Establishment of scRNA-seq atlases from mouse and human tooth
Single-cell (sc) omics has become a powerful tool to unravel a tissue’s cell landscape across health and disease. In recent years, sc transcriptomic interrogation has been applied to a variety of tooth tissues of both human and mouse, which has considerably advanced our fundamental understanding of tooth biology. Now, an overarching and integrated bird’s-view of the human and mouse tooth sc transcriptomic landscape would be a powerful multi-faceted tool for dental research, enabling further decipherment of tooth biology and development through constantly progressing state-of-the-art bioinformatic methods as well as the exploration of novel hypothesis-driven research. To this aim, we re-assessed and integrated recently published scRNA-sequencing datasets of different dental tissue types (healthy and diseased) from human and mouse to establish inclusive tooth sc atlases, and applied the consolidated data map to explore its power. For mouse tooth, we identified novel candidate transcriptional regulators of the ameloblast lineage. Regarding human tooth, we provide support for a developmental connection, not advanced before, between specific epithelial compartments. Taken together, we established inclusive mouse and human tooth sc atlases as powerful tools to potentiate innovative research into tooth biology, development and disease. In addition, to facilitate the easy exploration of the resulting tooth atlases by the community, loom files from the mouse and human atlases are provided. These files can be easily uploaded and browsed without bioinformatic experience, using an online application, SCope (available from https://scope.aertslab.org)
Steps to reproduce
Publicly available scRNA-seq datasets used in this study were retrieved from the Gene Expression Omnibus (GEO), FaceBase, ArrayExpress and Mendeley Data databases. For the mouse tooth atlas, the following datasets were used: GSE131204 (GEO), GSE120108 (GEO), FB00001104 (FaceBase), GSE146855 (GEO), GSE146123 (GEO), FB00001105 (FaceBase), GSE167989 (GEO), GSE168450 (GEO), GSE160358 (GEO). For the human healthy and diseased tooth atlases the following datasets were used: GSE146123 (GEO), GSE161267 (GEO), GSE167251 (GEO), https://data.mendeley.com/7ryrp25y6z (Mendeley Data), GSE167251 (GEO), E-MTAB-10596 (ArrayExpress), GSE185222 (GEO), GSE181688 (GEO). All datasets were individually imported in Seurat (v4.0.0) for further downstream analyses, in order to establish the tooth atlases. All code will be made available via GitHub (https://github.com/fhermans27/scRNAseq-tooth-atlas) upon publication. Loom files were established from the resulting mouse (including from the subclustered dental epithelium lineage) and human (including from diseased and integrated healthy and diseased atlases, as well as from subclustered dental epithelium) atlases using Loompy v2.0.17 (Linnarsson lab, www.loompy.org). Loom files can be uploaded to Scope, where they can be interactively explored (Aerts lab, scope.aertslab.org) without prior bioinformatic expertise.