@article {68251, title = {A scalable and modular automated pipeline for stitching of large electron microscopy datasets.}, journal = {eLife}, volume = {11}, year = {2022}, month = {2022 Jul 26}, abstract = {

Serial-section electronmicroscopy (ssEM) is themethod of choice for studyingmacroscopic biological samples at extremely high resolution in three dimensions. In the nervous system, nanometer-scale images are necessary to reconstruct dense neural wiring diagrams in the brain, so called connectomes. In order to use this data, consisting of up to 10 individual EM images, it must be assembled into a volume, requiring seamless 2D stitching from each physical section followed by 3D alignment of the stitched sections. The high throughput of ssEM necessitates 2D stitching to be done at the pace of imaging, which currently produces tens of terabytes per day. To achieve this, we present a modular volume assembly software pipeline ASAP (Assembly Stitching and Alignment Pipeline) that is scalable to datasets containing petabytes of data and parallelized to work in a distributed computational environment. The pipeline is built on top of the Render (27) services used in the volume assembly of the brain of adult Drosophilamelanogaster (30). It achieves high throughput by operating on themeta-data and transformations of each image stored in a database, thus eliminating the need to render intermediate output. ASAP ismodular, allowing for easy incorporation of new algorithms without significant changes in the workflow. The entire software pipeline includes a complete set of tools for stitching, automated quality control, 3D section alignment, and final rendering of the assembled volume to disk. ASAP has been deployed for continuous stitching of several large-scale datasets of the mouse visual cortex and human brain samples including one cubic millimeter of mouse visual cortex (28; 8) at speeds that exceed imaging. The pipeline also has multi-channel processing capabilities and can be applied to fluorescence and multi-modal datasets like array tomography.

}, issn = {2050-084X}, doi = {10.7554/eLife.76534}, author = {Mahalingam, Gayathri and Torres, Russel and Kapner, Daniel and Trautman, Eric T and Fliss, Tim and Seshamani, Shamishtaa and Perlman, Eric and Young, Rob and Kinn, Samuel and Buchanan, JoAnn and Takeno, Marc M and Yin, Wenjing and Bumbarger, Daniel J and Gwinn, Ryder P and Nyhus, Julie and Lein, Ed and Smith, Steven J and Reid, R Clay and Khairy, Khaled A and Saalfeld, Stephan and Collman, Forrest and Macarico da Costa, Nuno} } @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 {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 {47904, title = {Multi-scale correlation structure of gene expression in the brain.}, journal = {Neural networks : the official journal of the International Neural Network Society}, volume = {24}, year = {2011}, month = {2011 Nov}, pages = {933-42}, abstract = {

The mammalian brain is best understood as a multi-scale hierarchical neural system, in the sense that connection and function occur on multiple scales from micro to macro. Modern genomic-scale expression profiling can provide insight into methodologies that elucidate this architecture. We present a methodology for understanding the relationship of gene expression and neuroanatomy based on correlation between gene expression profiles across tissue samples. A resulting tool, NeuroBlast, can identify networks of genes co-expressed within or across neuroanatomic structures. The method applies to any data modality that can be mapped with sufficient spatial resolution, and provides a computation technique to elucidate neuroanatomy via patterns of gene expression on spatial and temporal scales. In addition, from the perspective of spatial location, we discuss a complementary technique that identifies gene classes that contribute to defining anatomic patterns.

}, keywords = {Algorithms, Animals, Atlases as Topic, Brain, Brain Chemistry, Data Mining, Databases, Genetic, Gene Expression, Mice, Mice, Inbred C57BL, Nerve Tissue Proteins, Tryptophan Hydroxylase}, issn = {1879-2782}, doi = {10.1016/j.neunet.2011.06.012}, author = {Hawrylycz, Mike and Ng, Lydia and Page, Damon and Morris, John and Lau, Chris and Faber, Sky and Faber, Vance and Sunkin, Susan and Menon, Vilas and Lein, Ed and Jones, Allan} }