September 3, 2014 | Ye Zhang, Kenian Chen, Steven A. Sloan, Mariko L. Bennett, Anja R. Scholze, Sean O’Keeffe, Hemali P. Phatnani, Paolo Guarnieri, Christine Caneda, Nadine Ruderisch, Shuyun Deng, Shane A. Liddelow, Chaolin Zhang, Richard Daneman, Tom Maniatis, Ben A. Barres, Jia Qian Wu
This study presents a comprehensive transcriptome and splicing database of eight major cell types in the mouse cerebral cortex: neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes. The authors used RNA sequencing (RNA-Seq) to generate a high-resolution transcriptome database, identifying thousands of novel cell type-enriched genes and splicing isoforms. They validated the purity of the isolated cell types using known cell-specific markers and compared the sensitivity of RNA-Seq with microarray analysis, finding that RNA-Seq identified more differentially expressed genes and had higher sensitivity in detecting low-abundance transcripts. The study also revealed distinct gene expression signatures, transcription factors, and signaling pathways for each cell type, providing insights into their unique functions and interactions. Additionally, the authors identified a large number of long noncoding RNAs (lncRNAs) with cell type-specific expression, highlighting their potential roles in brain development and function. The dataset and analysis tools are available on a user-friendly website to facilitate further research on brain cell types.This study presents a comprehensive transcriptome and splicing database of eight major cell types in the mouse cerebral cortex: neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes. The authors used RNA sequencing (RNA-Seq) to generate a high-resolution transcriptome database, identifying thousands of novel cell type-enriched genes and splicing isoforms. They validated the purity of the isolated cell types using known cell-specific markers and compared the sensitivity of RNA-Seq with microarray analysis, finding that RNA-Seq identified more differentially expressed genes and had higher sensitivity in detecting low-abundance transcripts. The study also revealed distinct gene expression signatures, transcription factors, and signaling pathways for each cell type, providing insights into their unique functions and interactions. Additionally, the authors identified a large number of long noncoding RNAs (lncRNAs) with cell type-specific expression, highlighting their potential roles in brain development and function. The dataset and analysis tools are available on a user-friendly website to facilitate further research on brain cell types.