@article {49496, title = {Distinct descending motor cortex pathways and their roles in movement.}, journal = {Nature}, volume = {563}, year = {2018}, month = {2018 Nov}, pages = {79-84}, abstract = {

Activity in the motor cortex predicts movements, seconds before they are initiated. This preparatory activity has been observed across cortical layers, including in descending pyramidal tract neurons in layer 5. A key question is how preparatory activity is maintained without causing movement, and is ultimately converted to a motor command to trigger appropriate movements. Here, using single-cell transcriptional profiling and axonal reconstructions, we identify two types of pyramidal tract neuron. Both types project to several targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex; populations of these neurons produced early preparatory activity that persisted until the movement was initiated. The second type projects to motor centres in the medulla and mainly\ produced late preparatory activity and motor commands. These results indicate that two types of motor cortex output neurons have specialized roles in motor control.

}, issn = {1476-4687}, doi = {10.1038/s41586-018-0642-9}, author = {Economo, Michael N and Viswanathan, Sarada and Tasic, Bosiljka and Bas, Erhan and Winnubst, Johan and Menon, Vilas and Graybuck, Lucas T and Nguyen, Thuc Nghi and Smith, Kimberly A and Yao, Zizhen and Wang, Lihua and Gerfen, Charles R and Chandrashekar, Jayaram and Zeng, Hongkui and Looger, Loren L and Svoboda, Karel} } @article {49495, title = {Shared and distinct transcriptomic cell types across neocortical areas.}, journal = {Nature}, volume = {563}, year = {2018}, month = {2018 Nov}, pages = {72-78}, abstract = {

The neocortex contains a multitude of cell types that are segregated into layers and functionally distinct areas. To investigate the diversity of cell types across the mouse neocortex, here we analysed 23,822 cells from two areas at distant poles of the mouse neocortex: the primary visual cortex and the anterior lateral motor cortex. We define 133 transcriptomic cell types by deep, single-cell RNA sequencing. Nearly all types of GABA (γ-aminobutyric acid)-containing neurons are shared across both areas, whereas most types of glutamatergic neurons were found in one of the two areas. By combining single-cell RNA sequencing and retrograde labelling, we match transcriptomic types of glutamatergic neurons to their long-range projection specificity. Our study establishes a combined transcriptomic and projectional taxonomy of cortical cell types from functionally distinct areas of the adult mouse cortex.

}, issn = {1476-4687}, doi = {10.1038/s41586-018-0654-5}, author = {Tasic, Bosiljka and Yao, Zizhen and Graybuck, Lucas T and Smith, Kimberly A and Nguyen, Thuc Nghi and Bertagnolli, Darren and Goldy, Jeff and Garren, Emma and Economo, Michael N and Viswanathan, Sarada and Penn, Osnat and Bakken, Trygve and Menon, Vilas and Miller, Jeremy and Fong, Olivia and Hirokawa, Karla E and Lathia, Kanan and Rimorin, Christine and Tieu, Michael and Larsen, Rachael and Casper, Tamara and Barkan, Eliza and Kroll, Matthew and Parry, Sheana and Shapovalova, Nadiya V and Hirschstein, Daniel and Pendergraft, Julie and Sullivan, Heather A and Kim, Tae Kyung and Szafer, Aaron and Dee, Nick and Groblewski, Peter and Wickersham, Ian and Cetin, Ali and Harris, Julie A and Levi, Boaz P and Sunkin, Susan M and Madisen, Linda and Daigle, Tanya L and Looger, Loren and Bernard, Amy and Phillips, John and Lein, Ed and Hawrylycz, Michael and Svoboda, Karel and Jones, Allan R and Koch, Christof and Zeng, Hongkui} } @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} }