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

PLOS ONE 2018 644 cites
2018:282019:412020:562021:702022:802023:882024:922025:942026:95
2018 2026
644

Bakken, Trygve E. and Hodge, Rebecca D. and Miller, Jeremy A. and Yao, Zizhen and Nguyen, Thuc Nghi and Aevermann, Brian and Barkan, Eliza and Bertagnolli, Darren and Casper, Tamara and Dee, Nick and Garren, Emma and Goldy, Jeff and Graybuck, Lucas T. and Kroll, Matthew and Lasken, Roger S. and Lathia, Kanan and Parry, Sheana and Rimorin, Christine and Scheuermann, Richard H. and Schork, Nicholas J. and Shehata, Soraya I. and Tieu, Michael and Phillips, John W. and Bernard, Amy and Smith, Kimberly A. and Zeng, Hongkui and Lein, Ed S. and Tasic, Bosiljka

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.

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