Parallel Single-Cell Multiomics Analysis of Neonatal Skin Reveals the Transitional Fibroblast States that Restrict Differentiation into Distinct Fates.
Parallel Single-Cell Multiomics Analysis of Neonatal Skin Reveals the Transitional Fibroblast States that Restrict Differentiation into Distinct Fates.
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DOI:
10.1016/j.jid.2021.11.032
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发表时间:
2022-07
影响因子:
6.5
通讯作者:
Driskell, Ryan R.
中科院分区:
文献类型:
--
作者:
Thompson, Sean M.;Phan, Quan M.;Winuthayanon, Sarayut;Driskell, Iwona M.;Driskell, Ryan R.
One of the keys to achieving skin regeneration lies within understanding the heterogeneity of neonatal fibroblasts, which support skin regeneration. However, the molecular underpinnings regulating the cellular states and fates of these cells are not fully understood. To investigate this, we performed a parallel multi-omics analysis by processing neonatal murine skin for single-cell ATAC-sequencing (scATAC-seq) and single-cell RNA-sequencing (scRNA-seq) separately. Our approach revealed that fibroblast clusters could be sorted into papillary and reticular lineages based on transcriptome profiling, as previously published. However, scATAC-seq analysis of neonatal fibroblast lineage markers, such as, Dpp4/CD26, Corin, and Dlk1 along with markers of myofibroblasts, revealed accessible chromatin in all fibroblast populations despite their lineage-specific transcriptome profiles. These results suggests that accessible chromatin does not always translate to gene expression and that many fibroblast lineage markers reflect a fibroblast state, which includes neonatal papillary, reticular, and myofibroblasts. This analysis also provides a possible explanation as to why these marker genes can be promiscuously expressed in different fibroblast populations under different conditions. Our scATAC-seq analysis also revealed that the functional lineage restriction between dermal papilla and adipocyte fates are regulated by distinct chromatin landscapes. Finally, we have developed a webtool for our multi-omics analysis: https://skinregeneration.org/scatacseq-and-scrnaseq-data-from-thompson-et-al-2021-2/.
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