Single-nucleus and single-cell transcriptomes compared in matched cortical cell types.
Trygve E BakkenRebecca D HodgeJeremy A MillerZizhen YaoThuc Nghi NguyenBrian AevermannEliza BarkanDarren BertagnolliTamara CasperNick DeeEmma GarrenJeff GoldyLucas T GrayMatthew KrollRoger S LaskenKanan LathiaSheana ParryChristine RimorinRichard H ScheuermannNicholas J SchorkSoraya I ShehataMichael TieuJohn W PhillipsAmy BernardKimberly A SmithHongkui ZengEd S LeinBosiljka TasicPublished in: PloS one (2018)
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.
Keyphrases
- single cell
- rna seq
- high throughput
- gene expression
- genome wide
- stem cells
- spinal cord
- transcription factor
- computed tomography
- healthcare
- magnetic resonance imaging
- mesenchymal stem cells
- magnetic resonance
- quantum dots
- resting state
- genome wide identification
- genome wide analysis
- functional connectivity
- bioinformatics analysis
- sensitive detection
- heat shock
- pi k akt
- ultrasound guided