Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain

By B. Lake, Rizi Ai, Gwendolyn E. Kaeser, N. Salathia, Y. Yung, R. Liu, Andre Wildberg, D. Gao, Ho-Lim Fung, Song Chen and 11 more. Science, 2016

921 citations recorded by the source corpus.

Single-nucleus gene expression Identifying the genes expressed at the level of a single cell nucleus can better help us understand the human brain. Blue et al. developed a single-nuclei sequencing technique, which they applied to cells in classically defined Brodmann areas from a postmortem brain. Clustering of gene expression showed concordance with the area of origin and defining 16 neuronal subtypes. Both excitatory and inhibitory neuronal subtypes show regional variations that define distinct cortical areas and exhibit how gene expression clusters may distinguish between distinct cortical areas. This method opens the door to widespread sampling of the genes expressed in a diseased brain and other tissues of interest. Science, this issue p. 1586 Individual neurons have specific transcriptomic signatures and transcriptomic heterogeneity. The human brain has enormously complex cellular diversity and connectivities fundamental to our neural functions, yet difficulties in interrogating individual neurons has impeded understanding of the underlying transcriptional landscape. We developed a scalable approach to sequence and quantify RNA molecules in isolated neuronal nuclei from a postmortem brain, generating 3227 sets of single-neuron data from six distinct regions of the cerebral cortex. Using an iterative clustering and classification approach, we identified 16 neuronal subtypes that were further annotated on the basis of known markers and cortical cytoarchitecture. These data demonstrate a robust and scalable method for identifying and categorizing single nuclear transcriptomes, revealing shared genes sufficient to distinguish previously unknown and orthologous neuronal subtypes as well as regional identity and transcriptomic heterogeneity within the human brain.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/90340cd554c743cdf0eb10b35549adf9b7fb8409

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Citations in the corpus

Massively-parallel single nucleus RNA-seq with DroNc-seq (2017, positive): “Notably, we found good congruence between our GABAergic sub-clusters and those previously defined 3,4,17 in mouse and human (Fig.”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, neutral): “This decreases mRNA amounts further, so that it has been suggested to count Intron mapping reads originating from nascent RNAs as part of single cell expression pro les [11].”
A single-cell atlas of entorhinal cortex from individuals with Alzheimer’s disease reveals cell-type-specific gene expression regulation (2019, positive): “…to pre-determine sample sizes but our sample sizes are similar or greater than those reported in previous publications (Tirosh et al 2016, Nature; Lake et al 2016, Science; Zhong et al, 2018, Nature) Data exclusions Two single-cell libraries were discarded due to high neuronal enrichment…”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, positive): “However, there remained striking heterogeneity within excitatory neurons, potentially reflecting differential expression and methylation in different layers of the PFC (Figure 5G) (Lake et al., 2016; Luo et al., 2017).”
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020, background): “…increased identification of molecularly-defined cell types in the human brain and implicated unique cell classes in risk for specific brain disorders (Darmanis et al., 2015; Hodge et al., 2019; Lake et al., 2016, 2018; Mathys et al., 2019; Nowakowski et al., 2017; Velmeshev et al., 2019).”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, background): “Several emerging scRNA-seq studies have focused on deepening our understanding of cells in the brain 102,103 .”
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain (2017, positive): “In this manner, we were able to identify epigenetic differences relevant to inhibitory neuron subtypes ( InA, InB ) distinguished by their developmental origin from subcortical regions of the medial or lateral/ caudal ganglionic eminences 4, 33, 34 (Fig.”
The Human Cell Atlas (2017, background): “…and hippocampus there are also a large number of transcriptionally 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…”
Integrative functional genomic analysis of human brain development and neuropsychiatric risks (2018, methodology): “20 PCW postmortem human brains as well as from single-nuclei sequencing of adult human brains (27).”