Researchers assembled a large single-cell atlas of tooth development, created a new trajectory-integration algorithm (TrajDTW), and combined tooth and bone atlases to identify conserved gene programs driving mineralization across tissues and species.
TrajDTW, a new algorithm, finds genes with matching trajectory dynamics across large, heterogeneous single-cell datasets to reveal robust developmental signals.
Integration of tooth and bone atlases uncovered shared molecular pathways for mineralization (bone, enamel, dentin) and cross-species conserved mesenchymal/odontoblast programs.
All processed data, interactive annotation, and code are publicly available for reuse: Tooth Atlas (Figshare), interactive viewer, TrajDTW GitHub, and full pipeline documentation.
The combined single-cell analysis identified a common differentiation architecture for mineralization, meaning that despite tissue-specific outcomes (bone vs enamel vs dentin), cells follow shared trajectory patterns and activate overlapping gene programs during mineral deposition. The cross-species work pointed to conserved mesenchymal/odontoblast programs between human and mouse, supporting the use of mouse models to study fundamental aspects of mineralization.
This work supplies a large, reusable tooth development atlas and an algorithmic approach to compare developmental trajectories across many datasets. Researchers studying skeletal and dental development, modelers of differentiation, and groups building regenerative strategies can use the atlas, TrajDTW, and code to investigate conserved gene programs, validate candidate regulatory genes, or design experiments that target shared mineralization machinery across tissues and species.
Researchers can apply TrajDTW to other organ atlases to test for shared differentiation architectures. Experimental follow-up can test candidate genes and pathways identified in the shared mineralization trajectory for roles in mineral deposition, tissue repair, or engineered tissue formation.
A large tooth single-cell atlas plus a trajectory-alignment algorithm reveal a shared molecular architecture for hard-tissue mineralization and conserved programs across species. The team made data and tools available so others can explore and build on these maps.
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