Amyloid β-peptide directly induces spontaneous calcium transients, delayed intercellular calcium waves and gliosis in rat cortical astrocytes

The contribution of astrocytes to the pathophysiology of AD (Alzheimer's disease) and the molecular and signalling mechanisms that potentially underlie them are still very poorly understood. However, there is mounting evidence that calcium dysregulation in astrocytes may be playing a key role. Intercellular calcium waves in astrocyte networks in vitro can be mechanically induced after Aβ (amyloid β-peptide) treatment, and spontaneously forming intercellular calcium waves have recently been shown in vivo in an APP (amyloid precursor protein)/PS1 (presenilin 1) Alzheimer's transgenic mouse model. However, spontaneous intercellular calcium transients and waves have not been observed in vitro in isolated astrocyte cultures in response to direct Aβ stimulation in the absence of potentially confounding signalling from other cell types. Here, we show that Aβ alone at relatively low concentrations is directly able to induce intracellular calcium transients and spontaneous intercellular calcium waves in isolated astrocytes in purified cultures, raising the possibility of a potential direct effect of Aβ exposure on astrocytes in vivo in the Alzheimer's brain. Waves did not occur immediately after Aβ treatment, but were delayed by many minutes before spontaneously forming, suggesting that intracellular signalling mechanisms required sufficient time to activate before intercellular effects at the network level become evident. Furthermore, the dynamics of intercellular calcium waves were heterogeneous, with distinct radial or longitudinal propagation orientations. Lastly, we also show that changes in the expression levels of the intermediate filament proteins GFAP (glial fibrillary acidic protein) and S100B are affected by Aβ-induced calcium changes differently, with GFAP being more dependent on calcium levels than S100B

Mapping the spatiotemporal dynamics of calcium signaling in cellular neural networks using optical flow

An optical flow gradient algorithm was applied to spontaneously forming net- works of neurons and glia in culture imaged by fluorescence optical microscopy in order to map functional calcium signaling with single pixel resolution. Optical flow estimates the direction and speed of motion of objects in an image between subsequent frames in a recorded digital sequence of images (i.e. a movie). Computed vector field outputs by the algorithm were able to track the spatiotemporal dynamics of calcium signaling pat- terns. We begin by briefly reviewing the mathematics of the optical flow algorithm, and then describe how to solve for the displacement vectors and how to measure their reliability. We then compare computed flow vectors with manually estimated vectors for the progression of a calcium signal recorded from representative astrocyte cultures. Finally, we applied the algorithm to preparations of primary astrocytes and hippocampal neurons and to the rMC-1 Muller glial cell line in order to illustrate the capability of the algorithm for capturing different types of spatiotemporal calcium activity. We discuss the imaging requirements, parameter selection and threshold selection for reliable measurements, and offer perspectives on uses of the vector data.Comment: 23 pages, 5 figures. Peer reviewed accepted version in press in Annals of Biomedical Engineerin

Mapping functional connectivity in cellular networks

My thesis is a collection of theoretical and practical techniques for mapping functional or effective connectivity in cellular neuronal networks, at the cell scale. This is a challenging scale to work with, primarily because of the difficulty in labeling and measuring the activities of networks of cells. It is also important as it underlies behavior, function, and complex diseases. I present methods to measure and quantify the dynamic activities of cells using the optical flow technique, which can identify activity and directions of information processing using calcium fluorescence measurements. I present a unified framework for simulation and estimation of neuronal activity, tailored towards interpretation of experimental data, and implemented in a fully parallel fashion on graphics processor unit (GPU) cards. This framework permits experimenters to estimate hidden quantities in collected data, using any neuronal or astrocyte model. I introduce a technique for mapping functional connectivity in neuronal networks, using experimental data and an arbitrary state space model. The technique makes some simplifications that reduces the dimensionality of the estimation problem, and shows excellent performance for networks of up to 30 possible independent incoming connections. While the framework and mapping algorithms use a state space, parametric representation of individual cell dynamics, I've also developed a time-embedded, nonparametric technique for estimating input-output relationships, and applied it to estimating current from voltage measurements and spikes from fluorescent calcium. Without any knowledge of the underlying neuronal dynamics, this technique can reconstruct a current signal from measured voltage in mouse pyramidal neurons with an R-value of 0.9. Finally, I present my findings and theoretical perspectives acquired while developing the framework and methods. Optimization as a means of estimating functional weights is especially challenging due to the topology of the parameter space, with small perturbations in weights resulting in drastically different simulated dynamics. High-dimensional spaces are prone to the curse of dimensionality, and network states represented in such spaces are not likely to be stable or typical. Finally, the effects of the concentration of measure, as I believe I've observed when mapping large networks, makes it unlikely that real-world networks have more than about 7 independent functional inputs at any given tim