ArticleBioData mining2026
Automatic extraction of mesenchymal stromal cells from single-cell RNA-sequencing data of human dental pulp.
Article in BioData mining, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHuman dental pulp is a complex tissue composed of diverse cell types, including mesenchymal stromal cells (MSCs), which are crucial for tissue repair and regeneration. Although single-cell RNA sequencing (scRNA-seq) data from human dental pulp have accumulated in recent years, MSC identification relies on manual verification of marker genes after clustering, which limits analytical efficiency and scalability. The choice of clustering resolution is determined empirically, potentially leading to under- or over-segmentation of MSCs or their mixing with other cell types. The objective of this study was to establish a computational workflow that automatically extracted uncultured MSCs from human dental pulp scRNA-seq data and to investigate the characteristics of freshly-isolated MSCs in dental pulp using multiple public datasets.
resultsA computational workflow was developed that automatically identified MSC populations given a predefined marker set from a scRNA-seq count matrix and systematically evaluated the performance of the marker set. The MSC marker set consisting of six genes (NT5E, THY1, ENG, FRZB, NOTCH3, MCAM) demonstrated higher cluster separation than the three basic MSC markers (NT5E, THY1, ENG) and consistently detected an MSC population across multiple independent dental pulp scRNA-seq datasets. Pseudo-bulk transcriptomic analysis of MSC populations extracted using this workflow indicated that MSCs in the dental pulp of patients with pulpitis themselves exhibited an inflammatory phenotype, characterized by substantial activation of inflammation-related pathways. Donor aging was associated primarily with reduced mesenchymal identity and metabolic alterations.
conclusionsThis study provides a framework for the automated extraction of dental pulp-derived MSCs and cross-dataset analysis. This framework is broadly applicable to the automated extraction of cell populations, such as dental pulp-derived MSCs, for which the annotation method is not yet well established, and is expected to be widely useful in single-cell analyses.
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