Chen Kevin G, Farley Kathryn O, Lassmann Timo
Precision Health, The Kids Research Institute Australia, 15 Hospital Ave, Nedlands, 6009, WA, Australia.
NAR Genom Bioinform. 2024 Dec 18;6(4):lqae180. doi: 10.1093/nargab/lqae180. eCollection 2024 Dec.
A robust understanding of the cellular mechanisms underlying diseases sets the foundation for the effective design of drugs and other interventions. The wealth of existing single-cell atlases offers the opportunity to uncover high-resolution information on expression patterns across various cell types and time points. To better understand the associations between cell types and diseases, we leveraged previously developed tools to construct a standardized analysis pipeline and systematically explored associations across four single-cell datasets, spanning a range of tissue types, cell types and developmental time periods. We utilized a set of existing tools to identify co-expression modules and temporal patterns per cell type and then investigated these modules for known disease and phenotype enrichments. Our pipeline reveals known and novel putative cell type-disease associations across all investigated datasets. In addition, we found that automatically discovered gene co-expression modules and temporal clusters are enriched for drug targets, suggesting that our analysis could be used to identify novel therapeutic targets.
对疾病背后细胞机制的深入理解为有效设计药物和其他干预措施奠定了基础。现有的丰富单细胞图谱提供了揭示跨各种细胞类型和时间点表达模式的高分辨率信息的机会。为了更好地理解细胞类型与疾病之间的关联，我们利用先前开发的工具构建了一个标准化分析流程，并系统地探索了跨越一系列组织类型、细胞类型和发育时期的四个单细胞数据集之间的关联。我们使用一组现有工具来识别每种细胞类型的共表达模块和时间模式，然后研究这些模块中已知疾病和表型的富集情况。我们的流程揭示了所有研究数据集中已知和新的潜在细胞类型与疾病的关联。此外，我们发现自动发现的基因共表达模块和时间簇富含药物靶点，这表明我们的分析可用于识别新的治疗靶点。
