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RNA-sequencing from single nuclei
Rashel V. Grindberg, Joyclyn L. Yee-Greenbaum, Michael J. McConnell, Mark Novotny, Andy L. O'Shaughnessy, Georgina M. Lambert, Marcos J. Araúzo-Bravo, Jun Lee, Max Fishman, Gillian E. Robbins, Xiaoying Lin, Pratap Venepally, Jonathan H. Badger, David W. Galbraith, Fred H. Gage and Roger S. Lasken
Proceedings of the National Academy of Sciences of the United States of America
Vol. 110, No. 49 (December 3, 2013), pp. 19802-19807
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/23758314
Page Count: 6
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It has recently been established that synthesis of double-stranded cDNA can be done from a single cell for use in DNA sequencing. Global gene expression can be quantified from the number of reads mapping to each gene, and mutations and mRNA splicing variants determined from the sequence reads. Here we demonstrate that this method of transcriptomic analysis can be done using the extremely low levels of mRNA in a single nucleus, isolated from a mouse neural progenitor cell line and from dissected hippocampal tissue. This method is characterized by excellent coverage and technical reproducibility. On average, more than 16,000 of the 24,057 mouse protein-coding genes were detected from single nuclei, and the amount of gene-expression variation was similar when measured between single nuclei and single cells. Several major advantages of the method exist: first, nuclei, compared with whole cells, have the advantage of being easily isolated from complex tissues and organs, such as those in the CNS. Second, the method can be widely applied to eukaryotic species, including those of different kingdoms. The method also provides insight into regulatory mechanisms specific to the nucleus. Finally, the method enables dissection of regulatory events at the single-cell level; pooling of 10 nuclei or 10 cells obscures some of the variability measured in transcript levels, implying that single nuclei and cells will be extremely useful in revealing the physiological state and interconnectedness of gene regulation in a manner that avoids the masking inherent to conventional transcriptomics using bulk cells or tissues.
Proceedings of the National Academy of Sciences of the United States of America © 2013 National Academy of Sciences