Advanced microstructured platforms for neuroscience: from lab-on-chips for circadian clock studies to next generation bionic 3D brain tissue models

In mammals, the suprachiasmatic nucleus (SCN) of the hypothalamus is considered the master circadian pacemaker which coordinates circadian rhythms in the central nervous system (CNS) and across the entire body. The SCN receives light input from the eyes through the retinohypothalamic tract and then it synchronizes other clocks in the CNS and periphery, thus orchestrating rhythms throughout the body. However, little is known about how so many cellular clocks within and across brain circuits can be effectively synchronized to entrain the coordinated expression of clock genes in cells distributed all over the brain. In this work I investigated the possible implication of two possible pathways: i) paracrine factors-mediated synchronization and ii) astrocytes-mediated synchronization. To study these pathways, I adopted an in vitro research model that I developed based on a lab-on-a-chip microfluidic device designed and realized in our laboratory. This device allows growing and compartmentalizing distinct neural populations connected through a network of astrocytes or through a cell-free channel in which the diffusion of paracrine factors is allowed. By taking advantage of this device, upon its validation, I synchronized neural clocks in one compartment and analyzed, in different experimental conditions, the induced expression of clock genes in a distant neural network grown in the second compartment. Results show that both pathways can be involved, but might have different roles. Neurons release factors that can diffuse to synchronize a neuronal population. The same factors can also synchronize astrocytes that, in turn, can transmit astrocyte-mediated molecular clocks to more distant neuronal populations. This is supported by experimental data obtained using microfluidic devices featuring different channel lengths. I found that paracrine factors-mediated synchronization occurs only in the case of a short distance between neuronal populations. On the contrary, interconnecting astrocytes define an active channel that can transfer molecular clocks to neural populations also at long distances. The study of possibly involved signaling factors indicate that paracrine factors-mediated synchronization occurs through GABA signaling, while astrocytes-mediated synchronization involves both GABA and glutamate. These findings strength the importance of the synergic regulation of clock genes among neurons and astrocytes, and identify a previously unknown role of astrocytes as active cells in distributing signals to regulate the expression of clock genes in the brain. Preliminary results also show a correlation between astrocyte reactivity and local alterations in neuronal synchronization, thus opening a new scenario for future studies in which disease-induced astrocyte reactivity might be linked to alterations in clock gene expression.Three-dimensional (3D) brain models hold great potential for the generation of functional in vitro models to advance studies on human brain development, diseases and possible therapies. The routine exploitation of such models, however, is hindered by the lack of technologies to chronically monitor the activity of neural aggregates in three dimensions. A promising new approach consists in growing bio-artificial 3D brain model systems with seamless tissue-integrated biosensing artificial microdevices. Such devices could provide a platform for in-tissue sensing of diverse biologically relevant parameters. To date there is very little information on how to control the extracellular integration of such microscale devices into neuronal 3D cell aggregates. In this direction, in the present work I contributed to investigated the growth of hybrid neurospheroids obtained by the aggregation of silicon sham microchips (100x100x50\u3bcm3) with primary cortical cells. Interestingly, by coating microchips with different adhesion-promoting molecules, we reveal that surface functionalization can tune the integration and final 3D location of self-standing microdevices into neurospheroids. Morphological and functional characterization suggests that the presence of an integrated microdevice does not alter spheroid growth, cellular composition, nor network activity and maturation. Finally, we also demonstrate the feasibility of separating cells and microchips from formed hybrid neurospheroids for further single-cell analysis, and quantifications confirm an unaltered ratio of neurons and glia. These results uncover the potential of surface-engineered self-standing microdevices to grow untethered three-dimensional brain-tissue models with inbuilt bioelectronic sensors at predefined sites

Parallel computing for brain simulation

[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not been understood yet how and why most of these abilities are produced. Aims: For decades, researchers have been trying to make computers reproduce these abilities, focusing on both understanding the nervous system and, on processing data in a more efficient way than before. Their aim is to make computers process information similarly to the brain. Important technological developments and vast multidisciplinary projects have allowed creating the first simulation with a number of neurons similar to that of a human brain. Conclusion: This paper presents an up-to-date review about the main research projects that are trying to simulate and/or emulate the human brain. They employ different types of computational models using parallel computing: digital models, analog models and hybrid models. This review includes the current applications of these works, as well as future trends. It is focused on various works that look for advanced progress in Neuroscience and still others which seek new discoveries in Computer Science (neuromorphic hardware, machine learning techniques). Their most outstanding characteristics are summarized and the latest advances and future plans are presented. In addition, this review points out the importance of considering not only neurons: Computational models of the brain should also include glial cells, given the proven importance of astrocytes in information processing.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028

Cortical circuit alterations precede motor impairments in Huntington's disease mice

Huntington's disease (HD) is a devastating hereditary movement disorder, characterized by degeneration of neurons in the striatum and cortex. Studies in human patients and mouse HD models suggest that disturbances of neuronal function in the neocortex play an important role in disease onset and progression. However, the precise nature and time course of cortical alterations in HD have remained elusive. Here, we use chronic in vivo two-photon calcium imaging to longitudinally monitor the activity of identified single neurons in layer 2/3 of the primary motor cortex in awake, behaving R6/2 transgenic HD mice and wildtype littermates. R6/2 mice show age-dependent changes in cortical network function, with an increase in activity that affects a large fraction of cells and occurs rather abruptly within one week, preceeding the onset of motor defects. Furthermore, quantitative proteomics demonstrate a pronounced downregulation of synaptic proteins in the cortex, and histological analyses in R6/2 mice and human HD autopsy cases reveal a reduction in perisomatic inhibitory synaptic contacts on layer 2/3 pyramidal cells. Taken together, our study provides a time-resolved description of cortical network dysfunction in behaving HD mice and points to disturbed excitation/inhibition balance as an important pathomechanism in HD

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

[Abstract] Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2014/049Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/039Instituto de Salud Carlos III; PI13/0028