Single-nucleus and single-cell transcriptomes compared in matched cortical cell types

作者：Trygve E. Bakken, Rebecca D. Hodge, Jeremy A. Miller, Zizhen Yao, Thuc Nghi Nguyen, Brian D. Aevermann, Eliza Barkan, Darren Bertagnolli, Tamara Casper, Nick Dee, Emma Garren, Jeff Goldy, Lucas T. Graybuck, Matthew Kroll, Roger S. Lasken, Kanan Lathia, Sheana Parry, Christine Rimorin, Richard H. Scheuermann, Nicholas J. Schork, Soraya I. Shehata, Michael Tieu, John W. Phillips, Amy Bernard, Kimberly A. Smith, Hongkui Zeng, Ed S. Lein, Bosiljka Tasic · 发表于：PLoS ONE · 年份：2018 · DOI：10.1371/journal.pone.0209648 · 被引用次数：680 · 研究领域：Single-cell and spatial transcriptomics、Neuroinflammation and Neurodegeneration Mechanisms、RNA Research and Splicing

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.