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Single-Nucleus RNA-Sequencing Profiling of Mouse Lung. Reduced Dissociation Bias and Improved Rare Cell-Type Detection Compared with Single-Cell RNA Sequencing.

ABSTRACT: 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.

SUBMITTER: Koenitzer JR

PROVIDER: S-EPMC7790136 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Single cell RNA sequencing (scRNA-seq) has rapidly gained popularity for profiling transcriptomes of hundreds to thousands of single cells. This technology has led to the discovery of novel cell types and revealed insights into the development of complex tissues. However, many technical challenges need to be overcome during data generation. Due to minute amounts of starting material, samples undergo extensive amplification, increasing technical variability. A solution for mitigating amplification biases is to include unique molecular identifiers (UMIs), which tag individual molecules. Transcript abundances are then estimated from the number of unique UMIs aligning to a specific gene, with PCR duplicates resulting in copies of the UMI not included in expression estimates. Methods: Here we investigate the effect of gene length bias in scRNA-Seq across a variety of datasets that differ in terms of capture technology, library preparation, cell types and species. Results: We find that scRNA-seq datasets that have been sequenced using a full-length transcript protocol exhibit gene length bias akin to bulk RNA-seq data. Specifically, shorter genes tend to have lower counts and a higher rate of dropout. In contrast, protocols that include UMIs do not exhibit gene length bias, with a mostly uniform rate of dropout across genes of varying length. Across four different scRNA-Seq datasets profiling mouse embryonic stem cells (mESCs), we found the subset of genes that are only detected in the UMI datasets tended to be shorter, while the subset of genes detected only in the full-length datasets tended to be longer. Conclusions: We find that the choice of scRNA-seq protocol influences the detection rate of genes, and that full-length datasets exhibit gene-length bias. In addition, despite clear differences between UMI and full-length transcript data, we illustrate that full-length and UMI data can be combined to reveal the underlying biology influencing expression of mESCs.

Dataset Information

Single-Nucleus RNA-Sequencing Profiling of Mouse Lung. Reduced Dissociation Bias and Improved Rare Cell-Type Detection Compared with Single-Cell RNA Sequencing.

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