Unique functional responses differentially map onto genetic subtypes of dopamine neurons.

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Title: Unique functional responses differentially map onto genetic subtypes of dopamine neurons.
Authors: Azcorra, Maite (AUTHOR), Gaertner, Zachary (AUTHOR), Davidson, Connor (AUTHOR), He, Qianzi (AUTHOR), Kim, Hailey (AUTHOR), Nagappan, Shivathmihai (AUTHOR), Hayes, Cooper K. (AUTHOR), Ramakrishnan, Charu (AUTHOR), Fenno, Lief (AUTHOR), Kim, Yoon Seok (AUTHOR), Deisseroth, Karl (AUTHOR), Longnecker, Richard (AUTHOR), Awatramani, Rajeshwar (AUTHOR), Dombeck, Daniel A. (AUTHOR)
Source: Nature Neuroscience. Oct2023, Vol. 26 Issue 10, p1762-1774. 13p.
Abstract: Dopamine neurons are characterized by their response to unexpected rewards, but they also fire during movement and aversive stimuli. Dopamine neuron diversity has been observed based on molecular expression profiles; however, whether different functions map onto such genetic subtypes remains unclear. In this study, we established that three genetic dopamine neuron subtypes within the substantia nigra pars compacta, characterized by the expression of Slc17a6 (Vglut2), Calb1 and Anxa1, each have a unique set of responses to rewards, aversive stimuli and accelerations and decelerations, and these signaling patterns are highly correlated between somas and axons within subtypes. Remarkably, reward responses were almost entirely absent in the Anxa1+ subtype, which instead displayed acceleration-correlated signaling. Our findings establish a connection between functional and genetic dopamine neuron subtypes and demonstrate that molecular expression patterns can serve as a common framework to dissect dopaminergic functions. The authors establish a connection between functional subtypes and genetic subtypes of dopamine neurons in mice and demonstrate that molecular expression patterns can serve as a common framework to dissect dopaminergic functions. [ABSTRACT FROM AUTHOR]
Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Dopamine neurons are characterized by their response to unexpected rewards, but they also fire during movement and aversive stimuli. Dopamine neuron diversity has been observed based on molecular expression profiles; however, whether different functions map onto such genetic subtypes remains unclear. In this study, we established that three genetic dopamine neuron subtypes within the substantia nigra pars compacta, characterized by the expression of Slc17a6 (Vglut2), Calb1 and Anxa1, each have a unique set of responses to rewards, aversive stimuli and accelerations and decelerations, and these signaling patterns are highly correlated between somas and axons within subtypes. Remarkably, reward responses were almost entirely absent in the Anxa1+ subtype, which instead displayed acceleration-correlated signaling. Our findings establish a connection between functional and genetic dopamine neuron subtypes and demonstrate that molecular expression patterns can serve as a common framework to dissect dopaminergic functions. The authors establish a connection between functional subtypes and genetic subtypes of dopamine neurons in mice and demonstrate that molecular expression patterns can serve as a common framework to dissect dopaminergic functions. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Nature Neuroscience is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Oct2023
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