Transcriptomic profiling of complex tissues by single-nucleus RNA-sequencing (snRNA-seq) presents 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 demonstrate that similarly high cell type resolution of closely related neuronal types can be achieved with both methods if intronic sequences are included in snRNA-seq analysis. More transcripts are detected in individual whole cells (~11,000 genes) than nuclei (~7,000 genes), but the majority of genes have similar detection across cells and nuclei. 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.

Summary:Recent advances in transcriptomic methods have increased experimental throughput to hundreds or thousands of samples, which are processed and visualized in parallel. Here, we present scrattch, short for single-cell RNA-seq analysis for transcriptomic type characterization. scrattch is a package for R designed to make publication-ready visualizations involving thousands of single cells quickly and easily. Though built with single-cell RNA-seq in mind, scrattch can be applied to any dataset that is provided in our straightforward data structure, and is compatible with SQLite and feather for on-disk storage.Availability and Implementation:Free access at [Allen Institute GitHub] and [Bioconductor?].