Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq

By Amit Zeisel, A. Muñoz-Manchado, Simone Codeluppi, P. Lönnerberg, Gioele La Manno, Anna Juréus, Sueli C Marques, H. Munguba, Liqun He, C. Betsholtz and 4 more. 2015

2,817 citations recorded by the source corpus.

Cellular diversity in the brain revealed The mammalian brain has an extraordinarily large number of cells. Although there are quite a few different cell types, many cells in any one category tend to look alike. Zeisel et al. analyzed the transcriptomes of mouse brain cells to reveal more than meets the eye. Interneurons of similar type were found in dissimilar regions of the brain. Oligodendrocytes that seemed to be all of one class were differentiated by their molecular signatures into a half-dozen classes. Microglia associated with blood vessels were distinguished from look-alike perivascular macrophages. Thus, the complex microanatomy of the brain can be revealed by the RNAs expressed in its cells. Science, this issue p. 1138 A close look at the genes expressed by cells in the brain reveals hidden and coordinated cellular complexity. The mammalian cerebral cortex supports cognitive functions such as sensorimotor integration, memory, and social behaviors. Normal brain function relies on a diverse set of differentiated cell types, including neurons, glia, and vasculature. Here, we have used large-scale single-cell RNA sequencing (RNA-seq) to classify cells in the mouse somatosensory cortex and hippocampal CA1 region. We found 47 molecularly distinct subclasses, comprising all known major cell types in the cortex. We identified numerous marker genes, which allowed alignment with known cell types, morphology, and location. We found a layer I interneuron expressing Pax6 and a distinct postmitotic oligodendrocyte subclass marked by Itpr2. Across the diversity of cortical cell types, transcription factors formed a complex, layered regulatory code, suggesting a mechanism for the maintenance of adult cell type identity.

Subfields: Immunology, Molecular Biology, Neurology

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Citations in the corpus

Prioritizing network communities (2018, background): “In the cell – cell similarity network, one could assume that top-ranked communities contain highly distinct marker genes 31 , while low-ranked communities contain marker genes whose expression levels are spread out beyond cells in the community.”
A deep generative model for single-cell RNA sequencing with application to detecting differentially expressed genes (2017, background): “A first dataset from [19] contains 3005 mouse cortex cells and gold-standard labels for seven distinct cell types.”
A Unique Microglia Type Associated with Restricting Development of Alzheimer's Disease. (2017, neutral): “…resolve immune heterogeneity in AD (Deng et al., 2014; Grün et al., 2015; Gury-BenAri et al., 2016; Jaitin et al., 2014; Junker and van Oudenaarden, 2014; La Manno et al., 2016; Mass et al., 2016; Matcovitch-Natan et al., 2016; Paul et al., 2015; Treutlein et al., 2014; Zeisel et al., 2015).”
A Mammalian enhancer trap resource for discovering and manipulating neuronal cell types (2016, methodology): “We also examined the expression of previously identified layer 6 marker genes (Molyneaux et al., 2007; Zeisel et al., 2015) and of genes used to generate BAC-Cre lines having layer 6 expression (Harris et al., 2014).”
Molecular Diversity and Specializations among the Cells of the Adult Mouse Brain (2018, background): “To date, single-cell gene expression studies have yielded cell-type classifications in the mouse cerebral cortex (Tasic et al., 2016; Zeisel et al., 2015), retina (Shekhar et al.”
Using neural networks for reducing the dimensions of single-cell RNA-Seq data (2017, methodology): “Given the number of cells that are profiled in each experiment, which can reach thousands (51,54), new methods are required for accurately and efficiently clustering these data while overcoming issues related to the stochastic nature of gene expression even in similar cells, noise and missing values”
The Human Cell Atlas (2017, background): “…defined types (Darmanis et al., 2015; Gokce et al., 2016; Habib et al., 2016; Lake et al., 2016; Pollen et al., 2014; Tasic et al., 2016; Zeisel et al., 2015), but it has been more difficult to find consensus between data modalities, and the relationship between transcriptomic types and…”