A comparison of high-content screening versus manual analysis to assay the effects of mesenchymal stem cell-conditioned medium on neurite outgrowth in vitro

Bone marrow mesenchymal stem cells (MSCs) promote nerve growth and functional recovery in animal models of spinal cord injury (SCI) to varying levels. The authors have tested high-content screening to examine the effects of MSC-conditioned medium (MSC-CM) on neurite outgrowth from the human neuroblastoma cell line SH-SY5Y and from explants of chick dorsal root ganglia (DRG). These analyses were compared to previously published methods that involved hand-tracing individual neurites. Both methods demonstrated that MSC-CM promoted neurite outgrowth. Each showed the proportion of SH-SY5Y cells with neurites increased by ~200% in MSC-CM within 48 h, and the number of neurites/SH-SY5Y cells was significantly increased in MSC-CM compared with control medium. For high-content screening, the analysis was performed within minutes, testing multiple samples of MSC-CM and in each case measuring >15,000 SH-SY5Y cells. In contrast, the manual measurement of neurite outgrowth from >200 SH-SY5Y cells in a single sample of MSC-CM took at least 1 h. High-content analysis provided additional measures of increased neurite branching in MSC-CM compared with control medium. MSC-CM was also found to stimulate neurite outgrowth in DRG explants using either method. The application of the high-content analysis was less well optimized for measuring neurite outgrowth from DRG explants than from SH-SY5Y cells.

Causality and synchronisation in complex systems with applications to neuroscience

This thesis presents an investigation, of synchronisation and causality, motivated by problems in computational neuroscience. The thesis addresses both theoretical and practical signal processing issues regarding the estimation of interdependence from a set of multivariate data generated by a complex underlying dynamical system. This topic is driven by a series of problems in neuroscience, which represents the principal background motive behind the material in this work. The underlying system is the human brain and the generative process of the data is based on modern electromagnetic neuroimaging methods . In this thesis, the underlying functional of the brain mechanisms are derived from the recent mathematical formalism of dynamical systems in complex networks. This is justified principally on the grounds of the complex hierarchical and multiscale nature of the brain and it offers new methods of analysis to model its emergent phenomena. A fundamental approach to study the neural activity is to investigate the connectivity pattern developed by the brain’s complex network. Three types of connectivity are important to study: 1) anatomical connectivity refering to the physical links forming the topology of the brain network; 2) effective connectivity concerning with the way the neural elements communicate with each other using the brain’s anatomical structure, through phenomena of synchronisation and information transfer; 3) functional connectivity, presenting an epistemic concept which alludes to the interdependence between data measured from the brain network. The main contribution of this thesis is to present, apply and discuss novel algorithms of functional connectivities, which are designed to extract different specific aspects of interaction between the underlying generators of the data. Firstly, a univariate statistic is developed to allow for indirect assessment of synchronisation in the local network from a single time series. This approach is useful in inferring the coupling as in a local cortical area as observed by a single measurement electrode. Secondly, different existing methods of phase synchronisation are considered from the perspective of experimental data analysis and inference of coupling from observed data. These methods are designed to address the estimation of medium to long range connectivity and their differences are particularly relevant in the context of volume conduction, that is known to produce spurious detections of connectivity. Finally, an asymmetric temporal metric is introduced in order to detect the direction of the coupling between different regions of the brain. The method developed in this thesis is based on a machine learning extensions of the well known concept of Granger causality. The thesis discussion is developed alongside examples of synthetic and experimental real data. The synthetic data are simulations of complex dynamical systems with the intention to mimic the behaviour of simple cortical neural assemblies. They are helpful to test the techniques developed in this thesis. The real datasets are provided to illustrate the problem of brain connectivity in the case of important neurological disorders such as Epilepsy and Parkinson’s disease. The methods of functional connectivity in this thesis are applied to intracranial EEG recordings in order to extract features, which characterize underlying spatiotemporal dynamics before during and after an epileptic seizure and predict seizure location and onset prior to conventional electrographic signs. The methodology is also applied to a MEG dataset containing healthy, Parkinson’s and dementia subjects with the scope of distinguishing patterns of pathological from physiological connectivity.

Scale-up of human mesenchymal stem cell culture:current technologies and future challenges

Regenerative medicine technologies have the potential to revolutionise human healthcare. However, whilst science has revealed the potential, and early products have shown the power of such therapies, there is now a need for the long-term supply of human stem cells in sufficient numbers to create reproducible and cost effective therapeutic products. The industrial platforms to be developed for human cell culture are in some ways analogous to those already developed for biopharmaceutical production using mammalian cells at large scales. However, there are a number of unique challenges that need to be addressed, largely because the quality of the cell is paramount, rather than the proteins that they express. © 2013 Elsevier Ltd.

Mesenchymal stem cell-conditioned medium accelerates skin wound healing:an in vitro study of fibroblast and keratinocyte scratch assays

We have used in vitro scratch assays to examine the relative contribution of dermal fibroblasts and keratinocytes in the wound repair process and to test the influence of mesenchymal stem cell (MSC) secreted factors on both skin cell types. Scratch assays were established using single cell and co-cultures of L929 fibroblasts and HaCaT keratinocytes, with wound closure monitored via time-lapse microscopy. Both in serum supplemented and serum free conditions, wound closure was faster in L929 fibroblast than HaCaT keratinocyte scratch assays, and in co-culture the L929 fibroblasts lead the way in closing the scratches. MSC-CM generated under serum free conditions significantly enhanced the wound closure rate of both skin cell types separately and in co-culture, whereas conditioned medium from L929 or HaCaT cultures had no significant effect. This enhancement of wound closure in the presence of MSC-CM was due to accelerated cell migration rather than increased cell proliferation. A number of wound healing mediators were identified in MSC-CM, including TGF-beta1, the chemokines IL-6, IL-8, MCP-1 and RANTES, and collagen type I, fibronectin, SPARC and IGFBP-7. This study suggests that the trophic activity of MSC may play a role in skin wound closure by affecting both dermal fibroblast and keratinocyte migration, along with a contribution to the formation of extracellular matrix.

Functional astrocyte-neuron lactate shuttle in a human stem cell-derived neuronal network

The NT2.D1 cell line is one of the most well-documented embryocarcinoma cell lines, and can be differentiated into neurons and astrocytes. Great focus has also been placed on defining the electrophysiological properties of the neuronal cells, and more recently we have investigated the functional properties of their associated astrocytes. We now show for the first time that human stem cell-derived astrocytes produce glycogen and that co-cultures of these cells demonstrate a functional astrocyte-neuron lactate shuttle (ANLS). The ANLS hypothesis proposes that during neuronal activity, glutamate released into the synaptic cleft is taken up by astrocytes and triggers glucose uptake, which is converted into lactate and released via monocarboxylate transporters for neuronal use. Using mixed cultures of NT2-derived neurons and astrocytes, we have shown that these cells modulate their glucose uptake in response to glutamate. Additionally, we demonstrate that in response to increased neuronal activity and under hypoglycaemic conditions, co-cultures modulate glycogen turnover and increase lactate production. Similar results were also shown after treatment with glutamate, potassium, isoproterenol, and dbcAMP. Together, these results demonstrate for the first time a functional ANLS in a human stem cell-derived co-culture. © 2013 ISCBFM.

Aβ(1-42) induced metabolic effects in a stem cell derived neuron and astrocyte network.

Alzheimer’s Disease (AD) is the most common form of dementia currently affecting more than 35 million people worldwide. Hypometabolism is a major feature of AD and appears decades before cognitive decline and pathological lesions. This has a detrimental impact on the brain which has a high energy demand. Current models of AD fail to mimic all the features of the disease, which has an impact on the development of new therapies. Human stem cell derived models of the brain have attracted a lot of attention in recent years as a tool to study neurodegenerative diseases. In this thesis, neurons and astrocytes derived from the human embryonal carcinoma cell line (NT2/D1) were utilised to determine the metabolic coupling between neurons and astrocytes with regards to responses to hypoglycaemia, neuromodulators and increase in neuronal activity. This model was then used to investigate the effects of Aß(1-42) on the metabolism of these NT2-derived co-cultures as well as pure astrocytes. Additionally primary cortical mixed neuronal and glial cultures were utilised to compare this model to a widely accepted in vitro model used in Alzheimer’s disease research. Co-cultures were found to respond to Aß(1-42) in similar way to human and in vivo models. Hypometabolism was characterised by changes in glucose metabolism, as well as lactate, pyruvate and glycogen. This led to a significant decrease in ATP and the ratio of NAD+/NADH. These results together with an increase in calcium oscillations and a decrease in GSH/GSSG ratio, suggests Aß-induces metabolic and oxidative stress. This situation could have detrimental effects in the brain which has a high energy demand, especially in terms of memory formation and antioxidant capacity.

MEG and complex systems

MEG beamformer algorithms work by making the assumption that correlated and spatially distinct local field potentials do not develop in the human brain. Despite this assumption, images produced by such algorithms concur with those from other non-invasive and invasive estimates of brain function. In this paper we set out to develop a method that could be applied to raw MEG data to explicitly test his assumption. We show that a promax rotation of MEG channel data can be used as an approximate estimator of the number of spatially distinct correlated sources in any frequency band.

A novel adaptive weight selection algorithm for multi-objective multi-agent reinforcement learning

To solve multi-objective problems, multiple reward signals are often scalarized into a single value and further processed using established single-objective problem solving techniques. While the field of multi-objective optimization has made many advances in applying scalarization techniques to obtain good solution trade-offs, the utility of applying these techniques in the multi-objective multi-agent learning domain has not yet been thoroughly investigated. Agents learn the value of their decisions by linearly scalarizing their reward signals at the local level, while acceptable system wide behaviour results. However, the non-linear relationship between weighting parameters of the scalarization function and the learned policy makes the discovery of system wide trade-offs time consuming. Our first contribution is a thorough analysis of well known scalarization schemes within the multi-objective multi-agent reinforcement learning setup. The analysed approaches intelligently explore the weight-space in order to find a wider range of system trade-offs. In our second contribution, we propose a novel adaptive weight algorithm which interacts with the underlying local multi-objective solvers and allows for a better coverage of the Pareto front. Our third contribution is the experimental validation of our approach by learning bi-objective policies in self-organising smart camera networks. We note that our algorithm (i) explores the objective space faster on many problem instances, (ii) obtained solutions that exhibit a larger hypervolume, while (iii) acquiring a greater spread in the objective space.

Stem cell therapies for ischemic cardiovascular diseases

Myocardial infarction results in loss of cardiac muscle and deficiency in cardiac performance. Likewise, peripheral artery disease can result in critical limb ischemia leading to reduced mobility, non-healing ulcers, gangrene and amputation. Both of these common conditions diminish quality of life and enhance risk of mortality. Successful advances in treatment have led to more people surviving incidences of myocardial infarction or living with peripheral artery disease. However, the current treatments are inadequate in repairing ischemic tissue. Over the last 5 years, a vast number of patents have been submitted concerning the use of stem cells, which correlates with the exponential growth in stem cell publications. Exploiting stem cell therapy offers a real potential in replacing ischemic tissue with functional cells. In this paper, we review recent patents concerning stem cell therapy that have the potential to provide or potentiate novel treatment for ischemic cardiovascular disease. In addition, we evaluate the promise of the inventions by describing some clinical trials that are currently taking place, as well as considering how current research on ischemic cardiovascular disease may change the patent landscape in the future.

Evolutionary Clustering of Complex Systems and Processes

In a paper the method of complex systems and processes clustering based use of genetic algorithm is offered. The aspects of its realization and shaping of fitness-function are considered. The solution of clustering task of Ukraine areas on socio-economic indexes is represented and comparative analysis with outcomes of classical methods is realized.

Adaptive Routing and Multi-Agent Control for Information Flows in IP-Networks

The principles of adaptive routing and multi-agent control for information flows in IP-networks.

Multi-Agent Information Processing and Adaptive Control in Global Telecommunication and Computer Networks

The problems and methods for adaptive control and multi-agent processing of information in global telecommunication and computer networks (TCN) are discussed. Criteria for controllability and communication ability (routing ability) of dataflows are described. Multi-agent model for exchange of divided information resources in global TCN has been suggested. Peculiarities for adaptive and intelligent control of dataflows in uncertain conditions and network collisions are analyzed.

Neural Approach in Multi-Agent Routing for Static Telecommunication Networks

The problem of multi-agent routing in static telecommunication networks with fixed configuration is considered. The problem is formulated in two ways: for centralized routing schema with the coordinator-agent (global routing) and for distributed routing schema with independent agents (local routing). For both schemas appropriate Hopfield neural networks (HNN) are constructed.