Single-cell RNA sequencing (scRNASeq) has advanced our understanding of lung biology, but its 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 RNA sequencing (snRNASeq) has addressed these deficiencies in other tissues, but no protocol exists for lung tissue. We present a snRNASeq protocol and compare its results with those of scRNASeq. 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 10× Genomics platform, and sequencing data were analyzed by Seurat. A total of 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 unique molecular identifiers 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. snRNASeq can be applied to archival murine lung samples, improves dissociation bias, eliminates artifactual gene expression, and provides similar gene detection compared with scRNASeq.
Keywords: bioinformatics; single cell; transcriptomics.