Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy.

Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 µm and report voltage correlations in pairs of neurons.

How Does Familiarity Breed Contempt?

Classifying sensory experiences as either novel or familiar represents a fundamental challenge to neural processing. In this issue of Cell, Hattori et al. describe a circuit mechanism by which a novel stimulus that initially interests a fruit fly turns into a familiar one.

Subcellular Imaging of Voltage and Calcium Signals Reveals Neural Processing In Vivo.

A mechanistic understanding of neural computation requires determining how information is processed as it passes through neurons and across synapses. However, it has been challenging to measure membrane potential changes in axons and dendrites in vivo. We use in vivo, two-photon imaging of novel genetically encoded voltage indicators, as well as calcium imaging, to measure sensory stimulus-evoked signals in the Drosophila visual system with subcellular resolution. Across synapses, we find major transformations in the kinetics, amplitude, and sign of voltage responses to light. We also describe distinct relationships between voltage and calcium signals in different neuronal compartments, a substrate for local computation. Finally, we demonstrate that ON and OFF selectivity, a key feature of visual processing across species, emerges through the transformation of membrane potential into intracellular calcium concentration. By imaging voltage and calcium signals to map information flow with subcellular resolution, we illuminate where and how critical computations arise.

Neurons Rho to Get in Shape for the Day.

Linking structural changes in neurons to animal behavior has proven challenging. New findings by Pesakou et al. tie daily cycles of axon arbor extension and retraction, mediated by Rho activity, to circadian and seasonal patterns of behavior in the fruit fly.

A network of cadherin-mediated interactions polarizes growth cones to determine targeting specificity.

Neuronal growth cones select synaptic partners through interactions with multiple cell surfaces in their environment. Many of these interactions are adhesive, yet it is unclear how growth cones integrate adhesive cues to direct their movements. Here, we examine the mechanisms that enable photoreceptors in the Drosophila visual system to choose synaptic partners. We demonstrate that the classical cadherin, N-cadherin, and an atypical cadherin, Flamingo, act redundantly to instruct the targeting choices made by every photoreceptor axon. These molecules gradually bias the spatial distribution of growth cone filopodia, polarizing each growth cone toward its future synaptic target before direct contact with the target occurs. We demonstrate that these molecules are localized to distinct subcellular domains and create a network of adhesive interactions distributed across many growth cones. Because this network comprises multiple redundant interactions, a complex wiring diagram can be constructed with extraordinary fidelity, suggesting a general principle.

A high-throughput behavioral screening platform for measuring chemotaxis by C. elegans.

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multiwell plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.

Coupling of activity, metabolism and behaviour across the Drosophila brain.

Coordinated activity across networks of neurons is a hallmark of both resting and active behavioural states in many species1-5. These global patterns alter energy metabolism over seconds to hours, which underpins the widespread use of oxygen consumption and glucose uptake as proxies of neural activity6,7. However, whether changes in neural activity are causally related to metabolic flux in intact circuits on the timescales associated with behaviour is unclear. Here we combine two-photon microscopy of the fly brain with sensors that enable the simultaneous measurement of neural activity and metabolic flux, across both resting and active behavioural states. We demonstrate that neural activity drives changes in metabolic flux, creating a tight coupling between these signals that can be measured across brain networks. Using local optogenetic perturbation, we demonstrate that even transient increases in neural activity result in rapid and persistent increases in cytosolic ATP, which suggests that neuronal metabolism predictively allocates resources to anticipate the energy demands of future activity. Finally, our studies reveal that the initiation of even minimal behavioural movements causes large-scale changes in the pattern of neural activity and energy metabolism, which reveals a widespread engagement of the brain. As the relationship between neural activity and energy metabolism is probably evolutionarily ancient and highly conserved, our studies provide a critical foundation for using metabolic proxies to capture changes in neural activity.

A positively tuned voltage indicator for extended electrical recordings in the brain.

Genetically encoded voltage indicators (GEVIs) enable optical recording of electrical signals in the brain, providing subthreshold sensitivity and temporal resolution not possible with calcium indicators. However, one- and two-photon voltage imaging over prolonged periods with the same GEVI has not yet been demonstrated. Here, we report engineering of ASAP family GEVIs to enhance photostability by inversion of the fluorescence-voltage relationship. Two of the resulting GEVIs, ASAP4b and ASAP4e, respond to 100-mV depolarizations with ≥180% fluorescence increases, compared with the 50% fluorescence decrease of the parental ASAP3. With standard microscopy equipment, ASAP4e enables single-trial detection of spikes in mice over the course of minutes. Unlike GEVIs previously used for one-photon voltage recordings, ASAP4b and ASAP4e also perform well under two-photon illumination. By imaging voltage and calcium simultaneously, we show that ASAP4b and ASAP4e can identify place cells and detect voltage spikes with better temporal resolution than commonly used calcium indicators. Thus, ASAP4b and ASAP4e extend the capabilities of voltage imaging to standard one- and two-photon microscopes while improving the duration of voltage recordings.

Motion-detecting circuits in flies: coming into view.

Visual motion cues provide animals with critical information about their environment and guide a diverse array of behaviors. The neural circuits that carry out motion estimation provide a well-constrained model system for studying the logic of neural computation. Through a confluence of behavioral, physiological, and anatomical experiments, taking advantage of the powerful genetic tools available in the fruit fly Drosophila melanogaster, an outline of the neural pathways that compute visual motion has emerged. Here we describe these pathways, the evidence supporting them, and the challenges that remain in understanding the circuits and computations that link sensory inputs to behavior. Studies in flies and vertebrates have revealed a number of functional similarities between motion-processing pathways in different animals, despite profound differences in circuit anatomy and structure. The fact that different circuit mechanisms are used to achieve convergent computational outcomes sheds light on the evolution of the nervous system.

Processing properties of ON and OFF pathways for Drosophila motion detection.

The algorithms and neural circuits that process spatio-temporal changes in luminance to extract visual motion cues have been the focus of intense research. An influential model, the Hassenstein-Reichardt correlator, relies on differential temporal filtering of two spatially separated input channels, delaying one input signal with respect to the other. Motion in a particular direction causes these delayed and non-delayed luminance signals to arrive simultaneously at a subsequent processing step in the brain; these signals are then nonlinearly amplified to produce a direction-selective response. Recent work in Drosophila has identified two parallel pathways that selectively respond to either moving light or dark edges. Each of these pathways requires two critical processing steps to be applied to incoming signals: differential delay between the spatial input channels, and distinct processing of brightness increment and decrement signals. Here we demonstrate, using in vivo patch-clamp recordings, that four medulla neurons implement these two processing steps. The neurons Mi1 and Tm3 respond selectively to brightness increments, with the response of Mi1 delayed relative to Tm3. Conversely, Tm1 and Tm2 respond selectively to brightness decrements, with the response of Tm1 delayed compared with Tm2. Remarkably, constraining Hassenstein-Reichardt correlator models using these measurements produces outputs consistent with previously measured properties of motion detectors, including temporal frequency tuning and specificity for light versus dark edges. We propose that Mi1 and Tm3 perform critical processing of the delayed and non-delayed input channels of the correlator responsible for the detection of light edges, while Tm1 and Tm2 play analogous roles in the detection of moving dark edges. Our data show that specific medulla neurons possess response properties that allow them to implement the algorithmic steps that precede the correlative operation in the Hassenstein-Reichardt correlator, revealing elements of the long-sought neural substrates of motion detection in the fly.

The cytoskeletal regulator Genghis khan is required for columnar target specificity in the Drosophila visual system.

A defining characteristic of neuronal cell type is the growth of axons and dendrites into specific layers and columns of the brain. Although differences in cell surface receptors and adhesion molecules are known to cause differences in synaptic specificity, differences in downstream signaling mechanisms that determine cell type-appropriate targeting patterns are unknown. Using a forward genetic screen in Drosophila, we identify the GTPase effector Genghis khan (Gek) as playing a crucial role in the ability of a subset of photoreceptor (R cell) axons to innervate appropriate target columns. In particular, single-cell mosaic analyses demonstrate that R cell growth cones lacking Gek function grow to the appropriate ganglion, but frequently fail to innervate the correct target column. Further studies reveal that R cell axons lacking the activity of the small GTPase Cdc42 display similar defects, providing evidence that these proteins regulate a common set of processes. Gek is expressed in all R cells, and a detailed structure-function analysis reveals a set of regulatory domains with activities that restrict Gek function to the growth cone. Although Gek does not normally regulate layer-specific targeting, ectopic expression of Gek is sufficient to alter the targeting choices made by another R cell type, the targeting of which is normally Gek independent. Thus, specific regulation of cytoskeletal responses to targeting cues is necessary for cell type-appropriate synaptic specificity.

A versatile in vivo system for directed dissection of gene expression patterns.

Tissue-specific gene expression using the upstream activating sequence (UAS)­GAL4 binary system has facilitated genetic dissection of many biological processes in Drosophila melanogaster. Refining GAL4 expression patterns or independently manipulating multiple cell populations using additional binary systems are common experimental goals. To simplify these processes, we developed a convertible genetic platform, the integrase swappable in vivo targeting element (InSITE) system. This approach allows GAL4 to be replaced with any other sequence, placing different genetic effectors under the control of the same regulatory elements. Using InSITE, GAL4 can be replaced with LexA or QF, allowing an expression pattern to be repurposed. GAL4 can also be replaced with GAL80 or split-GAL4 hemi-drivers, allowing intersectional approaches to refine expression patterns. The exchanges occur through efficient in vivo manipulations, making it possible to generate many swaps in parallel. This system is modular, allowing future genetic tools to be easily incorporated into the existing framework.

Differences in neural circuitry guiding behavioral responses to polarized light presented to either the dorsal or ventral retina in Drosophila.

Linearly polarized light (POL) serves as an important cue for many animals, providing navigational information, as well as directing them toward food sources and reproduction sites. Many insects detect the celestial polarization pattern, or the linearly polarized reflections off of surfaces, such as water. Much progress has been made toward characterizing both retinal detectors and downstream circuit elements responsible for celestial POL vision in different insect species, yet much less is known about the neural basis of how polarized reflections are detected. We previously established a novel, fully automated behavioral assay for studying the spontaneous orientation response of Drosophila melanogaster populations to POL stimuli presented to either the dorsal, or the ventral halves of the retina. We identified separate retinal detectors mediating these responses: the 'Dorsal Rim Area' (DRA), which had long been implicated in celestial POL vision, as well as a previously uncharacterized 'ventral polarization area' (VPA). In this study, we investigate whether DRA and VPA use the same or different downstream circuitry, for mediating spontaneous behavioral responses. We use homozygous mutants, or molecular genetic circuit-breaking tools (silencing, as well as rescue of synaptic activity), in combination with our behavioral paradigm. We show that responses to dorsal versus ventral stimulation involve previously characterized optic lobe neurons, like lamina monopolar cell L2 and medulla cell types Dm8/Tm5c. However, using different experimental conditions, we show that important differences exist between the requirement of these cell types downstream of DRA versus VPA. Therefore, while the neural circuits underlying behavioral responses to celestial and reflected POL cues share important building blocks, these elements play different functional roles within the network.

Walking Drosophila align with the e-vector of linearly polarized light through directed modulation of angular acceleration.

Understanding the mechanisms that link sensory stimuli to animal behavior is a central challenge in neuroscience. The quantitative description of behavioral responses to defined stimuli has led to a rich understanding of different behavioral strategies in many species. One important navigational cue perceived by many vertebrates and insects is the e-vector orientation of linearly polarized light. Drosophila manifests an innate orientation response to this cue ('polarotaxis'), aligning its body axis with the e-vector field. We have established a population-based behavioral paradigm for the genetic dissection of neural circuits guiding polarotaxis to both celestial as well as reflected polarized stimuli. However, the behavioral mechanisms by which flies align with a linearly polarized stimulus remain unknown. Here, we present a detailed quantitative description of Drosophila polarotaxis, systematically measuring behavioral parameters that are modulated by the stimulus. We show that angular acceleration is modulated during alignment, and this single parameter may be sufficient for alignment. Furthermore, using monocular deprivation, we show that each eye is necessary for modulating turns in the ipsilateral direction. This analysis lays the foundation for understanding how neural circuits guide these important visual behaviors.

Symmetries in stimulus statistics shape the form of visual motion estimators.

The estimation of visual motion has long been studied as a paradigmatic neural computation, and multiple models have been advanced to explain behavioral and neural responses to motion signals. A broad class of models, originating with the Reichardt correlator model, proposes that animals estimate motion by computing a temporal cross-correlation of light intensities from two neighboring points in visual space. These models provide a good description of experimental data in specific contexts but cannot explain motion percepts in stimuli lacking pairwise correlations. Here, we develop a theoretical formalism that can accommodate diverse stimuli and behavioral goals. To achieve this, we treat motion estimation as a problem of Bayesian inference. Pairwise models emerge as one component of the generalized strategy for motion estimation. However, correlation functions beyond second order enable more accurate motion estimation. Prior expectations that are asymmetric with respect to bright and dark contrast use correlations of both even and odd orders, and we show that psychophysical experiments using visual stimuli with symmetric probability distributions for contrast cannot reveal whether the subject uses odd-order correlators for motion estimation. This result highlights a gap in previous experiments, which have largely relied on symmetric contrast distributions. Our theoretical treatment provides a natural interpretation of many visual motion percepts, indicates that motion estimation should be revisited using a broader class of stimuli, demonstrates how correlation-based motion estimation is related to stimulus statistics, and provides multiple experimentally testable predictions.

Mapping the neural dynamics of locomotion across the Drosophila brain.

Locomotion engages widely distributed networks of neurons. However, our understanding of the spatial architecture and temporal dynamics of the networks that underpin walking remains incomplete. We use volumetric two-photon imaging to map neural activity associated with walking across the entire brain of Drosophila. We define spatially clustered neural signals selectively associated with changes in either forward or angular velocity, demonstrating that neurons with similar behavioral selectivity are clustered. These signals reveal distinct topographic maps in diverse brain regions involved in navigation, memory, sensory processing, and motor control, as well as regions not previously linked to locomotion. We identify temporal trajectories of neural activity that sweep across these maps, including signals that anticipate future movement, representing the sequential engagement of clusters with different behavioral specificities. Finally, we register these maps to a connectome and identify neural networks that we propose underlie the observed signals, setting a foundation for subsequent circuit dissection. Overall, our work suggests a spatiotemporal framework for the emergence and execution of complex walking maneuvers and links this brain-wide neural activity to single neurons and local circuits.

A recurrent neural circuit in Drosophila deblurs visual inputs.

A critical goal of vision is to detect changes in light intensity, even when these changes are blurred by the spatial resolution of the eye and the motion of the animal. Here we describe a recurrent neural circuit in Drosophila that compensates for blur and thereby selectively enhances the perceived contrast of moving edges. Using in vivo, two-photon voltage imaging, we measured the temporal response properties of L1 and L2, two cell types that receive direct synaptic input from photoreceptors. These neurons have biphasic responses to brief flashes of light, a hallmark of cells that encode changes in stimulus intensity. However, the second phase was often much larger than the first, creating an unusual temporal filter. Genetic dissection revealed that recurrent neural circuitry strongly shapes the second phase of the response, informing the structure of a dynamical model. By applying this model to moving natural images, we demonstrate that rather than veridically representing stimulus changes, this temporal processing strategy systematically enhances them, amplifying and sharpening responses. Comparing the measured responses of L2 to model predictions across both artificial and natural stimuli revealed that L2 tunes its properties as the model predicts in order to deblur images. Since this strategy is tunable to behavioral context, generalizable to any time-varying sensory input, and implementable with a common circuit motif, we propose that it could be broadly used to selectively enhance sharp and salient changes.

An efficient behavioral screening platform classifies natural products and other chemical cues according to their chemosensory valence in C. elegans.

Throughout history, humans have relied on plants as a source of medication, flavoring, and food. Plants synthesize large chemical libraries and release many of these compounds into the rhizosphere and atmosphere where they affect animal and microbe behavior. To survive, nematodes must have evolved the sensory capacity to distinguish plant-made small molecules (SMs) that are harmful and must be avoided from those that are beneficial and should be sought. This ability to classify chemical cues as a function of their value is fundamental to olfaction, and represents a capacity shared by many animals, including humans. Here, we present an efficient platform based on multi-well plates, liquid handling instrumentation, inexpensive optical scanners, and bespoke software that can efficiently determine the valence (attraction or repulsion) of single SMs in the model nematode, Caenorhabditis elegans. Using this integrated hardware-wetware-software platform, we screened 90 plant SMs and identified 37 that attracted or repelled wild-type animals, but had no effect on mutants defective in chemosensory transduction. Genetic dissection indicates that for at least 10 of these SMs, response valence emerges from the integration of opposing signals, arguing that olfactory valence is often determined by integrating chemosensory signals over multiple lines of information. This study establishes that C. elegans is an effective discovery engine for determining chemotaxis valence and for identifying natural products detected by the chemosensory nervous system.