Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain

By B. Lake, Song Chen, B. Sos, Jean Fan, Gwendolyn E. Kaeser, Y. Yung, T. Duong, D. Gao, J. Chun, Peter V. Kharchenko and 1 more. Nature Biotechnology, 2017

909 citations recorded by the source corpus.

Detailed characterization of the cell types in the human brain requires scalable experimental approaches to examine multiple aspects of the molecular state of individual cells, as well as computational integration of the data to produce unified cell-state annotations. Here we report improved high-throughput methods for single-nucleus droplet-based sequencing (snDrop-seq) and single-cell transposome hypersensitive site sequencing (scTHS-seq). We used each method to acquire nuclear transcriptomic and DNA accessibility maps for >60,000 single cells from human adult visual cortex, frontal cortex, and cerebellum. Integration of these data revealed regulatory elements and transcription factors that underlie cell-type distinctions, providing a basis for the study of complex processes in the brain, such as genetic programs that coordinate adult remyelination. We also mapped disease-associated risk variants to specific cellular populations, which provided insights into normal and pathogenic cellular processes in the human brain. This integrative multi-omics approach permits more detailed single-cell interrogation of complex organs and tissues.

Subfields: Molecular Biology, Neurology

View Paper: https://www.semanticscholar.org/paper/25648a2211b20fada4e722c14d7adb29d924e89c

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Citations in the corpus

Integrative functional genomic analysis of human brain development and neuropsychiatric risks (2018, methodology): “To help understand the temporal dynamics underlying this variation in gene expression, we analyzed our scRNA-seq data from embryonic fronto-parietal neocortical wall and mid-fetal fronto-parietal neocortical plate and adjacent subplate zone alongside our snRNA-seq data from adult human NCX and other”
Host-Viral Infection Maps Reveal Signatures of Severe COVID-19 Patients (2020, positive): “High-level analysis were performed using the R-based Pagoda2 pipeline (https://github.com/hms-dbmi/pagoda2/) (Lake et al., 2018) in addition to an in-house R script.”
Best practices for single-cell analysis across modalities (2023, background): “…that map multimodal data into a common feature space based on prior knowledge — such as assay for transposase-accessible chromatin (ATAC) regions to nearby transcripts — with subsequent application of single-cell data integration methods have been shown to result in information loss 135 .”
A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. (2019, background): “For comparison with normal brain cell types, two single-nuclei RNA-seq datasets of normal adult human cerebral cortex were retrieved from public repositories (Habib et al., 2017; Lake et al., 2018).”
A Single-Cell Atlas of In Vivo Mammalian Chromatin Accessibility. (2018, neutral): “Single-Cell Chromatin Accessibility versus Gene Expression A principal goal of the field is to construct a comprehensive atlas of mammalian cell types, which may require integrating atlases measuring different aspects of molecular biology (Lake et al., 2018).”
An atlas of the protein-coding genes in the human, pig, and mouse brain (2020, background): “More recently, brain atlas strategies have been launched on the basis of different approaches: fluorescence-activated cell sorting in mouse (7), antibody-based cell sorting in human (8), single-cell gene expression in mouse (9) and human (10, 11), and covariation analysis of transcriptomics expressi”