Townsend Hope A, Rosenberger Kaylee J, Vanderlinden Lauren A, Inamo Jun, Zhang Fan
Biofrontiers Institute, University of Colorado Boulder, Boulder, CO, United States.
Department of Molecular, Cellular, Developmental Biology, University of Colorado Boulder, Boulder, CO, United States.
Front Immunol. 2024 Dec 5;15:1454263. doi: 10.3389/fimmu.2024.1454263. eCollection 2024.
Understanding genetic underpinnings of immune-mediated inflammatory diseases is crucial to improve treatments. Single-cell RNA sequencing (scRNA-seq) identifies cell states expanded in disease, but often overlooks genetic causality due to cost and small genotyping cohorts. Conversely, large genome-wide association studies (GWAS) are commonly accessible.
We present a 3-step robust benchmarking analysis of integrating GWAS and scRNA-seq to identify genetically relevant cell states and genes in inflammatory diseases. First, we applied and compared the results of three recent algorithms, based on pathways (scGWAS), single-cell disease scores (scDRS), or both (scPagwas), according to accuracy/sensitivity and interpretability. While previous studies focused on coarse cell types, we used disease-specific, fine-grained single-cell atlases (183,742 and 228,211 cells) and GWAS data (Ns of 97,173 and 45,975) for rheumatoid arthritis (RA) and ulcerative colitis (UC). Second, given the lack of scRNA-seq for many diseases with GWAS, we further tested the tools' resolution limits by differentiating between similar diseases with only one fine-grained scRNA-seq atlas. Lastly, we provide a novel evaluation of noncoding SNP incorporation methods by testing which enabled the highest sensitivity/accuracy of known cell-state calls.
We first found that single-cell based tools scDRS and scPagwas called superior numbers of supported cell states that were overlooked by scGWAS. While scGWAS and scPagwas were advantageous for gene exploration, scDRS effectively accounted for batch effect and captured cellular heterogeneity of disease-relevance without single-cell genotyping. For noncoding SNP integration, we found a key trade-off between statistical power and confidence with positional (e.g. MAGMA) and non-positional approaches (e.g. chromatin-interaction, eQTL). Even when directly incorporating noncoding SNPs through 5' scRNA-seq measures of regulatory elements, non disease-specific atlases gave misleading results by not containing disease-tissue specific transcriptomic patterns. Despite this criticality of tissue-specific scRNA-seq, we showed that scDRS enabled deconvolution of two similar diseases with a single fine-grained scRNA-seq atlas and separate GWAS. Indeed, we identified supported and novel genetic-phenotype linkages separating RA and ankylosing spondylitis, and UC and crohn's disease. Overall, while noting evolving single-cell technologies, our study provides key findings for integrating expanding fine-grained scRNA-seq, GWAS, and noncoding SNP resources to unravel the complexities of inflammatory diseases.
了解免疫介导的炎症性疾病的遗传基础对于改善治疗方法至关重要。单细胞RNA测序（scRNA-seq）可识别疾病中扩增的细胞状态，但由于成本和基因分型队列较小，常常忽略了遗传因果关系。相反，大型全基因组关联研究（GWAS）通常是可获取的。
我们提出了一种用于整合GWAS和scRNA-seq的三步稳健基准分析，以识别炎症性疾病中与遗传相关的细胞状态和基因。首先，我们根据准确性/敏感性和可解释性，应用并比较了三种基于通路（scGWAS）、单细胞疾病评分（scDRS）或两者结合（scPagwas）的最新算法的结果。虽然先前的研究集中在粗略的细胞类型上，但我们使用了针对类风湿性关节炎（RA）和溃疡性结肠炎（UC）的疾病特异性、细粒度单细胞图谱（分别为183,742个和228,211个细胞）以及GWAS数据（样本量分别为97,173和45,975）。其次，鉴于许多有GWAS数据的疾病缺乏scRNA-seq数据，我们通过仅使用一个细粒度scRNA-seq图谱区分相似疾病，进一步测试了这些工具的分辨率极限。最后，我们通过测试哪种方法能使已知细胞状态调用具有最高的敏感性/准确性，对非编码SNP整合方法进行了新的评估。
我们首先发现，基于单细胞的工具scDRS和scPagwas识别出的得到支持的细胞状态数量比scGWAS更多，而scGWAS忽略了这些细胞状态。虽然scGWAS和scPagwas在基因探索方面具有优势，但scDRS有效地解决了批次效应问题，并且在无需单细胞基因分型的情况下捕获了与疾病相关的细胞异质性。对于非编码SNP整合，我们发现在统计功效与使用定位方法（如MAGMA）和非定位方法（如染色质相互作用、eQTL）的置信度之间存在关键权衡。即使通过调控元件的5' scRNA-seq测量直接纳入非编码SNP，非疾病特异性图谱由于不包含疾病组织特异性转录组模式，也会给出误导性结果。尽管组织特异性scRNA-seq至关重要，但我们表明scDRS能够使用单个细粒度scRNA-seq图谱和单独的GWAS对两种相似疾病进行反卷积分析。实际上，我们确定了区分RA和强直性脊柱炎以及UC和克罗恩病的得到支持的新的遗传-表型联系。总体而言，尽管注意到单细胞技术不断发展，但我们的研究为整合不断扩展的细粒度scRNA-seq、GWAS和非编码SNP资源以揭示炎症性疾病的复杂性提供了关键发现。
