Abstract
Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) affords some advantages over single-cell RNA-sequencing (scRNA-seq). snRNA-seq provides less biased cellular coverage, does not appear to suffer cell isolation-based transcriptional artifacts, and can be applied to archived frozen specimens. We used well-matched snRNA-seq and scRNA-seq datasets from mouse visual cortex to compare cell type detection. Although more transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), we demonstrate that closely related neuronal cell types can be similarly discriminated with both methods if intronic sequences are included in snRNA-seq analysis. We estimate that the nuclear proportion of total cellular mRNA varies from 20% to over 50% for large and small pyramidal neurons, respectively. Together, these results illustrate the high information content of nuclear RNA for characterization of cellular diversity in brain tissues.
Highlights
Cell types in mammalian brain have been defined based on various properties including their morphology, electrophysiology, and gene expression [1,2,3]. scRNA-seq has emerged as a highthroughput method for quantification of the majority of transcripts in thousands of cells [4]
Whole cells were collected as part of a larger study on cortical cell type diversity, which contains a complete description of all Cre-driver lines used for cell collection [6]
Gene expression was quantified as the sum of intronic and exonic reads per gene and was normalized as counts per million (CPM) and log2-transformed
Summary
Cell types in mammalian brain have been defined based on various properties including their morphology, electrophysiology, and gene expression [1,2,3]. scRNA-seq has emerged as a highthroughput method for quantification of the majority of transcripts in thousands of cells [4]. Cell types in mammalian brain have been defined based on various properties including their morphology, electrophysiology, and gene expression [1,2,3]. ScRNA-seq has emerged as a highthroughput method for quantification of the majority of transcripts in thousands of cells [4]. Similarities and differences in gene expression at the single cell level characterized by scRNAseq have revealed diverse cell types in many mouse brain regions, including neocortex [5,6,7], hypothalamus [8], and retina [9,10]. ScRNA-seq profiling does not provide an unbiased survey of neural cell types. Some cell types are more vulnerable to the tissue dissociation process and are underrepresented in the final data set.
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