Evidence map›Paper›PMID 39920788›Full record

ArticleStem cell research & therapy2025

Decoding SFRP2 progenitors in sustaining tooth growth at single-cell resolution.

Tianyuan Zhao, Qing Zhong, Zewen Sun, Xiaoyi Yu, Tianmeng Sun, Zhengwen An

Abstract read
In one paragraph

Article in Stem cell research & therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Article
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  4. International journal of molecular sciences · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Tianyuan ZhaoDepartment of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China.
Qing ZhongDepartment of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China.
Zewen SunDepartment of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China.
Xiaoyi YuDepartment of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China.
Tianmeng SunDepartment of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China.
Zhengwen AnDepartment of Oral Biology, School and Hospital of Stomatology, Jilin University, Changchun, China. wenny_an@jlu.edu.cn.ORCID http://orcid.org/0000-0003-0360-7388

Funding

National Key Research and Development Program of China 2022YFC2504200National Natural Science Foundation of China 82270960
6 · The paper itself

Abstract

backgroundSingle-cell transcriptomics has revolutionized tooth biology by uncovering previously unexplored areas. The mouse is a widely used model for studying human tissues and diseases, including dental pulp tissues. While human and mouse molars share many similarities, mouse incisors differ significantly from human teeth due to their continuous growth throughout their lifespan. The application of findings from mouse teeth to human disease remains insufficiently explored.

methodsLeveraging multiple single-cell datasets, we constructed a comprehensive dental pulp cell landscape to delineate tissue similarities and species-specific differences between humans and mice.

resultsWe identified a distinct cell population, Sfrp2

conclusionsOur study reveals a previously unrecognized subset of dental mesenchymal stem cells critical for tooth growth. This distinct subset, evolutionarily conserved between humans and mice, provides valuable insights into translational approaches for dental tissue regeneration and repair.

Indexed as

Membrane ProteinsMesenchymal Stem CellsStem CellsToothAnimalsDental PulpFibroblastsHumansMiceMolarSecreted Frizzled-Related ProteinsSingle-Cell AnalysisTwist-Related Protein 1Wnt Signaling PathwayMembrane ProteinsSecreted Frizzled-Related ProteinsSFRP2 protein, humanSfrp2 protein, mouseTwist-Related Protein 1Dental pulp stem cellsEvolutionSFRP2Single-cell transcriptomicsTissue growth

Identifiers

PMID39920788
PMCPMC11806734

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.