Probing the transcriptome of neuronal cell types
Abstract
Distinct neuronal cell types acquire and maintain their identity by expressing different genes. Recently it has become feasible to measure this cell type specific expression by isolating and amplifying mRNA from small populations of fluorescently labeled neurons and probing this mRNA with microarrays. Prior to this, most neuronal gene expression studies used tissue homogenates or randomly selected single cells and were, therefore, not well suited to studying transcriptional differences between cell types. Microarray studies of purified cell types have enabled investigators to identify the transcriptional signatures of, for example, subtypes of pyramidal neurons and interneurons in the neocortex, modulatory dopaminergic and serotonergic neurons, and the striatal neurons that form the so-called 'direct' and 'indirect' pathways through the basal ganglia. These studies are opening up new approaches to understanding brain circuitry, plasticity and pathology and are refining the concept of the neuronal cell type.
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The identity and physiological state of neuron types are largely determined by their patterns of gene expression.
“To a large extent, the identity and physiological state of neuron types are determined by their patterns of gene expression (Nelson et al., 2006, Hobert et al., 2010).”
From Cell-Type-Based Analysis of MicroRNA Profiles in the Mouse Brain (2012) · cites this paper as Nelson et al., 2006
Cites
Interneurons of the neocortical inhibitory system
Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004) · Nature Reviews Neuroscience · doi:10.1038/nrn1519 · Cited by 2,954 · see citing works
A gene expression atlas of the central nervous system based on bacterial artificial chromosomes
Gong, S., Zheng, C., Doughty, M. L., Losos, K., Didkovsky, N., Schambra, U. B., Nowak, N. J., Joyner, A., Leblanc, G., Hatten, M. E., et al. (2003) · Nature · doi:10.1038/nature02033 · Cited by 2,108 · see citing works
Brain-state- and cell-type-specific firing of hippocampal interneurons in vivo
Klausberger, T., Magill, P. J., Márton, L. F., Roberts, J. D. B., Cobden, P. M., Buzsáki, G., & Somogyi, P. (2003) · Nature · doi:10.1038/nature01374 · Cited by 1,345 · see citing works
Molecular taxonomy of major neuronal classes in the adult mouse forebrain
Sugino, K., Hempel, C. M., Miller, M. N., Hattox, A. M., Shapiro, P., Wu, C., Huang, Z. J., & Nelson, S. B. (2006) · Nature Neuroscience · doi:10.1038/nn1618 · Cited by 521 · see citing works
Self-citationKlausberger, T., Marton, L. F., O'Neill, J., Huck, J. H. J., Dalezios, Y., Fuentealba, P., Suen, W. Y., Papp, E., Kaneko, T., Watanabe, M., et al. (2005) · The Journal of Neuroscience · doi:10.1523/jneurosci.3269-05.2005 · Cited by 469 · see citing works
FACS-array profiling of striatal projection neuron subtypes in juvenile and adult mouse brains
Lobo, M. K., Karsten, S. L., Gray, M., Geschwind, D. H., & Yang, X. W. (2006) · Nature Neuroscience · doi:10.1038/nn1654 · Cited by 411 · see citing works
Classification of fusiform neocortical interneurons based on unsupervised clustering
Cauli, B., Porter, J. T., Tsuzuki, K., Lambolez, B., Rossier, J., Quenet, B., & Audinat, E. (2000) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.97.11.6144 · Cited by 316 · see citing works
Diversity in the Neuronal Machine
Soltesz, I. (2006) · doi:10.1093/acprof:oso/9780195177015.001.1 · Cited by 68 · see citing works
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A resource of Cre driver lines for genetic targeting of GABAergic neurons in cerebral cortex
Taniguchi, H., He, M., Wu, P., Kim, S., Paik, R., Sugino, K., Kvitsani, D., Fu, Y., Lu, J., Lin, Y., et al. (2011) · Neuron · doi:10.1016/j.neuron.2011.07.026 · Cited by 1,828 · see citing works
Self-citationFunctional organization of the transcriptome in human brain
Oldham, M. C., Konopka, G., Iwamoto, K., Langfelder, P., Kato, T., Horvath, S., & Geschwind, D. H. (2008) · Nature Neuroscience · doi:10.1038/nn.2207 · Cited by 752 · see citing works
Excitatory neuronal connectivity in the barrel cortex
Feldmeyer, D. (2012) · Frontiers in Neuroanatomy · doi:10.3389/fnana.2012.00024 · Cited by 298 · see citing works
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He, M., Liu, Y., Wang, X., Zhang, M. Q., Hannon, G. J., & Huang, Z. J. (2012) · Neuron · doi:10.1016/j.neuron.2011.11.010 · Cited by 277 · see citing works
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Geschwind, D. H., & Konopka, G. (2009) · Nature · doi:10.1038/nature08537 · Cited by 213 · see citing works
On the Independent Origins of Complex Brains and Neurons
Moroz, L. L. (2009) · Brain, Behavior and Evolution · doi:10.1159/000258665 · Cited by 209 · see citing works
Laminar dependence of neuronal correlations in visual cortex
Smith, M. A., Jia, X., Zandvakili, A., & Kohn, A. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00846.2012 · Cited by 129 · see citing works
The molecular and gene regulatory signature of a neuron
Hobert, O., Carrera, I., & Stefanakis, N. (2010) · Trends in Neurosciences · doi:10.1016/j.tins.2010.05.006 · Cited by 109 · see citing works
Molecular neuroanatomy: a generation of progress
Pollock, J. D., Wu, D.-Y., & Satterlee, J. S. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2013.11.001 · Cited by 36 · see citing works
A resource for transcriptomic analysis in the mouse brain
Plessy, C., Fagiolini, M., Wagatsuma, A., Harasawa, N., Kuji, T., Asaka-Oba, A., Kanzaki, Y., Fujishima, S., Waki, K., Nakahara, H., et al. (2008) · PLoS ONE · doi:10.1371/journal.pone.0003012 · Cited by 12 · see citing works
- Authors
- Nelson, S. B., Hempel, C., & Sugino, K.
- Venue
- Current Opinion in Neurobiology
- Publisher
- Elsevier BV
- Published
- 2006-10
- Volume
- 16
- Issue
- 5
- Pages
- 571-576
- ISSN
- 0959-4388
- Cited by
- 81
- References
- 8
- Language
- en
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