input to proximal apical dendrites in L4, consistent with previous work. Our analysis further suggests that TC inputs are biased toward certain branches and, within branches, synapses show significant clustering with an excess of TC synapse nearest neighbors within 5-15 μm compared to a random distribution. Thus, we show that AT is a sensitive and quantitative method to map specific types of synaptic input on the dendrites of entire neurons. We anticipate that this technique will be of wide utility for mapping functionally-relevant anatomical connectivity in neural circuits.

}, issn = {1662-5110}, doi = {10.3389/fncir.2013.00177}, author = {Rah, Jong-Cheol and Bas, Erhan and Colonell, Jennifer and Mishchenko, Yuriy and Karsh, Bill and Fetter, Richard D and Myers, Eugene W and Chklovskii, Dmitri B and Svoboda, Karel and Harris, Timothy D and Isaac, John T R} } @article {6145, title = {Unsupervised segmentation of noisy electron microscopy images using salient watersheds and region merging.}, journal = {BMC Bioinformatics}, volume = {14}, year = {2013}, month = {2013 Oct 4}, pages = {294}, abstract = {

Background: Segmenting electron microscopy (EM) images of cellular and subcellular processes in the nervous system is a key step in many bioimaging pipelines involving classification and labeling of ultrastructures. However, fully automated techniques to segment images are often susceptible to noise and heterogeneity in EM images (e.g. different histological preparations, different organisms, different brain regions, etc.). Supervised techniques to address this problem are often helpful but require large sets of training data, which are often difficult to obtain in practice, especially across many conditions. Results: We propose a new, principled unsupervised algorithm to segment EM images using a two-step approach: edge detection via salient watersheds following by robust region merging. We performed experiments to gather EM neuroimages of two organisms (mouse and fruit fly) using different histological preparations and generated manually curated ground-truth segmentations. We compared our algorithm against several state-of- the-art unsupervised segmentation algorithms and found superior performance using two standard measures of under-and over-segmentation error. Conclusions: Our algorithm is general and may be applicable to other large-scale segmentation problems for bioimages.

}, issn = {1471-2105}, doi = {10.1186/1471-2105-14-294}, author = {Navlakha, Saket and Ahammad, Parvez and Myers, Eugene W}, editor = {Myers, Eugene W} } @article {5200, title = {Automated tracking of whiskers in videos of head fixed rodents.}, journal = {PLoS Computational Biology}, volume = {8}, year = {2012}, month = {2012 Jul}, pages = {e1002591}, abstract = {

We have developed software for fully automated tracking of vibrissae (whiskers) in high-speed videos (\>500 Hz) of head-fixed, behaving rodents trimmed to a single row of whiskers. Performance was assessed against a manually curated dataset consisting of 1.32 million video frames comprising 4.5 million whisker traces. The current implementation detects whiskers with a recall of 99.998\% and identifies individual whiskers with 99.997\% accuracy. The average processing rate for these images was 8 Mpx/s/cpu (2.6 GHz Intel Core2, 2 GB RAM). This translates to 35 processed frames per second for a 640 px{\texttimes}352 px video of 4 whiskers. The speed and accuracy achieved enables quantitative behavioral studies where the analysis of millions of video frames is required. We used the software to analyze the evolving whisking strategies as mice learned a whisker-based detection task over the course of 6 days (8148 trials, 25 million frames) and measure the forces at the sensory follicle that most underlie haptic perception.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002591}, author = {Clack, Nathan G and O{\textquoteright}Connor, Daniel H and Huber, Daniel and Petreanu, Leopoldo and Hires, Andrew and Peron, Simon and Svoboda, Karel and Myers, Eugene W} } @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 {2327, title = {Automated tracking and analysis of centrosomes in early Caenorhabditis elegans embryos.}, journal = {Bioinformatics}, volume = {26}, year = {2010}, month = {2010 Jun 15}, pages = {i13-20}, abstract = {

The centrosome is a dynamic structure in animal cells that serves as a microtubule organizing center during mitosis and also regulates cell-cycle progression and sets polarity cues. Automated and reliable tracking of centrosomes is essential for genetic screens that study the process of centrosome assembly and maturation in the nematode Caenorhabditis elegans.

}, keywords = {Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Cell Division, Cell Lineage, Centrosome, Embryo, Fluorescence, Microscopy, Mitotic Spindle Apparatus, Nonmammalian}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btq190}, author = {Jaensch, Steffen and Decker, Markus and Hyman, Anthony A and Myers, Eugene W} } @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 {2328, title = {VAA3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets.}, journal = {Nature Biotechnology}, volume = {28}, year = {2010}, month = {2010 Apr}, pages = {348-53}, abstract = {

The V3D system provides three-dimensional (3D) visualization of gigabyte-sized microscopy image stacks in real time on current laptops and desktops. V3D streamlines the online analysis, measurement and proofreading of complicated image patterns by combining ergonomic functions for selecting a location in an image directly in 3D space and for displaying biological measurements, such as from fluorescent probes, using the overlaid surface objects. V3D runs on all major computer platforms and can be enhanced by software plug-ins to address specific biological problems. To demonstrate this extensibility, we built a V3D-based application, V3D-Neuron, to reconstruct complex 3D neuronal structures from high-resolution brain images. V3D-Neuron can precisely digitize the morphology of a single neuron in a fruitfly brain in minutes, with about a 17-fold improvement in reliability and tenfold savings in time compared with other neuron reconstruction tools. Using V3D-Neuron, we demonstrate the feasibility of building a 3D digital atlas of neurite tracts in the fruitfly brain.

}, keywords = {Computer Graphics, Computer-Assisted, Databases, Factual, Image Interpretation, Imaging, Information Storage and Retrieval, Microscopy, Radiology Information Systems, Three-Dimensional, User-Computer Interface}, issn = {1546-1696}, doi = {10.1038/nbt.1612}, author = {Peng, Hanchuan and Ruan, Zongcai and Long, Fuhui and Simpson, Julie H and Myers, Eugene W} } @article {2329, title = {Vibrissa-based object localization in head-fixed mice.}, journal = {The Journal of Neuroscience}, volume = {30}, year = {2010}, month = {2010 Feb 3}, pages = {1947-67}, abstract = {

Linking activity in specific cell types with perception, cognition, and action, requires quantitative behavioral experiments in genetic model systems such as the mouse. In head-fixed primates, the combination of precise stimulus control, monitoring of motor output, and physiological recordings over large numbers of trials are the foundation on which many conceptually rich and quantitative studies have been built. Choice-based, quantitative behavioral paradigms for head-fixed mice have not been described previously. Here, we report a somatosensory absolute object localization task for head-fixed mice. Mice actively used their mystacial vibrissae (whiskers) to sense the location of a vertical pole presented to one side of the head and reported with licking whether the pole was in a target (go) or a distracter (no-go) location. Mice performed hundreds of trials with high performance (\>90\% correct) and localized to \<0.95 mm (\<6 degrees of azimuthal angle). Learning occurred over 1-2 weeks and was observed both within and across sessions. Mice could perform object localization with single whiskers. Silencing barrel cortex abolished performance to chance levels. We measured whisker movement and shape for thousands of trials. Mice moved their whiskers in a highly directed, asymmetric manner, focusing on the target location. Translation of the base of the whiskers along the face contributed substantially to whisker movements. Mice tended to maximize contact with the go (rewarded) stimulus while minimizing contact with the no-go stimulus. We conjecture that this may amplify differences in evoked neural activity between trial types.

}, keywords = {Animals, Exploratory Behavior, Head Movements, Inbred C57BL, Mice, Movement, Physical, Restraint, Somatosensory Cortex, Space Perception, Touch, Vibrissae}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3762-09.2010}, author = {O{\textquoteright}Connor, Daniel H and Clack, Nathan G and Huber, Daniel and Komiyama, Takaki and Myers, Eugene W and Svoboda, Karel} } @article {2697, title = {Analysis of cell fate from single-cell gene expression profiles in C. elegans.}, journal = {Cell}, volume = {139}, year = {2009}, month = {2009 Oct 30}, pages = {623-33}, abstract = {

The C. elegans cell lineage provides a unique opportunity to look at how cell lineage affects patterns of gene expression. We developed an automatic cell lineage analyzer that converts high-resolution images of worms into a data table showing fluorescence expression with single-cell resolution. We generated expression profiles of 93 genes in 363 specific cells from L1 stage larvae and found that cells with identical fates can be formed by different gene regulatory pathways. Molecular signatures identified repeating cell fate modules within the cell lineage and enabled the generation of a molecular differentiation map that reveals points in the cell lineage when developmental fates of daughter cells begin to diverge. These results demonstrate insights that become possible using computational approaches to analyze quantitative expression from many genes in parallel using a digital gene expression atlas.

}, keywords = {Animals, Caenorhabditis elegans, Caenorhabditis elegans Proteins, Cell Differentiation, Cell Lineage, Gene Expression Profiling}, issn = {1097-4172}, doi = {10.1016/j.cell.2009.08.044}, author = {Liu, Xiao and Long, Fuhui and Peng, Hanchuan and Aerni, Sarah J and Jiang, Min and S{\'a}nchez-Blanco, Adolfo and Murray, John I and Preston, Elicia and Mericle, Barbara and Batzoglou, Serafim and Myers, Eugene W and Kim, Stuart K} } @article {2331, title = {VANO: a volume-object image annotation system.}, journal = {Bioinformatics}, volume = {25}, year = {2009}, month = {2009 Mar 1}, pages = {695-7}, abstract = {

Volume-object annotation system (VANO) is a cross-platform image annotation system that enables one to conveniently visualize and annotate 3D volume objects including nuclei and cells. An application of VANO typically starts with an initial collection of objects produced by a segmentation computation. The objects can then be labeled, categorized, deleted, added, split, merged and redefined. VANO has been used to build high-resolution digital atlases of the nuclei of Caenorhabditis elegans at the L1 stage and the nuclei of Drosophila melanogaster{\textquoteright}s ventral nerve cord at the late embryonic stage. AVAILABILITY: Platform independent executables of VANO, a sample dataset, and a detailed description of both its design and usage are available at research.janelia.org/peng/proj/vano. VANO is open-source for co-development.

}, keywords = {Animals, Caenorhabditis elegans, Computer-Assisted, Databases, Drosophila, Factual, Image Processing, Software, User-Computer Interface}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btp046}, author = {Peng, Hanchuan and Long, Fuhui and Myers, Eugene W} } @article {2695, title = {Straightening caenorhabditis elegans images.}, journal = {Bioinformatics}, volume = {24}, year = {2008}, month = {2008 Jan 15}, pages = {234-42}, abstract = {

MOTIVATION: Caenorhabditis elegans, a roundworm found in soil, is a widely studied model organism with about 1000 cells in the adult. Producing high-resolution fluorescence images of C.elegans to reveal biological insights is becoming routine, motivating the development of advanced computational tools for analyzing the resulting image stacks. For example, worm bodies usually curve significantly in images. Thus one must {\textquoteright}straighten{\textquoteright} the worms if they are to be compared under a canonical coordinate system. RESULTS: We develop a worm straightening algorithm (WSA) that restacks cutting planes orthogonal to a {\textquoteright}backbone{\textquoteright} that models the anterior-posterior axis of the worm. We formulate the backbone as a parametric cubic spline defined by a series of control points. We develop two methods for automatically determining the locations of the control points. Our experimental methods show that our approaches effectively straighten both 2D and 3D worm images.

}, keywords = {Algorithms, Animals, Caenorhabditis elegans, Computer-Assisted, Fluorescence, Image Enhancement, Image Interpretation, Imaging, Microscopy, Three-Dimensional}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btm569}, author = {Peng, Hanchuan and Long, Fuhui and Liu, Xiao and Kim, Stuart K and Myers, Eugene W} } @article {2154, title = {Automatic image analysis for gene expression patterns of fly embryos.}, journal = {BMC Cell Biology}, volume = {8}, year = {2007}, month = {2007 Jul 10}, pages = {S7}, abstract = {

Staining the mRNA of a gene via in situ hybridization (ISH) during the development of a D. melanogaster embryo delivers the detailed spatio-temporal pattern of expression of the gene. Many biological problems such as the detection of co-expressed genes, co-regulated genes, and transcription factor binding motifs rely heavily on the analyses of these image patterns. The increasing availability of ISH image data motivates the development of automated computational approaches to the analysis of gene expression patterns.

}, keywords = {Algorithms, Animals, Computational Biology, Computer-Assisted, Drosophila, Embryo, Gene Expression Profiling, Genes, Image Processing, In Situ Hybridization, Insect, Nonmammalian, Reproducibility of Results, Software Design}, issn = {1471-2121}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Zhou, Jie and Leung, Garmay and Eisen, Michael B and Myers, Eugene W} } @article {5344, title = {Straightening worm images.}, journal = {2007 4TH IEEE International Symposium on Biomedical Imagin : Macro to Nano, VOLS 1-3}, year = {2007}, month = {2007 Apr 12-15}, pages = {292-5}, abstract = {

C. elegans, a roundworm in soil is widely used in studying animal development and aging, cell differentiation, etc. Recentlv, high-resolution fluorescence images of C. elegans have become available, introducing several new image analysis applications. One problem is that worm bodies usually curve greatly in images, thus it is highly desired to straighten worms so that they can be compared easily under the same canonical coordinate system. We develop a worm straightening algorithm (WSA) using a cutting-plane restacking method, which aggregates the linear rotation transforms of a continuous sequence of cutting lines/planes orthogonal to the "backbone" of a worm to best approximate the nonlinearly bended worm body. We formulate the backbone as a parametric form of cubic spline of a series of control points. We develop two minimum-spanning-tree based methods to automatically determine the locations of control points. Our experimental methods show that our approach can effectively straighten both 2D and 3D worm images.

}, doi = {10.1109/ISBI.2007.356846}, author = {Peng, Hanchuan and Long, Fuhui and Myers, Eugene W} }