Single Nucleus RNASeq Profiling of Mouse Lung: Reduced Dissociation Bias and Improved Rare Cell Type Detection Compared with Single Cell RNASeq.

Single cell RNA-sequencing (scRNASeq) has advanced our understanding of lung biology, but utility is limited by the need for fresh samples, loss of cell types by death or inadequate dissociation, and transcriptional stress responses induced during tissue digestion. Single nucleus RNASeq (snRNASeq) has addressed these deficiencies in other tissues, but no protocol exists for lung. We present a snRNASeq protocol and compare its results with scRNA-seq. Two nuclear suspensions were prepared in lysis buffer on ice while one cell suspension was generated using enzymatic and mechanical dissociation. Cells and nuclei were processed using the 10x Genomics platform, and sequencing data analyzed by Seurat. 16,110 single nucleus and 11,934 single cell transcriptomes were generated. Gene detection rates were equivalent in snRNASeq and scRNASeq (~1,700 genes and 3,000 UMI per cell) when mapping intronic and exonic reads. In the combined data, 89% of epithelial cells were identified by snRNASeq, versus 22.2% of immune cells. snRNASeq transcriptomes are enriched for transcription factors and signaling proteins, with reduction in mitochondrial and stress response genes. Both techniques improved mesenchymal cell detection over previous studies. Homeostatic signaling relationships among alveolar cell types were defined by receptor-ligand mapping using snRNASeq data, revealing interplay among epithelial, mesenchymal, and capillary endothelial cells. Single nucleus RNASeq can be applied to archival murine lung samples, improves dissociation bias, eliminates artifactual gene expression and provides similar gene detection compared to scRNASeq.