Shared and distinct transcriptomic cell types across neocortical areas

By Bosiljka Tasic, Zizhen Yao, Lucas T. Graybuck, Kimberly A. Smith, T. Nguyen, Darren Bertagnolli, Jeff Goldy, Emma J. Garren, M. Economo, Sarada Viswanathan and 38 more. 2017

1,613 citations recorded by the source corpus.

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. Single-cell transcriptomics of more than 20,000 cells from two functionally distinct areas of the mouse neocortex identifies 133 transcriptomic types, and provides a foundation for understanding the diversity of cortical cell types.

Subfields: Immunology, Molecular Biology, Neurology

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Citations in the corpus

openTSNE: a modular Python library for t-SNE dimensionality reduction and embedding (2019, methodology): “We illustrate the benefits of creating different t-SNE plots of the same data set by generating four different embeddings of the data on single-cell gene expression in mouse brain (Tasic et al. 2018).”
A comparison of automatic cell identification methods for single-cell RNA sequencing data (2019, background): “The brain datasets, including Allen Mouse Brain (AMB), VISp, ALM (GSE115746), and MTG (phs001790), were downloaded from the Allen Institute Brain Atlas http://celltypes.brain-map.org/rnaseq.”
Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations (2019, methodology): “9 10 = 1 10 = 0.6 1 A B C = 0.6, Vip clusters Fig.5. Tasic et al. data set (n= 23822). (A) Standard t-SNE visualisation (= 1). Cluster ids and cluster colours are taken from the original publication [16]: cold colours for excitatory neurons, warm colours for inhibitory neurons, and grey/brown colour”
A Single-Cell Atlas of Cell Types, States, and Other Transcriptional Patterns from Nine Regions of the Adult Mouse Brain (2018, result): “This result is consistent with independent single-cell analyses in mouse (Tasic et al., 2017) and with theories of cortical specialization in humans and other primates (Krienen et al., 2016).”
Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution (2022, background): “For example, Lein et al. reported that Igsf21 and Rprm are highly expressed in the L5/L6 layers of the cortex 34 .”
Cell2location maps fine-grained cell types in spatial transcriptomics (2022, background): “22–24).”