Centre for Personalized Medicine, Linköping University, Linköping, Sweden.
Department of Internal Medicine, Region Jönköping County, Jönköping, Sweden.
Genome Med. 2019 Jul 30;11(1):47. doi: 10.1186/s13073-019-0657-3.
Genomic medicine has paved the way for identifying biomarkers and therapeutically actionable targets for complex diseases, but is complicated by the involvement of thousands of variably expressed genes across multiple cell types. Single-cell RNA-sequencing study (scRNA-seq) allows the characterization of such complex changes in whole organs.
The study is based on applying network tools to organize and analyze scRNA-seq data from a mouse model of arthritis and human rheumatoid arthritis, in order to find diagnostic biomarkers and therapeutic targets. Diagnostic validation studies were performed using expression profiling data and potential protein biomarkers from prospective clinical studies of 13 diseases. A candidate drug was examined by a treatment study of a mouse model of arthritis, using phenotypic, immunohistochemical, and cellular analyses as read-outs.
We performed the first systematic analysis of pathways, potential biomarkers, and drug targets in scRNA-seq data from a complex disease, starting with inflamed joints and lymph nodes from a mouse model of arthritis. We found the involvement of hundreds of pathways, biomarkers, and drug targets that differed greatly between cell types. Analyses of scRNA-seq and GWAS data from human rheumatoid arthritis (RA) supported a similar dispersion of pathogenic mechanisms in different cell types. Thus, systems-level approaches to prioritize biomarkers and drugs are needed. Here, we present a prioritization strategy that is based on constructing network models of disease-associated cell types and interactions using scRNA-seq data from our mouse model of arthritis, as well as human RA, which we term multicellular disease models (MCDMs). We find that the network centrality of MCDM cell types correlates with the enrichment of genes harboring genetic variants associated with RA and thus could potentially be used to prioritize cell types and genes for diagnostics and therapeutics. We validated this hypothesis in a large-scale study of patients with 13 different autoimmune, allergic, infectious, malignant, endocrine, metabolic, and cardiovascular diseases, as well as a therapeutic study of the mouse arthritis model.
Overall, our results support that our strategy has the potential to help prioritize diagnostic and therapeutic targets in human disease.
基因组医学为识别复杂疾病的生物标志物和治疗性作用靶点铺平了道路，但由于涉及数千个在多种细胞类型中表达的基因，情况变得复杂。单细胞 RNA 测序研究 (scRNA-seq) 允许对整个器官的这种复杂变化进行特征描述。
该研究基于应用网络工具对关节炎小鼠模型和人类类风湿关节炎的 scRNA-seq 数据进行组织和分析，以寻找诊断生物标志物和治疗靶点。使用来自 13 种疾病前瞻性临床研究的表达谱数据和潜在蛋白质生物标志物进行了诊断验证研究。使用关节炎小鼠模型的治疗研究来检查候选药物，将表型、免疫组织化学和细胞分析作为读出。
我们首次对关节炎小鼠模型炎症关节和淋巴结的 scRNA-seq 数据进行了系统性分析，研究了复杂疾病中的途径、潜在生物标志物和药物靶点。我们发现涉及数百种途径、生物标志物和药物靶点，这些途径、生物标志物和药物靶点在细胞类型之间差异很大。对关节炎小鼠模型和人类类风湿关节炎 (RA) 的 scRNA-seq 和 GWAS 数据的分析支持不同细胞类型中存在类似的致病机制分散。因此，需要系统的方法来确定生物标志物和药物的优先级。在这里，我们提出了一种基于使用关节炎小鼠模型和人类 RA 的 scRNA-seq 数据构建疾病相关细胞类型和相互作用的网络模型的优先级策略，我们称之为多细胞疾病模型 (MCDM)。我们发现 MCDM 细胞类型的网络中心性与富含与 RA 相关的遗传变异的基因富集相关，因此可能用于优先考虑诊断和治疗的细胞类型和基因。我们在对 13 种不同的自身免疫、过敏、感染、恶性、内分泌、代谢和心血管疾病患者的大规模研究以及关节炎小鼠模型的治疗研究中验证了这一假设。
总体而言，我们的结果支持我们的策略有可能帮助确定人类疾病的诊断和治疗靶点。
