Molecular taxonomy of major neuronal classes in the adult mouse forebrain

ArticleinNature Neuroscience 9(1):99-107 · February 2006with20 Reads
DOI: 10.1038/nn1618 · Source: PubMed
Identifying the neuronal cell types that comprise the mammalian forebrain is a central unsolved problem in neuroscience. Global gene expression profiles offer a potentially unbiased way to assess functional relationships between neurons. Here, we carried out microarray analysis of 12 populations of neurons in the adult mouse forebrain. Five of these populations were chosen from cingulate cortex and included several subtypes of GABAergic interneurons and pyramidal neurons. The remaining seven were derived from the somatosensory cortex, hippocampus, amygdala and thalamus. Using these expression profiles, we were able to construct a taxonomic tree that reflected the expected major relationships between these populations, such as the distinction between cortical interneurons and projection neurons. The taxonomic tree indicated highly heterogeneous gene expression even within a single region. This dataset should be useful for the classification of unknown neuronal subtypes, the investigation of specifically expressed genes and the genetic manipulation of specific neuronal circuit elements.
    • "All P-values from the bioinformatic analyses were adjusted using the Benjamini–Hochberg method. For cell types and immune response enrichment, we used the following lists of genes: neurons, astrocytes and oligodendrocytes (Cahoy et al, 2008), microglia (cured from Oldham et al, 2008), and glutamate/GABA (Sugino et al, 2006). For cross species comparison, we used lists from alcohol vapor-treated rats (Tapocik et al, 2013) and human alcoholics (Lewohl et al, 2011) to identify conserved alcoholresponsive microRNAs. "
    [Show abstract] [Hide abstract] ABSTRACT: Local translation of mRNAs in the synapse plays a major role in synaptic structure and function. Chronic alcohol use causes persistent changes in synaptic mRNA expression, possibly mediated by microRNAs localized in the synapse. We profiled the transcriptome of synaptoneurosomes (SN) obtained from the amygdala of mice that consumed 20% ethanol (alcohol) in a 30-day continuous two-bottle choice test to identify the microRNAs that target alcohol-induced mRNAs. SN are membrane vesicles containing pre- and post-synaptic compartments of neurons and astroglia and are a unique model for studying the synaptic transcriptome. We previously showed that chronic alcohol regulates mRNA expression in a coordinated manner. Here, we examine microRNAs and mRNAs from the same samples to define alcohol-responsive synaptic microRNAs and their predicted interactions with targeted mRNAs. The aim of the study was to identify the microRNA-mRNA synaptic interactions that are altered by alcohol. This was accomplished by comparing the effect of alcohol in SN vs. total homogenate preparations from the same samples. We used a combination of unbiased bioinformatics methods (differential expression, correlation, co-expression, microRNA-mRNA target prediction, co-targeting and cell type specific analyses) to identify key alcohol-sensitive microRNAs. Prediction analysis showed that a subset of alcohol-responsive microRNAs was predicted to target many alcohol-responsive mRNAs, providing a bidirectional analysis for identifying microRNA-mRNA interactions. We found microRNAs and mRNAs with overlapping patterns of expression that correlated with alcohol consumption. Cell-type specific analysis revealed that a significant number of alcohol-responsive mRNAs and microRNAs were unique to glutamate neurons and were predicted to target each other. Chronic alcohol appears to perturb the coordinated microRNA regulation of mRNAs in SN, a mechanism that may explain the aberrations in synaptic plasticity affecting the alcoholic brain.Neuropsychopharmacology accepted article preview online, 24 June 2015. doi:10.1038/npp.2015.179.
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    • "This suggests possible occlusion of relationships in more heterogeneous tissue than in more and more specific cell samples. Ideally, all analyses of this kind would be from single identified neurons (Schulz et al., 2007; Tobin et al., 2009; Kodama et al., 2012), or at least pooled neurons of a defined subclass (Sugino et al., 2006). Single cell data of this quality are technically feasible, although they would require preamplification of mRNA to have enough material for the depth of analyses provided here. "
    [Show abstract] [Hide abstract] ABSTRACT: Neural networks ultimately arrive at functional output via interaction of the excitability of individual neurons and their synaptic interactions. We investigated the relationships between voltage-gated ion channel and neurotransmitter receptor mRNA levels in mouse spinal cord at four different postnatal time points (P5, P11, P17, and adult) and three different adult cord levels (cervical, thoracic, and lumbosacral) using quantitative RT-PCR. Our analysis and data visualization are novel in that we chose a focal group of voltage-gated channel subunits and transmitter receptor subunits, performed absolute quantitation of mRNA copy number for each gene from a sample, and used multiple correlation analyses and correlation matrices to detect patterns in correlated mRNA levels across all genes of interest. These correlation profiles suggest that postnatal maturation of spinal cord includes changes among channel and receptor subunits that proceed from widespread co-regulation to more refined and distinct functional relationships.
    Full-text · Article · Jul 2014
    • "Other work has investigated a subpopulation of BLA neurons to examine its role in fear conditioning (Jasnow et al., 2013). Specifically, Jasnow et al. targeted the glutamatergic pyramidal cells found in the BLA by driving ChR2 expression with the Thy1 promoter, which limits expression to a specific subpopulation of glutamatergic cells in the BLA and other forebrain regions (Sugino et al., 2006). Optical stimulation of this specific class of BLA neurons during tone fear conditioning impaired retention of the learning while having no effect on the expression of freezing itself during the conditioning (Jasnow et al., 2013). "
    [Show abstract] [Hide abstract] ABSTRACT: Studies of amygdala functioning have occupied a significant place in the history of understanding how the brain controls behavior and cognition. Early work on the amygdala placed this small structure as a key component in the regulation of emotion and affective behavior. Over time, our understanding of its role in brain processes has expanded, as we have uncovered amygdala influences on memory, reward behavior, and overall functioning in many other brain regions. Studies have indicated that the amygdala has widespread connections with a variety of brain structures, from the prefrontal cortex to regions of the brainstem, that explain its powerful influence on other parts of the brain and behaviors mediated by those regions. Thus, many optogenetic studies have focused on harnessing the powers of this technique to elucidate the functioning of the amygdala in relation to motivation, fear, and memory as well as to determine how the amygdala regulates activity in other structures. For example, studies using optogenetics have examined how specific circuits within amygdala nuclei regulate anxiety. Other work has provided insight into how the basolateral and central amygdala nuclei regulate memory processing underlying aversive learning. Many experiments have taken advantage of optogenetics’ ability to target either genetically distinct subpopulations of neurons or the specific projections from the amygdala to other brain regions. Findings from such studies have provided evidence that particular patterns of activity in basolateral amygdala glutamatergic neurons are related to memory consolidation processes, while other work has indicated the critical nature of amygdala inputs to the prefrontal cortex and nucleus accumbens in regulating behavior dependent on those downstream structures. This review will examine the recent discoveries on amygdala functioning made through experiments using optogenetics, placing these findings in the context of the major questions in the field.
    Full-text · Article · Mar 2014
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