Division of Pulmonary and Critical Care, Department of Medicine.
Division of Nephrology, Department of Medicine, and.
Am J Respir Cell Mol Biol. 2020 Dec;63(6):739-747. doi: 10.1165/rcmb.2020-0095MA.
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.
单细胞 RNA 测序（scRNA-Seq）提高了我们对肺生物学的认识，但由于需要新鲜样本、死亡或不完全解离导致的细胞类型丢失以及组织消化过程中诱导的转录应激反应，其应用受到限制。单核 RNA 测序（snRNA-Seq）在其他组织中解决了这些缺陷，但肺组织中不存在该技术的协议。我们提出了一种 snRNA-Seq 协议，并将其结果与 scRNA-Seq 进行了比较。在冰上的裂解缓冲液中制备了两个核悬浮液，同时使用酶和机械解离生成一个细胞悬浮液。使用 10× Genomics 平台处理细胞和细胞核，并通过 Seurat 分析测序数据。共生成了 16110 个单核和 11934 个单细胞转录组。当映射内含子和外显子读子时，snRNA-Seq 和 scRNA-Seq 的基因检测率相当（每个细胞约有 1700 个基因和 3000 个独特分子标识符）。在组合数据中，snRNA-Seq 鉴定出 89%的上皮细胞，而免疫细胞仅鉴定出 22.2%。snRNA-Seq 转录组富含转录因子和信号蛋白，而线粒体和应激反应基因减少。与之前的研究相比，这两种技术都提高了间充质细胞的检测率。使用 snRNA-Seq 数据进行受体-配体映射，定义了肺泡细胞类型之间的稳态信号关系，揭示了上皮细胞、间充质细胞和毛细血管内皮细胞之间的相互作用。snRNA-Seq 可应用于存档的小鼠肺样本，可改善解离偏倚，消除人为的基因表达，并与 scRNA-Seq 相比提供相似的基因检测。
