DataSheet_1_Multicohort Analysis Identifies Monocyte Gene Signatures to Accurately Monitor Subset-Specific Changes in Human Diseases.pdf

Monocytes are crucial regulators of inflammation, and are characterized by three distinct subsets in humans, of which classical and non-classical are the most abundant. Different subsets carry out different functions and have been previously associated with multiple inflammatory conditions. Dissecting the contribution of different monocyte subsets to disease is currently limited by samples and cohorts, often resulting in underpowered studies and poor reproducibility. Publicly available transcriptome profiles provide an alternative source of data characterized by high statistical power and real-world heterogeneity. However, most transcriptome datasets profile bulk blood or tissue samples, requiring the use of in silico approaches to quantify changes in cell levels. Here, we integrated 853 publicly available microarray expression profiles of sorted human monocyte subsets from 45 independent studies to identify robust and parsimonious gene expression signatures, consisting of 10 genes specific to each subset. These signatures maintain their accuracy regardless of disease state in an independent cohort profiled by RNA-sequencing and are specific to their respective subset when compared to other immune cells from both myeloid and lymphoid lineages profiled across 6160 transcriptome profiles. Consequently, we show that these signatures can be used to quantify changes in monocyte subsets levels in expression profiles from patients in clinical trials. Finally, we show that proteins encoded by our signature genes can be used in cytometry-based assays to specifically sort monocyte subsets. Our results demonstrate the robustness, versatility, and utility of our computational approach and provide a framework for the discovery of new cellular markers.

单核细胞是炎症反应的关键调控因子，在人类体内可分为三种明确的亚群，其中经典型与非经典型单核细胞为丰度最高的两类。不同的单核细胞亚群行使各异的生物学功能，且既往研究显示其与多种炎症性疾病密切相关。当前，剖析不同单核细胞亚群对疾病的贡献度的研究受限于样本量与队列规模，常导致研究统计效力不足且可重复性较差。公开可用的转录组表达谱（transcriptome profiles）为研究提供了另一数据来源，其特点是统计效力高且兼具真实世界异质性。不过，大多数转录组数据集针对的是混合血液或组织样本，因此需要借助计算机模拟（in silico）方法来量化细胞比例的变化。本研究整合了来自45项独立研究的853份公开的分选人类单核细胞亚群的微阵列表达谱（microarray expression profiles），以筛选得到稳健且精简的基因表达特征（gene expression signatures），每个亚群对应10个特异性基因。这些基因特征在一项通过RNA测序（RNA-sequencing）获得的独立队列中，无论样本所处的疾病状态如何，均能保持良好的准确性；且在与6160份转录组谱中检测到的髓系与淋巴系来源的其他免疫细胞对比时，该特征仅对各自对应的单核细胞亚群具有特异性。因此，本研究证明这些基因特征可用于量化临床试验患者表达谱中的单核细胞亚群比例变化。最后，本研究证实，特征基因所编码的蛋白质可用于基于流式细胞术（cytometry-based assays）的实验，以特异性分选单核细胞亚群。本研究结果验证了所采用的计算方法的稳健性、通用性与实用性，并为新型细胞标志物的发现提供了研究框架。