Long-lasting internal states, like hunger, aggression, and sexual arousal, pattern ongoing behavior by defining how the sensory world is translated to specific actions that subserve the needs of an animal. Yet how enduring internal states shape sensory processing or behavior has remained unclear. In Drosophila, male flies will perform a lengthy and elaborate courtship ritual, triggered by activation of sexually-dimorphic P1 neurons, in which they faithfully follow and sing to a female. Here, by recording from males as they actively court a fictive ‘female’ in a virtual environment, we gain insight into how the salience of female visual cues is transformed by a male’s internal arousal state to give rise to persistent courtship pursuit. We reveal that the gain of LCt0a visual projection neurons is strongly increased during courtship, enhancing their sensitivity to moving targets. A simple network model based on the LCt0a circuit accurately predicts a male’s tracking of a female over hundreds of seconds, underscoring that LCt0a visual signals, once released by P1-mediated arousal, become coupled to motor pathways to deterministically control his visual pursuit. Furthermore, we find that P1 neuron activity correlates with fluctuations in the intensity of a male’s pursuit, and that their acute activation is sufficient to boost the gain of the LCt0 pathways. Together, these results reveal how alterations in a male’s internal arousal state can dynamically modulate the propagation of visual signals through a high-fidelity visuomotor circuit to guide his moment-to-moment performance of courtship.Competing Interest StatementThe authors have declared no competing interest.

This study provides a new perspective on the long-standing problem of the nature of the decapod crustacean blood-brain interface. Previous studies of crustacean blood-brain interface permeability have relied on invasive histological, immunohistochemical and electrophysiological techniques, indicating a leaky non-selective blood-brain barrier. The present investigation involves the use of magnetic resonance imaging (MRI), a method for non-invasive longitudinal tracking of tracers in real-time. Differential uptake rates of two molecularly distinct MRI contrast agents, namely manganese (Mn(II)) and Magnevist® (Gd-DTPA), were observed and quantified in the crayfish, Cherax destructor. Contrast agents were injected into the pericardium and uptake was observed with longitudinal MRI for approximately 14.5 h. Mn(II) was taken up quickly into neural tissue (within 6.5 min), whereas Gd-DTPA was not taken up into neural tissue and was instead restricted to the intracerebral vasculature or excreted into nearby sinuses. Our results provide evidence for a charge-selective intracerebral blood-brain interface in the crustacean nervous system, a structural characteristic once considered too complex for a lower-order arthropod.

The many roles of innexins, the molecules that form gap junctions in invertebrates, have been explored in numerous species. Here, we present a summary of innexin expression and function in two small, central pattern generating circuits found in crustaceans: the stomatogastric ganglion and the cardiac ganglion. The two ganglia express multiple innexin genes, exhibit varying combinations of symmetrical and rectifying gap junctions, as well as gap junctions within and across different cell types. Past studies have revealed correlations in ion channel and innexin expression in coupled neurons, as well as intriguing functional relationships between ion channel conductances and electrical coupling. Together, these studies suggest a putative role for innexins in correlating activity between coupled neurons at the levels of gene expression and physiological activity during development and in the adult animal.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: If cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell-type classification, we performed 2 forms of transcriptional profiling—RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from 2 small crustacean neuronal networks: The stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally defined neuron types can be classified by expression profile alone. The results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post hoc grouping, so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between 2 or more cell types. Therefore, this study supports the general utility of cell identification by transcriptional profiling, but adds a caution: It is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology, or innervation target can neuronal identity be unambiguously determined.

Understanding circuit organization depends on identification of cell types. Recent advances in transcriptional profiling methods have enabled classification of cell types by their gene expression. While exceptionally powerful and high throughput, the ground-truth validation of these methods is difficult: if cell type is unknown, how does one assess whether a given analysis accurately captures neuronal identity? To shed light on the capabilities and limitations of solely using transcriptional profiling for cell type classification, we performed two forms of transcriptional profiling – RNA-seq and quantitative RT-PCR, in single, unambiguously identified neurons from two small crustacean networks: the stomatogastric and cardiac ganglia. We then combined our knowledge of cell type with unbiased clustering analyses and supervised machine learning to determine how accurately functionally-defined neuron types can be classified by expression profile alone. Our results demonstrate that expression profile is able to capture neuronal identity most accurately when combined with multimodal information that allows for post-hoc grouping so analysis can proceed from a supervised perspective. Solely unsupervised clustering can lead to misidentification and an inability to distinguish between two or more cell types. Therefore, our study supports the general utility of cell identification by transcriptional profiling, but adds a caution: it is difficult or impossible to know under what conditions transcriptional profiling alone is capable of assigning cell identity. Only by combining multiple modalities of information such as physiology, morphology or innervation target can neuronal identity be unambiguously determined.SIGNIFICANCE STATEMENT Single cell transcriptional profiling has become a widespread tool in cell identification, particularly in the nervous system, based on the notion that genomic information determines cell identity. However, many cell type classification studies are unconstrained by other cellular attributes (e.g., morphology, physiology). Here, we systematically test how accurately transcriptional profiling can assign cell identity to well-studied anatomically- and functionally-identified neurons in two small neuronal networks. While these neurons clearly possess distinct patterns of gene expression across cell types, their expression profiles are not sufficient to unambiguously confirm their identity. We suggest that true cell identity can only be determined by combining gene expression data with other cellular attributes such as innervation pattern, morphology, or physiology.

It is often assumed that highly-branched neuronal structures perform compartmentalized computations. However, previously we showed that the Gastric Mill (GM) neuron in the crustacean stomatogastric ganglion (STG) operates like a single electrotonic compartment, despite having thousands of branch points and total cable length >10 mm (Otopalik et al., 2017a; 2017b). Here we show that compact electrotonic architecture is generalizable to other STG neuron types, and that these neurons present direction-insensitive, linear voltage integration, suggesting they pool synaptic inputs across their neuronal structures. We also show, using simulations of 720 cable models spanning a broad range of geometries and passive properties, that compact electrotonus, linear integration, and directional insensitivity in STG neurons arise from their neurite geometries (diameters tapering from 10-20 µm to \uline< 2 µm at their terminal tips). A broad parameter search reveals multiple morphological and biophysical solutions for achieving different degrees of passive electrotonic decrement and computational strategies in the absence of active properties.

The crustacean stomatogastric ganglion (STG) receives descending neuromodulatory inputs from three anterior ganglia: the paired commissural ganglia (CoGs), and the single esophageal ganglion (OG). In this paper, we provide the first detailed and quantitative analyses of the short- and long-term effects of removal of these descending inputs (decentralization) on the pyloric rhythm of the STG. Thirty minutes after decentralization, the mean frequency of the pyloric rhythm dropped from 1.20 Hz in control to 0.52 Hz. Whereas the relative phase of pyloric neuron activity was approximately constant across frequency in the controls, after decentralization this changed markedly. Nine control preparations kept for 5–6 d in vitro maintained pyloric rhythm frequencies close to their initial values. Nineteen decentralized preparations kept for 5–6 d dropped slightly in frequency from those seen at 30 min following decentralization, but then displayed stable activity over 6 d. Bouts of higher frequency activity were intermittently seen in both control and decentralized preparations, but the bouts began earlier and were more frequent in the decentralized preparations. Although the bouts may indicate that the removal of the modulatory inputs triggered changes in neuronal excitability, these changes did not produce obvious long-lasting changes in the frequency of the decentralized preparations.

Small central pattern generating circuits found in invertebrates have significant advantages for the study of the circuit mechanisms that generate brain rhythms. Experimental and computational studies of small oscillatory circuits reveal that similar rhythms can arise from disparate mechanisms. Animal-to-animal variation in the properties of single neurons and synapses may underly robust circuit performance, and can be revealed by perturbations. Neuromodulation can produce altered circuit performance but also ensure reliable circuit function.

Long-lasting internal arousal states motivate and pattern ongoing behaviour, enabling the temporary emergence of innate behavioural programs that serve the needs of an animal, such as fighting, feeding, and mating. However, how internal states shape sensory processing or behaviour remains unclear. In Drosophila, male flies perform a lengthy and elaborate courtship ritual that is triggered by the activation of sexually dimorphic P1 neurons1,2,3,4,5, during which they faithfully follow and sing to a female6,7. Here, by recording from males as they court a virtual ‘female’, we gain insight into how the salience of visual cues is transformed by a male’s internal arousal state to give rise to persistent courtship pursuit. The gain of LC10a visual projection neurons is selectively increased during courtship, enhancing their sensitivity to moving targets. A concise network model indicates that visual signalling through the LC10a circuit, once amplified by P1-mediated arousal, almost fully specifies a male’s tracking of a female. Furthermore, P1 neuron activity correlates with ongoing fluctuations in the intensity of a male’s pursuit to continuously tune the gain of the LC10a pathway. Together, these results reveal how a male’s internal state can dynamically modulate the propagation of visual signals through a high-fidelity visuomotor circuit to guide his moment-to-moment performance of courtship.

Gene expression analysis from single cells has become increasingly prominent across biological disciplines; thus, it is important to train students in these approaches. Here, we present an experimental and analysis pipeline that we developed for the Neural Systems & Behavior (NS&B) course at Marine Biological Laboratory. Our approach used the Maxwell® 16 LEV simplyRNA Tissue Kit and GoTaq® 2-Step RT-qPCR System for gene expression analysis from single neurons of the crustacean stomatogastric ganglion, a model system to study the generation of rhythmic motor patterns. We used double-stranded RNA to knockdown expression of a putative neuromodulator-activated sodium channel. We then examined the electrophysiological responses to known neuromodulators and confirmed that the response was reduced. Finally, we measured how mRNA levels of several ion channel genes changed in response. Our results provide new insights into the neural mechanisms underlying the generation and modulation of rhythmic motor patterns.