From Bite to Byte: Dental Structures Resolved at a Single-Cell Resolution.
IF: 8.924
Cited by: 10


The systematic classification of the cells that compose a tissue or an organ is key to understanding how these cells cooperate and interact as a functional unit. Our capacity to detect features that define cell identity has evolved from morphological and chemical analyses, through the use of predefined genetic markers, to unbiased transcriptomic and epigenetic profiling. The innovative technology of single-cell RNA sequencing (scRNA-seq) enables transcriptional profiling of thousands of individual cells. Since its development, scRNA-seq has been extensively applied to numerous organs and tissues in a wide range of animal models and human samples, thereby providing a plethora of fundamental biological insights into their development, homeostasis, and pathology. In this review, we present the findings of 3 recent studies that employed scRNA-seq to unravel the complexity of cellular composition in mammalian teeth. These findings offer an unprecedented catalogue of cell types in the mouse incisor, which is a convenient model system for studying continuous tooth growth. These studies identified novel cell types in the tooth epithelium and mesenchyme, as well as new markers for known cell types. Computational analyses of the data also uncovered the lineage and dynamics of cell states during ameloblast and odontoblast differentiation during both normal homeostasis and injury repair. The transcriptional differences between the mouse incisor and mouse and human molars uncover species-specific as well as shared features in tooth cell composition. Here, we highlight these findings and discuss important similarities and differences between these studies. We also discuss potential future applications of scRNA-seq in dental research and dentistry. Together, these studies demonstrate how the rapidly evolving technology of scRNA-seq can advance the study of tooth development and function and provide putative targets for regenerative approaches.


Spatial Transcriptomics
cell differentiation
sequence analysis
single-cell analysis
stem cells
tooth components

MeSH terms

Cell Differentiation
Gene Expression Profiling
Genetic Markers
Sequence Analysis, RNA
Single-Cell Analysis


Fresia, R
Marangoni, P
Burstyn-Cohen, T
Sharir, A

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