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      Diving into the brain: deep-brain imaging techniques in conscious animals

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          Abstract

          In most species, survival relies on the hypothalamic control of endocrine axes that regulate critical functions such as reproduction, growth, and metabolism. For decades, the complexity and inaccessibility of the hypothalamic–pituitary axis has prevented researchers from elucidating the relationship between the activity of endocrine hypothalamic neurons and pituitary hormone secretion. Indeed, the study of central control of endocrine function has been largely dominated by ‘traditional’ techniques that consist of studying in vitro or ex vivo isolated cell types without taking into account the complexity of regulatory mechanisms at the level of the brain, pituitary and periphery. Nowadays, by exploiting modern neuronal transfection and imaging techniques, it is possible to study hypothalamic neuron activity in situ, in real time, and in conscious animals. Deep-brain imaging of calcium activity can be performed through gradient-index lenses that are chronically implanted and offer a ‘window into the brain’ to image multiple neurons at single-cell resolution. With this review, we aim to highlight deep-brain imaging techniques that enable the study of neuroendocrine neurons in awake animals whilst maintaining the integrity of regulatory loops between the brain, pituitary and peripheral glands. Furthermore, to assist researchers in setting up these techniques, we discuss the equipment required and include a practical step-by-step guide to performing these deep-brain imaging studies.

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          Most cited references75

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          High-performance calcium sensors for imaging activity in neuronal populations and microcompartments

          Calcium imaging with genetically encoded calcium indicators (GECIs) is routinely used to measure neural activity in intact nervous systems. GECIs are frequently used in one of two different modes: to track activity in large populations of neuronal cell bodies, or to follow dynamics in subcellular compartments such as axons, dendrites and individual synaptic compartments. Despite major advances, calcium imaging is still limited by the biophysical properties of existing GECIs, including affinity, signal-to-noise ratio, rise and decay kinetics and dynamic range. Using structure-guided mutagenesis and neuron-based screening, we optimized the green fluorescent protein-based GECI GCaMP6 for different modes of in vivo imaging. The resulting jGCaMP7 sensors provide improved detection of individual spikes (jGCaMP7s,f), imaging in neurites and neuropil (jGCaMP7b), and may allow tracking larger populations of neurons using two-photon (jGCaMP7s,f) or wide-field (jGCaMP7c) imaging.
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            A high signal-to-noise Ca(2+) probe composed of a single green fluorescent protein.

            Recently, several groups have developed green fluorescent protein (GFP)-based Ca(2+) probes. When applied in cells, however, these probes are difficult to use because of a low signal-to-noise ratio. Here we report the development of a high-affinity Ca(2+) probe composed of a single GFP (named G-CaMP). G-CaMP showed an apparent K(d) for Ca(2+) of 235 nM. Association kinetics of Ca(2+) binding were faster at higher Ca(2+) concentrations, with time constants decreasing from 230 ms at 0.2 microM Ca(2+) to 2.5 ms at 1 microM Ca(2+). Dissociation kinetics (tau approximately 200 ms) are independent of Ca(2+) concentrations. In HEK-293 cells and mouse myotubes expressing G-CaMP, large fluorescent changes were observed in response to application of drugs or electrical stimulations. G-CaMP will be a useful tool for visualizing intracellular Ca2+ in living cells. Mutational analysis, together with previous structural information, suggests the residues that may alter the fluorescence of GFP.
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              Simultaneous Denoising, Deconvolution, and Demixing of Calcium Imaging Data.

              We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in vitro and in vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.

                Author and article information

                Journal
                J Endocrinol
                J. Endocrinol
                JOE
                The Journal of Endocrinology
                Bioscientifica Ltd (Bristol )
                0022-0795
                1479-6805
                August 2020
                07 May 2020
                : 246
                : 2
                : R33-R50
                Affiliations
                [1 ]College of Engineering , Mathematics and Physical Sciences, University of Exeter, Exeter, UK
                [2 ]EPSRC Centre for Predictive Modelling in Healthcare , University of Exeter, Exeter, UK
                [3 ]Bristol Medical School , Translational Health Sciences, University of Bristol, Bristol, UK
                [4 ]IGF , University of Montpellier, CNRS, INSERM, Montpellier, France
                Author notes
                Correspondence should be addressed to P Campos: p.campos@ 123456exeter.ac.uk
                Article
                JOE-20-0028
                10.1530/JOE-20-0028
                7354703
                32380471
                191a422a-3933-4138-937d-891300e12ea9
                © 2020 The authors

                This work is licensed under a Creative Commons Attribution 4.0 International License.

                History
                : 31 March 2020
                : 07 May 2020
                Categories
                Review

                Endocrinology & Diabetes
                neuroendocrinology,whole animal physiology,deep-brain imaging,neuronal activity,conscious animals

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