@article {68709, title = {BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets.}, journal = {Nature Methods}, volume = {20}, year = {2023}, month = {2023 Jun 01}, abstract = {

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

}, issn = {1548-7105}, doi = {10.1038/s41592-023-01848-5}, author = {Manubens-Gil, Linus and Zhou, Zhi and Chen, Hanbo and Ramanathan, Arvind and Liu, Xiaoxiao and Liu, Yufeng and Bria, Alessandro and Gillette, Todd and Ruan, Zongcai and Yang, Jian and Radojevi{\'c}, Miroslav and Zhao, Ting and Cheng, Li and Qu, Lei and Liu, Siqi and Bouchard, Kristofer E and Gu, Lin and Cai, Weidong and Ji, Shuiwang and Roysam, Badrinath and Wang, Ching-Wei and Yu, Hongchuan and Sironi, Amos and Iascone, Daniel Maxim and Zhou, Jie and Bas, Erhan and Conde-Sousa, Eduardo and Aguiar, Paulo and Li, Xiang and Li, Yujie and Nanda, Sumit and Wang, Yuan and Muresan, Leila and Fua, Pascal and Ye, Bing and He, Hai-Yan and Staiger, Jochen F and Peter, Manuel and Cox, Daniel N and Simonneau, Michel and Oberlaender, Marcel and Jefferis, Gregory and Ito, Kei and Gonzalez-Bellido, Paloma and Kim, Jinhyun and Rubel, Edwin and Cline, Hollis T and Zeng, Hongkui and Nern, Aljoscha and Chiang, Ann-Shyn and Yao, Jianhua and Roskams, Jane and Livesey, Rick and Stevens, Janine and Liu, Tianming and Dang, Chinh and Guo, Yike and Zhong, Ning and Tourassi, Georgia and Hill, Sean and Hawrylycz, Michael and Koch, Christof and Meijering, Erik and Ascoli, Giorgio A and Peng, Hanchuan} } @article {49495, title = {Shared and distinct transcriptomic cell types across neocortical areas.}, journal = {Nature}, volume = {563}, year = {2018}, month = {2018 Nov}, pages = {72-78}, abstract = {

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

}, issn = {1476-4687}, doi = {10.1038/s41586-018-0654-5}, author = {Tasic, Bosiljka and Yao, Zizhen and Graybuck, Lucas T and Smith, Kimberly A and Nguyen, Thuc Nghi and Bertagnolli, Darren and Goldy, Jeff and Garren, Emma and Economo, Michael N and Viswanathan, Sarada and Penn, Osnat and Bakken, Trygve and Menon, Vilas and Miller, Jeremy and Fong, Olivia and Hirokawa, Karla E and Lathia, Kanan and Rimorin, Christine and Tieu, Michael and Larsen, Rachael and Casper, Tamara and Barkan, Eliza and Kroll, Matthew and Parry, Sheana and Shapovalova, Nadiya V and Hirschstein, Daniel and Pendergraft, Julie and Sullivan, Heather A and Kim, Tae Kyung and Szafer, Aaron and Dee, Nick and Groblewski, Peter and Wickersham, Ian and Cetin, Ali and Harris, Julie A and Levi, Boaz P and Sunkin, Susan M and Madisen, Linda and Daigle, Tanya L and Looger, Loren and Bernard, Amy and Phillips, John and Lein, Ed and Hawrylycz, Michael and Svoboda, Karel and Jones, Allan R and Koch, Christof and Zeng, Hongkui} } @article {47894, title = {Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.}, journal = {Nature neuroscience}, volume = {19}, year = {2016}, month = {2016 Feb}, pages = {335-46}, abstract = {

Nervous systems are composed of various cell types, but the extent of cell type diversity is poorly understood. We constructed a cellular taxonomy of one cortical region, primary visual cortex, in adult mice on the basis of single-cell RNA sequencing. We identified 49 transcriptomic cell types, including 23 GABAergic, 19 glutamatergic and 7 non-neuronal types. We also analyzed cell type-specific mRNA processing and characterized genetic access to these transcriptomic types by many transgenic Cre lines. Finally, we found that some of our transcriptomic cell types displayed specific and differential electrophysiological and axon projection properties, thereby confirming that the single-cell transcriptomic signatures can be associated with specific cellular properties.

}, keywords = {Animals, Cell Line, Cerebral Cortex, Classification, gamma-Aminobutyric Acid, Gene Library, Genetic Markers, Glutamic Acid, Interneurons, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Neurons, RNA, Sequence Analysis, RNA, Transcriptome, Visual Cortex}, issn = {1546-1726}, doi = {10.1038/nn.4216}, author = {Tasic, Bosiljka and Menon, Vilas and Nguyen, Thuc Nghi and Kim, Tae Kyung and Jarsky, Tim and Yao, Zizhen and Levi, Boaz and Gray, Lucas T and Sorensen, Staci A and Dolbeare, Tim and Bertagnolli, Darren and Goldy, Jeff and Shapovalova, Nadiya and Parry, Sheana and Lee, Changkyu and Smith, Kimberly and Bernard, Amy and Madisen, Linda and Sunkin, Susan M and Hawrylycz, Michael and Koch, Christof and Zeng, Hongkui} } @article {46901, title = {BigNeuron: Large-scale 3D neuron reconstruction from optical microscopy images.}, journal = {Neuron}, volume = {87}, year = {2015}, month = {2015 Jul 15}, pages = {252-6}, abstract = {

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

Understanding the structure of single neurons is critical for understanding how they function within neural circuits. BigNeuron is a new community effort that combines modern bioimaging informatics, recent leaps in labeling and microscopy, and the widely recognized need for openness and standardization to provide a community resource for automated reconstruction of dendritic and axonal morphology of single neurons.

}, doi = {10.1016/j.neuron.2015.06.036}, author = {Peng, Hanchuan and Hawrylycz, Michael and Roskams, Jane and Hill, Sean and Spruston, Nelson and Meijering, Erik and Ascoli, Giorgio A} } @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 {47895, title = {Canonical genetic signatures of the adult human brain.}, journal = {Nature neuroscience}, volume = {18}, year = {2015}, month = {2015 Dec}, pages = {1832-44}, abstract = {

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

}, keywords = {Adult, Animals, Brain, Gene Regulatory Networks, Humans, Mice, Nerve Net, Transcriptome}, issn = {1546-1726}, doi = {10.1038/nn.4171}, author = {Hawrylycz, Michael and Miller, Jeremy A and Menon, Vilas and Feng, David and Dolbeare, Tim and Guillozet-Bongaarts, Angela L and Jegga, Anil G and Aronow, Bruce J and Lee, Chang-Kyu and Bernard, Amy and Glasser, Matthew F and Dierker, Donna L and Menche, J{\"o}rg and Szafer, Aaron and Collman, Forrest and Grange, Pascal and Berman, Kenneth A and Mihalas, Stefan and Yao, Zizhen and Stewart, Lance and Barab{\'a}si, Albert-L{\'a}szl{\'o} and Schulkin, Jay and Phillips, John and Ng, Lydia and Dang, Chinh and Haynor, David R and Jones, Allan and Van Essen, David C and Koch, Christof and Lein, Ed} } @article {6952, title = {Cell-type-based model explaining coexpression patterns of genes in the brain.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {111}, year = {2014}, month = {2014 Apr 8}, pages = {5397-402}, abstract = {

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

}, issn = {1091-6490}, doi = {10.1073/pnas.1312098111}, author = {Grange, Pascal and Bohland, Jason W and Okaty, Benjamin W and Sugino, Ken and Bokil, Hemant and Nelson, Sacha B and Ng, Lydia and Hawrylycz, Michael and Mitra, Partha P} } @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 {6491, title = {A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale.}, journal = {PLoS Computational Biology}, volume = {5}, year = {2009}, month = {2009 Mar}, pages = {e1000334}, abstract = {

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

}, keywords = {Animals, Brain, Databases, Factual, Humans, Macaca, Mice, Models, Nerve Net, Neuroanatomy, Neurological, Research Design}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1000334}, author = {Bohland, Jason W and Wu, Caizhi and Barbas, Helen and Bokil, Hemant and Bota, Mihail and Breiter, Hans C and Cline, Hollis T and Doyle, John C and Freed, Peter J and Greenspan, Ralph J and Haber, Suzanne N and Hawrylycz, Michael and Herrera, Daniel G and Hilgetag, Claus C and Huang, Z Josh and Jones, Allan and Jones, Edward G and Karten, Harvey J and Kleinfeld, David and K{\"o}tter, Rolf and Lester, Henry A and Lin, John M and Mensh, Brett D and Mikula, Shawn and Panksepp, Jaak and Price, Joseph L and Safdieh, Joseph and Saper, Clifford B and Schiff, Nicholas D and Schmahmann, Jeremy D and Stillman, Bruce W and Svoboda, Karel and Swanson, Larry W and Toga, Arthur W and Van Essen, David C and Watson, James D and Mitra, Partha P} }