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• W1542742417 abstract "The complexity of neural networks in the central nervous system is enormous, resulting not only from the vast combinatorial possibilities for neuronal connections, but also from the diversity of properties and functions of neuronal cells themselves. Systematic analysis of neuronal diversity over the last century, using techniques such as the Golgi method, has proved the existence of a large variety of morphological types of neurons. Because shapes and arborization patterns are the visible expression of neuronal connectivity, it is not surprising that different morphological cell types have so far without exception turned out to have distinct physiological functions. Only a few morphologically defined cell types, though, express usefully distinctive cytochemical markers, and those that share common markers do not necessarily display the same patterns of connections or electrophysiological properties. A high level of heterogeneity of function within morphological types is becoming increasingly apparent. Furthermore, in the cortex, there are probably many subtle variations in cellular properties and local circuitry, according to the specific function and development of different cortical areas. Ultimately, the function of these neurons and networks may become clear as a result of extracellular recordings in vivo (i.e. a ‘top-down’ approach), but the lack of knowledge about their properties and organization limits the ability to design appropriate experiments. Therefore, an increased understanding of the construction and organization of the local circuits of the brain is required, i.e. a ‘bottom-up’ approach. This entails a description of the cell types that are components of a neural centre, the identification of their chemical mediators, channels and receptors, and an analysis of their synaptic connections. Critical genetic and physiological experiments are then designed to understand cell functions. The size of this task, however, is staggering: the Golgi method has revealed 50 anatomical types of local circuit neurons in the monkey striate cortex, which has led to the suggestion that there could be as many as 100 different types of interneurons in each layer of the neocortex. Such extreme cellular heterogeneity has also significantly impaired gene expression analysis. To confront this complexity, specific populations must first be distinguished within the highly heterogeneous in vivo network/tissue. Over the last few years, high throughput techniques such as microarray have begun to offer the possibility for physiologists to bridge the gap in understanding between top-down and bottom-up knowledge of the brain, by systematic, and to an increasing extent automated, approaches to collecting genome-wide data for characterizing cells. These approaches are rapidly becoming almost standard tools in cellular neurophysiology labs, and bring with them a new set of issues relating to data analysis and experimental design. It is an opportune time therefore for The Journal of Physiology to review recent progress in applying functional genomics techniques to the brain. The articles in this special issue are distinguished not just by their focus on high throughput techniques, but also by a physiological emphasis on understanding how this new kind of data illuminates the dynamical function of neural systems. A common theme of these papers is a note of caution: high-throughput techniques often entail an increased level of noise, and the large-scale approach brings with it increased difficulty in scrutinizing the sense of each individual piece of data. These issues are particularly important to consider as we begin to construct open databases of functional genomic data. The community as a whole is only beginning to compare results from different groups on the same or similar systems, and to reach a consensus on the meaning of results. Yet, properly applied, these techniques offer unprecedented power for uncovering new interactions, connections, expression patterns, and gene regulatory networks. Over the last few years our understanding of the complexity of RNA species within a mammalian cell has completely changed: Gustincich et al. (2006) describe the current view of the transcriptome focusing on transcript diversity, the growing non-coding RNA world, the organization of transcriptional units in the genome and promoter structures. Mehler & Mattick (2006) review the emerging roles of non-coding RNAs – micro RNAs, small nucleolar RNAs and longer non-coding RNAs in controlling the development and function of the nervous system. A range of evidence suggests that these RNAs form complex networks that direct the trajectories of differentiation and development, via regulation of chromatin and RNA modifications, transcription, splicing, mRNA translation, and RNA stability as well as other mechanisms. Issues concerning a precise description of RNA patterns of expression in different neuronal cell types have been addressed in the following four papers. Brumwell & Curran (2006) review and compare the major developmental mouse brain gene expression mapping projects: BGEM/GenSat, GenePaint and Embryo Gene Expression Patterns Project/EMAP. They show specific examples of genes expressed transiently in development, and examples of transcription regulation. Alvarez-Bolado & Eichele (2006) describe the GenePaint database in more detail, and the principles underlying its implementation, which include robotics, automated or semiautomated sectioning, in situ hybridization and scanning, and on the analysis side, flexible searching, data mining, and deformable maps of expression. Coppola & Geschwind (2006) discuss the issues of resolution and accuracy of microarray studies of the brain, and review recent methods that make the best trade-off with the requirement for high throughput. The cellular complexity of the brain makes critical the separation of cells or the visualization of expression in very small regions, for example by voxelation and gene expression tomography. Then, particular cell types can be marked with genetic markers or histological tracers and separated by patch-clamp, by FACS, or by hand. Yano et al. (2006) review the progress in this area to date, and discuss in particular some of the technical issues of single-cell microarray, such as the problem of low copy numbers of mRNA, RNA loss during patch-clamp and amplification noise. The local control of translation has been shown to play a fundamental role in neuronal function. Therefore it is crucial to understand protein distribution and protein–protein interactions within specific cell types. Anderson & Grant (2006) review existing large-scale protein expression studies and the specific technical obstacles that need to be overcome before applying the scaling used in nucleic acid based approaches. Following the same line, Suzuki (2006) describes new genome-wide, high-throughput approaches for studying protein–protein interactions (PPI) in the brain. Comparison of different studies on the same system indicates a high level of false positives and negatives. The specificity of PPI for particular cell types calls for future integration of PPI databases with gene expression profile databases. The last series of reviews concern examples of genomic and proteomic approaches to the study of neuronal complexity. Thus, Ma (2006) reviews the use of high-throughput in situ hybridization in a large-scale effort to discover over 350 transcription factors with spatially or temporally restricted expression in the brain, and which thus appear to be involved in determining neuronal phenotype. Nakanishi & Okazawa (2006) describe an elegant tracing of the link between function and gene expression in establishing how a physiological cellular parameter, the membrane potential, controls the development of cerebellar granule neurons. Jacobs et al. (2006) focus on the development of mesodiencephalic dopaminergic neurons. Their cellular heterogeneity, based on anatomical position, may account for the difference in vulnerability of specific subgroups as observed in Parkinson's disease. Prakash & Wurst (2006) also discuss the developmental gene networks which generate dopaminergic neuron diversity. Finally, Greene (2006) reviews the important application of microarray approaches in dissecting types of dopaminergic neuron, which are involved in important disease states of the CNS, focusing on the differences between substantia nigra and ventral tegmental area neurons. Our aim for this special issue of The Journal of Physiology is to provide an accessible synthesis of current information on how gene expression regulates cell diversity in the CNS. We hope that this issue will contribute to further stimulating the interest of physiologists in this rapidly and fascinating new developing area of research." @default.
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• W1542742417 title "The mammalian transcriptome and the cellular complexity of the brain" @default.
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• W1542742417 doi "https://doi.org/10.1113/jphysiol.2006.118364" @default.
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