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Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons
Open accessAbstract
The lateral hypothalamic area (LHA) coordinates an array of fundamental behaviors, including sleeping, waking, feeding, stress and motivated behavior. The wide spectrum of functions ascribed to the LHA may be explained by a heterogeneous population of neurons, the full diversity of which is poorly understood. We employed a droplet-based single-cell RNA-sequencing approach to develop a comprehensive census of molecularly distinct cell types in the mouse LHA. Neuronal populations were classified based on fast neurotransmitter phenotype and expression of neuropeptides, transcription factors and synaptic proteins, among other gene categories. We define 15 distinct populations of glutamatergic neurons and 15 of GABAergic neurons, including known and novel cell types. We further characterize a novel population of somatostatin-expressing neurons through anatomical and behavioral approaches, identifying a role for these neurons in specific forms of innate locomotor behavior. This study lays the groundwork for better understanding the circuit-level underpinnings of LHA function. The lateral hypothalamic area (LHA) regulates fundamental aspects of innate behavior. However, the circuit-level underpinnings of LHA function are poorly understood given its cellular heterogeneity. Here, Mickelsen et al. employ a single-cell RNA-sequencing approach to classify molecularly distinct cell types in the mouse LHA.
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Cites
Hierarchical Grouping to Optimize an Objective Function
Ward, J. H. (1963) · Journal of the American Statistical Association · doi:10.1080/01621459.1963.10500845 · Cited by 20,598
A robust and high-throughput Cre reporting and characterization system for the whole mouse brain
Madisen, L., Zwingman, T. A., Sunkin, S. M., Oh, S. W., Zariwala, H. A., Gu, H., Ng, L. L., Palmiter, R. D., Hawrylycz, M. J., Jones, A. R., et al. (2010) · Nature Neuroscience · doi:10.1038/nn.2467 · Cited by 6,907
Genome-wide atlas of gene expression in the adult mouse brain
Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., Boe, A. F., Boguski, M. S., Brockway, K. S., Byrnes, E. J., et al. (2007) · Nature · doi:10.1038/nature05453 · Cited by 5,572
(2012) · doi:10.1016/j.jmoldx.2011.08.002 · Cited by 2,567
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
Leptin Action on GABAergic Neurons Prevents Obesity and Reduces Inhibitory Tone to POMC Neurons
Vong, L., Ye, C., Yang, Z., Choi, B., Chua, S., & Lowell, B. B. (2011) · Neuron · doi:10.1016/j.neuron.2011.05.028 · Cited by 1,235
Hypothalamic Orexin Neurons Regulate Arousal According to Energy Balance in Mice
Yamanaka, A., Beuckmann, C. T., Willie, J. T., Hara, J., Tsujino, N., Mieda, M., Tominaga, M., Yagami, K.-I., Sugiyama, F., Goto, K., et al. (2003) · Neuron · doi:10.1016/s0896-6273(03)00331-3 · Cited by 933
A molecular census of arcuate hypothalamus and median eminence cell types
Campbell, J. N., Macosko, E. Z., Fenselau, H., Pers, T. H., Lyubetskaya, A., Tenen, D., Goldman, M., Verstegen, A. M. J., Resch, J. M., McCarroll, S. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4495 · Cited by 817
Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity
Chen, R., Wu, X., Jiang, L., & Zhang, Y. (2017) · Cell Reports · doi:10.1016/j.celrep.2017.03.004 · Cited by 592
Visualizing Hypothalamic Network Dynamics for Appetitive and Consummatory Behaviors
Jennings, J. H., Ung, R. L., Resendez, S. L., Stamatakis, A. M., Taylor, J. G., Huang, J., Veleta, K., Kantak, P. A., Aita, M., Shilling-Scrivo, K., et al. (2015) · Cell · doi:10.1016/j.cell.2014.12.026 · Cited by 542
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Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320
Ren, J., Isakova, A., Friedmann, D., Zeng, J., Grutzner, S. M., Pun, A., Zhao, G. Q., Kolluru, S. S., Wang, R., Lin, R., et al. (2019) · eLife · doi:10.7554/elife.49424 · Cited by 304
“The rapid development of single-cell RNA sequencing (scRNA-seq) technology in recent years has provided a powerful tool for unbiased identification of transcriptomic cell types in the brain (Darmanis et al., 2015; Li et al., 2017; Mickelsen et al., 2019; Rosenberg et al., 2018; Saunders et al., 2018; Tasic et al., 201…”
MethodHypoMap—a unified single-cell gene expression atlas of the murine hypothalamus
Steuernagel, L., Lam, B. Y. H., Klemm, P., Dowsett, G. K. C., Bauder, C. A., Tadross, J. A., Hitschfeld, T. S., del Rio Martin, A., Chen, W., de Solis, A. J., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00657-y · Cited by 267
Multimodal analysis of cell types in a hypothalamic node controlling social behavior
Kim, D.-W., Yao, Z., Graybuck, L. T., Kim, T. K., Nguyen, T. N., Smith, K. A., Fong, O., Yi, L., Koulena, N., Pierson, N., et al. (2019) · Cell · doi:10.1016/j.cell.2019.09.020 · Cited by 259
Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex
Chen, A., Sun, Y., Lei, Y., Li, C., Liao, S., Meng, J., Bai, Y., Liu, Z., Liang, Z., Zhu, Z., et al. (2023) · Cell · doi:10.1016/j.cell.2023.06.009 · Cited by 232
Metadata
- Authors
- Mickelsen, L. E., Bolisetty, M., Chimileski, B. R., Fujita, A., Beltrami, E. J., Costanzo, J. T., Naparstek, J. R., Robson, P., & Jackson, A. C.
- Venue
- Nature Neuroscience
- Published
- 2019-03-11
- Volume
- 22
- Issue
- 4
- Pages
- 642-656
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- OpenAlex
- W2921807317
- Cited by
- 325
- References
- 69
- FWCI
- 10.74
- Type
- journal article
- Language
- en
- Open access
- green
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- Mickelsen, L. E., Bolisetty, M., Chimileski, B. R., Fujita, A., Beltrami, E. J., Costanzo, J. T., Naparstek, J. R., Robson, P., & Jackson, A. C. (2019). Single-cell transcriptomic analysis of the lateral hypothalamic area reveals molecularly distinct populations of inhibitory and excitatory neurons. Nature Neuroscience. https://doi.org/10.1038/s41593-019-0349-8
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