Cellular anatomy of the mouse primary motor cortex.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input-output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture

A multimodal cell census and atlas of the mammalian primary motor cortex

ABSTRACT We report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex (MOp or M1) as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties, and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Together, our results advance the collective knowledge and understanding of brain cell type organization: First, our study reveals a unified molecular genetic landscape of cortical cell types that congruently integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a unified taxonomy of transcriptomic types and their hierarchical organization that are conserved from mouse to marmoset and human. Third, cross-modal analysis provides compelling evidence for the epigenomic, transcriptomic, and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types and subtypes. Fourth, in situ single-cell transcriptomics provides a spatially-resolved cell type atlas of the motor cortex. Fifth, integrated transcriptomic, epigenomic and anatomical analyses reveal the correspondence between neural circuits and transcriptomic cell types. We further present an extensive genetic toolset for targeting and fate mapping glutamatergic projection neuron types toward linking their developmental trajectory to their circuit function. Together, our results establish a unified and mechanistic framework of neuronal cell type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties

Investigation of Data Modeling Strategies for Quantification of CT Image Quality

Thesis (Master's)--University of Washington, 2017-03Computed Tomography (CT) is a widely used medical imaging technology that plays a crucial role in pathology diagnosis and treatment management. The growing use of CT imaging raises the risk of undue radiation exposure, particularly for patients imaged multiple times. Thus, there is a need to maintain radiation exposure As Low As Reasonably Achievable (ALARA) while ensuring diagnostic quality images. In this study, we investigate data modeling strategies to quantify CT image quality (IQ), in order to guide protocol selection and attain ALARA. Specifically, we present a novel, Windowed Fourier-domain Distance Metric (WFDM) that is used to select regions-of-interest (ROI) by their degree of spatial variability. By selecting regions of low variation (ROI-LV), an estimate of the noise in that region can be made. CT IQ is defined as the inverse of this additive noise. Against the phantom CT images, the WFDM model is shown to correlate strongly to image noise (r > 0.76 (p 0.001)). As a CT IQ classifier, this model is comparatively analyzed against a fixed-size ROI (baseline) model and a Convolutional Neural Network (CNN), using phantom and patient CT images. The WFDM model and the CNN are shown to classify the phantom images accurately, with a mean accuracy of αWFDM ≤ 100%, and αCNN = 93.8%, respectively. The baseline model manages a mean accuracy of αB = 73.6% on the same phantom images. With the patient CT images, the baseline and WFDM accuracies drop to αB ≤ 49.5% and αWFDM ≤ 66.1%, respectively. The CNN, however, performs at 100% accuracy when tested with images from the same CT stack as the training set, but below 1.9% otherwise. This indicates the CNN focus on structural rather than textural features. Finally, the WFDM model is used to predict high/low trends in 84 pairs of patient CT images. These trends are set against the trends in x-ray flux at the time of acquisition, CTDIvol, which, for the same patient, directly correspond to CT IQ. The total percentage of image pairs with inverse trends is defined as the total percent error, which is found to be 30.95% and 21.43% for the baseline and WFDM models, respectively. However, this error drops to 0% for CTDIvol changes of at least 40.0% for the baseline model and 27.5% for the WFDM model, respectively. Thus, for every patient that has been previously imaged, the WFDM model can be used to predict optimal parameters for adequate CT image acquisition. Future work will investigate the impact of WFDM parameters, such as the window sizes and transformation technique, on CT image quality assessment. In addition, the WFDM model can also be applied to pre-process CT images followed by CNN data models for CT image texture identification

A multimodal cell census and atlas of the mammalian primary motor cortex

none258Here we report the generation of a multimodal cell census and atlas of the mammalian primary motor cortex as the initial product of the BRAIN Initiative Cell Census Network (BICCN). This was achieved by coordinated large-scale analyses of single-cell transcriptomes, chromatin accessibility, DNA methylomes, spatially resolved single-cell transcriptomes, morphological and electrophysiological properties and cellular resolution input-output mapping, integrated through cross-modal computational analysis. Our results advance the collective knowledge and understanding of brain cell-type organization1-5. First, our study reveals a unified molecular genetic landscape of cortical cell types that integrates their transcriptome, open chromatin and DNA methylation maps. Second, cross-species analysis achieves a consensus taxonomy of transcriptomic types and their hierarchical organization that is conserved from mouse to marmoset and human. Third, in situ single-cell transcriptomics provides a spatially resolved cell-type atlas of the motor cortex. Fourth, cross-modal analysis provides compelling evidence for the transcriptomic, epigenomic and gene regulatory basis of neuronal phenotypes such as their physiological and anatomical properties, demonstrating the biological validity and genomic underpinning of neuron types. We further present an extensive genetic toolset for targeting glutamatergic neuron types towards linking their molecular and developmental identity to their circuit function. Together, our results establish a unifying and mechanistic framework of neuronal cell-type organization that integrates multi-layered molecular genetic and spatial information with multi-faceted phenotypic properties.openCallaway, Edward M.; Dong, Hong-Wei; Ecker, Joseph R.; Hawrylycz, Michael J.; Huang, Z. Josh; Lein, Ed S.; Ngai, John; Osten, Pavel; Ren, Bing; Tolias, Andreas Savas; White, Owen; Zeng, Hongkui; Zhuang, Xiaowei; Ascoli, Giorgio A.; Behrens, M. Margarita; Chun, Jerold; Feng, Guoping; Gee, James C.; Ghosh, Satrajit S.; Halchenko, Yaroslav O.; Hertzano, Ronna; Lim, Byung Kook; Martone, Maryann E.; Ng, Lydia; Pachter, Lior; Ropelewski, Alexander J.; Tickle, Timothy L.; Yang, X. William; Zhang, Kun; Bakken, Trygve E.; Berens, Philipp; Daigle, Tanya L.; Harris, Julie A.; Jorstad, Nikolas L.; Kalmbach, Brian E.; Kobak, Dmitry; Li, Yang Eric; Liu, Hanqing; Matho, Katherine S.; Mukamel, Eran A.; Naeemi, Maitham; Scala, Federico; Tan, Pengcheng; Ting, Jonathan T.; Xie, Fangming; Zhang, Meng; Zhang, Zhuzhu; Zhou, Jingtian; Zingg, Brian; Armand, Ethan; Yao, Zizhen; Bertagnolli, Darren; Casper, Tamara; Crichton, Kirsten; Dee, Nick; Diep, Dinh; Ding, Song-Lin; Dong, Weixiu; Dougherty, Elizabeth L.; Fong, Olivia; Goldman, Melissa; Goldy, Jeff; Hodge, Rebecca D.; Hu, Lijuan; Keene, C. Dirk; Krienen, Fenna M.; Kroll, Matthew; Lake, Blue B.; Lathia, Kanan; Linnarsson, Sten; Liu, Christine S.; Macosko, Evan Z.; McCarroll, Steven A.; McMillen, Delissa; Nadaf, Naeem M.; Nguyen, Thuc Nghi; Palmer, Carter R.; Pham, Thanh; Plongthongkum, Nongluk; Reed, Nora M.; Regev, Aviv; Rimorin, Christine; Romanow, William J.; Savoia, Steven; Siletti, Kimberly; Smith, Kimberly; Sulc, Josef; Tasic, Bosiljka; Tieu, Michael; Torkelson, Amy; Tung, Herman; van Velthoven, Cindy T. J.; Vanderburg, Charles R.; Yanny, Anna Marie; Fang, Rongxin; Hou, Xiaomeng; Lucero, Jacinta D.; Osteen, Julia K.; Pinto-Duarte, Antonio; Poirion, Olivier; Preissl, Sebastian; Wang, Xinxin; Aldridge, Andrew I.; Bartlett, Anna; Boggeman, Lara; O’Connor, Carolyn; Castanon, Rosa G.; Chen, Huaming; Fitzpatrick, Conor; Luo, Chongyuan; Nery, Joseph R.; Nunn, Michael; Rivkin, Angeline C.; Tian, Wei; Dominguez, Bertha; Ito-Cole, Tony; Jacobs, Matthew; Jin, Xin; Lee, Cheng-Ta; Lee, Kuo-Fen; Miyazaki, Paula Assakura; Pang, Yan; Rashid, Mohammad; Smith, Jared B.; Vu, Minh; Williams, Elora; Biancalani, Tommaso; Booeshaghi, A. Sina; Crow, Megan; Dudoit, Sandrine; Fischer, Stephan; Gillis, Jesse; Hu, Qiwen; Kharchenko, Peter V.; Niu, Sheng-Yong; Ntranos, Vasilis; Purdom, Elizabeth; Risso, Davide; de Bézieux, Hector Roux; Somasundaram, Saroja; Street, Kelly; Svensson, Valentine; Vaishnav, Eeshit Dhaval; Van den Berge, Koen; Welch, Joshua D.; An, Xu; Bateup, Helen S.; Bowman, Ian; Chance, Rebecca K.; Foster, Nicholas N.; Galbavy, William; Gong, Hui; Gou, Lin; Hatfield, Joshua T.; Hintiryan, Houri; Hirokawa, Karla E.; Kim, Gukhan; Kramer, Daniel J.; Li, Anan; Li, Xiangning; Luo, Qingming; Muñoz-Castañeda, Rodrigo; Stafford, David A.; Feng, Zhao; Jia, Xueyan; Jiang, Shengdian; Jiang, Tao; Kuang, Xiuli; Larsen, Rachael; Lesnar, Phil; Li, Yaoyao; Li, Yuanyuan; Liu, Lijuan; Peng, Hanchuan; Qu, Lei; Ren, Miao; Ruan, Zongcai; Shen, Elise; Song, Yuanyuan; Wakeman, Wayne; Wang, Peng; Wang, Yimin; Wang, Yun; Yin, Lulu; Yuan, Jing; Zhao, Sujun; Zhao, Xuan; Narasimhan, Arun; Palaniswamy, Ramesh; Banerjee, Samik; Ding, Liya; Huilgol, Dhananjay; Huo, Bingxing; Kuo, Hsien-Chi; Laturnus, Sophie; Li, Xu; Mitra, Partha P.; Mizrachi, Judith; Wang, Quanxin; Xie, Peng; Xiong, Feng; Yu, Yang; Eichhorn, Stephen W.; Berg, Jim; Bernabucci, Matteo; Bernaerts, Yves; Cadwell, Cathryn René; Castro, Jesus Ramon; Dalley, Rachel; Hartmanis, Leonard; Horwitz, Gregory D.; Jiang, Xiaolong; Ko, Andrew L.; Miranda, Elanine; Mulherkar, Shalaka; Nicovich, Philip R.; Owen, Scott F.; Sandberg, Rickard; Sorensen, Staci A.; Tan, Zheng Huan; Allen, Shona; Hockemeyer, Dirk; Lee, Angus Y.; Veldman, Matthew B.; Adkins, Ricky S.; Ament, Seth A.; Bravo, Héctor Corrada; Carter, Robert; Chatterjee, Apaala; Colantuoni, Carlo; Crabtree, Jonathan; Creasy, Heather; Felix, Victor; Giglio, Michelle; Herb, Brian R.; Kancherla, Jayaram; Mahurkar, Anup; McCracken, Carrie; Nickel, Lance; Olley, Dustin; Orvis, Joshua; Schor, Michael; Hood, Greg; Dichter, Benjamin; Grauer, Michael; Helba, Brian; Bandrowski, Anita; Barkas, Nikolaos; Carlin, Benjamin; D’Orazi, Florence D.; Degatano, Kylee; Gillespie, Thomas H.; Khajouei, Farzaneh; Konwar, Kishori; Thompson, Carol; Kelly, Kathleen; Mok, Stephanie; Sunkin, SusanCallaway, Edward M.; Dong, Hong-Wei; Ecker, Joseph R.; Hawrylycz, Michael J.; Huang, Z. Josh; Lein, Ed S.; Ngai, John; Osten, Pavel; Ren, Bing; Tolias, Andreas Savas; White, Owen; Zeng, Hongkui; Zhuang, Xiaowei; Ascoli, Giorgio A.; Behrens, M. Margarita; Chun, Jerold; Feng, Guoping; Gee, James C.; Ghosh, Satrajit S.; Halchenko, Yaroslav O.; Hertzano, Ronna; Lim, Byung Kook; Martone, Maryann E.; Ng, Lydia; Pachter, Lior; Ropelewski, Alexander J.; Tickle, Timothy L.; Yang, X. William; Zhang, Kun; Bakken, Trygve E.; Berens, Philipp; Daigle, Tanya L.; Harris, Julie A.; Jorstad, Nikolas L.; Kalmbach, Brian E.; Kobak, Dmitry; Li, Yang Eric; Liu, Hanqing; Matho, Katherine S.; Mukamel, Eran A.; Naeemi, Maitham; Scala, Federico; Tan, Pengcheng; Ting, Jonathan T.; Xie, Fangming; Zhang, Meng; Zhang, Zhuzhu; Zhou, Jingtian; Zingg, Brian; Armand, Ethan; Yao, Zizhen; Bertagnolli, Darren; Casper, Tamara; Crichton, Kirsten; Dee, Nick; Diep, Dinh; Ding, Song-Lin; Dong, Weixiu; Dougherty, Elizabeth L.; Fong, Olivia; Goldman, Melissa; Goldy, Jeff; Hodge, Rebecca D.; Hu, Lijuan; Keene, C. Dirk; Krienen, Fenna M.; Kroll, Matthew; Lake, Blue B.; Lathia, Kanan; Linnarsson, Sten; Liu, Christine S.; Macosko, Evan Z.; Mccarroll, Steven A.; Mcmillen, Delissa; Nadaf, Naeem M.; Nguyen, Thuc Nghi; Palmer, Carter R.; Pham, Thanh; Plongthongkum, Nongluk; Reed, Nora M.; Regev, Aviv; Rimorin, Christine; Romanow, William J.; Savoia, Steven; Siletti, Kimberly; Smith, Kimberly; Sulc, Josef; Tasic, Bosiljka; Tieu, Michael; Torkelson, Amy; Tung, Herman; van Velthoven, Cindy T. J.; Vanderburg, Charles R.; Yanny, Anna Marie; Fang, Rongxin; Hou, Xiaomeng; Lucero, Jacinta D.; Osteen, Julia K.; Pinto-Duarte, Antonio; Poirion, Olivier; Preissl, Sebastian; Wang, Xinxin; Aldridge, Andrew I.; Bartlett, Anna; Boggeman, Lara; O’Connor, Carolyn; Castanon, Rosa G.; Chen, Huaming; Fitzpatrick, Conor; Luo, Chongyuan; Nery, Joseph R.; Nunn, Michael; Rivkin, Angeline C.; Tian, Wei; Dominguez, Bertha; Ito-Cole, Tony; Jacobs, Matthew; Jin, Xin; Lee, Cheng-Ta; Lee, Kuo-Fen; Miyazaki, Paula Assakura; Pang, Yan; Rashid, Mohammad; Smith, Jared B.; Vu, Minh; Williams, Elora; Biancalani, Tommaso; Booeshaghi, A. Sina; Crow, Megan; Dudoit, Sandrine; Fischer, Stephan; Gillis, Jesse; Hu, Qiwen; Kharchenko, Peter V.; Niu, Sheng-Yong; Ntranos, Vasilis; Purdom, Elizabeth; Risso, Davide; de Bézieux, Hector Roux; Somasundaram, Saroja; Street, Kelly; Svensson, Valentine; Vaishnav, Eeshit Dhaval; Van den Berge, Koen; Welch, Joshua D.; An, Xu; Bateup, Helen S.; Bowman, Ian; Chance, Rebecca K.; Foster, Nicholas N.; Galbavy, William; Gong, Hui; Gou, Lin; Hatfield, Joshua T.; Hintiryan, Houri; Hirokawa, Karla E.; Kim, Gukhan; Kramer, Daniel J.; Li, Anan; Li, Xiangning; Luo, Qingming; Muñoz-Castañeda, Rodrigo; Stafford, David A.; Feng, Zhao; Jia, Xueyan; Jiang, Shengdian; Jiang, Tao; Kuang, Xiuli; Larsen, Rachael; Lesnar, Phil; Li, Yaoyao; Li, Yuanyuan; Liu, Lijuan; Peng, Hanchuan; Qu, Lei; Ren, Miao; Ruan, Zongcai; Shen, Elise; Song, Yuanyuan; Wakeman, Wayne; Wang, Peng; Wang, Yimin; Wang, Yun; Yin, Lulu; Yuan, Jing; Zhao, Sujun; Zhao, Xuan; Narasimhan, Arun; Palaniswamy, Ramesh; Banerjee, Samik; Ding, Liya; Huilgol, Dhananjay; Huo, Bingxing; Kuo, Hsien-Chi; Laturnus, Sophie; Li, Xu; Mitra, Partha P.; Mizrachi, Judith; Wang, Quanxin; Xie, Peng; Xiong, Feng; Yu, Yang; Eichhorn, Stephen W.; Berg, Jim; Bernabucci, Matteo; Bernaerts, Yves; Cadwell, Cathryn René; Castro, Jesus Ramon; Dalley, Rachel; Hartmanis, Leonard; Horwitz, Gregory D.; Jiang, Xiaolong; Ko, Andrew L.; Miranda, Elanine; Mulherkar, Shalaka; Nicovich, Philip R.; Owen, Scott F.; Sandberg, Rickard; Sorensen, Staci A.; Tan, Zheng Huan; Allen, Shona; Hockemeyer, Dirk; Lee, Angus Y.; Veldman, Matthew B.; Adkins, Ricky S.; Ament, Seth A.; Bravo, Héctor Corrada; Carter, Robert; Chatterjee, Apaala; Colantuoni, Carlo; Crabtree, Jonathan; Creasy, Heather; Felix, Victor; Giglio, Michelle; Herb, Brian R.; Kancherla, Jayaram; Mahurkar, Anup; Mccracken, Carrie; Nickel, Lance; Olley, Dustin; Orvis, Joshua; Schor, Michael; Hood, Greg; Dichter, Benjamin; Grauer, Michael; Helba, Brian; Bandrowski, Anita; Barkas, Nikolaos; Carlin, Benjamin; D’Orazi, Florence D.; Degatano, Kylee; Gillespie, Thomas H.; Khajouei, Farzaneh; Konwar, Kishori; Thompson, Carol; Kelly, Kathleen; Mok, Stephanie; Sunkin, Susa