@article {47896, title = {Genome engineering of isogenic human ES cells to model autism disorders.}, journal = {Nucleic acids research}, volume = {43}, year = {2015}, month = {2015 May 26}, pages = {e65}, abstract = {

Isogenic pluripotent stem cells are critical tools for studying human neurological diseases by allowing one to study the effects of a mutation in a fixed genetic background. Of particular interest are the spectrum of autism disorders, some of which are monogenic such as Timothy syndrome (TS); others are multigenic such as the microdeletion and microduplication syndromes of the 16p11.2 chromosomal locus. Here, we report engineered human embryonic stem cell (hESC) lines for modeling these two disorders using locus-specific endonucleases to increase the efficiency of homology-directed repair (HDR). We developed a system to: (1) computationally identify unique transcription activator-like effector nuclease (TALEN) binding sites in the genome using a new software program, TALENSeek, (2) assemble the TALEN genes by combining golden gate cloning with modified constructs from the FLASH protocol, and (3) test the TALEN pairs in an amplification-based HDR assay that is more sensitive than the typical non-homologous end joining assay. We applied these methods to identify, construct, and test TALENs that were used with HDR donors in hESCs to generate an isogenic TS cell line in a scarless manner and to model the 16p11.2 copy number disorder without modifying genomic loci with high sequence similarity.

}, keywords = {Autistic Disorder, Binding Sites, Cell Engineering, Cell Line, Child Development Disorders, Pervasive, Deoxyribonucleases, Embryonic Stem Cells, Gene Targeting, Genome, Human, Humans, Long QT Syndrome, Models, Genetic, Recombinational DNA Repair, Software, Syndactyly}, issn = {1362-4962}, doi = {10.1093/nar/gkv164}, author = {Martinez, Refugio A and Stein, Jason L and Krostag, Anne-Rachel F and Nelson, Angelique M and Marken, John S and Menon, Vilas and May, Ryan C and Yao, Zizhen and Kaykas, Ajamete and Geschwind, Daniel H and Grimley, Joshua S} } @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} }