7 results on '"Gouwens, N."'
Search Results
2. Use of Radiation Within the Last Year of Life Among Cancer Patients
- Author
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Tseng, Y.D., primary, Gouwens, N., additional, Lo, S.S., additional, Halasz, L.M., additional, Mezheritsky, I., additional, and Loggers, E., additional
- Published
- 2017
- Full Text
- View/download PDF
3. A guide to the BRAIN Initiative Cell Census Network data ecosystem.
- Author
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Hawrylycz M, Martone ME, Ascoli GA, Bjaalie JG, Dong HW, Ghosh SS, Gillis J, Hertzano R, Haynor DR, Hof PR, Kim Y, Lein E, Liu Y, Miller JA, Mitra PP, Mukamel E, Ng L, Osumi-Sutherland D, Peng H, Ray PL, Sanchez R, Regev A, Ropelewski A, Scheuermann RH, Tan SZK, Thompson CL, Tickle T, Tilgner H, Varghese M, Wester B, White O, Zeng H, Aevermann B, Allemang D, Ament S, Athey TL, Baker C, Baker KS, Baker PM, Bandrowski A, Banerjee S, Bishwakarma P, Carr A, Chen M, Choudhury R, Cool J, Creasy H, D'Orazi F, Degatano K, Dichter B, Ding SL, Dolbeare T, Ecker JR, Fang R, Fillion-Robin JC, Fliss TP, Gee J, Gillespie T, Gouwens N, Zhang GQ, Halchenko YO, Harris NL, Herb BR, Hintiryan H, Hood G, Horvath S, Huo B, Jarecka D, Jiang S, Khajouei F, Kiernan EA, Kir H, Kruse L, Lee C, Lelieveldt B, Li Y, Liu H, Liu L, Markuhar A, Mathews J, Mathews KL, Mezias C, Miller MI, Mollenkopf T, Mufti S, Mungall CJ, Orvis J, Puchades MA, Qu L, Receveur JP, Ren B, Sjoquist N, Staats B, Tward D, van Velthoven CTJ, Wang Q, Xie F, Xu H, Yao Z, Yun Z, Zhang YR, Zheng WJ, and Zingg B
- Subjects
- Animals, Humans, Mice, Ecosystem, Neurons, Brain, Neurosciences
- 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., Competing Interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: AR is a co-founder and equity holder of Celsius Therapeutics, an equity holder in Immunitas Therapeutics and, until 31 July 2020, was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Asimov, and Neogene Therapeutics. From 1 August 2020, AR is an employee of Genentech and has equity in Roche. AR is a named inventor on multiple patents related to single cell and spatial genomics filed by or issued to the Broad Institute., (Copyright: © 2023 Hawrylycz et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Published
- 2023
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- View/download PDF
4. Integrating EM and Patch-seq data: Synaptic connectivity and target specificity of predicted Sst transcriptomic types.
- Author
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Gamlin CR, Schneider-Mizell CM, Mallory M, Elabbady L, Gouwens N, Williams G, Mukora A, Dalley R, Bodor A, Brittain D, Buchanan J, Bumbarger D, Kapner D, Kinn S, Mahalingam G, Seshamani S, Takeno M, Torres R, Yin W, Nicovich PR, Bae JA, Castro MA, Dorkenwald S, Halageri A, Jia Z, Jordan C, Kemnitz N, Lee K, Li K, Lu R, Macrina T, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Silversmith W, Turner NL, Wong W, Wu J, Yu S, Berg J, Jarsky T, Lee B, Seung HS, Zeng H, Reid RC, Collman F, da Costa NM, and Sorensen SA
- Abstract
Neural circuit function is shaped both by the cell types that comprise the circuit and the connections between those cell types
1 . Neural cell types have previously been defined by morphology2, 3 , electrophysiology4, 5 , transcriptomic expression6-8 , connectivity9-13 , or even a combination of such modalities14-16 . More recently, the Patch-seq technique has enabled the characterization of morphology (M), electrophysiology (E), and transcriptomic (T) properties from individual cells17-20 . Using this technique, these properties were integrated to define 28, inhibitory multimodal, MET-types in mouse primary visual cortex21 . It is unknown how these MET-types connect within the broader cortical circuitry however. Here we show that we can predict the MET-type identity of inhibitory cells within a large-scale electron microscopy (EM) dataset and these MET-types have distinct ultrastructural features and synapse connectivity patterns. We found that EM Martinotti cells, a well defined morphological cell type22, 23 known to be Somatostatin positive (Sst+)24, 25 , were successfully predicted to belong to Sst+ MET-types. Each identified MET-type had distinct axon myelination patterns and synapsed onto specific excitatory targets. Our results demonstrate that morphological features can be used to link cell type identities across imaging modalities, which enables further comparison of connectivity in relation to transcriptomic or electrophysiological properties. Furthermore, our results show that MET-types have distinct connectivity patterns, supporting the use of MET-types and connectivity to meaningfully define cell types.- Published
- 2023
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5. Consistent cross-modal identification of cortical neurons with coupled autoencoders.
- Author
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Gala R, Budzillo A, Baftizadeh F, Miller J, Gouwens N, Arkhipov A, Murphy G, Tasic B, Zeng H, Hawrylycz M, and Sümbül U
- Abstract
Consistent identification of neurons in different experimental modalities is a key problem in neuroscience. Although methods to perform multimodal measurements in the same set of single neurons have become available, parsing complex relationships across different modalities to uncover neuronal identity is a growing challenge. Here we present an optimization framework to learn coordinated representations of multimodal data and apply it to a large multimodal dataset profiling mouse cortical interneurons. Our approach reveals strong alignment between transcriptomic and electrophysiological characterizations, enables accurate cross-modal data prediction, and identifies cell types that are consistent across modalities.
- Published
- 2021
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6. Identification of genetic markers for cortical areas using a Random Forest classification routine and the Allen Mouse Brain Atlas.
- Author
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Weed N, Bakken T, Graddis N, Gouwens N, Millman D, Hawrylycz M, and Waters J
- Subjects
- Adult, Algorithms, Animals, Humans, Mice, Cerebral Cortex metabolism, Gene Expression Profiling methods, Genetic Markers, Models, Biological, Models, Statistical
- Abstract
The mammalian neocortex is subdivided into a series of cortical areas that are functionally and anatomically distinct and are often distinguished in brain sections using histochemical stains and other markers of protein expression. We searched the Allen Mouse Brain Atlas, a database of gene expression, for novel markers of cortical areas. To screen for genes that change expression at area borders, we employed a random forest algorithm and binary region classification. Novel genetic markers were identified for 19 of 39 areas and provide code that quickly and efficiently searches the Allen Mouse Brain Atlas. Our results demonstrate the utility of the random forest algorithm for cortical area classification and we provide code that may be used to facilitate the identification of genetic markers of cortical and subcortical structures and perhaps changes in gene expression in disease states., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2019
- Full Text
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7. Generalized leaky integrate-and-fire models classify multiple neuron types.
- Author
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Teeter C, Iyer R, Menon V, Gouwens N, Feng D, Berg J, Szafer A, Cain N, Zeng H, Hawrylycz M, Koch C, and Mihalas S
- Subjects
- Action Potentials physiology, Animals, Cell Line, Cerebral Cortex cytology, Cerebral Cortex physiology, Cluster Analysis, Electrophysiological Phenomena, Mice, Mice, Transgenic, Neurons cytology, Models, Neurological, Neurons classification, Neurons physiology
- Abstract
There is a high diversity of neuronal types in the mammalian neocortex. To facilitate construction of system models with multiple cell types, we generate a database of point models associated with the Allen Cell Types Database. We construct a set of generalized leaky integrate-and-fire (GLIF) models of increasing complexity to reproduce the spiking behaviors of 645 recorded neurons from 16 transgenic lines. The more complex models have an increased capacity to predict spiking behavior of hold-out stimuli. We use unsupervised methods to classify cell types, and find that high level GLIF model parameters are able to differentiate transgenic lines comparable to electrophysiological features. The more complex model parameters also have an increased ability to differentiate between transgenic lines. Thus, creating simple models is an effective dimensionality reduction technique that enables the differentiation of cell types from electrophysiological responses without the need for a priori-defined features. This database will provide a set of simplified models of multiple cell types for the community to use in network models.
- Published
- 2018
- Full Text
- View/download PDF
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