Transformation of odor selectivity from projection neurons to single mushroom body neurons mapped with dual-color calcium imaging

Drosophila aux orchestrates the phosphorylation-dependent assembly of the lysosomal V-ATPase in glia and contributes to SNCA/α-synuclein degradation

Glia contribute to the neuropathology of Parkinson disease (PD), but how they react opposingly to be beneficial or detrimental under pathological conditions, like promoting or eliminating SNCA/α-syn (synuclein alpha) inclusions, remains elusive. Here we present evidence that aux (auxilin), the Drosophila homolog of the PD risk factor GAK (cyclin G associated kinase), regulates the lysosomal degradation of SNCA/α-syn in glia. Lack of glial GAK/aux increases the lysosome number and size, regulates lysosomal acidification and hydrolase activity, and ultimately blocks the degradation of substrates including SNCA/α-syn. Whereas SNCA/α-syn accumulates prominently in lysosomes devoid of glial aux, levels of injected SNCA/α-syn preformed fibrils are further enhanced in the absence of microglial GAK. Mechanistically, aux mediates phosphorylation at the serine 543 of Vha44, the V1 C subunit of the vacuolar-type H+-translocating ATPase (V-ATPase), and regulates its assembly to control proper acidification of the lysosomal milieu. Expression of Vha44, but not the Vha44 variant lacking S543 phosphorylation, restores lysosome acidity, locomotor deficits, and DA neurodegeneration upon glial aux depletion, linking this pathway to PD. Our findings identify a phosphorylation-dependent switch controlling V-ATPase assembly for lysosomal SNCA/α-syn degradation in glia. Targeting the clearance of glial SNCA/α-syn inclusions via this lysosomal pathway could potentially be a therapeutic approach to ameliorate the disease progression in PD.Abbreviation: aux: auxilin; GAK: cyclin G associated kinase; LTG: LysoTracker Green; LTR: LysoTracker Red; MR: Magic Red; PD: Parkinson disease; SNCA/a-syn: synuclein alpha; V-ATPase: vacuolar-type H+-translocating ATPase.

Sensory encoding and memory in the mushroom body: signals, noise, and variability

To survive in changing environments, animals need to learn to associate specific sensory stimuli with positive or negative valence. How do they form stimulus-specific memories to distinguish between positively/negatively associated stimuli and other irrelevant stimuli? Solving this task is one of the functions of the mushroom body, the associative memory center in insect brains. Here we summarize recent work on sensory encoding and memory in the Drosophila mushroom body, highlighting general principles such as pattern separation, sparse coding, noise and variability, coincidence detection, and spatially localized neuromodulation, and placing the mushroom body in comparative perspective with mammalian memory systems.

Experience-dependent tuning of the olfactory system

Insects rely on their sense of smell to navigate complex environments and make decisions regarding food and reproduction. However, in natural settings, the odors that convey this information may come mixed with environmental odors that can obscure their perception. Therefore, recognizing the presence of informative odors involves generalization and discrimination processes, which can be facilitated when there is a high contrast between stimuli, or the internal representation of the odors of interest outcompetes that of concurrent ones. The first two layers of the olfactory system, which involve the detection of odorants by olfactory receptor neurons and their encoding by the first postsynaptic partners in the antennal lobe, are critical for achieving such optimal representation. In this review, we summarize evidence indicating that experience-dependent changes adjust these two levels of the olfactory system. These changes are discussed in the context of the advantages they provide for detection of informative odors.

Input density tunes Kenyon cell sensory responses in the Drosophila mushroom body

The ability to discriminate sensory stimuli with overlapping features is thought to arise in brain structures called expansion layers, where neurons carrying information about sensory features make combinatorial connections onto a much larger set of cells. For 50 years, expansion coding has been a prime topic of theoretical neuroscience, which seeks to explain how quantitative parameters of the expansion circuit influence sensory sensitivity, discrimination, and generalization. Here, we investigate the developmental events that produce the quantitative parameters of the arthropod expansion layer, called the mushroom body. Using Drosophila melanogaster as a model, we employ genetic and chemical tools to engineer changes to circuit development. These allow us to produce living animals with hypothesis-driven variations on natural expansion layer wiring parameters. We then test the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input density, but not cell number, tunes neuronal odor selectivity. Simple odor discrimination behavior is maintained when the Kenyon cell number is reduced and augmented by Kenyon cell number expansion. Animals with increased input density to each Kenyon cell show increased overlap in Kenyon cell odor responses and become worse at odor discrimination tasks.

Practical considerations in an era of multicolor optogenetics

The ability to control synaptic communication is indispensable to modern neuroscience. Until recently, only single-pathway manipulations were possible due to limited availability of opsins activated by distinct wavelengths. However, extensive protein engineering and screening efforts have drastically expanded the optogenetic toolkit, ushering in an era of multicolor approaches for studying neural circuits. Nonetheless, opsins with truly discrete spectra are scarce. Experimenters must therefore take care to avoid unintended cross-activation of optogenetic tools (crosstalk). Here, we demonstrate the multidimensional nature of crosstalk in a single model synaptic pathway, testing stimulus wavelength, irradiance, duration, and opsin choice. We then propose a "lookup table" method for maximizing the dynamic range of opsin responses on an experiment-by-experiment basis.

SpaRCe: Improved Learning of Reservoir Computing Systems Through Sparse Representations

"Sparse" neural networks, in which relatively few neurons or connections are active, are common in both machine learning and neuroscience. While, in machine learning, "sparsity" is related to a penalty term that leads to some connecting weights becoming small or zero, in biological brains, sparsity is often created when high spiking thresholds prevent neuronal activity. Here, we introduce sparsity into a reservoir computing network via neuron-specific learnable thresholds of activity, allowing neurons with low thresholds to contribute to decision-making but suppressing information from neurons with high thresholds. This approach, which we term "SpaRCe," optimizes the sparsity level of the reservoir without affecting the reservoir dynamics. The read-out weights and the thresholds are learned by an online gradient rule that minimizes an error function on the outputs of the network. Threshold learning occurs by the balance of two opposing forces: reducing interneuronal correlations in the reservoir by deactivating redundant neurons, while increasing the activity of neurons participating in correct decisions. We test SpaRCe on classification problems and find that threshold learning improves performance compared to standard reservoir computing. SpaRCe alleviates the problem of catastrophic forgetting, a problem most evident in standard echo state networks (ESNs) and recurrent neural networks in general, due to increasing the number of task-specialized neurons that are included in the network decisions.

Hacking brain development to test models of sensory coding

Animals can discriminate myriad sensory stimuli but can also generalize from learned experience. You can probably distinguish the favorite teas of your colleagues while still recognizing that all tea pales in comparison to coffee. Tradeoffs between detection, discrimination, and generalization are inherent at every layer of sensory processing. During development, specific quantitative parameters are wired into perceptual circuits and set the playing field on which plasticity mechanisms play out. A primary goal of systems neuroscience is to understand how material properties of a circuit define the logical operations-computations--that it makes, and what good these computations are for survival. A cardinal method in biology-and the mechanism of evolution--is to change a unit or variable within a system and ask how this affects organismal function. Here, we make use of our knowledge of developmental wiring mechanisms to modify hard-wired circuit parameters in the Drosophila melanogaster mushroom body and assess the functional and behavioral consequences. By altering the number of expansion layer neurons (Kenyon cells) and their dendritic complexity, we find that input number, but not cell number, tunes odor selectivity. Simple odor discrimination performance is maintained when Kenyon cell number is reduced and augmented by Kenyon cell expansion.

Prolonged exposure of neonatal mice to sevoflurane leads to hyper-ramification in microglia, reduced contacts between microglia and synapses, and defects in adult behavior

Background

Prolonged exposure to general anesthetics during development is known to cause neurobehavioral abnormalities, but the cellular and molecular mechanisms involved are unclear. Microglia are the resident immune cells in the central nervous system and play essential roles in normal brain development.

Materials and methods

In the study, postnatal day 7 (P7) C57BL/6 mice were randomly assigned to two groups. In the sevoflurane (SEVO), mice were exposed to 2.5% sevoflurane for 4 h. In the control group, mice were exposed to carrier gas (30% O2/70% N2) for 4 h. Fixed brain slices from P14 to P21 mice were immunolabeled for ionized calcium-binding adapter molecule 1 (IBA-1) to visualize microglia. The morphological analysis of microglia in the somatosensory cortex was performed using ImageJ and Imaris software. Serial block face scanning electron microscopy (SBF-SEM) was performed to assess the ultrastructure of the microglia and the contacts between microglia and synapse in P14 and P21 mice. The confocal imaging of brain slices was performed to assess microglia surveillance in resting and activated states in P14 and P21 mice. Behavioral tests were used to assess the effect of microglia depletion and repopulation on neurobehavioral abnormalities caused by sevoflurane exposure.

Results

The prolonged exposure of neonatal mice to sevoflurane induced microglia hyper-ramification with an increase in total branch length, arborization area, and branch complexity 14  days after exposure. Prolonged neonatal sevoflurane exposure reduced contacts between microglia and synapses, without affecting the surveillance of microglia in the resting state or responding to laser-induced focal brain injury. These neonatal changes in microglia were associated with anxiety-like behaviors in adult mice. Furthermore, microglial depletion before sevoflurane exposure and subsequent repopulation in the neonatal brain mitigated anxiety-like behaviors caused by sevoflurane exposure.

Conclusion

Our experiments indicate that general anesthetics may harm the developing brain, and microglia may be an essential target of general anesthetic-related developmental neurotoxicity.

A feedback control principle common to several biological and engineered systems

Feedback control is used by many distributed systems to optimize behaviour. Traditional feedback control algorithms spend significant resources to constantly sense and stabilize a continuous control variable of interest, such as vehicle speed for implementing cruise control, or body temperature for maintaining homeostasis. By contrast, discrete-event feedback (e.g. a server acknowledging when data are successfully transmitted, or a brief antennal interaction when an ant returns to the nest after successful foraging) can reduce costs associated with monitoring a continuous variable; however, optimizing behaviour in this setting requires alternative strategies. Here, we studied parallels between discrete-event feedback control strategies in biological and engineered systems. We found that two common engineering rules-additive-increase, upon positive feedback, and multiplicative-decrease, upon negative feedback, and multiplicative-increase multiplicative-decrease-are used by diverse biological systems, including for regulating foraging by harvester ant colonies, for maintaining cell-size homeostasis, and for synaptic learning and adaptation in neural circuits. These rules support several goals of these systems, including optimizing efficiency (i.e. using all available resources); splitting resources fairly among cooperating agents, or conversely, acquiring resources quickly among competing agents; and minimizing the latency of responses, especially when conditions change. We hypothesize that theoretical frameworks from distributed computing may offer new ways to analyse adaptation behaviour of biology systems, and in return, biological strategies may inspire new algorithms for discrete-event feedback control in engineering.

Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

Neural circuits use homeostatic compensation to achieve consistent behavior despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly's olfactory memory center. In a computational model, we show that under sparse coding conditions, memory performance is degraded when the mushroom body's principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model's compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.

A Correspondence Between Normalization Strategies in Artificial and Biological Neural Networks

A fundamental challenge at the interface of machine learning and neuroscience is to uncover computational principles that are shared between artificial and biological neural networks. In deep learning, normalization methods such as batch normalization, weight normalization, and their many variants help to stabilize hidden unit activity and accelerate network training, and these methods have been called one of the most important recent innovations for optimizing deep networks. In the brain, homeostatic plasticity represents a set of mechanisms that also stabilize and normalize network activity to lie within certain ranges, and these mechanisms are critical for maintaining normal brain function. In this article, we discuss parallels between artificial and biological normalization methods at four spatial scales: normalization of a single neuron's activity, normalization of synaptic weights of a neuron, normalization of a layer of neurons, and normalization of a network of neurons. We argue that both types of methods are functionally equivalent-that is, both push activation patterns of hidden units toward a homeostatic state, where all neurons are equally used-and we argue that such representations can improve coding capacity, discrimination, and regularization. As a proof of concept, we develop an algorithm, inspired by a neural normalization technique called synaptic scaling, and show that this algorithm performs competitively against existing normalization methods on several data sets. Overall, we hope this bidirectional connection will inspire neuroscientists and machine learners in three ways: to uncover new normalization algorithms based on established neurobiological principles; to help quantify the trade-offs of different homeostatic plasticity mechanisms used in the brain; and to offer insights about how stability may not hinder, but may actually promote, plasticity.

Synthesis of Conserved Odor Object Representations in a Random, Divergent-Convergent Network

Animals are capable of recognizing mixtures and groups of odors as a unitary object. However, how odor object representations are generated in the brain remains elusive. Here, we investigate sensory transformation between the primary olfactory center and its downstream region, the mushroom body (MB), in Drosophila and show that clustered representations for mixtures and groups of odors emerge in the MB at the population and single-cell levels. Decoding analyses demonstrate that neurons selective for mixtures and groups enhance odor generalization. Responses of these neurons and those selective for individual odors all emerge in an experimentally well-constrained model implementing divergent-convergent, random connectivity between the primary center and the MB. Furthermore, we found that relative odor representations are conserved across animals despite this random connectivity. Our results show that the generation of distinct representations for individual odors and groups and mixtures of odors in the MB can be understood in a unified computational and mechanistic framework.

Habituation as a neural algorithm for online odor discrimination

Habituation is a form of simple memory that suppresses neural activity in response to repeated, neutral stimuli. This process is critical in helping organisms guide attention toward the most salient and novel features in the environment. Here, we follow known circuit mechanisms in the fruit fly olfactory system to derive a simple algorithm for habituation. We show, both empirically and analytically, that this algorithm is able to filter out redundant information, enhance discrimination between odors that share a similar background, and improve detection of novel components in odor mixtures. Overall, we propose an algorithmic perspective on the biological mechanism of habituation and use this perspective to understand how sensory physiology can affect odor perception. Our framework may also help toward understanding the effects of habituation in other more sophisticated neural systems.

The Drosophila Mushroom Body: From Architecture to Algorithm in a Learning Circuit

The Drosophila brain contains a relatively simple circuit for forming Pavlovian associations, yet it achieves many operations common across memory systems. Recent advances have established a clear framework for Drosophila learning and revealed the following key operations: a) pattern separation, whereby dense combinatorial representations of odors are preprocessed to generate highly specific, nonoverlapping odor patterns used for learning; b) convergence, in which sensory information is funneled to a small set of output neurons that guide behavioral actions; c) plasticity, where changing the mapping of sensory input to behavioral output requires a strong reinforcement signal, which is also modulated by internal state and environmental context; and d) modularization, in which a memory consists of multiple parallel traces, which are distinct in stability and flexibility and exist in anatomically well-defined modules within the network. Cross-module interactions allow for higher-order effects where past experience influences future learning. Many of these operations have parallels with processes of memory formation and action selection in more complex brains.

Phosphoproteomic profiling of oxycodone‑treated spinal cord of rats with cancer‑induced bone pain

Treatment of cancer‑induced bone pain (CIBP) is challenging in clinical settings. Oxycodone (OXY) is used to treat CIBP; however, a lack of understanding of the mechanisms underlying CIBP limits the application of OXY. In the present study, all rats were randomly divided into three groups: The sham group, the CIBP group, and the OXY group. Then, a rat model of CIBP was established by inoculation of Walker 256 tumor cells from rat tibia. Phosphoproteomic profiling of the OXY‑treated spinal dorsal cords of rats with CIBP was performed, and 1,679 phosphorylated proteins were identified, of which 160 proteins were significantly different between the CIBP and sham groups, and 113 proteins were significantly different between the CIBP and OXY groups. Gene Ontology analysis revealed that these proteins mainly clustered as synaptic‑associated cellular components; among these, disks large homolog 3 expression was markedly increased in rats with CIBP and was reversed by OXY treatment. Subsequent domain analysis of the differential proteins revealed several significant synaptic‑associated domains. In conclusion, synaptic‑associated cellular components may be critical in OXY‑induced analgesia in rats with CIBP.

Astroglial dysfunctions drive aberrant synaptogenesis and social behavioral deficits in mice with neonatal exposure to lengthy general anesthesia

Lengthy use of general anesthetics (GAs) causes neurobehavioral deficits in the developing brain, which has raised significant clinical concerns such that the United States Food and Drug Administration (FDA) is warning on the use of GAs in children younger than 3 years. However, the molecular and cellular mechanisms for GAs-induced neurotoxicity remain largely unknown. Here, we report that sevoflurane (Sevo), a commonly used GA in pediatrics, caused compromised astrocyte morphogenesis spatiotemporally correlated to synaptic overgrowth, with reduced synaptic function in developing cortex in a regional-, exposure-length-, and age-specific manner. Sevo disrupted astrocyte Ca²⁺ homeostasis both acutely and chronically, which led to the down-regulation of Ezrin, an actin-binding membrane-bound protein, which we found was critically involved in astrocyte morphogenesis in vivo. Importantly, overexpression of astrocyte Ezrin rescued astrocytic and neuronal dysfunctions and fully corrected deficits in social behaviors in developing mice with lengthy Sevo exposure. Our data uncover that, in addition to neurons, astrocytes may represent important targets for GAs to exert toxic effects and that astrocyte morphological integrity is crucial for synaptogenesis and neurological behaviors.

The extended use of general anesthetics can cause neurobehavioral deficits in the developing brain, leading to clinical concerns regarding their use in children younger than 3 years. This study shows that general anesthetics target glial cells to disrupt neural circuit formation in the developing brain, an effect that may underlie the observed learning, cognitive, or emotional deficits.

Functional organization of intrinsic and feedback presynaptic inputs in the primary visual cortex

In the primary visual cortex (V1) of many mammalian species, neurons are spatially organized according to their preferred orientation into a highly ordered map. However, whether and how the various presynaptic inputs to V1 neurons are organized relative to the neuronal orientation map remain unclear. To address this issue, we constructed genetically encoded calcium indicators targeting axon boutons in two colors and used them to map the organization of axon boutons of V1 intrinsic and V2-V1 feedback projections in tree shrews. Both connections are spatially organized into maps according to the preferred orientations of axon boutons. Dual-color calcium imaging showed that V1 intrinsic inputs are precisely aligned to the orientation map of V1 cell bodies, while the V2-V1 feedback projections are aligned to the V1 map with less accuracy. Nonselective integration of intrinsic presynaptic inputs around the dendritic tree is sufficient to reproduce cell body orientation preference. These results indicate that a precisely aligned map of intrinsic inputs could reinforce the neuronal map in V1, a principle that may be prevalent for brain areas with function maps.

A Presynaptic Function of Shank Protein in Drosophila

Human genetic studies support that loss-of-function mutations in the SH3 domain and ankyrin repeat containing family proteins (SHANK1-3), the large synaptic scaffolding proteins enriched at the postsynaptic density of excitatory synapses, are causative for autism spectrum disorder and other neuropsychiatric disorders in humans. To better understand the in vivo functions of Shank and facilitate dissection of neuropathology associated with SHANK mutations in human, we generated multiple mutations in the Shank gene, the only member of the SHANK family in Drosophila melanogaster Both male and female Shank null mutants were fully viable and fertile with no apparent morphological or developmental defects. Expression analysis revealed apparent enrichment of Shank in the neuropils of the CNS. Specifically, Shank coexpressed with another PSD scaffold protein, Homer, in the calyx of mushroom bodies in the brain. Consistent with high expression in mushroom body calyces, Shank mutants show an abnormal calyx structure and reduced olfactory acuity. These morphological and functional phenotypes were fully rescued by pan-neuronal reexpression of Shank, and only partially rescued by presynaptic but no rescue by postsynaptic reexpression of Shank. Our findings thus establish a previously unappreciated presynaptic function of Shank.SIGNIFICANCE STATEMENT Mutations in SHANK family genes are causative for idiopathic autism spectrum disorder. To understand the neural function of Shank, a large scaffolding protein enriched at the postsynaptic densities, we examined the role of Drosophila Shank in synapse development at the peripheral neuromuscular junctions and the central mushroom body calyx. Our results demonstrate that, in addition to its conventional postsynaptic function, Shank also acts presynaptically in synapse development in the brain. This study offers novel insights into the synaptic role of Shank.

Neural signatures of dynamic stimulus selection in Drosophila

Many animals orient using visual cues, but how a single cue is selected from among many is poorly understood. Here we show that Drosophila ring neurons-central brain neurons implicated in navigation-display visual stimulus selection. Using in vivo two-color two-photon imaging with genetically encoded calcium indicators, we demonstrate that individual ring neurons inherit simple-cell-like receptive fields from their upstream partners. Stimuli in the contralateral visual field suppressed responses to ipsilateral stimuli in both populations. Suppression strength depended on when and where the contralateral stimulus was presented, an effect stronger in ring neurons than in their upstream inputs. This history-dependent effect on the temporal structure of visual responses, which was well modeled by a simple biphasic filter, may determine how visual references are selected for the fly's internal compass. Our approach highlights how two-color calcium imaging can help identify and localize the origins of sensory transformations across synaptically connected neural populations.

K+ channel reorganization and homeostatic plasticity during postembryonic development: biophysical and genetic analyses in acutely dissociated Drosophila central neurons

Intrinsic electric activities of neurons play important roles in establishing and refining neural circuits during development. However, how the underlying ionic currents undergo postembryonic reorganizations remains largely unknown. Using acutely dissociated neurons from larval, pupal, and adult Drosophila brains, we show drastic re-assemblies and compensatory regulations of voltage-gated (IKv) and Ca2+-activated (IK(Ca)) K+ currents during postembryonic development. Larval and adult neurons displayed prominent fast-inactivating IKv, mediated by the Shaker (Sh) channel to a large extent, while in the same neurons IK(Ca) was far smaller in amplitude. In contrast, pupal neurons were characterized by large sustained IKv and prominent IK(Ca), encoded predominantly by the slowpoke (slo) gene. Surprisingly, deletion of Sh in the ShM null mutant removed inactivating, transient IKv from large portions of neurons at all stages. Interestingly, elimination of Sh currents was accompanied by upregulation of non-Sh transient IKv. In comparison, the slo1 mutation abolished the vast majority of IK(Ca), particularly at the pupal stage. Strikingly, the deficiency of IK(Ca) in slo pupae was compensated by the transient component of IKv mediated by Sh channels. Thus, IK(Ca) appears to play critical roles in pupal development and its absence induces functional compensations from a specific transient IKv current. While mutants lacking either Sh or slo currents survived normally, Sh;;slo double mutants deficient in both failed to survive through pupal metamorphosis. Together, our data highlight significant reorganizations and homeostatic compensations of K+ currents during postembryonic development and uncover previously unrecognized roles for Sh and slo in this plastic process.

Sensory Response of Transplanted Astrocytes in Adult Mammalian Cortex In Vivo

Glial precursor transplantation provides a potential therapy for brain disorders. Before its clinical application, experimental evidence needs to indicate that engrafted glial cells are functionally incorporated into the existing circuits and become essential partners of neurons for executing fundamental brain functions. While previous experiments supporting for their functional integration have been obtained under in vitro conditions using slice preparations, in vivo evidence for such integration is still lacking. Here, we utilized in vivo two-photon Ca²⁺ imaging along with immunohistochemistry, fluorescent indicator labeling-based axon tracing and correlated light/electron microscopy to analyze the profiles and the functional status of glial precursor cell-derived astrocytes in adult mouse neocortex. We show that after being transplanted into somatosensory cortex, precursor-derived astrocytes are able to survive for more than a year and respond with Ca²⁺ signals to sensory stimulation. These sensory-evoked responses are mediated by functionally-expressed nicotinic receptors and newly-established synaptic contacts with the host cholinergic afferents. Our results provide in vivo evidence for a functional integration of transplanted astrocytes into adult mammalian neocortex, representing a proof-of-principle for sensory cortex remodeling through addition of essential neural elements. Moreover, we provide strong support for the use of glial precursor transplantation to understand glia-related neural development in vivo.

Activity-dependent FMRP requirements in development of the neural circuitry of learning and memory

The activity-dependent refinement of neural circuit connectivity during critical periods of brain development is essential for optimized behavioral performance. We hypothesize that this mechanism is defective in fragile X syndrome (FXS), the leading heritable cause of intellectual disability and autism spectrum disorders. Here, we use optogenetic tools in the Drosophila FXS disease model to test activity-dependent dendritogenesis in two extrinsic neurons of the mushroom body (MB) learning and memory brain center: (1) the input projection neuron (PN) innervating Kenyon cells (KCs) in the MB calyx microglomeruli and (2) the output MVP2 neuron innervated by KCs in the MB peduncle. Both input and output neuron classes exhibit distinctive activity-dependent critical period dendritic remodeling. MVP2 arbors expand in Drosophila mutants null for fragile X mental retardation 1 (dfmr1), as well as following channelrhodopsin-driven depolarization during critical period development, but are reduced by halorhodopsin-driven hyperpolarization. Optogenetic manipulation of PNs causes the opposite outcome--reduced dendritic arbors following channelrhodopsin depolarization and expanded arbors following halorhodopsin hyperpolarization during development. Importantly, activity-dependent dendritogenesis in both neuron classes absolutely requires dfmr1 during one developmental window. These results show that dfmr1 acts in a neuron type-specific activity-dependent manner for sculpting dendritic arbors during early-use, critical period development of learning and memory circuitry in the Drosophila brain.

The Q-System: A Versatile Expression System for Drosophila

Binary expression systems are flexible and versatile genetic tools in Drosophila. The Q-system is a recently developed repressible binary expression system that offers new possibilities for transgene expression and genetic manipulations. In this review chapter, we focus on current state-of-the-art Q-system tools and reagents. We also discuss in vivo applications of the Q-system, together with GAL4/UAS and LexA/LexAop systems, for simultaneous expression of multiple effectors, intersectional labeling, and clonal analysis.

CRASP: CFP reconstitution across synaptic partners

Mapping the pattern of connectivity between neurons is widely regarded to be critical for understanding the nervous system. GRASP (GFP reconstitution across synaptic partners) has been used as a promising method for mapping neuronal connectivity, but is currently available in the green color only, limiting its potential applications. Here we demonstrate CRASP (CFP reconstitution across synaptic partners), a cyan-colored version of GRASP. We validated the system in HEK 293T cells, and generated transgenic Drosophila lines to show that the system could reliably detect neuronal contacts in the brain. Furthermore, we showed that the CRASP signal could be selectively amplified using standard immunohistochemistry methods. The CRASP system adds to the toolkit available to researchers for mapping neuronal connectivity, and substantially expands the potential application of GRASP-like strategies.

Sleep- and wake-dependent changes in neuronal activity and reactivity demonstrated in fly neurons using in vivo calcium imaging

Sleep in Drosophila shares many features with mammalian sleep, but it remains unknown whether spontaneous and evoked activity of individual neurons change with the sleep/wake cycle in flies as they do in mammals. Here we used calcium imaging to assess how the Kenyon cells in the fly mushroom bodies change their activity and reactivity to stimuli during sleep, wake, and after short or long sleep deprivation. As before, sleep was defined as a period of immobility of >5 min associated with a reduced behavioral response to a stimulus. We found that calcium levels in Kenyon cells decline when flies fall asleep and increase when they wake up. Moreover, calcium transients in response to two different stimuli are larger in awake flies than in sleeping flies. The activity of Kenyon cells is also affected by sleep/wake history: in awake flies, more cells are spontaneously active and responding to stimuli if the last several hours (5-8 h) before imaging were spent awake rather than asleep. By contrast, long wake (≥29 h) reduces both baseline and evoked neural activity and decreases the ability of neurons to respond consistently to the same repeated stimulus. The latter finding may underlie some of the negative effects of sleep deprivation on cognitive performance and is consistent with the occurrence of local sleep during wake as described in behaving rats. Thus, calcium imaging uncovers new similarities between fly and mammalian sleep: fly neurons are more active and reactive in wake than in sleep, and their activity tracks sleep/wake history.

A long Stokes shift red fluorescent Ca2+ indicator protein for two-photon and ratiometric imaging

The introduction of calcium ion (Ca(2+)) indicators based on red fluorescent proteins (RFPs) has created new opportunities for multicolour visualization of intracellular Ca(2+) dynamics. However, one drawback of these indicators is that they have optimal two-photon excitation outside the near-infrared window (650-1,000 nm) where tissue is most transparent to light. To address this shortcoming, we developed a long Stokes shift RFP-based Ca(2+) indicator, REX-GECO1, with optimal two-photon excitation at <1,000 nm. REX-GECO1 fluoresces at 585 nm when excited at 480 nm or 910 nm by a one- or two-photon process, respectively. We demonstrate that REX-GECO1 can be used as either a ratiometric or intensiometric Ca(2+) indicator in organotypic hippocampal slice cultures (one- and two-photon) and the visual system of albino tadpoles (two-photon). Furthermore, we demonstrate single excitation wavelength two-colour Ca(2+) and glutamate imaging in organotypic cultures.

Astrocyte-like glial cells physiologically regulate olfactory processing through the modification of ORN-PN synaptic strength in Drosophila

Astrocyte-like glial cells are abundant in the central nervous system of adult Drosophila and exhibit morphology similar to astrocytes of mammals. Previous evidence has shown that astrocyte-like glial cells are strongly associated with synapses in the antennal lobe (AL), the first relay of the olfactory system, where olfactory receptor neurons (ORNs) transmit information into projection neurons (PNs). However, the function of astrocyte-like glia in the AL remains obscure. In this study, using in vivo calcium imaging, we found that astrocyte-like glial cells exhibited spontaneous microdomain calcium elevations. Using simultaneous manipulation of glial activity and monitoring of neuronal function, we found that the astrocyte-like glial activation, but not ensheathing glial activation, could inhibit odor-evoked responses of PNs. Ensheathing glial cells are another subtype of glia, and are of functional importance in the AL. Electrophysiological experiments indicated that astrocyte-like glial activation decreased the amplitude and slope of excitatory postsynaptic potentials evoked through electrical stimulation of the antennal nerve. These results suggest that astrocyte-like glial cells may regulate olfactory processing through negative regulation of ORN-PN synaptic strength. Beyond the antennal lobe we observed astrocyte-like glial spontaneous calcium activities in the ventromedial protocerebrum, indicating that astrocyte-like glial spontaneous calcium elevations might be general in the adult fly brain. Overall, our study demonstrates a new function for astrocyte-like glial cells in the physiological modulation of olfactory information transmission, possibly through regulating ORN-PN synapse strength.

A single GABAergic neuron mediates feedback of odor-evoked signals in the mushroom body of larval Drosophila

Inhibition has a central role in defining the selectivity of the responses of higher order neurons to sensory stimuli. However, the circuit mechanisms of regulation of these responses by inhibitory neurons are still unclear. In Drosophila, the mushroom bodies (MBs) are necessary for olfactory memory, and by implication for the selectivity of learned responses to specific odors. To understand the circuitry of inhibition in the calyx (the input dendritic region) of the MBs, and its relationship with MB excitatory activity, we used the simple anatomy of the Drosophila larval olfactory system to identify any inhibitory inputs that could contribute to the selectivity of MB odor responses. We found that a single neuron accounts for all detectable GABA innervation in the calyx of the MBs, and that this neuron has pre-synaptic terminals in the calyx and post-synaptic branches in the MB lobes (output axonal area). We call this neuron the larval anterior paired lateral (APL) neuron, because of its similarity to the previously described adult APL neuron. Reconstitution of GFP partners (GRASP) suggests that the larval APL makes extensive contacts with the MB intrinsic neurons, Kenyon Cells (KCs), but few contacts with incoming projection neurons (PNs). Using calcium imaging of neuronal activity in live larvae, we show that the larval APL responds to odors, in a manner that requires output from KCs. Our data suggest that the larval APL is the sole GABAergic neuron that innervates the MB input region and carries inhibitory feedback from the MB output region, consistent with a role in modulating the olfactory selectivity of MB neurons.

Olfactory coding in the insect brain: data and conjectures

Much progress has been made recently in understanding how olfactory coding works in insect brains. Here, I propose a wiring diagram for the major steps from the first processing network (the antennal lobe) to behavioral readout. I argue that the sequence of lateral inhibition in the antennal lobe, non-linear synapses, threshold-regulating gated spring network, selective lateral inhibitory networks across glomeruli, and feedforward inhibition to the lateral protocerebrum cover most of the experimental results from different research groups and model species. I propose that the main difference between mushroom bodies and the lateral protocerebrum is not about learned vs. innate behavior. Rather, mushroom bodies perform odor identification, whereas the lateral protocerebrum performs odor evaluation (both learned and innate). I discuss the concepts of labeled line and combinatorial coding and postulate that, under restrictive experimental conditions, these networks lead to an apparent existence of 'labeled line' coding for special odors. Modulatory networks are proposed as switches between different evaluating systems in the lateral protocerebrum. A review of experimental data and theoretical conjectures both contribute to this synthesis, creating new hypotheses for future research.

Parallel pathways convey olfactory information with opposite polarities in Drosophila

In insects, olfactory information received by peripheral olfactory receptor neurons (ORNs) is conveyed from the antennal lobes (ALs) to higher brain regions by olfactory projection neurons (PNs). Despite the knowledge that multiple types of PNs exist, little is known about how these different neuronal pathways work cooperatively. Here we studied the Drosophila GABAergic mediolateral antennocerebral tract PNs (mlPNs), which link ipsilateral AL and lateral horn (LH), in comparison with the cholinergic medial tract PNs (mPNs). We examined the connectivity of mlPNs in ALs and found that most mlPNs received inputs from both ORNs and mPNs and participated in AL network function by forming gap junctions with other AL neurons. Meanwhile, mlPNs might innervate LH neurons downstream of mPNs, exerting a feedforward inhibition. Using dual-color calcium imaging, which enables a simultaneous monitoring of neural activities in two groups of PNs, we found that mlPNs exhibited robust odor responses overlapping with, but broader than, those of mPNs. Moreover, preferentially down-regulation of GABA in most mlPNs caused abnormal courtship and aggressive behaviors in male flies. These findings demonstrate that in Drosophila, olfactory information in opposite polarities are carried coordinately by two parallel and interacted pathways, which could be essential for appropriate behaviors.

Tools for resolving functional activity and connectivity within intact neural circuits

Mammalian neural circuits are sophisticated biological systems that choreograph behavioral processes vital for survival. While the inherent complexity of discrete neural circuits has proven difficult to decipher, many parallel methodological developments promise to help delineate the function and connectivity of molecularly defined neural circuits. Here, we review recent technological advances designed to precisely monitor and manipulate neural circuit activity. We propose a holistic, multifaceted approach for unraveling how behavioral states are manifested through the cooperative interactions between discrete neurocircuit elements.

Long-period rhythmic synchronous firing in a scale-free network

Stimulus information is encoded in the spatial-temporal structures of external inputs to the neural system. The ability to extract the temporal information of inputs is fundamental to brain function. It has been found that the neural system can memorize temporal intervals of visual inputs in the order of seconds. Here we investigate whether the intrinsic dynamics of a large-size neural circuit alone can achieve this goal. The network models we consider have scale-free topology and the property that hub neurons are difficult to be activated. The latter is implemented by either including abundant electrical synapses between neurons or considering chemical synapses whose efficacy decreases with the connectivity of the postsynaptic neuron. We find that hub neurons trigger synchronous firing across the network, loops formed by low-degree neurons determine the rhythm of synchronous firing, and the hardness of exciting hub neurons avoids epileptic firing of the network. Our model successfully reproduces the experimentally observed rhythmic synchronous firing with long periods and supports the notion that the neural system can process temporal information through the dynamics of local circuits in a distributed way.

Design principles of the sparse coding network and the role of "sister cells" in the olfactory system of Drosophila

Sensory systems face the challenge to represent sensory inputs in a way to allow easy readout of sensory information by higher brain areas. In the olfactory system of the fly drosopohila melanogaster, projection neurons (PNs) of the antennal lobe (AL) convert a dense activation of glomeruli into a sparse, high-dimensional firing pattern of Kenyon cells (KCs) in the mushroom body (MB). Here we investigate the design principles of the olfactory system of drosophila in regard to the capabilities to discriminate odor quality from the MB representation and its robustness to different types of noise. We focus on understanding the role of highly correlated homotypic projection neurons (“sister cells”) found in the glomeruli of flies. These cells are coupled by gap-junctions and receive almost identical sensory inputs, but target randomly different KCs in MB. We show that sister cells might play a crucial role in increasing the robustness of the MB odor representation to noise. Computationally, sister cells thus might help the system to improve the generalization capabilities in face of noise without impairing the discriminability of odor quality at the same time.

Integration of the olfactory code across dendritic claws of single mushroom body neurons

In the olfactory system, sensory inputs are arranged in different glomerular channels, which respond in combinatorial ensembles to the various chemical features of an odor. Here we investigate where and how this combinatorial code is read out deeper in the brain. We exploit the unique morphology of neurons in the mushroom body (MB), which receive input on large dendritic claws. Imaging odor responses of these dendritic claws shows that input channels with distinct odor tuning converge on individual MB neurons. We determined how these inputs interact to drive the cell to spike threshold using intracellular recordings to examine MB responses to optogenetically controlled input. Our results provide an elegant explanation for the characteristic selectivity of MB neurons: these cells receive different types of input, and require those inputs to be coactive in order to spike. These results establish the MB as an important site of integration in the fly olfactory system.

Mushroom body miscellanea: transgenic Drosophila strains expressing anatomical and physiological sensor proteins in Kenyon cells

The fruit fly Drosophila melanogaster represents a key model organism for analyzing how neuronal circuits regulate behavior. The mushroom body in the central brain is a particularly prominent brain region that has been intensely studied in several insect species and been implicated in a variety of behaviors, e.g., associative learning, locomotor activity, and sleep. Drosophila melanogaster offers the advantage that transgenes can be easily expressed in neuronal subpopulations, e.g., in intrinsic mushroom body neurons (Kenyon cells). A number of transgenes has been described and engineered to visualize the anatomy of neurons, to monitor physiological parameters of neuronal activity, and to manipulate neuronal function artificially. To target the expression of these transgenes selectively to specific neurons several sophisticated bi- or even multipartite transcription systems have been invented. However, the number of transgenes that can be combined in the genome of an individual fly is limited in practice. To facilitate the analysis of the mushroom body we provide a compilation of transgenic fruit flies that express transgenes under direct control of the Kenyon-cell specific promoter, mb247. The transgenes expressed are fluorescence reporters to analyze neuroanatomical aspects of the mushroom body, proteins to restrict ectopic gene expression to mushroom bodies, or fluorescent sensors to monitor physiological parameters of neuronal activity of Kenyon cells. Some of the transgenic animals compiled here have been published already, whereas others are novel and characterized here for the first time. Overall, the collection of transgenic flies expressing sensor and reporter genes in Kenyon cells facilitates combinations with binary transcription systems and might, ultimately, advance the physiological analysis of mushroom body function.