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Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. Because individual muscles may be used in many different behaviors, MN activity must be flexibly coordinated by dedicated premotor circuitry, the organization of which remains largely unknown. Here, we use comprehensive reconstruction of neuron anatomy and synaptic connectivity from volumetric electron microscopy (i.e., connectomics) to analyze the wiring logic of motor circuits controlling theDrosophilaleg and wing. We find that both leg and wing premotor networks are organized into modules that link MNs innervating muscles with related functions. However, the connectivity patterns within leg and wing motor modules are distinct. Leg premotor neurons exhibit proportional gradients of synaptic input onto MNs within each module, revealing a novel circuit basis for hierarchical MN recruitment. In comparison, wing premotor neurons lack proportional synaptic connectivity, which may allow muscles to be recruited in different combinations or with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

To move the body, the brain must precisely coordinate patterns of activity among diverse populations of motor neurons. Here, we use in vivo calcium imaging, electrophysiology, and behavior to understand how genetically-identified motor neurons control flexion of the fruit fly tibia. We find that leg motor neurons exhibit a coordinated gradient of anatomical, physiological, and functional properties. Large, fast motor neurons control high force, ballistic movements while small, slow motor neurons control low force, postural movements. Intermediate neurons fall between these two extremes. This hierarchical organization resembles the size principle, first proposed as a mechanism for establishing recruitment order among vertebrate motor neurons. Recordings in behaving flies confirmed that motor neurons are typically recruited in order from slow to fast. However, we also find that fast, intermediate, and slow motor neurons receive distinct proprioceptive feedback signals, suggesting that the size principle is not the only mechanism that dictates motor neuron recruitment. Overall, this work reveals the functional organization of the fly leg motor system and establishes Drosophila as a tractable system for investigating neural mechanisms of limb motor control.

SUMMARYMany animals use coordinated limb movements to interact with and navigate through the environment. To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to map synaptic connectivity within a neuronal network that controls limb movements. We present a synapse-resolution EM dataset containing the ventral nerve cord (VNC) of an adult femaleDrosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we reconstructed 507 motor neurons, including all those that control the legs and wings. We show that a specific class of leg sensory neurons directly synapse onto the largest-caliber motor neuron axons on both sides of the body, representing a unique feedback pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM data acquisition more accessible and affordable to the scientific community.

To investigate circuit mechanisms underlying locomotor behavior, we used serial-section electron microscopy (EM) to acquire a synapse-resolution dataset containing the ventral nerve cord (VNC) of an adult female Drosophila melanogaster. To generate this dataset, we developed GridTape, a technology that combines automated serial-section collection with automated high-throughput transmission EM. Using this dataset, we studied neuronal networks that control leg and wing movements by reconstructing all 507 motor neurons that control the limbs. We show that a specific class of leg sensory neurons synapses directly onto motor neurons with the largest-caliber axons on both sides of the body, representing a unique pathway for fast limb control. We provide open access to the dataset and reconstructions registered to a standard atlas to permit matching of cells between EM and light microscopy data. We also provide GridTape instrumentation designs and software to make large-scale EM more accessible and affordable to the scientific community.

SummaryTo move the body, the brain must precisely coordinate patterns of activity among diverse populations of motor neurons. In many species, including vertebrates, the motor neurons innervating a given muscle fire in a specific order that is determined by a gradient of cellular size and electrical excitability. This hierarchy allows premotor circuits to recruit motor neurons of increasing force capacity in a task-dependent manner. However, it remains unclear whether such a size principle also applies to species with more compact motor systems, such as the fruit fly,Drosophila melanogaster, which has just 53 motor neurons per leg. Usingin vivocalcium imaging and electrophysiology, we found that genetically-identified motor neurons controlling flexion of the fly tibia exhibit a gradient of anatomical, physiological, and functional properties consistent with the size principle. Large, fast motor neurons control high force, ballistic movements while small, slow motor neurons control low force, postural movements. Intermediate neurons fall between these two extremes. In behaving flies, motor neurons are recruited in order from slow to fast. This hierarchical organization suggests that slow and fast motor neurons control distinct motor regimes. Indeed, we find that optogenetic manipulation of each motor neuron type has distinct effects on the behavior of walking flies.

Stimulus or context dependent routing of neural signals through parallel pathways can permit flexible processing of diverse inputs. For example, work in mouse shows that rod photoreceptor signals are routed through several retinal pathways, each specialized for different light levels. This light level-dependent routing of rod signals has been invoked to explain several human perceptual results, but it has not been tested in primate retina. Here we show, surprisingly, that rod signals traverse the primate retina almost exclusively through a single pathway, regardless of light level. Indeed, identical experiments in mouse and primate reveal large differences in how rod signals traverse the retina. These results require reevaluating human perceptual results in terms of flexible computation within this single pathway. This includes a prominent speeding of rod signals with light level – which we show is inherited directly from the rods photoreceptors themselves rather than from different pathways with different kinetics.

Vision in dim light, when photons are scarce, requires reliable signaling of the arrival of single photons. Rod photoreceptors accomplish this task through the use of a G-protein-coupled transduction cascade that amplifies the activity of single active rhodopsin molecules. This process is one of the best understood signaling cascades in biology, yet quantitative measurements of the amplitude and kinetics of the rod's response in mice vary by a factor of ∼2 across studies. What accounts for these discrepancies? We used several experimental approaches to reconcile differences in published properties of rod responses. First, we used suction electrode recordings from single rods to compare measurements across a range of recording conditions. Second, we compared measurements of single-cell photocurrents to estimates of rod function fromin vitroelectroretinograms. Third, we assayed the health of the post-receptor retinal tissue in these different conditions. Several salient points emerge from these experiments: (1) recorded responses can be altered dramatically by how the retina is stored; (2) the kinetics of the recovery of responses to bright but not dim flashes are strongly sensitive to the extracellular concentration of magnesium; (3) experimental conditions that produce very different single-photon responses measured in single rods produce near identical derived rod responses from the electroretinogram. The dependence of rod responses on experimental conditions will be a key consideration in efforts to extract general principles of G-protein signaling from studies of phototransduction and to relate these signals to downstream mechanisms that facilitate visual sensitivity.

Rod photoreceptors generate measurable responses to single-photon activation of individual molecules of the G protein-coupled receptor (GPCR), rhodopsin. Timely rhodopsin desensitization depends on phosphorylation and arrestin binding, which quenches G protein activation. Rhodopsin phosphorylation has been measured biochemically at C-terminal serine residues, suggesting that these residues are critical for producing fast, low-noise responses. The role of native threonine residues is unclear. We compared single-photon responses from rhodopsin lacking native serine or threonine phosphorylation sites. Contrary to expectation, serine-only rhodopsin generated prolonged step-like single-photon responses that terminated abruptly and randomly, whereas threonine-only rhodopsin generated responses that were only modestly slower than normal. We show that the step-like responses of serine-only rhodopsin reflect slow and stochastic arrestin binding. Thus, threonine sites play a privileged role in promoting timely arrestin binding and rhodopsin desensitization. Similar coordination of phosphorylation and arrestin binding may more generally permit tight control of the duration of GPCR activity. DOI: http://dx.doi.org/10.7554/eLife.05981.001, eLife digest ‘Rod’ cells in the eye enable us to see in starlight. Inside these cells, a protein called rhodopsin is activated by light, which leads to an electrical signal being produced that travels to the brain. The duration of the electrical signal depends on the time it takes for the rhodopsin to be deactivated. Rhodopsin is a member of a large class of receptor proteins known as G protein-coupled receptors that regulate many processes throughout the body. Previous studies have shown that rhodopsin is deactivated by the attachment of phosphate groups to the protein. This allows another protein called arrestin to bind to rhodopsin. The phosphates can be attached to particular amino acids—the building blocks of proteins—at one end of rhodopsin. Three of these are a type of amino acid called serine. Previous work has shown that light increases the speed at which phosphate groups are added to these serines, suggesting that they are important for producing rapid electrical signals. The other three amino acids are of a different type—called threonine—but it is less clear what role they play in deactivating rhodopsin. Here, Azevedo et al. studied mutant forms of rhodopsin that were missing the serines or threonines in mice. The experiments show that loss of the serines only slightly slowed the electrical signals. However, loss of the threonines resulted in much slower electrical signals that ended at random times. This was due to rhodopsin being less able to bind to arrestin. Azevedo et al. propose a new model for how rhodopsin is deactivated. Once light activates the protein, phosphate groups are rapidly added to the serines, which begins to lower the activity of rhodopsin. However, it is the slower addition of phosphates to the threonines that is essential to promote arrestin binding and fully deactivate the protein. Other proteins belonging to the G protein-coupled receptor family also have these serines and threonines, and thus, may be regulated in a similar way. DOI: http://dx.doi.org/10.7554/eLife.05981.002

To better understand biophysical mechanisms of mechanosensory processing, we investigated two cell types in the Drosophila brain (A2 and B1 cells) that are postsynaptic to antennal vibration receptors. A2 cells receive excitatory synaptic currents in response to both directions of movement: thus, twice per vibration cycle. The membrane acts as a low-pass filter, so that voltage and spiking mainly track the vibration envelope rather than individual cycles. By contrast, B1 cells are excited by only forward or backward movement, meaning they are sensitive to vibration phase. They receive oscillatory synaptic currents at the stimulus frequency, and they bandpass filter these inputs to favor specific frequencies. Different cells prefer different frequencies, due to differences in their voltage-gated conductances. Both Na+ and K+ conductances suppress low-frequency synaptic inputs, so cells with larger voltage-gated conductances prefer higher frequencies. These results illustrate how membrane properties and voltage-gated conductances can extract distinct stimulus features into parallel channels.

Reliable signal transduction via G-protein-coupled receptors requires proper receptor inactivation. For example, signals originating from single rhodopsin molecules vary little from one to the next, requiring reproducible inactivation of rhodopsin by phosphorylation and arrestin binding. We determined how reduced concentrations of rhodopsin kinase (GRK1) and/or arrestin1 influenced the kinetics and variability of the single-photon responses of mouse rod photoreceptors. These experiments revealed that arrestin, in addition to its role in quenching the activity of rhodopsin, can tune the kinetics of rhodopsin phosphorylation by competing with GRK1. This competition influenced the variability of the active lifetime of rhodopsin. Biasing the competition in favor of GRK1 revealed that rhodopsin remained active through much of the single-photon response under the conditions of our experiments. This long-lasting rhodopsin activity can explain the characteristic time course of single-photon response variability. Indeed, explaining the late time-to-peak of the variance required an active lifetime of rhodopsin approximately twice that of the G-protein transducin. Competition between arrestins and kinases may be a general means of influencing signals mediated by G-protein-coupled receptors, particularly when activation of a few receptors produces signals of functional importance.

Significant variation exists in published measurements of the kinetics and sensitivity of mouse rod photoreceptors; this variation includes ∼2× differences in the amplitude of the single-photon response and the half-maximal flash strength, and ∼1.5× differences in the time-to-peak of the dim flash response. The most obvious discrepancy between these studies is in the conditions used to store and perfuse the retina during the course of the experiment. Here we characterize changes in phototransduction produced by different recording conditions. Consistent with past work, the sensitivity and response kinetics depended strongly on recording conditions. These differences resulted, at least in part, from an apparent change in relative time constants of rhodopsin and transducin inactivation. These results underscore the importance of identifying experimental conditions that closely resemble the physiological environment of the retina to relate properties of phototransduction to downstream processing and visually-guided behavior.

Imaging neuronal networks provides a foundation for understanding the nervous system, but resolving dense nanometer-scale structures over large volumes remains challenging for light microscopy (LM) and electron microscopy (EM). Here we show that X-ray holographic nano-tomography (XNH) can image millimeter-scale volumes with sub-100-nm resolution, enabling reconstruction of dense wiring in Drosophila melanogaster and mouse nervous tissue. We performed correlative XNH and EM to reconstruct hundreds of cortical pyramidal cells and show that more superficial cells receive stronger synaptic inhibition on their apical dendrites. By combining multiple XNH scans, we imaged an adult Drosophila leg with sufficient resolution to comprehensively catalog mechanosensory neurons and trace individual motor axons from muscles to the central nervous system. To accelerate neuronal reconstructions, we trained a convolutional neural network to automatically segment neurons from XNH volumes. Thus, XNH bridges a key gap between LM and EM, providing a new avenue for neural circuit discovery. Kuan, Phelps, et al. used synchrotron X-ray imaging and deep learning to map dense neuronal wiring in fly and mouse tissue, enabling examination of individual cells and connectivity in circuits governing motor control and perceptual decision-making.