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Register a new userOptogenetic manipulation of neural activity in C. elegans: from synapse to circuits and behavior
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The emerging field of optogenetics allows for optical activation or inhibition of neurons and other tissue in the nervous system. In 2005 optogenetic proteins were expressed in the nematode C. elegans for the first time. Since then, C. elegans has served as a powerful platform upon which to conduct optogenetic investigations of synaptic function, circuit dynamics and the neuronal basis of behavior. The C. elegans nervous system, consisting of 302 neurons, whose connectivity and morphology has been mapped completely, drives a rich repertoire of behaviors that are quantifiable by video microscopy. This model organisms compact nervous system, quantifiable behavior, genetic tractability and optical accessibility make it especially amenable to optogenetic interrogation. Channelrhodopsin-2 (ChR2), halorhodopsin (NpHR/Halo) and other common optogenetic proteins have all been expressed in C. elegans. Moreover recent advances leveraging molecular genetics and patterned light illumination have now made it possible to target photoactivation and inhibition to single cells and to do so in worms as they behave freely. Here we describe techniques and methods for optogenetic manipulation in C. elegans. We review recent work using optogenetics and C. elegans for neuroscience investigations at the level of synapses, circuits and behavior.
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We present a high-throughput optogenetic illumination system capable of simultaneous closed-loop light delivery to specified targets in populations of moving Caenorhabditis elegans. The instrument addresses three technical challenges: it delivers targeted illumination to specified regions of the animals body such as its head or tail; it automatically delivers stimuli triggered upon the animals behavior; and it achieves high throughput by targeting many animals simultaneously. The instrument was used to optogenetically probe the animals behavioral response to competing mechanosensory stimuli in the the anterior and posterior soft touch receptor neurons. Responses to more than $10^4$ stimulus events from a range of anterior-posterior intensity combinations were measured. The animals probability of sprinting forward in response to a mechanosensory stimulus depended on both the anterior and posterior stimulation intensity, while the probability of reversing depended primarily on the posterior stimulation intensity. We also probed the animals response to mechanosensory stimulation during the onset of turning, a relatively rare behavioral event, by delivering stimuli automatically when the animal began to turn. Using this closed-loop approach, over $10^3$ stimulus events were delivered during turning onset at a rate of 9.2 events per worm-hour, a greater than 25-fold increase in throughput compared to previous investigations. These measurements validate with greater statistical power previous findings that turning acts to gate mechanosensory evoked reversals. Compared to previous approaches, the current system offers targeted optogenetic stimulation to specific body regions or behaviors with many-fold increases in throughput to better constrain quantitative models of sensorimotor processing.
A quantitative understanding of how sensory signals are transformed into motor outputs places useful constraints on brain function and helps reveal the brains underlying computations. We investigate how the nematode C. elegans responds to time-varying mechanosensory signals using a high-throughput optogenetic assay and automated behavior quantification. In the prevailing picture of the touch circuit, the animals behavior is determined by which neurons are stimulated and by the stimulus amplitude. In contrast, we find that the behavioral response is tuned to temporal properties of mechanosensory signals, like its integral and derivative, that extend over many seconds. Mechanosensory signals, even in the same neurons, can be tailored to elicit different behavioral responses. Moreover, we find that the animals response also depends on its behavioral context. Most dramatically, the animal ignores all tested mechanosensory stimuli during turns. Finally, we present a linear-nonlinear model that predicts the animals behavioral response to stimulus.
The roundworm C. elegans exhibits robust escape behavior in response to rapidly rising temperature. The behavior lasts for a few seconds, shows history dependence, involves both sensory and motor systems, and is too complicated to model mechanistically using currently available knowledge. Instead we model the process phenomenologically, and we use the Sir Isaac dynamical inference platform to infer the model in a fully automated fashion directly from experimental data. The inferred model requires incorporation of an unobserved dynamical variable, and is biologically interpretable. The model makes accurate predictions about the dynamics of the worm behavior, and it can be used to characterize the functional logic of the dynamical system underlying the escape response. This work illustrates the power of modern artificial intelligence to aid in discovery of accurate and interpretable models of complex natural systems.
Given the inner complexity of the human nervous system, insight into the dynamics of brain activity can be gained from understanding smaller and simpler organisms, such as the nematode C. Elegans. The behavioural and structural biology of these organisms is well-known, making them prime candidates for benchmarking modelling and simulation techniques. In these complex neuronal collections, classical, white-box modelling techniques based on intrinsic structural or behavioural information are either unable to capture the profound nonlinearities of the neuronal response to different stimuli or generate extremely complex models, which are computationally intractable. In this paper we show how the nervous system of C. Elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, we target the use of state of the art recurrent neural networks architectures such as LSTMs and GRUs and compare these architectures in terms of their properties and their accuracy as well as the complexity of the resulting models. We show that GRU models with a hidden layer size of 4 units are able to accurately reproduce with high accuracy the systems response to very different stimuli.
The ability to acquire large-scale recordings of neuronal activity in awake and unrestrained animals poses a major challenge for studying neural coding of animal behavior. We present a new instrument capable of recording intracellular calcium transients from every neuron in the head of a freely behaving C. elegans with cellular resolution while simultaneously recording the animals position, posture and locomotion. We employ spinning-disk confocal microscopy to capture 3D volumetric fluorescent images of neurons expressing the calcium indicator GCaMP6s at 5 head-volumes per second. Two cameras simultaneously monitor the animals position and orientation. Custom software tracks the 3D position of the animals head in real-time and adjusts a motorized stage to keep it within the field of view as the animal roams freely. We observe calcium transients from 78 neurons and correlate this activity with the animals behavior. Across worms, multiple neurons show significant correlations with modes of behavior corresponding to forward, backward, and turning locomotion. By comparing the 3D positions of these neurons with a known atlas, our results are consistent with previous single-neuron studies and demonstrate the existence of new candidate neurons for behavioral circuits.
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