Imaging cytoplasmic cAMP in mouse brainstem neurons

<p>Abstract</p> <p>Background</p> <p>cAMP is an ubiquitous second messenger mediating various neuronal functions, often as a consequence of increased intracellular Ca²⁺levels. While imaging of calcium is commonly used in neuroscience applications, probing for cAMP levels has not yet been performed in living vertebrate neuronal tissue before.</p> <p>Results</p> <p>Using a strictly neuron-restricted promoter we virally transduced neurons in the organotypic brainstem slices which contained pre-Bötzinger complex, constituting the rhythm-generating part of the respiratory network. Fluorescent cAMP sensor Epac1-camps was expressed both in neuronal cell bodies and neurites, allowing us to measure intracellular distribution of cAMP, its absolute levels and time-dependent changes in response to physiological stimuli. We recorded [cAMP]_ichanges in the micromolar range after modulation of adenylate cyclase, inhibition of phosphodiesterase and activation of G-protein-coupled metabotropic receptors. [cAMP]_ilevels increased after membrane depolarisation and release of Ca²⁺from internal stores. The effects developed slowly and reached their maximum after transient [Ca²⁺]_ielevations subsided. Ca²⁺-dependent [cAMP]_itransients were suppressed after blockade of adenylate cyclase with 0.1 mM adenylate cyclase inhibitor 2'5'-dideoxyadenosine and potentiated after inhibiting phosphodiesterase with isobutylmethylxanthine and rolipram. During paired stimulations, the second depolarisation and Ca²⁺release evoked bigger cAMP responses. These effects were abolished after inhibition of protein kinase A with H-89 pointing to the important role of phosphorylation of calcium channels in the potentiation of [cAMP]_itransients.</p> <p>Conclusion</p> <p>We constructed and characterized a neuron-specific cAMP probe based on Epac1-camps. Using viral gene transfer we showed its efficient expression in organotypic brainstem preparations. Strong fluorescence, resistance to photobleaching and possibility of direct estimation of [cAMP] levels using dual wavelength measurements make the probe useful in studies of neurons and the mechanisms of their plasticity. Epac1-camps was applied to examine the crosstalk between Ca²⁺and cAMP signalling and revealed a synergism of actions of these two second messengers.</p

Regulation of Spike Timing-Dependent Plasticity of Olfactory Inputs in Mitral Cells in the Rat Olfactory Bulb

The recent history of activity input onto granule cells (GCs) in the main olfactory bulb can affect the strength of lateral inhibition, which functions to generate contrast enhancement. However, at the plasticity level, it is unknown whether and how the prior modification of lateral inhibition modulates the subsequent induction of long-lasting changes of the excitatory olfactory nerve (ON) inputs to mitral cells (MCs). Here we found that the repetitive stimulation of two distinct excitatory inputs to the GCs induced a persistent modification of lateral inhibition in MCs in opposing directions. This bidirectional modification of inhibitory inputs differentially regulated the subsequent synaptic plasticity of the excitatory ON inputs to the MCs, which was induced by the repetitive pairing of excitatory postsynaptic potentials (EPSPs) with postsynaptic bursts. The regulation of spike timing-dependent plasticity (STDP) was achieved by the regulation of the inter-spike-interval (ISI) of the postsynaptic bursts. This novel form of inhibition-dependent regulation of plasticity may contribute to the encoding or processing of olfactory information in the olfactory bulb

Human activity learning for assistive robotics using a classifier ensemble

Assistive robots in ambient assisted living environments can be equipped with learning capabilities to effectively learn and execute human activities. This paper proposes a human activity learning (HAL) system for application in assistive robotics. An RGB-depth sensor is used to acquire information of human activities, and a set of statistical, spatial and temporal features for encoding key aspects of human activities are extracted from the acquired information of human activities. Redundant features are removed and the relevant features used in the HAL model. An ensemble of three individual classifiers—support vector machines (SVMs), K-nearest neighbour and random forest - is employed to learn the activities. The performance of the proposed system is improved when compared with the performance of other methods using a single classifier. This approach is evaluated on experimental dataset created for this work and also on a benchmark dataset—the Cornell Activity Dataset (CAD-60). Experimental results show the overall performance achieved by the proposed system is comparable to the state of the art and has the potential to benefit applications in assistive robots for reducing the time spent in learning activities