Autor: |
Trygve E Bakken, Rebecca D Hodge, Jeremy A Miller, Zizhen Yao, Thuc Nghi Nguyen, Brian 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 |
Jazyk: |
angličtina |
Rok vydání: |
2018 |
Předmět: |
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Zdroj: |
PLoS ONE, Vol 13, Iss 12, p e0209648 (2018) |
Druh dokumentu: |
article |
ISSN: |
1932-6203 |
DOI: |
10.1371/journal.pone.0209648 |
Popis: |
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. |
Databáze: |
Directory of Open Access Journals |
Externí odkaz: |
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