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

By Ye Zhang, Kenian Chen, S. Sloan, Mariko L. Bennett, A. Scholze, S. O’Keeffe, Hemali P. Phatnani, P. Guarnieri, Christine Caneda, N. Ruderisch and 7 more. 2014

4,590 citations recorded by the source corpus.

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.

Subfields: Cancer Research, Molecular Biology

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Citations in the corpus

Human hippocampal neurogenesis drops sharply in children to undetectable levels in adults (2018, neutral): “The simplest explanation is that these cells are dividing local glia, many of which are known to express SOX2+ 25,26 Extended Data Fig.”
Gamma frequency entrainment attenuates amyloid load and modifies microglia (2016, positive): “To understand the cellular and molecular mechanisms from our RNA-seq data, 14 of publicly available RNA-seq datasets 35 were processed for cell-type-specific analysis.”
ROS Generation in Microglia: Understanding Oxidative Stress and Inflammation in Neurodegenerative Disease (2020, background): “There remains a small increase in ROS production in response to LPS and α-synuclein in these NOX2 knockout cultures, likely a result of NOX4, since this isoform is expressed at the next highest level in microglia [72].”
Developmental heterogeneity of microglia and brain myeloid cells revealed by deep single-cell RNA sequencing (2018, neutral): “They all expressed microglial signature genes such as P2ry12 and Slc2a5 (Figure 1G) (Bennett et al., 2016; Zhang et al., 2014), and these cells were largely isolated from the CD45low gate, whereas cells from other “non-microglia” clusters were almost exclusively from the CD45hi gate (Figure 1D).”
Neuronal activity-dependent control of postnatal neurogenesis and gliogenesis (2018, neutral): “OPCs receive synaptic inputs from axons in both gray (Bergles et al. 2000, Jabs et al. 2005, Lin & Bergles 2004) and white matter (Káradóttir et al. 2005, 2008; Kukley et al. 2007; Ziskin et al. 2007).”
Cell biology of astrocyte-synapse interactions (2017, neutral): “…Down syndrome astrocytes show decreased expression of multiple synaptogenic proteins, including TSP1 and Gpc6, and like with fragile X syndrome, the addition of TSP1 to Down syndrome astrocytes is sufficient to rescue some of the synaptogenic deficits (Chen et al., 2014; Garcia et al., 2010).”
Temporal Tracking of Microglia Activation in Neurodegeneration at Single-Cell Resolution (2017, methodology): “To verify that the cells isolated were indeed microglia cells, we compared the average expression profile of cells isolated from CK control mice with previously published expression profiles of specific brain cell types and with the expression profiles of monocytes and tissue-resident macrophages (Z”
The Aging Astrocyte Transcriptome from Multiple Regions of the Mouse Brain (2018, methodology): “To quantify astrocyte enrichment, we developed a list of cell-specific markers from published RNA-seq and microarray data of purified cortical cell types from the developing brain (see Table S1 for a list; Cahoy et al., 2008; Zhang et al., 2014).”
Amyloid β oligomers constrict human capillaries in Alzheimer’s disease via signaling to pericytes (2019, background): “These data suggest that NOX4 in pericytes or endothelial cells (27–29), rather thanNOX2 in immune cells (28, 29), is the NADPH oxidase mainly responsible for generating the ROS that evoke capillary constriction.”
New tools for studying microglia in the mouse and human CNS (2016, methodology): “We calculated percentile ranks for several canonical activation genes for our naïve and LPS-stimulated samples, as well as five published datasets (15, 17, 30, 33, 34); these datasets were generated using CD45 or Cx3cr1 rather than a specific microglia marker, such as Tmem119, and used enzymatic dig”
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq (2017, methodology): “However, this consistency is partial, reflecting the differences between differentiation programs as measured in mice (29) and as we observe in IDH-A and IDH-O tumors.”