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单细胞 RNA 测序揭示了细胞异质性,并为小儿睡眠呼吸暂停提供了特征。

Single-cell RNA-seq uncovers cellular heterogeneity and provides a signature for paediatric sleep apnoea.

机构信息

Department of Child Health, Child Health Research Institute, School of Medicine, University of Missouri, Columbia, MO, USA.

Pulmonary, Critical Care and Sleep Medicine, Yale School of Medicine, New Haven, CT, USA.

出版信息

Eur Respir J. 2023 Feb 9;61(2). doi: 10.1183/13993003.01465-2022. Print 2023 Feb.

Abstract

BACKGROUND

Obstructive sleep apnoea (OSA) is a highly prevalent disease and a major cause of systemic inflammation leading to neurocognitive, behavioural, metabolic and cardiovascular dysfunction in children and adults. However, the impact of OSA on the heterogeneity of circulating immune cells remains to be determined.

METHODS

We applied single-cell transcriptomics analysis (scRNA-seq) to identify OSA-induced changes in transcriptional landscape in peripheral blood mononuclear cell (PBMC) composition, which uncovered severity-dependent differences in several cell lineages. Furthermore, a machine-learning approach was used to combine scRNAs-seq cell-specific markers with those differentially expressed in OSA.

RESULTS

scRNA-seq demonstrated OSA-induced heterogeneity in cellular composition and enabled the identification of previously undescribed cell types in PBMCs. We identified a molecular signature consisting of 32 genes, which distinguished OSA patients from various controls with high precision (area under the curve 0.96) and accuracy (93% positive predictive value and 95% negative predictive value) in an independent PBMC bulk RNA expression dataset.

CONCLUSION

OSA deregulates systemic immune function and displays a molecular signature that can be assessed in standard cellular RNA without the need for pre-analytical cell separation, thereby making the assay amenable to application in a molecular diagnostic setting.

摘要

背景

阻塞性睡眠呼吸暂停(OSA)是一种高发疾病,也是导致儿童和成人全身炎症、神经认知、行为、代谢和心血管功能障碍的主要原因。然而,OSA 对循环免疫细胞异质性的影响仍有待确定。

方法

我们应用单细胞转录组分析(scRNA-seq)来识别外周血单个核细胞(PBMC)组成中转录景观中由 OSA 引起的变化,这揭示了几个细胞谱系中与严重程度相关的差异。此外,还使用机器学习方法将 scRNA-seq 细胞特异性标志物与 OSA 中差异表达的标志物结合起来。

结果

scRNA-seq 显示 OSA 诱导了细胞组成的异质性,并能够在 PBMC 中识别以前未描述的细胞类型。我们确定了一个由 32 个基因组成的分子特征,该特征可以区分 OSA 患者与各种对照,在独立的 PBMC 批量 RNA 表达数据集中具有高精度(曲线下面积 0.96)和准确性(93%的阳性预测值和 95%的阴性预测值)。

结论

OSA 会使全身免疫功能失调,并表现出一种分子特征,可通过标准细胞 RNA 进行评估,而无需进行预分析的细胞分离,从而使该检测适用于分子诊断应用。

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