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191 results on '"Welch, Joshua D."'

1. Opportunities and challenges of single-cell and spatially resolved genomics methods for neuroscience discovery

Author

Bonev, Boyan, Gonçalo, Castelo-Branco, Chen, Fei, Codeluppi, Simone, Corces, M. Ryan, Fan, Jean, Heiman, Myriam, Harris, Kenneth, Inoue, Fumitaka, Kellis, Manolis, Levine, Ariel, Lotfollahi, Mo, Luo, Chongyuan, Maynard, Kristen R., Nitzan, Mor, Ramani, Vijay, Satijia, Rahul, Schirmer, Lucas, Shen, Yin, Sun, Na, Green, Gilad S., Theis, Fabian, Wang, Xiao, Welch, Joshua D., Gokce, Ozgun, Konopka, Genevieve, Liddelow, Shane, Macosko, Evan, Bayraktar, Omer, Habib, Naomi, and Nowakowski, Tomasz J.

Published
2024
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3. Mapping Cell Fate Transition in Space and Time

Author

Gu, Yichen, Liu, Jialin, Li, Chen, Welch, Joshua D., Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, van Leeuwen, Jan, Series Editor, Hutchison, David, Editorial Board Member, Kanade, Takeo, Editorial Board Member, Kittler, Josef, Editorial Board Member, Kleinberg, Jon M., Editorial Board Member, Kobsa, Alfred, Series Editor, Mattern, Friedemann, Editorial Board Member, Mitchell, John C., Editorial Board Member, Naor, Moni, Editorial Board Member, Nierstrasz, Oscar, Series Editor, Pandu Rangan, C., Editorial Board Member, Sudan, Madhu, Series Editor, Terzopoulos, Demetri, Editorial Board Member, Tygar, Doug, Editorial Board Member, Weikum, Gerhard, Series Editor, Vardi, Moshe Y, Series Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, and Ma, Jian, editor

Published
2024
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4. A Bilevel Optimization Method for Tensor Recovery Under Metric Learning Constraints

Author

Bagherian, Maryam, Tarzanagh, Davoud A., Dinov, Ivo, and Welch, Joshua D.

Subjects
Mathematics - Optimization and Control
Abstract

Tensor completion and tensor decomposition are important problems in many domains. In this work, we leverage the connection between these problems to learn a distance metric that improves both decomposition and completion. We show that the optimal Mahalanobis distance metric for the completion task is closely related to the Tucker decomposition of the completed tensor. Then, we formulate a bilevel optimization problem to perform joint tensor completion and decomposition, subject to metric learning constraints. The metric learning constraints also allow us to flexibly incorporate similarity side information and coupled matrices, when available, into the tensor recovery process. We derive an algorithm to solve the bilevel optimization problem and prove its global convergence. When evaluated on real data, our approach performs significantly better compared to previous methods.

Published
2022

5. A transcriptomic and epigenomic cell atlas of the mouse primary motor cortex

Author

Yao, Zizhen, Liu, Hanqing, Xie, Fangming, Fischer, Stephan, Adkins, Ricky S, Aldridge, Andrew I, Ament, Seth A, Bartlett, Anna, Behrens, M Margarita, Van den Berge, Koen, Bertagnolli, Darren, de Bézieux, Hector Roux, Biancalani, Tommaso, Booeshaghi, A Sina, Bravo, Héctor Corrada, Casper, Tamara, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather, Crichton, Kirsten, Crow, Megan, Dee, Nick, Dougherty, Elizabeth L, Doyle, Wayne I, Dudoit, Sandrine, Fang, Rongxin, Felix, Victor, Fong, Olivia, Giglio, Michelle, Goldy, Jeff, Hawrylycz, Mike, Herb, Brian R, Hertzano, Ronna, Hou, Xiaomeng, Hu, Qiwen, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, Li, Yang Eric, Lucero, Jacinta D, Luo, Chongyuan, Mahurkar, Anup, McMillen, Delissa, Nadaf, Naeem M, Nery, Joseph R, Nguyen, Thuc Nghi, Niu, Sheng-Yong, Ntranos, Vasilis, Orvis, Joshua, Osteen, Julia K, Pham, Thanh, Pinto-Duarte, Antonio, Poirion, Olivier, Preissl, Sebastian, Purdom, Elizabeth, Rimorin, Christine, Risso, Davide, Rivkin, Angeline C, Smith, Kimberly, Street, Kelly, Sulc, Josef, Svensson, Valentine, Tieu, Michael, Torkelson, Amy, Tung, Herman, Vaishnav, Eeshit Dhaval, Vanderburg, Charles R, van Velthoven, Cindy, Wang, Xinxin, White, Owen R, Huang, Z Josh, Kharchenko, Peter V, Pachter, Lior, Ngai, John, Regev, Aviv, Tasic, Bosiljka, Welch, Joshua D, Gillis, Jesse, Macosko, Evan Z, Ren, Bing, Ecker, Joseph R, Zeng, Hongkui, and Mukamel, Eran A

Subjects
Human Genome, Neurosciences, Genetics, Bioengineering, Biotechnology, 1.1 Normal biological development and functioning, Underpinning research, Neurological, Animals, Atlases as Topic, Datasets as Topic, Epigenesis, Genetic, Epigenomics, Female, Gene Expression Profiling, Male, Mice, Motor Cortex, Neurons, Organ Specificity, Reproducibility of Results, Single-Cell Analysis, Transcriptome, General Science & Technology
Abstract

Single-cell transcriptomics can provide quantitative molecular signatures for large, unbiased samples of the diverse cell types in the brain1-3. With the proliferation of multi-omics datasets, a major challenge is to validate and integrate results into a biological understanding of cell-type organization. Here we generated transcriptomes and epigenomes from more than 500,000 individual cells in the mouse primary motor cortex, a structure that has an evolutionarily conserved role in locomotion. We developed computational and statistical methods to integrate multimodal data and quantitatively validate cell-type reproducibility. The resulting reference atlas-containing over 56 neuronal cell types that are highly replicable across analysis methods, sequencing technologies and modalities-is a comprehensive molecular and genomic account of the diverse neuronal and non-neuronal cell types in the mouse primary motor cortex. The atlas includes a population of excitatory neurons that resemble pyramidal cells in layer 4 in other cortical regions4. We further discovered thousands of concordant marker genes and gene regulatory elements for these cell types. Our results highlight the complex molecular regulation of cell types in the brain and will directly enable the design of reagents to target specific cell types in the mouse primary motor cortex for functional analysis.

Published
2021

8. Single-Cell Transcriptomic Analyses of Cell Fate Transitions during Human Cardiac Reprogramming

Author

Zhou, Yang, Liu, Ziqing, Welch, Joshua D, Gao, Xu, Wang, Li, Garbutt, Tiffany, Keepers, Benjamin, Ma, Hong, Prins, Jan F, Shen, Weining, Liu, Jiandong, and Qian, Li

Subjects
Biochemistry and Cell Biology, Biological Sciences, Cardiovascular, Genetics, Heart Disease, Underpinning research, Aetiology, 2.1 Biological and endogenous factors, 1.1 Normal biological development and functioning, Animals, Cell Differentiation, Cell Lineage, Cellular Reprogramming, Cellular Reprogramming Techniques, Fibroblasts, Humans, Myocytes, Cardiac, Sequence Analysis, RNA, Single-Cell Analysis, Transcriptome, CELL reprogramming, RNA velocity, SLICER, cell fate index, fibroblast, iCM, single-cell RNA-seq, Medical and Health Sciences, Developmental Biology, Biological sciences, Biomedical and clinical sciences
Abstract

Direct cellular reprogramming provides a powerful platform to study cell plasticity and dissect mechanisms underlying cell fate determination. Here, we report a single-cell transcriptomic study of human cardiac (hiCM) reprogramming that utilizes an analysis pipeline incorporating current data normalization methods, multiple trajectory prediction algorithms, and a cell fate index calculation we developed to measure reprogramming progression. These analyses revealed hiCM reprogramming-specific features and a decision point at which cells either embark on reprogramming or regress toward their original fibroblast state. In combination with functional screening, we found that immune-response-associated DNA methylation is required for hiCM induction and validated several downstream targets of reprogramming factors as necessary for productive hiCM reprograming. Collectively, this single-cell transcriptomics study provides detailed datasets that reveal molecular features underlying hiCM determination and rigorous analytical pipelines for predicting cell fate conversion.

Published
2019

9. Single-Cell Multi-omic Velocity Infers Dynamic and Decoupled Gene Regulation

Author

Li, Chen, Virgilio, Maria, Collins, Kathleen L., Welch, Joshua D., Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, and Pe'er, Itsik, editor

Published
2022
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12. Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte

Author

Liu, Ziqing, Wang, Li, Welch, Joshua D, Ma, Hong, Zhou, Yang, Vaseghi, Haley Ruth, Yu, Shuo, Wall, Joseph Blake, Alimohamadi, Sahar, Zheng, Michael, Yin, Chaoying, Shen, Weining, Prins, Jan F, Liu, Jiandong, and Qian, Li

Subjects
Biochemistry and Cell Biology, Biological Sciences, Generic health relevance, Algorithms, Animals, Cell Lineage, Cellular Reprogramming, Down-Regulation, Fibroblasts, GATA4 Transcription Factor, Heterogeneous-Nuclear Ribonucleoproteins, MEF2 Transcription Factors, Mice, Myocytes, Cardiac, Polypyrimidine Tract-Binding Protein, RNA Splicing, RNA, Messenger, Single-Cell Analysis, T-Box Domain Proteins, Transcriptome, General Science & Technology
Abstract

Direct lineage conversion offers a new strategy for tissue regeneration and disease modelling. Despite recent success in directly reprogramming fibroblasts into various cell types, the precise changes that occur as fibroblasts progressively convert to the target cell fates remain unclear. The inherent heterogeneity and asynchronous nature of the reprogramming process renders it difficult to study this process using bulk genomic techniques. Here we used single-cell RNA sequencing to overcome this limitation and analysed global transcriptome changes at early stages during the reprogramming of mouse fibroblasts into induced cardiomyocytes (iCMs). Using unsupervised dimensionality reduction and clustering algorithms, we identified molecularly distinct subpopulations of cells during reprogramming. We also constructed routes of iCM formation, and delineated the relationship between cell proliferation and iCM induction. Further analysis of global gene expression changes during reprogramming revealed unexpected downregulation of factors involved in mRNA processing and splicing. Detailed functional analysis of the top candidate splicing factor, Ptbp1, revealed that it is a critical barrier for the acquisition of cardiomyocyte-specific splicing patterns in fibroblasts. Concomitantly, Ptbp1 depletion promoted cardiac transcriptome acquisition and increased iCM reprogramming efficiency. Additional quantitative analysis of our dataset revealed a strong correlation between the expression of each reprogramming factor and the progress of individual cells through the reprogramming process, and led to the discovery of new surface markers for the enrichment of iCMs. In summary, our single-cell transcriptomics approaches enabled us to reconstruct the reprogramming trajectory and to uncover intermediate cell populations, gene pathways and regulators involved in iCM induction.

Published
2017

20. Author Correction: Community-wide hackathons to identify central themes in single-cell multi-omics

Author

Cao, Kim-Anh Lê, Abadi, Al J., Davis-Marcisak, Emily F., Hsu, Lauren, Arora, Arshi, Coullomb, Alexis, Deshpande, Atul, Feng, Yuzhou, Jeganathan, Pratheepa, Loth, Melanie, Meng, Chen, Mu, Wancen, Pancaldi, Vera, Sankaran, Kris, Righelli, Dario, Singh, Amrit, Sodicoff, Joshua S., Stein-O’Brien, Genevieve L., Subramanian, Ayshwarya, Welch, Joshua D., You, Yue, Argelaguet, Ricard, Carey, Vincent J., Dries, Ruben, Greene, Casey S., Holmes, Susan, Love, Michael I., Ritchie, Matthew E., Yuan, Guo-Cheng, Culhane, Aedin C., and Fertig, Elana

Published
2021
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21. Community-wide hackathons to identify central themes in single-cell multi-omics

Author

Lê Cao, Kim-Anh, Abadi, Al J., Davis-Marcisak, Emily F., Hsu, Lauren, Arora, Arshi, Coullomb, Alexis, Deshpande, Atul, Feng, Yuzhou, Jeganathan, Pratheepa, Loth, Melanie, Meng, Chen, Mu, Wancen, Pancaldi, Vera, Sankaran, Kris, Righelli, Dario, Singh, Amrit, Sodicoff, Joshua S., Stein-O’Brien, Genevieve L., Subramanian, Ayshwarya, Welch, Joshua D., You, Yue, Argelaguet, Ricard, Carey, Vincent J., Dries, Ruben, Greene, Casey S., Holmes, Susan, Love, Michael I., Ritchie, Matthew E., Yuan, Guo-Cheng, Culhane, Aedin C., and Fertig, Elana

Published
2021
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26. Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation

Author

Varshney, Arushi, primary, Manickam, Nandini, additional, Orchard, Peter, additional, Tovar, Adelaide, additional, Zhang, Zhenhao, additional, Feng, Fan, additional, Erdos, Michael R, additional, Narisu, Narisu, additional, Ventresca, Christa, additional, Nishino, Kirsten, additional, Rai, Vivek, additional, Stringham, Heather M, additional, Jackson, Anne U, additional, Tamsen, Tricia, additional, Gao, Chao, additional, Yang, Mao, additional, Koues, Olivia I, additional, Welch, Joshua D, additional, Burant, Charles F, additional, Williams, L Keoki, additional, Jenkinson, Chris, additional, DeFronzo, Ralph A, additional, Norton, Luke, additional, Saramies, Jouko, additional, Lakka, Timo A, additional, Laakso, Markku, additional, Tuomilehto, Jaakko, additional, Mohlke, Karen L, additional, Kitzman, Jacob O, additional, Koistinen, Heikki A, additional, Liu, Jie, additional, Boehnke, Michael, additional, Collins, Francis S, additional, Scott, Laura J, additional, and Parker, Stephen C J, additional

Published
2023
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29. Transcription factor FOXF1 identifies compartmentally distinct mesenchymal cells with a role in lung allograft fibrogenesis

Author

Braeuer, Russell R., Walker, Natalie M., Misumi, Keizo, Mazzoni-Putman, Serina, Aoki, Yoshiro, Liao, Ruohan, Vittal, Ragini, Kleer, Gabriel G., Wheeler, David S., Sexton, Jonathan Z., Farver, Carol F., Welch, Joshua D., and Lama, Vibha N.

Subjects
Lungs -- Transplantation, Fibrosis -- Risk factors -- Genetic aspects -- Development and progression, Stem cells -- Genetic aspects -- Physiological aspects -- Health aspects, Health care industry
Abstract

In this study, we demonstrate that forkhead box F1 (FOXF1), a mesenchymal transcriptional factor essential for lung development, was retained in a topographically distinct mesenchymal stromal cell population along the bronchovascular space in an adult lung and identify this distinct subset of collagen-expressing cells as key players in lung allograft remodeling and fibrosis. Using Foxf1-tdTomato BAC (Foxfi-tdTomato) and Foxf1-tdTomato Col1a1-GFP mice, we show that [Lin.sup.-][Foxf1.sup.+] cells encompassed the stem cell antigen [1.sup.+][CD34.sup.+] ([Sca1.sup.+][CD34.sup.+]) subset of collagen 1-expressing mesenchymal cells (MCs) with a capacity to generate CFU and lung epithelial organoids. Histologically, FOXF1- expressing MCs formed a 3D network along the conducting airways; FOXF1 was noted to be conspicuously absent in MCs in the alveolar compartment. Bulk and single-cell RNA-Seq confirmed distinct transcriptional signatures of [Foxf1.sup.-] and [Foxf1.sup.-] MCs, with Foxf1-expressing cells delineated by their high expression of the transcription factor glioma-associated oncogene 1 (Gli1) and low expression of integrin [alpha]8 (Itga), versus other collagen-expressing MCs. [FOXF1.sup.+][Gli1.sup.+] MCs showed proximity to Sonic hedgehog-expressing (Shh-expressing) bronchial epithelium, and mesenchymal expression of Foxf1 and Gli1 was found to be dependent on paracrine Shh signaling in epithelial organoids. Using a murine lung transplant model, we show dysregulation of epithelial-mesenchymal SHH/GLI1/ FOXF1 crosstalk and expansion of this specific peribronchial MC population in chronically rejecting fibrotic lung allografts., Introduction It is increasingly recognized that characterization of mesenchymal cell (MC) populations within distinct anatomic locations is key to understanding local homeostatic and regenerative processes as well as delineating unique [...]

Published
2021
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30. Endosteal stem cells at the bone‐blood interface: A double‐edged sword for rapid bone formation: Bone marrow endosteal stem cells provide a robust source of bone‐making osteoblasts both in normal and abnormal bone formation.

Author

Matsushita, Yuki, Liu, Jialin, Chu, Angel Ka Yan, Ono, Wanida, Welch, Joshua D., and Ono, Noriaki

Subjects
STEM cells, BONE growth, TUMOR suppressor genes, BONE marrow, BONE regeneration, NEOPLASTIC cell transformation, OSTEOBLASTS, CELL populations
Abstract

Endosteal stem cells are a subclass of bone marrow skeletal stem cell populations that are particularly important for rapid bone formation occurring in growth and regeneration. These stem cells are strategically located near the bone surface in a specialized microenvironment of the endosteal niche. These stem cells are abundant in young stages but eventually depleted and replaced by other stem cell types residing in a non‐endosteal perisinusoidal niche. Single‐cell molecular profiling and in vivo cell lineage analyses play key roles in discovering endosteal stem cells. Importantly, endosteal stem cells can transform into bone tumor‐making cells when deleterious mutations occur in tumor suppressor genes. The emerging hypothesis is that osteoblast‐chondrocyte transitional identities confer a special subset of endosteal stromal cells with stem cell‐like properties, which may make them susceptible for tumorigenic transformation. Endosteal stem cells are likely to represent an important therapeutic target of bone diseases caused by aberrant bone formation. [ABSTRACT FROM AUTHOR]

Published
2024
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37. A multimodal cell census and atlas of the mammalian primary motor cortex

Author

Callaway, 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, Susan, BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, [missing], BICCN contributing principal investigators, [missing], Principal manuscript editors, [missing], Manuscript writing and figure generation, [missing], Analysis coordination, [missing], Integrated data analysis, [missing], scRNA-seq and snRNA-seq data generation and processing, [missing], ATAC-seq data generation and processing, [missing], Methylcytosine data production and analysis, [missing], Epi-retro-seq data generation and processing, [missing], ‘Omics data analysis, [missing], Tracing and connectivity data generation, [missing], Morphology data generation and reconstruction, [missing], OLST/STPT and other data generation, [missing], Morphology, connectivity and imaging analysis, [missing], Spatially resolved single-cell transcriptomics (MERFISH), [missing], Multimodal profiling (Patch-seq), [missing], Transgenic tools, [missing], NeMO archive and analytics, [missing], Brain Image Library (BIL) archive, [missing], DANDI archive, [missing], Brain Cell Data Center (BCDC), [missing], Project management, [missing], and BRAIN Initiative Cell Census Network (BICCN), [missing]

Subjects
Epigenomics, Male, INTERNEURONS, ORGANIZATION, MOUSE, Molecular neuroscience, Neural circuits, CLASSIFICATION, Article, Mice, Atlases as Topic, Glutamates, Species Specificity, Animals, Humans, TRANSCRIPTOMICS, SPECIFICATION, NEURONS, In Situ Hybridization, Fluorescence, Phylogeny, Neurons, Multidisciplinary, Gene Expression Profiling, Biology and Life Sciences, PATHWAYS, Brain, Callithrix, Censuses, Cellular neuroscience, Mathematics and Statistics, SINGLE, Organ Specificity, MORPHOLOGY, Motor cortex, Female, Single-Cell Analysis, Transcriptome
Abstract

Here 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., The BRAIN Initiative Cell Census Network has constructed a multimodal cell census and atlas of the mammalian primary motor cortex in a landmark effort towards understanding brain cell-type diversity, neural circuit organization and brain function.

Published
2021

43. Additional file 1 of SquiggleNet: real-time, direct classification of nanopore signals

Author

Bao, Yuwei, Wadden, Jack, Erb-Downward, John R., Ranjan, Piyush, Zhou, Weichen, McDonald, Torrin L., Mills, Ryan E., Boyle, Alan P., Dickson, Robert P., Blaauw, David, and Welch, Joshua D.

Abstract

Additional file 1 Supplement. Details on data collection, model parameters, hyperparameter tuning, details on the efficiency comparison experiment method, details on the theoretical throughput estimation model are included in the Supplement. Additional experiment results, including DNA methylation, model interpretation, model performance trained on a different dataset, model performance on different test dataset composition ratio and on odd/even chromosomes, are also presented in the Supplement [24���28].

Published
2021
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46. An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Author

Yao, Zizhen, Liu, Hanqing, Xie, Fangming, Fischer, Stephan, Adkins, Ricky S., Aldrige, Andrew I., Ament, Seth A., Bartlett, Ann, Behrens, M. Margarita, Van den Berge, Koen, Bertagnolli, Darren, Biancalani, Tommaso, Booeshaghi, A. Sina, Corrada Bravo, Hector, Casper, Tamara, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather, Crichton, Kirsten, Crow, Megan, Dee, Nick, Dougherty, Elizabeth L., Doyle, Wayne I., Dudoit, Sandrine, Fang, Rongxin, Felix, Victor, Fong, Olivia, Giglio, Michelle, Goldy, Jeff, Hawrylycz, Michael, Roux de Bezieux, Hector, Herb, Brian R., Hertzano, Ronna, Hou, Xiaomeng, Hu, Qiwen, Huang, Z. Josh, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, Li, Yang Eric, Lucero, Jacinta D., Luo, Chongyuan, Mahurkar, Anup, McMillen, Delissa, Nadaf, Naeem M., Nery, Joseph R., Nguyen, Thuc Nghi, Niu, Sheng-Yong, Ntranos, Vasilis, Orvis, Joshua, Osteen, Julia K., Pham, Thanh, Pinto-Duarte, Antonio, Poirion, Olivier, Preissl, Sebastian, Purdom, Elizabeth, Rimorin, Christine, Risso, Davide, Rivkin, Angeline C., Smith, Kimberly, Street, Kelly, Sulc, Josef, Svensson, Valentine, Tieu, Michael, Torkelson, Amy, Tung, Herman, Vaishnav, Eeshit Dhaval, Vanderburg, Charles R., van Velthoven, Cindy, Wang, Xinxin, White, Owen, Gillis, Jesse, Kharchenko, Peter V., Ngai, John, Pachter, Lior, Regev, Aviv, Tasic, Bosiljka, Welch, Joshua D., Ecker, Joseph R., Macosko, Evan, Ren, Bing, Zeng, Hongkui, Mukamel, Eran A., Yao, Zizhen, Liu, Hanqing, Xie, Fangming, Fischer, Stephan, Adkins, Ricky S., Aldrige, Andrew I., Ament, Seth A., Bartlett, Ann, Behrens, M. Margarita, Van den Berge, Koen, Bertagnolli, Darren, Biancalani, Tommaso, Booeshaghi, A. Sina, Corrada Bravo, Hector, Casper, Tamara, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather, Crichton, Kirsten, Crow, Megan, Dee, Nick, Dougherty, Elizabeth L., Doyle, Wayne I., Dudoit, Sandrine, Fang, Rongxin, Felix, Victor, Fong, Olivia, Giglio, Michelle, Goldy, Jeff, Hawrylycz, Michael, Roux de Bezieux, Hector, Herb, Brian R., Hertzano, Ronna, Hou, Xiaomeng, Hu, Qiwen, Huang, Z. Josh, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, Li, Yang Eric, Lucero, Jacinta D., Luo, Chongyuan, Mahurkar, Anup, McMillen, Delissa, Nadaf, Naeem M., Nery, Joseph R., Nguyen, Thuc Nghi, Niu, Sheng-Yong, Ntranos, Vasilis, Orvis, Joshua, Osteen, Julia K., Pham, Thanh, Pinto-Duarte, Antonio, Poirion, Olivier, Preissl, Sebastian, Purdom, Elizabeth, Rimorin, Christine, Risso, Davide, Rivkin, Angeline C., Smith, Kimberly, Street, Kelly, Sulc, Josef, Svensson, Valentine, Tieu, Michael, Torkelson, Amy, Tung, Herman, Vaishnav, Eeshit Dhaval, Vanderburg, Charles R., van Velthoven, Cindy, Wang, Xinxin, White, Owen, Gillis, Jesse, Kharchenko, Peter V., Ngai, John, Pachter, Lior, Regev, Aviv, Tasic, Bosiljka, Welch, Joshua D., Ecker, Joseph R., Macosko, Evan, Ren, Bing, Zeng, Hongkui, and Mukamel, Eran A.

Abstract

Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.

Published
2020

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