MCMNA：单细胞水平构建帕金森病多尺度转录组网络模型
Multiscale Transcriptomic Network Models of Parkinson’s Disease at the Single Cell Level
研究团队提出基于 metacell 的多尺度网络分析方法 MCMNA，用于系统解析帕金森病黑质 snRNA-seq 数据中的细胞类型特异基因调控结构。该方法通过 metacell 聚合缓解单细胞数据的稀疏性，构建稳健的基因调控网络，并结合基因共表达模块、metacell 差异表达与贝叶斯因果网络，推断候选驱动基因及其层级关系。作者称该多尺度网络模型可为疾病机制研究和治疗靶点优先级排序提供框架。
来源：GigaScience · doi.org
Multiple single nuclei RNA-sequencing (snRNA-seq) studies of the vulnerable brain regions of Parkinson’s Disease (PD) have revealed alterations in brain cell populations and cell type specific transcriptomes. However, a systematic analysis of cell-type–resolved gene regulatory architecture in PD is lacking. Here, we develop an integrative, meta-cell based multiscale network analysis (MCMNA) of snRNA-seq data from the substantia nigra to systematically uncover molecular mechanisms and identify potential therapeutic targets for PD. MCMNA overcomes the inherent sparsity of single cell data by leveraging metacell-based aggregation, enabling construction of robust gene regulatory networks. Gene co-expression network analysis identifies cell-type specific gene modules associated with PD. Integration of meta-cell based differential gene expression and a Bayesian causal network systematically reveals putative driver genes and their hierarchies. Our multiscale network models provide a framework for prioritizing candidate molecular regulators and pathways for further investigation of disease mechanisms and therapeutic targets.