Transcriptional Landscape of the Prenatal Human Brain

Summary The anatomical and functional architecture of the human brain is largely determined by prenatal transcriptional processes. We describe an anatomically comprehensive atlas of mid-gestational human brain, including de novo reference atlases, in situ hybridization, ultra-high resolution magnetic resonance imaging (MRI) and microarray analysis on highly discrete laser microdissected brain regions. In developing cerebral cortex, transcriptional differences are found between different proliferative and postmitotic layers, wherein laminar signatures reflect cellular composition and developmental processes. Cytoarchitectural differences between human and mouse have molecular correlates, including species differences in gene expression in subplate, although surprisingly we find minimal differences between the inner and human-expanded outer subventricular zones. Both germinal and postmitotic cortical layers exhibit fronto-temporal gradients, with particular enrichment in frontal lobe. Finally, many neurodevelopmental disorder and human evolution-related genes show patterned expression, potentially underlying unique features of human cortical formation. These data provide a rich, freely-accessible resource for understanding human brain development

Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data

With the emergence of genome-wide colorimetric in situ hybridization (ISH) data sets such as the Allen Brain Atlas, it is important to understand the relationship between this gene expression modality and those derived from more quantitative based technologies. This study introduces a novel method for standardized relative quantification of colorimetric ISH signal that enables a large-scale cross-platform expression level comparison of ISH with two publicly available microarray brain data sources

Molecular and Anatomical Signatures of Sleep Deprivation in the Mouse Brain

Sleep deprivation (SD) leads to a suite of cognitive and behavioral impairments, and yet the molecular consequences of SD in the brain are poorly understood. Using a systematic immediate-early gene (IEG) mapping to detect neuronal activation, the consequences of SD were mapped primarily to forebrain regions. SD was found to both induce and suppress IEG expression (and thus neuronal activity) in subregions of neocortex, striatum, and other brain regions. Laser microdissection and cDNA microarrays were used to identify the molecular consequences of SD in seven brain regions. In situ hybridization (ISH) for 222 genes selected from the microarray data and other sources confirmed that robust molecular changes were largely restricted to the forebrain. Analysis of the ISH data for 222 genes (publicly accessible at http://sleep.alleninstitute.org ) provided a molecular and anatomic signature of the effects of SD on the brain. The suprachiasmatic nucleus (SCN) and the neocortex exhibited differential regulation of the same genes, such that in the SCN genes exhibited time-of-day effects while in the neocortex, genes exhibited only SD and waking (W) effects. In the neocortex, SD activated gene expression in areal-, layer-, and cell type-specific manner. In the forebrain, SD preferentially activated excitatory neurons, as demonstrated by double-labeling, except for striatum which consists primarily of inhibitory neurons. These data provide a characterization of the anatomical and cell type-specific signatures of SD on neuronal activity and gene expression that may account for the associated cognitive and behavioral effects

Cross-platform comparison of global dynamic range for microarray, ISH, and SAGE

Dynamic range of signal intensities in the striatum (Str; solid lines) and hypothalamus (Hyp; dashed lines) observed in GNF (green lines), Teragenomics (Tera; red lines), ABA (blue lines), and SAGE (aqua line) data sets (striatum only). The data are plotted on a log scale for 1,270 of the highest expression values. Genes on each curve are sorted independently so that only the relative range of values is preserved. The compressed dynamic range at the highest levels in ISH quantification compared to the microarray and SAGE platforms is notable.<p><b>Copyright information:</b></p><p>Taken from "Quantitative methods for genome-scale analysis of hybridization and correlation with microarray data"</p><p>http://genomebiology.com/2008/9/1/R23</p><p>Genome Biology 2008;9(1):R23-R23.</p><p>Published online 30 Jan 2008</p><p>PMCID:PMC2395252.</p><p></p

Intra- and cross-platform comparison between GNF and Teragenomics data sets for the striatum

Scatter plots showing correlation of expression levels between replicates in Teragenomics (TERA; left panel), replicates in GNF (center panel), and cross-platform for Teragenomics and GNF (right panel). Correlations and gene numbers are from Table 3. Scatter plots for the other five structures are shown in Figure S2 in Additional data file 1.<p><b>Copyright information:</b></p><p>Taken from "Quantitative methods for genome-scale analysis of hybridization and correlation with microarray data"</p><p>http://genomebiology.com/2008/9/1/R23</p><p>Genome Biology 2008;9(1):R23-R23.</p><p>Published online 30 Jan 2008</p><p>PMCID:PMC2395252.</p><p></p