@article {47895, title = {Canonical genetic signatures of the adult human brain.}, journal = {Nature neuroscience}, volume = {18}, year = {2015}, month = {2015 Dec}, pages = {1832-44}, abstract = {

The structure and function of the human brain are highly stereotyped, implying a conserved molecular program responsible for its development, cellular structure and function. We applied a correlation-based metric called differential stability to assess reproducibility of gene expression patterning across 132 structures in six individual brains, revealing mesoscale genetic organization. The genes with the highest differential stability are highly biologically relevant, with enrichment for brain-related annotations, disease associations, drug targets and literature citations. Using genes with high differential stability, we identified 32 anatomically diverse and reproducible gene expression signatures, which represent distinct cell types, intracellular components and/or associations with neurodevelopmental and neurodegenerative disorders. Genes in neuron-associated compared to non-neuronal networks showed higher preservation between human and mouse; however, many diversely patterned genes displayed marked shifts in regulation between species. Finally, highly consistent transcriptional architecture in neocortex is correlated with resting state functional connectivity, suggesting a link between conserved gene expression and functionally relevant circuitry.

}, keywords = {Adult, Animals, Brain, Gene Regulatory Networks, Humans, Mice, Nerve Net, Transcriptome}, issn = {1546-1726}, doi = {10.1038/nn.4171}, author = {Hawrylycz, Michael and Miller, Jeremy A and Menon, Vilas and Feng, David and Dolbeare, Tim and Guillozet-Bongaarts, Angela L and Jegga, Anil G and Aronow, Bruce J and Lee, Chang-Kyu and Bernard, Amy and Glasser, Matthew F and Dierker, Donna L and Menche, J{\"o}rg and Szafer, Aaron and Collman, Forrest and Grange, Pascal and Berman, Kenneth A and Mihalas, Stefan and Yao, Zizhen and Stewart, Lance and Barab{\'a}si, Albert-L{\'a}szl{\'o} and Schulkin, Jay and Phillips, John and Ng, Lydia and Dang, Chinh and Haynor, David R and Jones, Allan and Van Essen, David C and Koch, Christof and Lein, Ed} } @article {47897, title = {A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain.}, journal = {Neuron}, volume = {83}, year = {2014}, month = {2014 Jul 16}, pages = {309-23}, abstract = {

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for \~{}2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).

}, keywords = {Animals, Brain, Brain Mapping, Gene Expression, Gene Expression Profiling, Gene Expression Regulation, Developmental, Mice}, issn = {1097-4199}, doi = {10.1016/j.neuron.2014.05.033}, author = {Thompson, Carol L and Ng, Lydia and Menon, Vilas and Martinez, Salvador and Lee, Chang-Kyu and Glattfelder, Katie and Sunkin, Susan M and Henry, Alex and Lau, Christopher and Dang, Chinh and Garcia-Lopez, Raquel and Martinez-Ferre, Almudena and Pombero, Ana and Rubenstein, John L R and Wakeman, Wayne B and Hohmann, John and Dee, Nick and Sodt, Andrew J and Young, Rob and Smith, Kimberly and Nguyen, Thuc-Nghi and Kidney, Jolene and Kuan, Leonard and Jeromin, Andreas and Kaykas, Ajamete and Miller, Jeremy and Page, Damon and Orta, Geri and Bernard, Amy and Riley, Zackery and Smith, Simon and Wohnoutka, Paul and Hawrylycz, Michael J and Puelles, Luis and Jones, Allan R} } @article {47898, title = {Improving reliability and absolute quantification of human brain microarray data by filtering and scaling probes using RNA-Seq.}, journal = {BMC genomics}, volume = {15}, year = {2014}, month = {2014}, pages = {154}, abstract = {

BACKGROUND: High-throughput sequencing is gradually replacing microarrays as the preferred method for studying mRNA expression levels, providing nucleotide resolution and accurately measuring absolute expression levels of almost any transcript, known or novel. However, existing microarray data from clinical, pharmaceutical, and academic settings represent valuable and often underappreciated resources, and methods for assessing and improving the quality of these data are lacking.

RESULTS: To quantitatively assess the quality of microarray probes, we directly compare RNA-Seq to Agilent microarrays by processing 231 unique samples from the Allen Human Brain Atlas using RNA-Seq. Both techniques provide highly consistent, highly reproducible gene expression measurements in adult human brain, with RNA-Seq slightly outperforming microarray results overall. We show that RNA-Seq can be used as ground truth to assess the reliability of most microarray probes, remove probes with off-target effects, and scale probe intensities to match the expression levels identified by RNA-Seq. These sequencing scaled microarray intensities (SSMIs) provide more reliable, quantitative estimates of absolute expression levels for many genes when compared with unscaled intensities. Finally, we validate this result in two human cell lines, showing that linear scaling factors can be applied across experiments using the same microarray platform.

CONCLUSIONS: Microarrays provide consistent, reproducible gene expression measurements, which are improved using RNA-Seq as ground truth. We expect that our strategy could be used to improve probe quality for many data sets from major existing repositories.

}, keywords = {Brain, Cluster Analysis, Computational Biology, Gene Expression, Gene Expression Profiling, High-Throughput Nucleotide Sequencing, Humans, Neocortex, Oligonucleotide Array Sequence Analysis, Reproducibility of Results, Sequence Analysis, RNA, Transcriptome}, issn = {1471-2164}, doi = {10.1186/1471-2164-15-154}, author = {Miller, Jeremy A and Menon, Vilas and Goldy, Jeff and Kaykas, Ajamete and Lee, Chang-Kyu and Smith, Kimberly A and Shen, Elaine H and Phillips, John W and Lein, Ed S and Hawrylycz, Mike J} }