@article {66008, title = {Survey of spiking in the mouse visual system reveals functional hierarchy.}, journal = {Nature}, volume = {592(7852):86-92}, year = {2021}, month = {2021 Jan 20}, pages = {86-92}, abstract = {

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-03171-x}, author = {Siegle, Joshua H and Jia, Xiaoxuan and Durand, Severine and Gale, Sam and Bennett, Corbett and Graddis, Nile and Heller, Greggory and Ramirez, Tamina K and Choi, Hannah and Luviano, Jennifer A and Groblewski, Peter A and Ahmed, Ruweida and Arkhipov, Anton and Bernard, Amy and Billeh, Yazan N and Brown, Dillan and Buice, Michael A and Cain, Nicolas and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Chvilicek, Maggie and Cox, Timothy C and Dai, Kael and Denman, Daniel J and de Vries, Saskia E J and Dietzman, Roald and Esposito, Luke and Farrell, Colin and Feng, David and Galbraith, John and Garrett, Marina and Gelfand, Emily C and Hancock, Nicole and Harris, Julie A and Howard, Robert and Hu, Brian and Hytnen, Ross and Iyer, Ramakrishnan and Jessett, Erika and Johnson, Katelyn and Kato, India and Kiggins, Justin and Lambert, Sophie and Lecoq, Jerome and Ledochowitsch, Peter and Lee, Jung Hoon and Leon, Arielle and Li, Yang and Liang, Elizabeth and Long, Fuhui and Mace, Kyla and Melchior, Jose and Millman, Daniel and Mollenkopf, Tyler and Nayan, Chelsea and Ng, Lydia and Ngo, Kiet and Nguyen, Thuyahn and Nicovich, Philip R and North, Kat and Ocker, Gabriel Koch and Ollerenshaw, Doug and Oliver, Michael and Pachitariu, Marius and Perkins, Jed and Reding, Melissa and Reid, David and Robertson, Miranda and Ronellenfitch, Kara and Seid, Sam and Slaughterbeck, Cliff and Stoecklin, Michelle and Sullivan, David and Sutton, Ben and Swapp, Jackie and Thompson, Carol and Turner, Kristen and Wakeman, Wayne and Whitesell, Jennifer D and Williams, Derric and Williford, Ali and Young, Rob and Zeng, Hongkui and Naylor, Sarah and Phillips, John W and Reid, R Clay and Mihalas, Stefan and Olsen, Shawn R and Koch, Christof} } @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 {45418, title = {Atlas-builder software and the eNeuro atlas: resources for developmental biology and neuroscience.}, journal = {Development}, volume = {141}, year = {2014}, month = {2014 Jun}, pages = {2524-32}, abstract = {

A major limitation in understanding embryonic development is the lack of cell type-specific markers. Existing gene expression and marker atlases provide valuable tools, but they typically have one or more limitations: a lack of single-cell resolution; an inability to register multiple expression patterns to determine their precise relationship; an inability to be upgraded by users; an inability to compare novel patterns with the database patterns; and a lack of three-dimensional images. Here, we develop new {\textquoteright}atlas-builder{\textquoteright} software that overcomes each of these limitations. A newly generated atlas is three-dimensional, allows the precise registration of an infinite number of cell type-specific markers, is searchable and is open-ended. Our software can be used to create an atlas of any tissue in any organism that contains stereotyped cell positions. We used the software to generate an {\textquoteright}eNeuro{\textquoteright} atlas of the Drosophila embryonic CNS containing eight transcription factors that mark the major CNS cell types (motor neurons, glia, neurosecretory cells and interneurons). We found neuronal, but not glial, nuclei occupied stereotyped locations. We added 75 new Gal4 markers to the atlas to identify over 50\% of all interneurons in the ventral CNS, and these lines allowed functional access to those interneurons for the first time. We expect the atlas-builder software to benefit a large proportion of the developmental biology community, and the eNeuro atlas to serve as a publicly accessible hub for integrating neuronal attributes - cell lineage, gene expression patterns, axon/dendrite projections, neurotransmitters--and linking them to individual neurons.

}, keywords = {Animals, Axons, Cell Lineage, Central Nervous System, Computational Biology, Databases, Genetic, Dendrites, Drosophila melanogaster, Drosophila Proteins, Gene Expression Profiling, Gene Expression Regulation, Developmental, Genetic Markers, Interneurons, Mice, Neurons, Neurotransmitter Agents, Rats, Software}, issn = {1477-9129}, doi = {10.1242/dev.108720}, author = {Heckscher, Ellie S and Long, Fuhui and Layden, Michael J and Chuang, Chein-Hui and Manning, Laurina and Richart, Jourdain and Pearson, Joseph C and Crews, Stephen T and Peng, Hanchuan and Myers, Eugene and Doe, Chris Q} } @article {45064, title = {Virtual finger boosts three-dimensional imaging and microsurgery as well as terabyte volume image visualization and analysis.}, journal = {Nature Communications}, volume = {5}, year = {2014}, month = {2014 Jul 11}, pages = {4342}, abstract = {

Three-dimensional (3D) bioimaging, visualization and data analysis are in strong need of powerful 3D exploration techniques. We develop virtual finger (VF) to generate 3D curves, points and regions-of-interest in the 3D space of a volumetric image with a single finger operation, such as a computer mouse stroke, or click or zoom from the 2D-projection plane of an image as visualized with a computer. VF provides efficient methods for acquisition, visualization and analysis of 3D images for roundworm, fruitfly, dragonfly, mouse, rat and human. Specifically, VF enables instant 3D optical zoom-in imaging, 3D free-form optical microsurgery, and 3D visualization and annotation of terabytes of whole-brain image volumes. VF also leads to orders of magnitude better efficiency of automated 3D reconstruction of neurons and similar biostructures over our previous systems. We use VF to generate from images of 1,107 Drosophila GAL4 lines a projectome of a Drosophila brain.

}, issn = {2041-1723}, doi = {10.1038/ncomms5342}, author = {Peng, Hanchuan and Tang, Jianyong and Xiao, Hang and Bria, Alessandro and Zhou, Jianlong and Butler, Victoria and Zhou, Zhi and Gonzalez-Bellido, Paloma T and Oh, Seung W and Chen, Jichao and Mitra, Ananya and Tsien, Richard W and Zeng, Hongkui and Ascoli, Giorgio A and Iannello, Giulio and Hawrylycz, Michael and Myers, Eugene and Long, Fuhui} } @article {6284, title = {Clonal development and organization of the adult Drosophila central brain.}, journal = {Current biology : CB}, volume = {23}, year = {2013}, month = {2013 Apr 22}, pages = {633-43}, abstract = {BACKGROUND: The insect brain can be divided into neuropils that are formed by neurites of both local and remote origin. The complexity of the interconnections obscures how these neuropils are established and interconnected through development. The Drosophila central brain develops from a fixed number of neuroblasts (NBs) that deposit neurons in regional clusters. RESULTS: By determining individual NB clones and pursuing their projections into specific neuropils, we unravel the regional development of the brain neural network. Exhaustive clonal analysis revealed 95 stereotyped neuronal lineages with characteristic cell-body locations and neurite trajectories. Most clones show complex projection patterns, but despite the complexity, neighboring clones often coinnervate the same local neuropil or neuropils and further target a restricted set of distant neuropils. CONCLUSIONS: These observations argue for regional clonal development of both neuropils and neuropil connectivity throughout the Drosophila central brain.}, issn = {1879-0445}, doi = {10.1016/j.cub.2013.02.057}, author = {Yu, Hung-Hsiang and Awasaki, Takeshi and Schroeder, Mark David and Long, Fuhui and Yang, Jacob S and He, Yisheng and Ding, Peng and Kao, Jui-Chun and Wu, Gloria Yueh-Yi and Peng, Hanchuan and Myers, Gene and Lee, Tzumin} } @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 {4975, title = {Visualization and analysis of 3D microscopic images.}, journal = {PLoS Computational Biology}, volume = {8}, year = {2012}, month = {2012 Jun}, pages = {e1002519}, abstract = {

In a wide range of biological studies, it is highly desirable to visualize and analyze three-dimensional (3D) microscopic images. In this primer, we first introduce several major methods for visualizing typical 3D images and related multi-scale, multi-time-point, multi-color data sets. Then, we discuss three key categories of image analysis tasks, namely segmentation, registration, and annotation. We demonstrate how to pipeline these visualization and analysis modules using examples of profiling the single-cell gene-expression of C. elegans and constructing a map of stereotyped neurite tracts in a fruit fly brain.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1002519}, author = {Long, Fuhui and Zhou, Jianlong and Peng, Hanchuan} } @article {2700, title = {Automated reconstruction of neuronal morphology based on local geometrical and global structural models.}, journal = {Neuroinformatics}, volume = {9}, year = {2011}, month = {2011 May 6}, pages = {247-61}, abstract = {

Digital reconstruction of neurons from microscope images is an important and challenging problem in neuroscience. In this paper, we propose a model-based method to tackle this problem. We first formulate a model structure, then develop an algorithm for computing it by carefully taking into account morphological characteristics of neurons, as well as the image properties under typical imaging protocols. The method has been tested on the data sets used in the DIADEM competition and produced promising results for four out of the five data sets.

}, issn = {1559-0089}, doi = {10.1007/s12021-011-9120-3}, author = {Zhao, Ting and Xie, Jun and Amat, Fernando and Clack, Nathan and Ahammad, Parvez and Peng, Hanchuan and Long, Fuhui and Myers, Eugene} } @conference {2721, title = {Automatic 3D neuron tracing using all-paths pruning.}, booktitle = {Conference on Intelligent Systems for Molecular Biology}, year = {2011}, month = {2011}, address = {Vienna}, abstract = {

Motivation: Digital reconstruction, or tracing, of 3D neuron structures is critical toward reverse engineering the wiring and functions of a brain. However, despite a number of existing studies, this task is still challenging, especially when a 3D microscopic image has low signal-to-noise ratio (SNR) and fragmented neuron segments. Published work can handle these hard situations only by introducing global prior information, such as where a neurite segment starts and terminates. However, manual incorporation of such global information can be very time consuming. Thus, a completely automatic approach for these hard situations is highly desirable.

Results: We have developed an automatic graph algorithm, called the all-path pruning (APP), to trace the 3D structure of a neuron. To avoid potential mis-tracing of some parts of a neuron, an APP first produces an initial over-reconstruction, by tracing the optimal geodesic shortest path from the seed location to every possible destination voxel/pixel location in the image. Since the initial reconstruction contains all the possible paths and thus could contain redundant structural components (SC), we simplify the entire reconstruction without compromising its connectedness by pruning the redundant structural elements, using a new maximal-covering minimal-redundant (MCMR) subgraph algorithm. We show that MCMR has a linear computational complexity and will converge. We examined the performance of our method using challenging 3D neuronal image datasets of model organisms (e.g. fruit fly).

}, author = {Long, Fuhui and Peng, Hanchuan and Myers, Eugene} } @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 {5297, title = {Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions.}, journal = {Neuroinformatics}, volume = {9}, year = {2011}, month = {2011 Sep}, pages = {103-5}, keywords = {Algorithms, Animals, Computer-Assisted, Data Mining, Humans, Image Cytometry, Image Processing, Imaging, Neuroanatomical Tract-Tracing Techniques, Neurons, Software, Software Design, Three-Dimensional}, issn = {1559-0089}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Zhao, Ting and Myers, Eugene} } @article {3839, title = {Simultaneous recognition and segmentation of cells: application in C. elegans.}, journal = {Bioinformatics}, volume = {27}, year = {2011}, month = {2011 Aug 17}, pages = {2895-902}, abstract = {

MOTIVATION: Automatic recognition of cell identities is critical for quantitative measurement, targeting, and manipulation of cells of model animals at single-cell resolution. It has been shown to be a powerful tool for studying gene expression and regulation, cell lineages, and cell fates. Existing methods first segment cells, before applying a recognition algorithm in the second step. As a result, the segmentation errors in the first step directly affect and complicate the subsequent cell recognition step. Moreover, in new experimental settings, some of the image features that have been previously relied upon to recognize cells may not be easy to reproduce, due to limitations on the number of color channels available for fluorescent imaging or to the cost of building transgenic animals. An approach that is more accurate and relies on only a single signal channel is clearly desirable. RESULTS: We have developed a new method, called SRS (for Simultaneous Recognition and Segmentation of cells), and applied it to 3D image stacks of the model organism C. elegans. Given a 3D image stack of the animal and a 3D atlas of target cells, SRS is effectively an atlas-guided voxel classification process: cell recognition is realized by smoothly deforming the atlas to best fit the image, where the segmentation is obtained naturally via classification of all image voxels. The method achieved a 97.7\% overall recognition accuracy in recognizing a key class of marker cells, the body wall muscle (BWM) cells, on a data set of 175 C. elegans image stacks containing 14,118 manually curated BWM cells providing the "ground-truth" for accuracy. This result was achieved without any additional fiducial image features. SRS also automatically identified 14 of the image stacks as involving {\textpm}90-degree rotations. With these stacks excluded from the data set, the recognition accuracy rose to 99.1\%. We also show SRS is generally applicable to other cell-types, e.g. intestinal cells. AVAILABILITY: The supplementary movies can be downloaded from our website http://penglab.janelia.org/proj/celegans_seganno. The method has been implemented as a plug-in program within the V3D system (http://penglab.janelia.org/proj/v3d) and will be released in the V3D plugin source code repository.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btr480}, author = {Qu, Lei and Long, Fuhui and Liu, Xiao and Kim, Stuart and Myers, Eugene and Peng, Hanchuan} } @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 {2149, title = {A 3D digital atlas of C. elegans and its application to single-cell analyses.}, journal = {Nature Methods}, volume = {6}, year = {2009}, month = {2009 Sep}, pages = {667-72}, abstract = {

We built a digital nuclear atlas of the newly hatched, first larval stage (L1) of the wild-type hermaphrodite of Caenorhabditis elegans at single-cell resolution from confocal image stacks of 15 individual worms. The atlas quantifies the stereotypy of nuclear locations and provides other statistics on the spatial patterns of the 357 nuclei that could be faithfully segmented and annotated out of the 558 present at this developmental stage. We then developed an automated approach to assign cell names to each nucleus in a three-dimensional image of an L1 worm. We achieved 86\% accuracy in identifying the 357 nuclei automatically. This computational method will allow high-throughput single-cell analyses of the post-embryonic worm, such as gene expression analysis, or ablation or stimulation of cells under computer control in a high-throughput functional screen.

}, keywords = {Animals, Automatic Data Processing, Biological, Caenorhabditis elegans, Cell Lineage, Cell Nucleus, Models}, issn = {1548-7105}, doi = {10.1007/s12021-010-9090-x}, author = {Long, Fuhui and Peng, Hanchuan and Liu, Xiao and Kim, Stuart K and Myers, Eugene} } @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} } @conference {2719, title = {Automatic recognition of cells (ARC) for 3D images of C. elegans.}, booktitle = {Proceedings of the 2008 Conference on Computational Molecular Biology (RECOMB) (Singapore, 2008)}, year = {2008}, month = {2008 Apr 2}, abstract = {

The development of high-resolution microscopy makes possible the high-throughput screening of cellular information, such as gene expression at single cell resolution. One of the critical enabling techniques yet to be developed is the automatic recognition or annotation of specific cells in a 3D image stack. In this paper, we present a novel graph-based algorithm, ARC, that determines cell identities in a 3D confocal image of C. elegans based on their highly stereotyped arrangement. This is an essential step in our work on gene expression analysis of C. elegans at the resolution of single cells. Our ARC method integrates both the absolute and relative spatial locations of cells in a C. elegans body. It uses a marker-guided, spatially-constrained, two-stage bipartite matching to find the optimal match between cells in a subject image and cells in 15 template images that have been manually annotated and vetted. We applied ARC to the recognition of cells in 3D confocal images of the first larval stage (L1) of C. elegans hermaphrodites, and achieved an average accuracy of 94.91\%.

}, author = {Long, Fuhui and Peng, Hanchuan and Liu, X and Kim, Stuart K and Myers, Eugene} } @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 {2718, title = {Automatic segmentation of nuclei in 3D microscopy images of C. elegans.}, journal = {2007 4TH IEEE International Symposium on Biomedical Imaging: Macro to Nano, VOLS 1-3}, year = {2007}, month = {2007 Apr 12-15}, pages = {536-9}, abstract = {

Automatic segmentation of nuclei in 3D microscopy images is essential for many biological studies including high throughput analysis of gene expression level, morphology, and phenotypes in single cell level. The complexity and variability of the microscopy images present many difficulties to the traditional image segmentation methods. In this paper, we present a new method based on 3D watershed algorithm to segment such images. By using both the intensity information of the image and the geometry information of the appropriately detected foreground mask, our method is robust to intensity fluctuation within nuclei and at the same time sensitive to the intensity and geometrical cues between nuclei. Besides, the method can automatically correct potential segmentation errors by using several post-processing steps. We tested this algorithm on the 3D confocal images of C.elegans, an organism that has been widely used in biological studies. Our results show that the algorithm can segment nuclei in high accuracy despite the non-uniform background, tightly clustered nuclei with different sizes and shapes, fluctuated intensities, and hollow-shaped staining patterns in the images.

}, author = {Long, Fuhui and Peng, Hanchuan and Myers, Eugene} } @article {2155, title = {Phenotype clustering of breast epithelial cells in confocal images based on nuclear protein distribution analysis.}, journal = {BMC Cell Biology}, volume = {8 }, year = {2007}, month = {2007 Jul 10}, pages = {S3}, abstract = {

The distribution of chromatin-associated proteins plays a key role in directing nuclear function. Previously, we developed an image-based method to quantify the nuclear distributions of proteins and showed that these distributions depended on the phenotype of human mammary epithelial cells. Here we describe a method that creates a hierarchical tree of the given cell phenotypes and calculates the statistical significance between them, based on the clustering analysis of nuclear protein distributions.

}, keywords = {Breast, Breast Neoplasms, Cell Line, Cluster Analysis, Computer-Assisted, Confocal, Epithelial Cells, Female, Humans, Image Processing, Imaging, Microscopy, Models, Nuclear Proteins, Phenotype, Statistical, Three-Dimensional}, issn = {1471-2121}, doi = {10.1007/s12021-010-9090-x}, author = {Long, Fuhui and Peng, Hanchuan and Sudar, Damir and Leli{\`e}vre, Sophie A and Knowles, David 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} } @article {2158, title = {Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy.}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {27}, year = {2005}, month = {2005 Aug}, pages = {1226-38}, abstract = {

Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we first derive an equivalent form, called minimal-redundancy-maximal-relevance criterion (mRMR), for first-order incremental feature selection. Then, we present a two-stage feature selection algorithm by combining mRMR and other more sophisticated feature selectors (e.g., wrappers). This allows us to select a compact set of superior features at very low cost. We perform extensive experimental comparison of our algorithm and other methods using three different classifiers (naive Bayes, support vector machine, and linear discriminate analysis) and four different data sets (handwritten digits, arrhythmia, NCI cancer cell lines, and lymphoma tissues). The results confirm that mRMR leads to promising improvement on feature selection and classification accuracy.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Cluster Analysis, Computer Simulation, Computer-Assisted, Diagnosis, Humans, Information Storage and Retrieval, Models, Neoplasms, Numerical Analysis, Pattern Recognition, Statistical}, issn = {0162-8828}, doi = {10.1007/s12021-010-9090-x}, author = {Peng, Hanchuan and Long, Fuhui and Ding, Chris} }