Updated skin transcriptomic atlas depicted by reciprocal contribution of single-nucleus RNA sequencing and single-cell RNA sequencing

Background: Single-cell RNA sequencing (scRNA-seq) has advanced our understanding of skin biology, but its utility is restricted by the requirement of fresh samples, inadequate dissociation-induced cell loss or death, and activation during tissue digestion. Single-nucleus RNA sequencing (snRNA-seq) can use frozen, hard-to-dissociate materials, which might be a promising method to circumvent the limitations of scRNA-seq for the skin tissue.Objective: To profile skin cells using snRNA-seq in parallel with scRNA-seq. Methods: We performed snRNA-seq in parallel with scRNA-seq for the bisected skin sample of one person and integrated previously published scRNA-seq data for analysis. We comparatively analyzed the differences in cell proportions and gene expression between the two methods. The differentiation trajectories of keratinocytes and fibroblasts were analyzed by Slingshot analysis.Results: snRNA-seq was less susceptible to contamination from mitochondrial and ribosomal RNA, and exhibited a greater capacity to detect transcription factors. snRNA-seq identified more spatially and func-tionally relevant keratinocyte clusters that constitute cell trajectories with expected differentiation dy-namics. Novel markers, e.g., LYPD3, EMP2, and CSTB, were revealed for different differentiation stages of keratinocytes, and NFIB and GRHL1 were identified as transcription factors involving in the proliferation and functional differentiation of keratinocytes. Fibroblasts were found in a state of activation in scRNA-seq. And scRNA-seq detected a greater number of immune cells.Conclusions: We generated an updated atlas of the skin transcriptome based on the reciprocal contribution of scRNA-seq and snRNA-seq.(c) 2023 Japanese Society for Investigative Dermatology. Published by Elsevier B.V. All rights reserved.

Automated summary

This study used snRNA-seq and scRNA-seq in parallel to analyze skin cells, and found that snRNA-seq had advantages in avoiding contamination from mitochondrial and ribosomal RNA, and was able to better detect transcription factors. Through Slingshot analysis, snRNA-seq identified more spatially and functionally relevant keratinocyte clusters that exhibited expected differentiation dynamics.