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1. Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus.

Author

Herb, Brian, Glover, Hannah, Bhaduri, Aparna, Colantuoni, Carlo, Bale, Tracy, Siletti, Kimberly, Hodge, Rebecca, Lein, Ed, Doege, Claudia, Ament, Seth, and Kriegstein, Arnold

Subjects
Mice, Animals, Humans, Hypothalamus, Neurons, Transcriptome, Gene Expression Profiling, Genomics
Abstract

The development and diversity of neuronal subtypes in the human hypothalamus has been insufficiently characterized. To address this, we integrated transcriptomic data from 241,096 cells (126,840 newly generated) in the prenatal and adult human hypothalamus to reveal a temporal trajectory from proliferative stem cell populations to mature hypothalamic cell types. Iterative clustering of the adult neurons identified 108 robust transcriptionally distinct neuronal subtypes representing 10 hypothalamic nuclei. Pseudotime trajectories provided insights into the genes driving formation of these nuclei. Comparisons to single-cell transcriptomic data from the mouse hypothalamus suggested extensive conservation of neuronal subtypes despite certain differences in species-enriched gene expression. The uniqueness of hypothalamic neuronal lineages was examined developmentally by comparing excitatory lineages present in cortex and inhibitory lineages in ganglionic eminence, revealing both distinct and shared drivers of neuronal maturation across the human forebrain. These results provide a comprehensive transcriptomic view of human hypothalamus development through gestation and adulthood at cellular resolution.

Published
2023

3. A guide to the BRAIN Initiative Cell Census Network data ecosystem

Author

Hawrylycz, Michael, Martone, Maryann E, Ascoli, Giorgio A, Bjaalie, Jan G, Dong, Hong-Wei, Ghosh, Satrajit S, Gillis, Jesse, Hertzano, Ronna, Haynor, David R, Hof, Patrick R, Kim, Yongsoo, Lein, Ed, Liu, Yufeng, Miller, Jeremy A, Mitra, Partha P, Mukamel, Eran, Ng, Lydia, Osumi-Sutherland, David, Peng, Hanchuan, Ray, Patrick L, Sanchez, Raymond, Regev, Aviv, Ropelewski, Alex, Scheuermann, Richard H, Tan, Shawn Zheng Kai, Thompson, Carol L, Tickle, Timothy, Tilgner, Hagen, Varghese, Merina, Wester, Brock, White, Owen, Zeng, Hongkui, Aevermann, Brian, Allemang, David, Ament, Seth, Athey, Thomas L, Baker, Cody, Baker, Katherine S, Baker, Pamela M, Bandrowski, Anita, Banerjee, Samik, Bishwakarma, Prajal, Carr, Ambrose, Chen, Min, Choudhury, Roni, Cool, Jonah, Creasy, Heather, D’Orazi, Florence, Degatano, Kylee, Dichter, Benjamin, Ding, Song-Lin, Dolbeare, Tim, Ecker, Joseph R, Fang, Rongxin, Fillion-Robin, Jean-Christophe, Fliss, Timothy P, Gee, James, Gillespie, Tom, Gouwens, Nathan, Zhang, Guo-Qiang, Halchenko, Yaroslav O, Harris, Nomi L, Herb, Brian R, Hintiryan, Houri, Hood, Gregory, Horvath, Sam, Huo, Bingxing, Jarecka, Dorota, Jiang, Shengdian, Khajouei, Farzaneh, Kiernan, Elizabeth A, Kir, Huseyin, Kruse, Lauren, Lee, Changkyu, Lelieveldt, Boudewijn, Li, Yang, Liu, Hanqing, Liu, Lijuan, Markuhar, Anup, Mathews, James, Mathews, Kaylee L, Mezias, Chris, Miller, Michael I, Mollenkopf, Tyler, Mufti, Shoaib, Mungall, Christopher J, Orvis, Joshua, Puchades, Maja A, Qu, Lei, Receveur, Joseph P, Ren, Bing, Sjoquist, Nathan, Staats, Brian, Tward, Daniel, van Velthoven, Cindy TJ, Wang, Quanxin, Xie, Fangming, Xu, Hua, Yao, Zizhen, and Yun, Zhixi

Subjects
Biological Sciences, Genetics, Data Science, Neurosciences, Mental Health, 1.1 Normal biological development and functioning, Neurological, Animals, Humans, Mice, Brain, Ecosystem, Neurons, Agricultural and Veterinary Sciences, Medical and Health Sciences, Developmental Biology, Agricultural, veterinary and food sciences, Biological sciences, Biomedical and clinical sciences
Abstract

Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Published
2023

4. 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

5. Comparative cellular analysis of motor cortex in human, marmoset and mouse

Author

Bakken, Trygve E, Jorstad, Nikolas L, Hu, Qiwen, Lake, Blue B, Tian, Wei, Kalmbach, Brian E, Crow, Megan, Hodge, Rebecca D, Krienen, Fenna M, Sorensen, Staci A, Eggermont, Jeroen, Yao, Zizhen, Aevermann, Brian D, Aldridge, Andrew I, Bartlett, Anna, Bertagnolli, Darren, Casper, Tamara, Castanon, Rosa G, Crichton, Kirsten, Daigle, Tanya L, Dalley, Rachel, Dee, Nick, Dembrow, Nikolai, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Fang, Rongxin, Fischer, Stephan, Goldman, Melissa, Goldy, Jeff, Graybuck, Lucas T, Herb, Brian R, Hou, Xiaomeng, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, van Lew, Baldur, Li, Yang Eric, Liu, Christine S, Liu, Hanqing, Lucero, Jacinta D, Mahurkar, Anup, McMillen, Delissa, Miller, Jeremy A, Moussa, Marmar, Nery, Joseph R, Nicovich, Philip R, Niu, Sheng-Yong, Orvis, Joshua, Osteen, Julia K, Owen, Scott, Palmer, Carter R, Pham, Thanh, Plongthongkum, Nongluk, Poirion, Olivier, Reed, Nora M, Rimorin, Christine, Rivkin, Angeline, Romanow, William J, Sedeño-Cortés, Adriana E, Siletti, Kimberly, Somasundaram, Saroja, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Wang, Xinxin, Xie, Fangming, Yanny, Anna Marie, Zhang, Renee, Ament, Seth A, Behrens, M Margarita, Bravo, Hector Corrada, Chun, Jerold, Dobin, Alexander, Gillis, Jesse, Hertzano, Ronna, Hof, Patrick R, Höllt, Thomas, Horwitz, Gregory D, Keene, C Dirk, Kharchenko, Peter V, Ko, Andrew L, Lelieveldt, Boudewijn P, Luo, Chongyuan, Mukamel, Eran A, Pinto-Duarte, António, Preissl, Sebastian, Regev, Aviv, Ren, Bing, Scheuermann, Richard H, Smith, Kimberly, Spain, William J, White, Owen R, Koch, Christof, Hawrylycz, Michael, Tasic, Bosiljka, Macosko, Evan Z, McCarroll, Steven A, and Ting, Jonathan T

Subjects
Human Genome, Neurosciences, Genetics, Biotechnology, Underpinning research, 1.1 Normal biological development and functioning, Neurological, Animals, Atlases as Topic, Callithrix, Epigenesis, Genetic, Epigenomics, Female, GABAergic Neurons, Gene Expression Profiling, Glutamates, Humans, In Situ Hybridization, Fluorescence, Male, Mice, Middle Aged, Motor Cortex, Neurons, Organ Specificity, Phylogeny, Single-Cell Analysis, Species Specificity, Transcriptome, General Science & Technology
Abstract

The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals1. Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations.

Published
2021

10. Author Correction: Comparative cellular analysis of motor cortex in human, marmoset and mouse

Author

Bakken, Trygve E., Jorstad, Nikolas L., Hu, Qiwen, Lake, Blue B., Tian, Wei, Kalmbach, Brian E., Crow, Megan, Hodge, Rebecca D., Krienen, Fenna M., Sorensen, Staci A., Eggermont, Jeroen, Yao, Zizhen, Aevermann, Brian D., Aldridge, Andrew I., Bartlett, Anna, Bertagnolli, Darren, Casper, Tamara, Castanon, Rosa G., Crichton, Kirsten, Daigle, Tanya L., Dalley, Rachel, Dee, Nick, Dembrow, Nikolai, Diep, Dinh, Ding, Song-Lin, Dong, Weixiu, Fang, Rongxin, Fischer, Stephan, Goldman, Melissa, Goldy, Jeff, Graybuck, Lucas T., Herb, Brian R., Hou, Xiaomeng, Kancherla, Jayaram, Kroll, Matthew, Lathia, Kanan, van Lew, Baldur, Li, Yang Eric, Liu, Christine S., Liu, Hanqing, Lucero, Jacinta D., Mahurkar, Anup, McMillen, Delissa, Miller, Jeremy A., Moussa, Marmar, Nery, Joseph R., Nicovich, Philip R., Niu, Sheng-Yong, Orvis, Joshua, Osteen, Julia K., Owen, Scott, Palmer, Carter R., Pham, Thanh, Plongthongkum, Nongluk, Poirion, Olivier, Reed, Nora M., Rimorin, Christine, Rivkin, Angeline, Romanow, William J., Sedeño-Cortés, Adriana E., Siletti, Kimberly, Somasundaram, Saroja, Sulc, Josef, Tieu, Michael, Torkelson, Amy, Tung, Herman, Wang, Xinxin, Xie, Fangming, Yanny, Anna Marie, Zhang, Renee, Ament, Seth A., Behrens, M. Margarita, Bravo, Hector Corrada, Chun, Jerold, Dobin, Alexander, Gillis, Jesse, Hertzano, Ronna, Hof, Patrick R., Höllt, Thomas, Horwitz, Gregory D., Keene, C. Dirk, Kharchenko, Peter V., Ko, Andrew L., Lelieveldt, Boudewijn P., Luo, Chongyuan, Mukamel, Eran A., Pinto-Duarte, António, Preiss, Sebastian, Regev, Aviv, Ren, Bing, Scheuermann, Richard H., Smith, Kimberly, Spain, William J., White, Owen R., Koch, Christof, Hawrylycz, Michael, Tasic, Bosiljka, Macosko, Evan Z., McCarroll, Steven A., Ting, Jonathan T., Zeng, Hongkui, Zhang, Kun, Feng, Guoping, Ecker, Joseph R., Linnarsson, Sten, and Lein, Ed S.

Published
2022
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11. gEAR: Gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

Author

Orvis, Joshua, Gottfried, Brian, Kancherla, Jayaram, Adkins, Ricky S., Song, Yang, Dror, Amiel A., Olley, Dustin, Rose, Kevin, Chrysostomou, Elena, Kelly, Michael C., Milon, Beatrice, Matern, Maggie S., Azaiez, Hela, Herb, Brian, Colantuoni, Carlo, Carter, Robert L., Ament, Seth A., Kelley, Matthew W., White, Owen, Bravo, Hector Corrada, Mahurkar, Anup, and Hertzano, Ronna

Published
2021
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12. The single-cell opioid responses in the context of HIV (SCORCH) consortium

Author

Ament, Seth A., Campbell, Rianne R., Lobo, Mary Kay, Receveur, Joseph P., Agrawal, Kriti, Borjabad, Alejandra, Byrareddy, Siddappa N., Chang, Linda, Clarke, Declan, Emani, Prashant, Gabuzda, Dana, Gaulton, Kyle J., Giglio, Michelle, Giorgi, Federico M., Gok, Busra, Guda, Chittibabu, Hadas, Eran, Herb, Brian R., Hu, Wen, Huttner, Anita, Ishmam, Mohammad R., Jacobs, Michelle M., Kelschenbach, Jennifer, Kim, Dong-Wook, Lee, Cheyu, Liu, Shuhui, Liu, Xiaokun, Madras, Bertha K., Mahurkar, Anup A., Mash, Deborah C., Mukamel, Eran A., Niu, Meng, O’Connor, Richard M., Pagan, Chelsea M., Pang, Alina P. S., Pillai, Piya, Repunte-Canonigo, Vez, Ruzicka, W. Brad, Stanley, Jay, Tickle, Timothy, Tsai, Shang-Yi A., Wang, Allen, Wills, Lauren, Wilson, Alyssa M., Wright, Susan N., Xu, Siwei, Yang, Junchen, Zand, Maryam, Zhang, Le, Zhang, Jing, Akbarian, Schahram, Buch, Shilpa, Cheng, Christine S., Corley, Michael J., Fox, Howard S., Gerstein, Mark, Gummuluru, Suryaram, Heiman, Myriam, Ho, Ya-Chi, Kellis, Manolis, Kenny, Paul J., Kluger, Yuval, Milner, Teresa A., Moore, David J., Morgello, Susan, Ndhlovu, Lishomwa C., Rana, Tariq M., Sanna, Pietro Paolo, Satterlee, John S., Sestan, Nenad, Spector, Stephen A., Spudich, Serena, Tilgner, Hagen U., Volsky, David J., White, Owen R., Williams, Dionne W., and Zeng, Hongkui

Abstract

Substance use disorders (SUD) and drug addiction are major threats to public health, impacting not only the millions of individuals struggling with SUD, but also surrounding families and communities. One of the seminal challenges in treating and studying addiction in human populations is the high prevalence of co-morbid conditions, including an increased risk of contracting a human immunodeficiency virus (HIV) infection. Of the ~15 million people who inject drugs globally, 17% are persons with HIV. Conversely, HIV is a risk factor for SUD because chronic pain syndromes, often encountered in persons with HIV, can lead to an increased use of opioid pain medications that in turn can increase the risk for opioid addiction. We hypothesize that SUD and HIV exert shared effects on brain cell types, including adaptations related to neuroplasticity, neurodegeneration, and neuroinflammation. Basic research is needed to refine our understanding of these affected cell types and adaptations. Studying the effects of SUD in the context of HIV at the single-cell level represents a compelling strategy to understand the reciprocal interactions among both conditions, made feasible by the availability of large, extensively-phenotyped human brain tissue collections that have been amassed by the Neuro-HIV research community. In addition, sophisticated animal models that have been developed for both conditions provide a means to precisely evaluate specific exposures and stages of disease. We propose that single-cell genomics is a uniquely powerful technology to characterize the effects of SUD and HIV in the brain, integrating data from human cohorts and animal models. We have formed the Single-Cell Opioid Responses in the Context of HIV (SCORCH) consortium to carry out this strategy.

Published
2024
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13. Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data

Author

Wang, Yichen, primary, Sarfraz, Irzam, additional, Teh, Wei Kheng, additional, Sokolov, Artem, additional, Herb, Brian R., additional, Creasy, Heather H., additional, Virshup, Isaac, additional, Dries, Ruben, additional, Degatano, Kylee, additional, Mahurkar, Anup, additional, Schnell, Daniel J., additional, Madrigal, Pedro, additional, Hilton, Jason, additional, Gehlenborg, Nils, additional, Tickle, Timothy, additional, and Campbell, Joshua D., additional

Published
2023
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14. The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain

Author

Ament, Seth A, primary, Adkins, Ricky S, additional, Carter, Robert, additional, Chrysostomou, Elena, additional, Colantuoni, Carlo, additional, Crabtree, Jonathan, additional, Creasy, Heather H, additional, Degatano, Kylee, additional, Felix, Victor, additional, Gandt, Peter, additional, Garden, Gwenn A, additional, Giglio, Michelle, additional, Herb, Brian R, additional, Khajouei, Farzaneh, additional, Kiernan, Elizabeth, additional, McCracken, Carrie, additional, McDaniel, Kennedy, additional, Nadendla, Suvarna, additional, Nickel, Lance, additional, Olley, Dustin, additional, Orvis, Joshua, additional, Receveur, Joseph P, additional, Schor, Mike, additional, Sonthalia, Shreyash, additional, Tickle, Timothy L, additional, Way, Jessica, additional, Hertzano, Ronna, additional, Mahurkar, Anup A, additional, and White, Owen R, additional

Published
2022
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16. The Neuroscience Multi-Omic Archive: A BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain

Author

Ament, Seth A., primary, Adkins, Ricky S., additional, Carter, Robert, additional, Chrysostomou, Elena, additional, Colantuoni, Carlo, additional, Crabtree, Jonathan, additional, Creasy, Heather H., additional, Degatano, Kylee, additional, Felix, Victor, additional, Gandt, Peter, additional, Garden, Gwenn A., additional, Giglio, Michelle, additional, Herb, Brian R., additional, Khajouei, Farzaneh, additional, Kiernan, Elizabeth, additional, McCracken, Carrie, additional, McDaniel, Kennedy, additional, Nadendla, Suvarna, additional, Nickel, Lance, additional, Olley, Dustin, additional, Orvis, Joshua, additional, Receveur, Joseph P., additional, Schor, Mike, additional, Tickle, Timothy L., additional, Way, Jessica, additional, Hertzano, Ronna, additional, Mahurkar, Anup A., additional, and White, Owen R, additional

Published
2022
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19. Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain.

Author

Ament, Seth A, Adkins, Ricky S, Carter, Robert, Chrysostomou, Elena, Colantuoni, Carlo, Crabtree, Jonathan, Creasy, Heather H, Degatano, Kylee, Felix, Victor, Gandt, Peter, Garden, Gwenn A, Giglio, Michelle, Herb, Brian R, Khajouei, Farzaneh, Kiernan, Elizabeth, McCracken, Carrie, McDaniel, Kennedy, Nadendla, Suvarna, Nickel, Lance, and Olley, Dustin

Published
2023
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20. 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

21. Cochlear organoids reveal epigenetic and transcriptional programs of postnatal hair cell differentiation from supporting cells

Author

Kalra, Gurmannat, primary, Lenz, Danielle, additional, Abdul-Aziz, Dunia, additional, Hanna, Craig, additional, Herb, Brian, additional, Colantuoni, Carlo, additional, Milon, Beatrice, additional, Saxena, Madhurima, additional, Shetty, Amol C, additional, Hertzano, Ronna P, additional, Shivdasani, Ramesh, additional, Ament, Seth A, additional, and Edge, Albert SB, additional

Published
2021
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22. Single-cell genomics reveals region-specific developmental trajectories underlying neuronal diversity in the human hypothalamus

Author

Herb, Brian R., primary, Glover, Hannah J., additional, Bhaduri, Aparna, additional, Colantuoni, Carlo, additional, Bale, Tracy L., additional, Siletti, Kimberly, additional, Linnarsson, Sten, additional, Hodge, Rebecca, additional, Lein, Ed, additional, Kriegstein, Arnold R., additional, Doege, Claudia A., additional, and Ament, Seth A., additional

Published
2021
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24. NeMO analytics‐AD: The neuroscience multi‐omic visualization and analysis platform, now extended to support Alzheimer’s disease

Author

Chrysostomou, Elena, primary, Prater, Katherine E., additional, Song, Yang, additional, Orvis, Joshua, additional, Kancherla, Jayaram, additional, Herb, Brian, additional, Ament, Seth, additional, Bravo, Hector C., additional, Mahurkar, Anup A., additional, White, Owen, additional, Garden, Gwenn A., additional, and Hertzano, Ronna, additional

Published
2020
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27. gEAR: gene Expression Analysis Resource portal for community-driven, multi-omic data exploration

Author

Orvis, Joshua, primary, Gottfried, Brian, additional, Kancherla, Jayaram, additional, Adkins, Ricky S., additional, Song, Yang, additional, Dror, Amiel A., additional, Olley, Dustin, additional, Rose, Kevin, additional, Chrysostomou, Elena, additional, Kelly, Michael C., additional, Milon, Beatrice, additional, Matern, Maggie S., additional, Azaiez, Hela, additional, Herb, Brian, additional, Colantuoni, Carlo, additional, Carter, Robert L., additional, Ament, Seth A., additional, Kelley, Matthew W., additional, White, Owen, additional, Bravo, Hector Corrada, additional, Mahurkar, Anup, additional, and Hertzano, Ronna, additional

Published
2020
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30. Evolution of cellular diversity in primary motor cortex of human, marmoset monkey, and mouse

Author

Bakken, Trygve E., primary, Jorstad, Nikolas L., additional, Hu, Qiwen, additional, Lake, Blue B., additional, Tian, Wei, additional, Kalmbach, Brian E., additional, Crow, Megan, additional, Hodge, Rebecca D., additional, Krienen, Fenna M., additional, Sorensen, Staci A., additional, Eggermont, Jeroen, additional, Yao, Zizhen, additional, Aevermann, Brian D., additional, Aldridge, Andrew I., additional, Bartlett, Anna, additional, Bertagnolli, Darren, additional, Casper, Tamara, additional, Castanon, Rosa G., additional, Crichton, Kirsten, additional, Daigle, Tanya L., additional, Dalley, Rachel, additional, Dee, Nick, additional, Dembrow, Nikolai, additional, Diep, Dinh, additional, Ding, Song-Lin, additional, Dong, Weixiu, additional, Fang, Rongxin, additional, Fischer, Stephan, additional, Goldman, Melissa, additional, Goldy, Jeff, additional, Graybuck, Lucas T., additional, Herb, Brian R., additional, Hou, Xiaomeng, additional, Kancherla, Jayaram, additional, Kroll, Matthew, additional, Lathia, Kanan, additional, van Lew, Baldur, additional, Li, Yang Eric, additional, Liu, Christine S., additional, Liu, Hanqing, additional, Lucero, Jacinta D., additional, Mahurkar, Anup, additional, McMillen, Delissa, additional, Miller, Jeremy A., additional, Moussa, Marmar, additional, Nery, Joseph R., additional, Nicovich, Philip R., additional, Orvis, Joshua, additional, Osteen, Julia K., additional, Owen, Scott, additional, Palmer, Carter R., additional, Pham, Thanh, additional, Plongthongkum, Nongluk, additional, Poirion, Olivier, additional, Reed, Nora M., additional, Rimorin, Christine, additional, Rivkin, Angeline, additional, Romanow, William J., additional, Sedeño-Cortés, Adriana E., additional, Siletti, Kimberly, additional, Somasundaram, Saroja, additional, Sulc, Josef, additional, Tieu, Michael, additional, Torkelson, Amy, additional, Tung, Herman, additional, Wang, Xinxin, additional, Xie, Fangming, additional, Yanny, Anna Marie, additional, Zhang, Renee, additional, Ament, Seth A., additional, Behrens, M. Margarita, additional, Bravo, Hector Corrada, additional, Chun, Jerold, additional, Dobin, Alexander, additional, Gillis, Jesse, additional, Hertzano, Ronna, additional, Hof, Patrick R., additional, Höllt, Thomas, additional, Horwitz, Gregory D., additional, Keene, C. Dirk, additional, Kharchenko, Peter V., additional, Ko, Andrew L., additional, Lelieveldt, Boudewijn P., additional, Luo, Chongyuan, additional, Mukamel, Eran A., additional, Preissl, Sebastian, additional, Regev, Aviv, additional, Ren, Bing, additional, Scheuermann, Richard H., additional, Smith, Kimberly, additional, Spain, William J., additional, White, Owen R., additional, Koch, Christof, additional, Hawrylycz, Michael, additional, Tasic, Bosiljka, additional, Macosko, Evan Z., additional, McCarroll, Steven A., additional, Ting, Jonathan T., additional, Zeng, Hongkui, additional, Zhang, Kun, additional, Feng, Guoping, additional, Ecker, Joseph R., additional, Linnarsson, Sten, additional, and Lein, Ed S., additional

Published
2020
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31. An integrated transcriptomic and epigenomic atlas of mouse primary motor cortex cell types

Author

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

Published
2020
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32. Additional file 3: of Defense against territorial intrusion is associated with DNA methylation changes in the honey bee brain

Author

Herb, Brian, Shook, Molly, Fields, Christopher, and Robinson, Gene

Abstract

Figure S1. Results of permutation testing for DEG/CpG overlap and comparison with Cingolani et al. 2013 [9]. (a-f) Minute 5 and Minute 120 suggestive CpGs were overlapped with gene expression differences of Aggressive and Control bees from Shpigler et al. (2017a) [4]. To test the significance of the overlap, we chose at random the same number of significantly different DEGs, checked the overlap and repeated this process 1000 times. For each plot, the blue histogram represents the distribution of random trials and the red vertical line represents the true number of overlapping DEGs with suggestive CpGs. Note that both minute 5 and minute 120 suggestive CpGs had a highly significant overlap with only minute 120 DEGs. (g-h) A similar approach was taken to test the significance of the overlap between AHB / EHB differences and minute 120 DMRs and suggestive CpGs. In both cases the true overlap was greater than the 1000 permutations. (PDF 478Â kb)

Published
2018
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38. Differential methylation of tissue- and cancer-specific CpG island shores distinguishes human induced pluripotent stem cells, embryonic stem cells and fibroblasts

Author

Doi, Akiko, primary, Park, In-Hyun, additional, Wen, Bo, additional, Murakami, Peter, additional, Aryee, Martin J, additional, Irizarry, Rafael, additional, Herb, Brian, additional, Ladd-Acosta, Christine, additional, Rho, Junsung, additional, Loewer, Sabine, additional, Miller, Justine, additional, Schlaeger, Thorsten, additional, Daley, George Q, additional, and Feinberg, Andrew P, additional

Published
2009
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39. Epigenetics as an answer to Darwin's "special difficulty".

Author

Herb, Brian R.

Subjects
EPIGENETICS, DNA methylation, HISTONE genetics, NATURAL selection, PHEROMONES
Abstract

Epigenetic modifications produce distinct phenotypes from the same genome through genome-wide transcriptional control. Recently, DNA methylation in honeybees and histone modifications in ants were found to assist the formation of caste phenotypes during development and adulthood. This insight allows us to revisit one of Darwin's greatest challenges to his natural selection theory; the derivation of multiple forms of sterileworkers within eusocial species. Differential feeding of larvae creates two distinct developmental paths between queens and workers, with workers further refined by pheromone cues. Flexible epigenetic control provides a mechanism to interpret the milieu of social cues that create distinct worker sub-caste phenotypes. Recent findings suggest a distinct use for DNA methylation before and after adult emergence. Further, a comparison of genes that are differentially methylated and transcriptionally altered upon pheromone signaling suggests that epigenetics can play a key role in mediating pheromone signals to derive sub-caste phenotypes. Epigenetic modifications may provide a molecular mechanism to Darwin's "special difficulty" and explain the emergence of multiple sub-phenotypes among sterile individuals. [ABSTRACT FROM AUTHOR]

Published
2014
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41. NeMO–AD, a new neuroscience multi‐omic visualization and analysis platform for Alzheimer's disease research.

Author

Chrysostomou, Elena, McDaniel, Kennedy E, Prater, Katherine E, Casas, Brandon A, Song, Yang, Orvis, Joshua, Kancherla, Jayaram, Herb, Brian, Bravo, Hector C, Mahurkar, Anup A, White, Owen, Ament, Seth, Garden, Gwenn A, and Hertzano, Ronna

Abstract

Background: Recent years have resulted in tremendous progress in understanding Alzheimer's disease (AD) via the generation and analysis of multi‐omic data. In addition, the BRAIN initiative has been mapping gene expression in the normal brain. However, a barrier for the broad usage of these data has been limited access and the requirement for informatics support to allow biologists to meaningfully access this wealth of information. To address this challenge, we extended the Neuroscience Multi‐Omic (NeMO) Analytics (nemoanalytics.org) platform to include a focus on Alzheimer's disease ("NeMO‐AD"). Method: NeMO Analytics gene expression tools are powered by gEAR (umgear.org) and consist of six components: (i) An expression browser, for visualization of gene expression across multiple datasets; (ii) A dataset compare tool for comparing all the genes between two groups; (iii) An elaborate single cell workbench; (iv) A dataset uploader that allows the user to upload their own multi‐omic data; (v) A dataset curator that allows the user to define visualization preferences (e.g., data presentation as bar, line, x‐y scatter, violin plot, tSNE, or SVG); and (vi) A dataset manager, which allows you to organize datasets into profiles and manage privacy and dataset sharing. NeMO Analytics also allows the visualization of epigenetic data via integration of the Epiviz tool (https://epiviz.github.io/). Result: Currently, NeMO‐AD consists of 32 impactful AD datasets, including bulk RNA‐seq (40.6%), microarray (43.8), single‐cell RNA‐seq (12.5%) and single‐nuclei RNA‐seq (3.1%) from both human (40.6%) and mouse models (59.4%). The datasets have been organized into 8 different "profiles" (collections of similarly themed datasets, allowing for side‐by‐side visualization): specific brain regions, experimental models of AD, microglia, therapeutic targets, AD progression, molecular brain aging, biomarkers and other. Users can create their own profiles and upload their own datasets which can be made public, private or shared with collaborator(s). Conclusion: NeMO‐AD offers biologists access to impactful multi‐omic data relevant to Alzheimer's disease that can be explored without assistance from bioinformaticians. The broad usage of this platform to share and disseminate data could result in a more rapid progression of AD research. [ABSTRACT FROM AUTHOR]

Published
2021
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43. MAKING THE MOST OF A WEAK DOLLAR.

Author

Herb, Brian

Abstract

The article provides tips in making the most of a decline exchange rate for U.S. dollar when traveling overseas. It is wise to get travel quotes in U.S. dollars in order to safeguard against rising prices. Choosing the right location to travel is also important because there are still countries which offers exceptional value for the dollar. Stay in areas where locals frequent in order to avoid places where prices are increased for those catering to tourists.

Published
2008

44. ChemInform Abstract: Selective Conversion of Enol Ethers into Alcohols in the Presence of Alkenes Using Hg(OAc)2—NaBH4.

Author

Crouch, R. David, Mehlmann, John F., Herb, Brian R., Mitten, Jeffrey V., and Dai, H. George

Abstract

ChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Published
1999
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45. Transcriptional signatures of fentanyl use in the mouse ventral tegmental area.

Author

Fox ME, Montemarano A, Ostman AE, Basu M, Herb B, Ament SA, and Fox LD

Abstract

Synthetic opioids such as fentanyl contribute to the vast majority of opioid-related overdose deaths, but fentanyl use remains broadly understudied. Like other substances with misuse potential, opioids cause lasting molecular adaptations to brain reward circuits, including neurons in the ventral tegmental area (VTA). The VTA contains numerous cell types that play diverse roles in opioid use and relapse, however it is unknown how fentanyl experience alters the transcriptional landscape in specific subtypes. Here, we performed single nuclei RNA sequencing to study transcriptional programs in fentanyl experienced mice. Male and female C57/BL6 mice self-administered intravenous fentanyl (1.5 µg/kg/infusion) or saline for 10 days. After 24 hr abstinence, VTA nuclei were isolated and prepared for sequencing on the 10X platform. We identified different patterns of gene expression across cell types. In dopamine neurons, we found enrichment of genes involved in growth hormone signaling. In dopamine-glutamate-GABA combinatorial neurons, and some GABA neurons, we found enrichment of genes involved in Pi3k-Akt signaling. In glutamate neurons, we found enrichment of genes involved in cholinergic signaling. We identified transcriptional regulators for the differentially expressed genes in each neuron cluster, including downregulation of transcriptional repressor Bcl6, and upregulation of Wnt signaling partner Tcf4. We also compared the fentanyl-induced gene expression changes identified in mouse VTA with a published rat dataset in bulk VTA, and found overlap in genes related to GABAergic signaling and extracellular matrix interaction. Together, we provide a comprehensive picture of how fentanyl self-administration alters the transcriptional landscape of the mouse VTA, that serves for the foundation for future mechanistic studies.

Published
2023
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46. Matrix and analysis metadata standards (MAMS) to facilitate harmonization and reproducibility of single-cell data.

Author

Wang Y, Sarfraz I, Teh WK, Sokolov A, Herb BR, Creasy HH, Virshup I, Dries R, Degatano K, Mahurkar A, Schnell DJ, Madrigal P, Hilton J, Gehlenborg N, Tickle T, and Campbell JD

Abstract

A large number of genomic and imaging datasets are being produced by consortia that seek to characterize healthy and disease tissues at single-cell resolution. While much effort has been devoted to capturing information related to biospecimen information and experimental procedures, the metadata standards that describe data matrices and the analysis workflows that produced them are relatively lacking. Detailed metadata schema related to data analysis are needed to facilitate sharing and interoperability across groups and to promote data provenance for reproducibility. To address this need, we developed the Matrix and Analysis Metadata Standards (MAMS) to serve as a resource for data coordinating centers and tool developers. We first curated several simple and complex "use cases" to characterize the types of feature-observation matrices (FOMs), annotations, and analysis metadata produced in different workflows. Based on these use cases, metadata fields were defined to describe the data contained within each matrix including those related to processing, modality, and subsets. Suggested terms were created for the majority of fields to aid in harmonization of metadata terms across groups. Additional provenance metadata fields were also defined to describe the software and workflows that produced each FOM. Finally, we developed a simple list-like schema that can be used to store MAMS information and implemented in multiple formats. Overall, MAMS can be used as a guide to harmonize analysis-related metadata which will ultimately facilitate integration of datasets across tools and consortia. MAMS specifications, use cases, and examples can be found at https://github.com/single-cell-mams/mams/., Competing Interests: DECLARATIONS The authors declare that they have no competing interests.

Published
2023
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47. The Neuroscience Multi-Omic Archive: a BRAIN Initiative resource for single-cell transcriptomic and epigenomic data from the mammalian brain.

Author

Ament SA, Adkins RS, Carter R, Chrysostomou E, Colantuoni C, Crabtree J, Creasy HH, Degatano K, Felix V, Gandt P, Garden GA, Giglio M, Herb BR, Khajouei F, Kiernan E, McCracken C, McDaniel K, Nadendla S, Nickel L, Olley D, Orvis J, Receveur JP, Schor M, Sonthalia S, Tickle TL, Way J, Hertzano R, Mahurkar AA, and White OR

Subjects
Animals, Mice, Genomics, Mammals, Primates, Epigenomics, Multiomics, Transcriptome, Brain cytology, Brain metabolism, Databases, Genetic
Abstract

Scalable technologies to sequence the transcriptomes and epigenomes of single cells are transforming our understanding of cell types and cell states. The Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative Cell Census Network (BICCN) is applying these technologies at unprecedented scale to map the cell types in the mammalian brain. In an effort to increase data FAIRness (Findable, Accessible, Interoperable, Reusable), the NIH has established repositories to make data generated by the BICCN and related BRAIN Initiative projects accessible to the broader research community. Here, we describe the Neuroscience Multi-Omic Archive (NeMO Archive; nemoarchive.org), which serves as the primary repository for genomics data from the BRAIN Initiative. Working closely with other BRAIN Initiative researchers, we have organized these data into a continually expanding, curated repository, which contains transcriptomic and epigenomic data from over 50 million brain cells, including single-cell genomic data from all of the major regions of the adult and prenatal human and mouse brains, as well as substantial single-cell genomic data from non-human primates. We make available several tools for accessing these data, including a searchable web portal, a cloud-computing interface for large-scale data processing (implemented on Terra, terra.bio), and a visualization and analysis platform, NeMO Analytics (nemoanalytics.org)., (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Published
2023
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