ExRNA Atlas analysis reveals distinct extracellular RNA cargo types present across human biofluids.

To develop a map of cell-cell communication mediated by extracellular RNA, the NIH Extracellular RNA Communication Consortium created the exRNA Atlas resource (https://exrna-atlas.org). The Atlas version 4P1 hosts 5,309 exRNA-seq and exRNA qPCR profiles from 19 studies and a suite of analysis and visualization tools. To analyze variation between profiles, we apply computational deconvolution. The analysis leads to a model with six exRNA cargo types (CT1, CT2, CT3A, CT3B, CT3C, CT4), each detectable in multiple biofluids (serum, plasma, CSF, saliva, urine). Five of the cargo types associate with known vesicular and non-vesicular (lipoprotein and ribonucleoprotein) exRNA carriers. To validate utility of this model, we re-analyze an exercise response study by deconvolution to identify physiologically relevant response pathways that were not detected previously. To enable wide application of this model, as part of the exRNA Atlas resource, we provide tools for deconvolution and analysis of user-provided case-control studies.

为绘制介导细胞间通讯的细胞外RNA的图谱，美国国家卫生研究院（NIH）细胞外RNA通讯联盟构建了细胞外RNA图谱资源（https://exrna-atlas.org）。该图谱的4P1版本汇集了来自19项研究的5,309个细胞外RNA测序（exRNA-seq）和细胞外RNA定量PCR（qPCR）分析结果，并提供了一系列分析与可视化工具。为了分析这些结果之间的差异，我们采用了计算去卷积技术。该分析构建了一个包含六种细胞外RNA载体类型（CT1、CT2、CT3A、CT3B、CT3C、CT4）的模型，这些类型均可在多种生物流体（血清、血浆、脑脊液、唾液、尿液）中检测到。其中五种载体类型与已知的囊泡和非囊泡（脂蛋白和核糖核蛋白）细胞外RNA载体相关联。为了验证该模型的有效性，我们通过去卷积技术重新分析了运动反应研究，以识别先前未检测到的生理相关反应途径。为了使该模型得到广泛的应用，作为细胞外RNA图谱资源的一部分，我们提供了去卷积与分析用户提供的病例对照研究工具。