An RNA-Sequencing Transcriptome and Splicing Database of Glia, Neurons, and Vascular Cells of the Cerebral Cortex

База данных транскриптома и вариантов сплайсинга, полученная методом RNA-секвенирования, для глии, нейронов и сосудистых клеток коры головного мозга
Ben A. Barres, Shane A. Liddelow, Richard Daneman, Mariko L. Bennett, Tom Maniatis, Paolo Guarnieri, Ye Zhang, Sean O’Keeffe, Jia Qian Wu, Kenian Chen, Hemali Phatnani, Steven A. Sloan, Anja R. Scholze, Christine Caneda, Nadine Ruderisch, Shuyun Deng, Chaolin Zhang
2014-09-03

PKM2 splicingRNA sequencing transcriptomealternative splicingcell type–enriched genesglia neurons vascular cells
The major cell classes of the brain differ in their developmental processes, metabolism, signaling, and function. To better understand the functions and interactions of the cell types that comprise these classes, we acutely purified representative populations of neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes from mouse cerebral cortex. We generated a transcriptome database for these eight cell types by RNA sequencing and used a sensitive algorithm to detect alternative splicing events in each cell type. Bioinformatic analyses identified thousands of new cell type-enriched genes and splicing isoforms that will provide novel markers for cell identification, tools for genetic manipulation, and insights into the biology of the brain. For example, our data provide clues as to how neurons and astrocytes differ in their ability to dynamically regulate glycolytic flux and lactate generation attributable to unique splicing of PKM2, the gene encoding the glycolytic enzyme pyruvate kinase. This dataset will provide a powerful new resource for understanding the development and function of the brain. To ensure the widespread distribution of these datasets, we have created a user-friendly website (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) that provides a platform for analyzing and comparing transciption and alternative splicing profiles for various cell classes in the brain.
1
Applied a sensitive algorithm to detect alternative splicing events, identifying thousands of cell type-enriched splicing isoforms.
2
Bioinformatic analyses discovered thousands of new cell type-enriched genes that can serve as markers and tools for genetic manipulation.
3
Generated an RNA-seq transcriptome database for eight purified mouse cerebral cortex cell types (neurons, astrocytes, OPCs, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, pericytes).
4
Identified cell type–specific splicing of PKM2 suggesting mechanistic differences between neurons and astrocytes in regulating glycolytic flux and lactate generation.
5
Provided a publicly accessible website hosting the dataset for analysis and comparison of transcription and alternative splicing across brain cell classes.

Purified populations of eight mouse cerebral cortex cell types (neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes) used to generate an RNA-sequencing transcriptome and splicing database

Cell-type-specific transcriptome profiles and alternative splicing events detected by RNA-sequencing, including identification of cell type-enriched genes and splicing isoforms

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2014-09-03
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Ben A. Barres
Shane A. Liddelow
Richard Daneman
Mariko L. Bennett
Tom Maniatis
Paolo Guarnieri
Ye Zhang
Sean O’Keeffe
Jia Qian Wu
Kenian Chen
Hemali Phatnani
Steven A. Sloan
Anja R. Scholze
Christine Caneda
Nadine Ruderisch
Shuyun Deng
Chaolin Zhang
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