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209 Publications
Showing 151-160 of 209 resultsAnatomical, molecular, and physiological interactions between astrocytes and neuronal synapses regulate information processing in the brain. The fruit fly Drosophila melanogaster has become a valuable experimental system for genetic manipulation of the nervous system and has enormous potential for elucidating mechanisms that mediate neuron-glia interactions. Here, we show the first electrophysiological recordings from Drosophila astrocytes and characterize their spatial and physiological relationship with particular synapses. Astrocyte intrinsic properties were found to be strongly analogous to those of vertebrate astrocytes, including a passive current-voltage relationship, low membrane resistance, high capacitance, and dye-coupling to local astrocytes. Responses to optogenetic stimulation of glutamatergic pre-motor neurons were correlated directly with anatomy using serial electron microscopy reconstructions of homologous identified neurons and surrounding astrocytic processes. Robust bidirectional communication was present: neuronal activation triggered astrocytic glutamate transport via Eaat1, and blocking Eaat1 extended glutamatergic interneuron-evoked inhibitory post-synaptic currents in motor neurons. The neuronal synapses were always located within a micron of an astrocytic process, but none were ensheathed by those processes. Thus, fly astrocytes can modulate fast synaptic transmission via neurotransmitter transport within these anatomical parameters. This article is protected by copyright. All rights reserved.
Summary Multiple division cycles without growth are a characteristic feature of early embryogenesis. The female germline loads proteins and RNAs into oocytes to support these divisions, which lack many quality control mechanisms operating in somatic cells undergoing growth. Here, we describe a small RNA-Argonaute pathway that ensures early embryonic divisions in C. elegans by employing catalytic slicing activity to broadly tune, instead of silence, germline gene expression. Misregulation of one target, a kinesin-13 microtubule depolymerase, underlies a major phenotype associated with pathway loss. Tuning of target transcript levels is guided by the density of homologous small RNAs, whose generation must ultimately be related to target sequence. Thus, the tuning action of a small RNA-catalytic Argonaute pathway generates oocytes capable of supporting embryogenesis. We speculate that the specialized nature of germline chromatin led to the emergence of small RNA-catalytic Argonaute pathways in the female germline as a post-transcriptional control layer to optimize oocyte composition.
The estrogen receptor (ER), glucocorticoid receptor (GR), and forkhead box protein 1 (FoxA1) are significant factors in breast cancer progression. FoxA1 has been implicated in establishing ER-binding patterns though its unique ability to serve as a pioneer factor. However, the molecular interplay between ER, GR, and FoxA1 requires further investigation. Here we show that ER and GR both have the ability to alter the genomic distribution of the FoxA1 pioneer factor. Single-molecule tracking experiments in live cells reveal a highly dynamic interaction of FoxA1 with chromatin in vivo. Furthermore, the FoxA1 factor is not associated with detectable footprints at its binding sites throughout the genome. These findings support a model wherein interactions between transcription factors and pioneer factors are highly dynamic. Moreover, at a subset of genomic sites, the role of pioneer can be reversed, with the steroid receptors serving to enhance binding of FoxA1.
The emerging field of connectomics aims to unlock the mysteries of the brain by understanding the connectivity between neurons. To map this connectivity, we acquire thousands of electron microscopy (EM) images with nanometer-scale resolution. After aligning these images, the resulting dataset has the potential to reveal the shapes of neurons and the synaptic connections between them. However, imaging the brain of even a tiny organism like the fruit fly yields terabytes of data. It can take years of manual effort to examine such image volumes and trace their neuronal connections. One solution is to apply image segmentation algorithms to help automate the tracing tasks. In this paper, we propose a novel strategy to apply such segmentation on very large datasets that exceed the capacity of a single machine. Our solution is robust to potential segmentation errors which could otherwise severely compromise the quality of the overall segmentation, for example those due to poor classifier generalizability or anomalies in the image dataset. We implement our algorithms in a Spark application which minimizes disk I/O, and apply them to a few large EM datasets, revealing both their effectiveness and scalability. We hope this work will encourage external contributions to EM segmentation by providing 1) a flexible plugin architecture that deploys easily on different cluster environments and 2) an in-memory representation of segmentation that could be conducive to new advances.
Apache Spark is a popular open-source platform for large-scale data processing that is well-suited for iterative machine learning tasks. In this paper we present MLlib, Spark’s open-source distributed machine learning library. MLlib provides efficient functionality for a wide range of learning settings and includes several underlying statistical, optimization, and linear algebra primitives. Shipped with Spark, MLlib supports several languages and provides a high-level API that leverages Spark’s rich ecosystem to simplify the development of end-to-end machine learning pipelines. MLlib has experienced a rapid growth due to its vibrant open-source community of over 140 contributors, and includes extensive documentation to support further growth and to let users quickly get up to speed.
Clonal analysis is helping us understand the dynamics of cell replacement in homeostatic adult tissues (Simons and Clevers, 2011). Such an analysis, however, has not yet been achieved for continuously growing adult tissues, but is essential if we wish to understand the architecture of adult organs. The retinas of lower vertebrates grow throughout life from retinal stem cells (RSCs) and retinal progenitor cells (RPCs) at the rim of the retina, called the ciliary marginal zone (CMZ). Here, we show that RSCs reside in a niche at the extreme periphery of the CMZ and divide asymmetrically along a radial (peripheral to central) axis, leaving one daughter in the peripheral RSC niche and the other more central where it becomes an RPC. We also show that RPCs of the CMZ have clonal sizes and compositions that are statistically similar to progenitor cells of the embryonic retina and fit the same stochastic model of proliferation. These results link embryonic and postembryonic cell behaviour, and help to explain the constancy of tissue architecture that has been generated over a lifetime.
Tracking single molecules in living cells provides invaluable information on their environment and on the interactions that underlie their motion. New experimental techniques now permit the recording of large amounts of individual trajectories, enabling the implementation of advanced statistical tools for data analysis. In this primer, we present a Bayesian approach toward treating these data, and we discuss how it can be fruitfully employed to infer physical and biochemical parameters from single-molecule trajectories.
Genetically encoded calcium indicators (GECIs) allow measurement of activity in large populations of neurons and in small neuronal compartments, over times of milliseconds to months. Although GFP-based GECIs are widely used for in vivo neurophysiology, GECIs with red-shifted excitation and emission spectra have advantages for in vivo imaging because of reduced scattering and absorption in tissue, and a consequent reduction in phototoxicity. However, current red GECIs are inferior to the state-of-the-art GFP-based GCaMP6 indicators for detecting and quantifying neural activity. Here we present improved red GECIs based on mRuby (jRCaMP1a, b) and mApple (jRGECO1a), with sensitivity comparable to GCaMP6. We characterized the performance of the new red GECIs in cultured neurons and in mouse, Drosophila, zebrafish and C. elegans in vivo. Red GECIs facilitate deep-tissue imaging, dual-color imaging together with GFP-based reporters, and the use of optogenetics in combination with calcium imaging.
UNLABELLED: Astrocytes tile the entire CNS, but their functions within neural circuits in health and disease remain incompletely understood. We used genetically encoded Ca(2+)and glutamate indicators to explore the rules for astrocyte engagement in the corticostriatal circuit of adult wild-type (WT) and Huntington's disease (HD) model mice at ages not accompanied by overt astrogliosis (at approximately postnatal days 70-80). WT striatal astrocytes displayed extensive spontaneous Ca(2+)signals, but did not respond to cortical stimulation, implying that astrocytes were largely disengaged from cortical input in healthy tissue. In contrast, in HD model mice, spontaneous Ca(2+)signals were significantly reduced in frequency, duration, and amplitude, but astrocytes responded robustly to cortical stimulation with evoked Ca(2+)signals. These action-potential-dependent astrocyte Ca(2+)signals were mediated by neuronal glutamate release during cortical stimulation, accompanied by prolonged extracellular glutamate levels near astrocytes and tightly gated by Glt1 glutamate transporters. Moreover, dysfunctional Ca(2+)and glutamate signaling that was observed in HD model mice was largely, but not completely, rescued by astrocyte specific restoration of Kir4.1, emphasizing the important contributions of K(+)homeostatic mechanisms that are known to be reduced in HD model mice. Overall, our data show that astrocyte engagement in the corticostriatal circuit is markedly altered in HD. Such prodromal astrocyte dysfunctions may represent novel therapeutic targets in HD and other brain disorders. SIGNIFICANCE STATEMENT: We report how early-onset astrocyte dysfunction without detectable astrogliosis drives disease-related processes in a mouse model of Huntington's disease (HD). The cellular mechanisms involve astrocyte homeostasis and signaling mediated by Kir4.1, Glt1, and Ca(2+) The data show that the rules for astrocyte engagement in a neuronal circuit are fundamentally altered in a brain disease caused by a known molecular defect and that fixing early homeostasis dysfunction remedies additional cellular deficits. Overall, our data suggest that key aspects of altered striatal function associated with HD may be triggered, at least in part, by dysfunctional astrocytes, thereby providing details of an emerging striatal microcircuit mechanism in HD. Such prodromal changes in astrocytes may represent novel therapeutic targets.
In the absence of salient sensory cues to guide behavior, animals must still execute sequences of motor actions in order to forage and explore. How such successive motor actions are coordinated to form global locomotion trajectories is unknown. We mapped the structure of larval zebrafish swim trajectories in homogeneous environments and found that trajectories were characterized by alternating sequences of repeated turns to the left and to the right. Using whole-brain light-sheet imaging, we identified activity relating to the behavior in specific neural populations that we termed the anterior rhombencephalic turning region (ARTR). ARTR perturbations biased swim direction and reduced the dependence of turn direction on turn history, indicating that the ARTR is part of a network generating the temporal correlations in turn direction. We also find suggestive evidence for ARTR mutual inhibition and ARTR projections to premotor neurons. Finally, simulations suggest the observed turn sequences may underlie efficient exploration of local environments.