Genetic and optical targeting of neural circuits and behavior—zebrafish in the spotlight. Curr Opinion Neurobiol

University of California, San Francisco, Department of Physiology, San Francisco, CA 94158-2324, USA.
Current opinion in neurobiology (Impact Factor: 6.63). 09/2009; 19(5):553-60. DOI: 10.1016/j.conb.2009.08.001
Source: PubMed


Methods to label neurons and to monitor their activity with genetically encoded fluorescent reporters have been a staple of neuroscience research for several years. The recent introduction of photoswitchable ion channels and pumps, such as channelrhodopsin (ChR2), halorhodopsin (NpHR), and light-gated glutamate receptor (LiGluR), is enabling remote optical manipulation of neuronal activity. The translucent brains of zebrafish offer superior experimental conditions for optogenetic approaches in vivo. Enhancer and gene trapping approaches have generated hundreds of Gal4 driver lines in which the expression of UAS-linked effectors can be targeted to subpopulations of neurons. Local photoactivation of genetically targeted LiGluR, ChR2, or NpHR has uncovered novel functions for specific areas and cell types in zebrafish behavior. Because the manipulation is restricted to times and places where genetics (cell types) and optics (beams of light) intersect, this method affords excellent resolving power for the functional analysis of neural circuitry.

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    • "Intriguingly, fish perform the OKR in response to rotational motion and the OMR mainly in response to translational motion, and thus, rotation and translation are tightly linked with different behavioral output. Here we took advantage of recent advances in optogenetics and functional imaging (Baier and Scott, 2009; Wyart and Del Bene, 2011; Del Bene and Wyart, 2012; McLean and Fetcho, 2011). We first functionally localized the APT using optogenetic gain-of-function and lossof-function manipulations. "
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    ABSTRACT: Animals respond to whole-field visual motion with compensatory eye and body movements in order to stabilize both their gaze and position with respect to their surroundings. In zebrafish, rotational stimuli need to be distinguished from translational stimuli to drive the optokinetic and the optomotor responses, respectively. Here, we systematically characterize the neural circuits responsible for these operations using a combination of optogenetic manipulation and in vivo calcium imaging during optic flow stimulation. By recording the activity of thousands of neurons within the area pretectalis (APT), we find four bilateral pairs of clusters that process horizontal whole-field motion and functionally classify eleven prominent neuron types with highly selective response profiles. APT neurons are prevalently direction selective, either monocularly or binocularly driven, and hierarchically organized to distinguish between rotational and translational optic flow. Our data predict a wiring diagram of a neural circuit tailored to drive behavior that compensates for self-motion.
    Neuron 03/2014; 81(6):1344-59. DOI:10.1016/j.neuron.2014.02.043 · 15.05 Impact Factor
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    • "In the presented study we show how optogenetic experiments can be combined with modeling to infer the dynamics of a neural circuit module for integration. In the last few years, the field of optogenetics has provided a powerful set of techniques to perform gain- and loss-of-function experiments (reviewed in Zhang et al., 2007a; Luo et al., 2008; Fenno et al., 2011) and has been applied to zebrafish (Szobota et al., 2007; Douglass et al., 2008; Arrenberg et al., 2009; Baier and Scott, 2009; Zhu et al., 2009; Schoonheim et al., 2010). A fundamental problem in interpreting the effects of optogenetic stimulations is that a system's response is a combination of the stimulation magnitude and the intrinsic network dynamics. "
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    ABSTRACT: Many neural systems can store short-term information in persistently firing neurons. Such persistent activity is believed to be maintained by recurrent feedback among neurons. This hypothesis has been fleshed out in detail for the oculomotor integrator (OI) for which the so-called "line attractor" network model can explain a large set of observations. Here we show that there is a plethora of such models, distinguished by the relative strength of recurrent excitation and inhibition. In each model, the firing rates of the neurons relax toward the persistent activity states. The dynamics of relaxation can be quite different, however, and depend on the levels of recurrent excitation and inhibition. To identify the correct model, we directly measure these relaxation dynamics by performing optogenetic perturbations in the OI of zebrafish expressing halorhodopsin or channelrhodopsin. We show that instantaneous, inhibitory stimulations of the OI lead to persistent, centripetal eye position changes ipsilateral to the stimulation. Excitatory stimulations similarly cause centripetal eye position changes, yet only contralateral to the stimulation. These results show that the dynamics of the OI are organized around a central attractor state-the null position of the eyes-which stabilizes the system against random perturbations. Our results pose new constraints on the circuit connectivity of the system and provide new insights into the mechanisms underlying persistent activity.
    Frontiers in Neural Circuits 02/2014; 8:10. DOI:10.3389/fncir.2014.00010 · 3.60 Impact Factor
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    • "As the experimental applications of ChR2 move to include biophysically diverse interneurons (Markram et al., 2004; Katzel et al., 2011; Schultheis et al., 2011; English et al., 2012; Chiu et al., 2013; Owen et al., 2013), a fuller understanding of its possibilities and limitations becomes essential. Although ChR2 expression, trafficking, and activation has been achieved in most types of nervous tissue (Li et al., 2005; Nagel et al., 2005; Bi et al., 2006; Schroll et al., 2006; Adamantidis et al., 2007; Zhang et al., 2007; Douglass et al., 2008; Mahoney et al., 2008; Baier and Scott, 2009; Guo et al., 2009; Han et al., 2009; Liu et al., 2009; Zhu et al., 2009; Gourine et al., 2010; Hagglund et al., 2010; Diester and et al, 2011; Figueiredo et al., 2011; Sasaki et al., 2012; Ljaschenko et al., 2013), little consideration has been given to the kinetics that constrain light-induced firing properties in different neuronal subtypes. Factors that affect the degree of neuronal photostimulation, including intrinsic differences in firing dynamics, membrane properties, and channel composition differ among neuronal cell types. "
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    ABSTRACT: eLife digest The brain is a highly complex structure composed of trillions of interconnecting nerve cells. The pattern of connections between these cells gives rise to the various brain circuits that govern how the brain functions. Understanding how the brain is wired together is important for determining how ‘faulty circuits’ contribute to various neurological disorders. New optogenetic technique tools allow neuroscientists to turn on specific neurons simply by shining light on them. These techniques involve genetically manipulating the organisms so that their neurons express proteins that are activated when they are exposed to light of a particular wavelength. However, it is important to understand the limitations of this approach—including the possibility that the light might actually turn off some neurons—when using it to study animal behavior. Now, Herman, Huang et al. show that shining light pulses for long durations onto neurons expressing a light-activated protein called channelrhodopsin-2 causes the neurons to become silenced rather than activated. Moreover, certain types of neurons, called interneurons, are more susceptible to this effect—termed ‘depolarization block’—than the other types of neurons. Researchers need to be mindful of this effect when channelrhodopsin-2 is used in optogenetic experiments to study the behavior of living animals. However, this silencing property could be useful in experiments that investigate situations in which depolarization block is thought to contribute to brain function and health: such as in the treatments of schizophrenia and Parkinson’s disease. DOI:
    eLife Sciences 01/2014; 3:e01481. DOI:10.7554/eLife.01481 · 9.32 Impact Factor
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