Conservation of neuron-astrocyte coordinated activity among sensory processing centers of the developing brain.

Afferent neurons in developing sensory organs exhibit a prolonged period of burst firing prior to the onset of sensory experience. This intrinsically generated activity propagates from the periphery through central processing centers to promote the survival and physiological maturation of neurons and refine their synaptic connectivity. Recent studies in the auditory system indicate that these bursts of action potentials also trigger metabotropic glutamate receptor-mediated calcium increases within astrocytes that are spatially and temporally correlated with neuronal events; however, it is not known if this phenomenon occurs in other sensory modalities. Here we show using in vivo simultaneous imaging of neuronal and astrocyte calcium activity in awake mouse pups that waves of retinal ganglion cell activity induce spatially and temporally correlated waves of astrocyte activity in the superior colliculus that depend on metabotropic glutamate receptors mGluR5 and mGluR3. Astrocyte calcium transients reliably occurred with each neuronal wave, but peaked more than one second after neuronal events. Despite differences in the temporal features of spontaneous activity in auditory and visual processing regions, individual astrocytes exhibited similar overall calcium activity patterns, providing a conserved mechanism to synchronize neuronal and astrocyte maturation within discrete sensory domains.

Norepinephrine changes behavioral state via astroglial purinergic signaling.

Both neurons and glia communicate via diffusible neuromodulatory substances, but the substrates of computation in such neuromodulatory networks are unclear. During behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine drives fast excitation and delayed inhibition of behavior and circuit activity. We find that the inhibitory arm of this feedforward motif is implemented by astroglial purinergic signaling. Neuromodulator imaging, behavioral pharmacology, and perturbations of neurons and astroglia reveal that norepinephrine triggers astroglial release of adenosine triphosphate, extracellular conversion into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. This work, along with a companion piece by Lefton and colleagues demonstrating an analogous pathway mediating the effect of norepinephrine on synaptic connectivity in mice, identifies a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in norepinephrine-mediated behavioral and brain state transitions.

Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new or improved biosensors and microscopy technology. This creates major computational challenges to extract and quantify biologically meaningful spatiotemporal patterns embedded within complex and rich data sources, many of which cannot be captured with existing methods. Here, we introduce Activity Quantification and Analysis (AQuA2), a fast, accurate, and versatile data analysis platform built upon advanced machine learning techniques. It decomposes complex live imaging-based datasets into elementary signaling events, allowing accurate and unbiased quantification of molecular activities and identification of consensus functional units. We demonstrate applications across a wide range of biosensors, cell types, organs, animal models, and imaging modalities. As exemplar findings, we show how AQuA2 identified drug-dependent interactions between neurons and astroglia, and distinct sensorimotor signal propagation patterns in the mouse spinal cord.

Norepinephrine links astrocytic activity to regulation of cortical state.

Cortical state, defined by population-level neuronal activity patterns, determines sensory perception. While arousal-associated neuromodulators-including norepinephrine (NE)-reduce cortical synchrony, how the cortex resynchronizes remains unknown. Furthermore, general mechanisms regulating cortical synchrony in the wake state are poorly understood. Using in vivo imaging and electrophysiology in mouse visual cortex, we describe a critical role for cortical astrocytes in circuit resynchronization. We characterize astrocytes' calcium responses to changes in behavioral arousal and NE, and show that astrocytes signal when arousal-driven neuronal activity is reduced and bi-hemispheric cortical synchrony is increased. Using in vivo pharmacology, we uncover a paradoxical, synchronizing response to Adra1a receptor stimulation. We reconcile these results by demonstrating that astrocyte-specific deletion of Adra1a enhances arousal-driven neuronal activity, while impairing arousal-related cortical synchrony. Our findings demonstrate that astrocytic NE signaling acts as a distinct neuromodulatory pathway, regulating cortical state and linking arousal-associated desynchrony to cortical circuit resynchronization.

Accurate quantification of astrocyte and neurotransmitter fluorescence dynamics for single-cell and population-level physiology.

Recent work examining astrocytic physiology centers on fluorescence imaging, due to development of sensitive fluorescent indicators and observation of spatiotemporally complex calcium activity. However, the field remains hindered in characterizing these dynamics, both within single cells and at the population level, because of the insufficiency of current region-of-interest-based approaches to describe activity that is often spatially unfixed, size-varying and propagative. Here we present an analytical framework that releases astrocyte biologists from region-of-interest-based tools. The Astrocyte Quantitative Analysis (AQuA) software takes an event-based perspective to model and accurately quantify complex calcium and neurotransmitter activity in fluorescence imaging datasets. We apply AQuA to a range of ex vivo and in vivo imaging data and use physiologically relevant parameters to comprehensively describe the data. Since AQuA is data-driven and based on machine learning principles, it can be applied across model organisms, fluorescent indicators, experimental modes, and imaging resolutions and speeds, enabling researchers to elucidate fundamental neural physiology.