Equivalent high-resolution identification of neuronal cell types with single-nucleus and single-cell RNA-sequencing
Trygve E. Bakken,
Rebecca D Hodge,
Jeremy M Miller,
Thuc N Nguyen,
Lucas T Gray,
Roger S Lasken,
Richard H. Scheuermann,
Nicholas J. Schork,
Soraya I Shehata,
John W. Phillips,
Kimberly A Smith,
Ed S Lein,
Posted 25 Dec 2017
bioRxiv DOI: 10.1101/239749 (published DOI: 10.1371/journal.pone.0209648)
Posted 25 Dec 2017
Transcriptional profiling of complex tissues by RNA-sequencing of single nuclei presents some advantages over whole cell analysis. It enables unbiased cellular coverage, lack of cell isolation-based transcriptional effects, and application to archived frozen specimens. Using a well-matched pair of single-nucleus RNA-seq (snRNA-seq) and single-cell RNA-seq (scRNA-seq) SMART-Seq v4 datasets from mouse visual cortex, we demonstrate that similarly high-resolution clustering of closely related neuronal types can be achieved with both methods if intronic sequences are included in nuclear RNA-seq analysis. More transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), but the majority of genes have similar detection across cells and nuclei. 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.
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