MSCsDB: a database of single-cell transcriptomic profiles and in-depth comprehensive analyses of human mesenchymal stem cells

Abstract

AbstractMesenchymal stem cells (MSCs) possess multipotent properties that make them promising candidates for immunomodulation and regenerative medicine. However, MSC heterogeneity poses challenges to their research reproducibility and clinical application. The emergence of single-cell RNA sequencing (scRNA-seq) technology has enabled a thorough examination of MSC heterogeneity, underscoring the necessity for a specialized platform to systematically analyze the published datasets derived from MSC scRNA-seq experiments. However, large-scale integration and in-depth exploration of MSC scRNA-seq datasets to comprehensively depict their developmental patterns, relationships, and knowledge are still lacking. Here, we present MSCsDB (http://mscsdb.jflab.ac.cn:18088/index/), an interactive database that has been constructed using high-quality scRNA-seq datasets from all published sources on MSCs. MSCsDB provides a one-stop interactive query for regulon activities, gene ontology enrichment, signature gene visualization and transcription factor regulon analysis. Additionally, the dedicated module within MSCsDB was developed to facilitate the evaluation of MSC quality, thereby promoting the standardization of MSC subtype usage. Notably, MSCsDB enables users to analyze their MSCs scRNA-seq data directly, yielding visually appealing outputs of exceptional quality that can be conveniently downloaded via email. Furthermore, MSCsDB integrates the current comprehensive MSC atlas taxonomy, which includes 470,000 cells and 5 tissues from 26 subjects, as publicly available references. These references provide molecular characterization and phenotypic prediction for annotating MSC subsets. In summary, MSCsDB serves as a user-friendly and contemporary data repository for human MSCs, offering a dedicated platform that enables users to effectively conduct comprehensive analyses on their individual MSCs scRNA-seq data.