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Dive into the research topics where Tim Dolbeare is active.

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Featured researches published by Tim Dolbeare.


Nature | 2012

An anatomically comprehensive atlas of the adult human brain transcriptome

Michael Hawrylycz; Ed Lein; Angela L. Guillozet-Bongaarts; Elaine H. Shen; Lydia Ng; Jeremy A. Miller; Louie N. van de Lagemaat; Kimberly A. Smith; Amanda Ebbert; Zackery L. Riley; Chris Abajian; Christian F. Beckmann; Amy Bernard; Darren Bertagnolli; Andrew F. Boe; Preston M. Cartagena; M. Mallar Chakravarty; Mike Chapin; Jimmy Chong; Rachel A. Dalley; Barry Daly; Chinh Dang; Suvro Datta; Nick Dee; Tim Dolbeare; Vance Faber; David Feng; David Fowler; Jeff Goldy; Benjamin W. Gregor

Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive microarray profiling of ∼900 neuroanatomically precise subdivisions in two individuals. Transcriptional regulation varies enormously by anatomical location, with different regions and their constituent cell types displaying robust molecular signatures that are highly conserved between individuals. Analysis of differential gene expression and gene co-expression relationships demonstrates that brain-wide variation strongly reflects the distributions of major cell classes such as neurons, oligodendrocytes, astrocytes and microglia. Local neighbourhood relationships between fine anatomical subdivisions are associated with discrete neuronal subtypes and genes involved with synaptic transmission. The neocortex displays a relatively homogeneous transcriptional pattern, but with distinct features associated selectively with primary sensorimotor cortices and with enriched frontal lobe expression. Notably, the spatial topography of the neocortex is strongly reflected in its molecular topography—the closer two cortical regions, the more similar their transcriptomes. This freely accessible online data resource forms a high-resolution transcriptional baseline for neurogenetic studies of normal and abnormal human brain function.


Nature | 2014

Transcriptional landscape of the prenatal human brain

Jeremy A. Miller; Song Lin Ding; Susan M. Sunkin; Kimberly A. Smith; Lydia Ng; Aaron Szafer; Amanda Ebbert; Zackery L. Riley; Joshua J. Royall; Kaylynn Aiona; James M. Arnold; Crissa Bennet; Darren Bertagnolli; Krissy Brouner; Stephanie Butler; Shiella Caldejon; Anita Carey; Christine Cuhaciyan; Rachel A. Dalley; Nick Dee; Tim Dolbeare; Benjamin Facer; David Feng; Tim P. Fliss; Garrett Gee; Jeff Goldy; Lindsey Gourley; Benjamin W. Gregor; Guangyu Gu; Robert Howard

The anatomical and functional architecture of the human brain is mainly determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of the mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high-resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser-microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and post-mitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and outer subventricular zones even though the outer zone is expanded in humans. Both germinal and post-mitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in the frontal lobe. Finally, many neurodevelopmental disorder and human-evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development.


Nature Neuroscience | 2016

Adult mouse cortical cell taxonomy revealed by single cell transcriptomics

Bosiljka Tasic; Vilas Menon; Thuc Nghi Nguyen; Tae Kyung Kim; Tim Jarsky; Zizhen Yao; Boaz P. Levi; Lucas T. Gray; Staci A. Sorensen; Tim Dolbeare; Darren Bertagnolli; Jeff Goldy; Nadiya V. Shapovalova; Sheana Parry; Chang-Kyu Lee; Kimberly A. Smith; Amy Bernard; Linda Madisen; Susan M. Sunkin; Michael Hawrylycz; Christof Koch; Hongkui Zeng

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


Nucleic Acids Research | 2012

Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system

Susan M. Sunkin; Lydia Ng; Christopher Lau; Tim Dolbeare; Terri L. Gilbert; Carol L. Thompson; Michael Hawrylycz; Chinh Dang

The Allen Brain Atlas (http://www.brain-map.org) provides a unique online public resource integrating extensive gene expression data, connectivity data and neuroanatomical information with powerful search and viewing tools for the adult and developing brain in mouse, human and non-human primate. Here, we review the resources available at the Allen Brain Atlas, describing each product and data type [such as in situ hybridization (ISH) and supporting histology, microarray, RNA sequencing, reference atlases, projection mapping and magnetic resonance imaging]. In addition, standardized and unique features in the web applications are described that enable users to search and mine the various data sets. Features include both simple and sophisticated methods for gene searches, colorimetric and fluorescent ISH image viewers, graphical displays of ISH, microarray and RNA sequencing data, Brain Explorer software for 3D navigation of anatomy and gene expression, and an interactive reference atlas viewer. In addition, cross data set searches enable users to query multiple Allen Brain Atlas data sets simultaneously. All of the Allen Brain Atlas resources can be accessed through the Allen Brain Atlas data portal.


Nature Neuroscience | 2015

Canonical genetic signatures of the adult human brain

Michael Hawrylycz; Jeremy A. Miller; Vilas Menon; David Feng; Tim Dolbeare; Angela L. Guillozet-Bongaarts; Anil G. Jegga; Bruce J. Aronow; Chang Kyu Lee; Amy Bernard; Matthew F. Glasser; Donna L. Dierker; Jörg Menche; Aaron Szafer; Forrest Collman; Pascal Grange; Kenneth A. Berman; Stefan Mihalas; Zizhen Yao; Lance Stewart; Albert-László Barabási; Jay Schulkin; John Phillips; Lydia Ng; Chinh Dang; David R. Haynor; Allan R. Jones; David C. Van Essen; Christof Koch; Ed Lein

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


Nature | 2016

A comprehensive transcriptional map of primate brain development

Trygve E. Bakken; Jeremy A. Miller; Song Lin Ding; Susan M. Sunkin; Kimberly A. Smith; Lydia Ng; Aaron Szafer; Rachel A. Dalley; Joshua J. Royall; Tracy Lemon; Sheila Shapouri; Kaylynn Aiona; James M. Arnold; Jeffrey L. Bennett; Darren Bertagnolli; Kristopher Bickley; Andrew F. Boe; Krissy Brouner; Stephanie Butler; Emi J. Byrnes; Shiella Caldejon; Anita Carey; Shelby Cate; Mike Chapin; Jefferey Chen; Nick Dee; Tsega Desta; Tim Dolbeare; Nadia Dotson; Amanda Ebbert

The transcriptional underpinnings of brain development remain poorly understood, particularly in humans and closely related non-human primates. We describe a high-resolution transcriptional atlas of rhesus monkey (Macaca mulatta) brain development that combines dense temporal sampling of prenatal and postnatal periods with fine anatomical division of cortical and subcortical regions associated with human neuropsychiatric disease. Gene expression changes more rapidly before birth, both in progenitor cells and maturing neurons. Cortical layers and areas acquire adult-like molecular profiles surprisingly late in postnatal development. Disparate cell populations exhibit distinct developmental timing of gene expression, but also unexpected synchrony of processes underlying neural circuit construction including cell projection and adhesion. Candidate risk genes for neurodevelopmental disorders including primary microcephaly, autism spectrum disorder, intellectual disability, and schizophrenia show disease-specific spatiotemporal enrichment within developing neocortex. Human developmental expression trajectories are more similar to monkey than rodent, although approximately 9% of genes show human-specific regulation with evidence for prolonged maturation or neoteny compared to monkey.


The Journal of Comparative Neurology | 2016

Comprehensive cellular-resolution atlas of the adult human brain

Song-Lin Ding; Joshua J. Royall; Susan M. Sunkin; Lydia Ng; Benjamin Facer; Phil Lesnar; Angie Guillozet-Bongaarts; Bergen McMurray; Aaron Szafer; Tim Dolbeare; Allison Stevens; Lee S. Tirrell; Thomas Benner; Shiella Caldejon; Rachel A. Dalley; Nick Dee; Christopher Lau; Julie Nyhus; Melissa Reding; Zackery L. Riley; David Sandman; Elaine Shen; Andre van der Kouwe; Ani Varjabedian; Michelle Write; Lilla Zöllei; Chinh Dang; James A. Knowles; Christof Koch; John Phillips

Detailed anatomical understanding of the human brain is essential for unraveling its functional architecture, yet current reference atlases have major limitations such as lack of whole‐brain coverage, relatively low image resolution, and sparse structural annotation. We present the first digital human brain atlas to incorporate neuroimaging, high‐resolution histology, and chemoarchitecture across a complete adult female brain, consisting of magnetic resonance imaging (MRI), diffusion‐weighted imaging (DWI), and 1,356 large‐format cellular resolution (1 µm/pixel) Nissl and immunohistochemistry anatomical plates. The atlas is comprehensively annotated for 862 structures, including 117 white matter tracts and several novel cyto‐ and chemoarchitecturally defined structures, and these annotations were transferred onto the matching MRI dataset. Neocortical delineations were done for sulci, gyri, and modified Brodmann areas to link macroscopic anatomical and microscopic cytoarchitectural parcellations. Correlated neuroimaging and histological structural delineation allowed fine feature identification in MRI data and subsequent structural identification in MRI data from other brains. This interactive online digital atlas is integrated with existing Allen Institute for Brain Science gene expression atlases and is publicly accessible as a resource for the neuroscience community. J. Comp. Neurol. 524:3127–3481, 2016.


eLife | 2017

Neuropathological and transcriptomic characteristics of the aged brain

Jeremy A. Miller; Angela L. Guillozet-Bongaarts; Laura E. Gibbons; Nadia Postupna; Anne Renz; Allison Beller; Susan M. Sunkin; Lydia Ng; Shannon E. Rose; Kimberly A. Smith; Aaron Szafer; Chris Barber; Darren Bertagnolli; Kristopher Bickley; Krissy Brouner; Shiella Caldejon; Mike Chapin; Mindy L Chua; Natalie M Coleman; Eiron Cudaback; Christine Cuhaciyan; Rachel A. Dalley; Nick Dee; Tsega Desta; Tim Dolbeare; Nadezhda Dotson; Michael Fisher; Nathalie Gaudreault; Garrett Gee; Terri L. Gilbert

As more people live longer, age-related neurodegenerative diseases are an increasingly important societal health issue. Treatments targeting specific pathologies such as amyloid beta in Alzheimer’s disease (AD) have not led to effective treatments, and there is increasing evidence of a disconnect between traditional pathology and cognitive abilities with advancing age, indicative of individual variation in resilience to pathology. Here, we generated a comprehensive neuropathological, molecular, and transcriptomic characterization of hippocampus and two regions cortex in 107 aged donors (median = 90) from the Adult Changes in Thought (ACT) study as a freely-available resource (http://aging.brain-map.org/). We confirm established associations between AD pathology and dementia, albeit with increased, presumably aging-related variability, and identify sets of co-expressed genes correlated with pathological tau and inflammation markers. Finally, we demonstrate a relationship between dementia and RNA quality, and find common gene signatures, highlighting the importance of properly controlling for RNA quality when studying dementia.


bioRxiv | 2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex

Saskia de Vries; Jerome Lecoq; Michael Buice; Peter Groblewski; Gabriel Koch Ocker; Michael Oliver; David Feng; Nicholas Cain; Peter Ledochowitsch; Daniel Millman; Kate Roll; Marina Garrett; Tom Keenan; Leonard Kuan; Stefan Mihalas; Shawn Olsen; Carol L. Thompson; Wayne Wakeman; Jack Waters; Derric Williams; Chris Barber; Nathan Berbesque; Brandon Blanchard; Nicholas Bowles; Shiella Caldejon; Linzy Casal; Andrew Cho; Sissy Cross; Chinh Dang; Tim Dolbeare

To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.


Science | 2018

An anatomic transcriptional atlas of human glioblastoma

Ralph B. Puchalski; Nameeta Shah; Jeremy A. Miller; Rachel A. Dalley; Steve R. Nomura; Jae-Guen Yoon; Kimberly A. Smith; Michael Lankerovich; Darren Bertagnolli; Kris Bickley; Andrew F. Boe; Krissy Brouner; Stephanie Butler; Shiella Caldejon; Mike Chapin; Suvro Datta; Nick Dee; Tsega Desta; Tim Dolbeare; Nadezhda Dotson; Amanda Ebbert; David Feng; Xu Feng; Michael Fisher; Garrett Gee; Jeff Goldy; Lindsey Gourley; Benjamin W. Gregor; Guangyu Gu; Nika Hejazinia

Anatomically correct tumor genomics Glioblastoma is the most lethal form of human brain cancer. The genomic alterations and gene expression profiles characterizing this tumor type have been widely studied. Puchalski et al. created the Ivy Glioblastoma Atlas, a freely available online resource for the research community. The atlas, a collaborative effort between bioinformaticians and pathologists, maps molecular features of glioblastomas, such as transcriptional signatures, to histologically defined anatomical regions of the tumors. The relationships identified in this atlas, in conjunction with associated databases of clinical and genomic information, could provide new insights into the pathogenesis, diagnosis, and treatment of glioblastoma. Science, this issue p. 660 An online resource maps the molecular genetic features of glioblastoma, a lethal brain cancer, to its anatomic features. Glioblastoma is an aggressive brain tumor that carries a poor prognosis. The tumor’s molecular and cellular landscapes are complex, and their relationships to histologic features routinely used for diagnosis are unclear. We present the Ivy Glioblastoma Atlas, an anatomically based transcriptional atlas of human glioblastoma that aligns individual histologic features with genomic alterations and gene expression patterns, thus assigning molecular information to the most important morphologic hallmarks of the tumor. The atlas and its clinical and genomic database are freely accessible online data resources that will serve as a valuable platform for future investigations of glioblastoma pathogenesis, diagnosis, and treatment.

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Lydia Ng

Allen Institute for Brain Science

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Darren Bertagnolli

Allen Institute for Brain Science

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Jeremy A. Miller

Allen Institute for Brain Science

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Kimberly A. Smith

Allen Institute for Brain Science

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Nick Dee

Allen Institute for Brain Science

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Rachel A. Dalley

Allen Institute for Brain Science

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Shiella Caldejon

Allen Institute for Brain Science

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Susan M. Sunkin

Allen Institute for Brain Science

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Aaron Szafer

Allen Institute for Brain Science

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Chinh Dang

Allen Institute for Brain Science

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