Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.

Genetic control of the dynamic transcriptional response to immune stimuli and glucocorticoids at single-cell resolution.
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单细胞分辨率下对免疫刺激和糖皮质激素的动态转录反应的遗传控制。

DOI:
10.1101/gr.276765.122
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发表时间:
2023-06
期刊:
影响因子:
7
通讯作者:
Pique-Regi, Roger
Pique-Regi, Roger
中科院分区:
生物学1区
文献类型:
--
作者:
Resztak, Justyna A.;Wei, Julong;Zilioli, Samuele;Sendler, Edward;Alazizi, Adnan;Mair-Meijers, Henriette E.;Wu, Peijun;Wen, Xiaoquan;Slatcher, Richard B.;Zhou, Xiang;Luca, Francesca;Pique-Regi, Roger

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合成糖皮质激素,如地塞米松,已被用作许多免疫疾病的治疗方法,如哮喘和最近的严重COVID-19。单细胞数据可以捕获关于转录变异性和动力学的更细粒度的细节,以更好地了解药物反应中个体间变异的分子基础。在这里,我们使用单细胞RNA-seq研究了96名非裔美国儿童活化外周血单核细胞中糖皮质激素的转录反应动力学。我们使用新的统计方法来计算基因表达变异性的平均独立测量值和转录响应伪时间的测量值。使用这些方法,我们发现糖皮质激素逆转免疫刺激对基因表达均值和变异性的影响。我们的新措施的基因表达响应动力学,对角线性判别分析的基础上,分离个别细胞的响应状态的基础上,他们的转录谱,并允许我们确定不同的动态模式的基因表达沿着的响应伪时间。我们确定了调节基因表达平均值和变异性的遗传变异,包括治疗特异性效应,并显示了基因表达反应的转录动力学的广泛遗传调节。
Synthetic glucocorticoids, such as dexamethasone, have been used as a treatment for many immune conditions, such as asthma and, more recently, severe COVID-19. Single-cell data can capture more fine-grained details on transcriptional variability and dynamics to gain a better understanding of the molecular underpinnings of inter-individual variation in drug response. Here, we used single-cell RNA-seq to study the dynamics of the transcriptional response to glucocorticoids in activated peripheral blood mononuclear cells from 96 African American children. We used novel statistical approaches to calculate a mean-independent measure of gene expression variability and a measure of transcriptional response pseudotime. Using these approaches, we showed that glucocorticoids reverse the effects of immune stimulation on both gene expression mean and variability. Our novel measure of gene expression response dynamics, based on the diagonal linear discriminant analysis, separated individual cells by response status on the basis of their transcriptional profiles and allowed us to identify different dynamic patterns of gene expression along the response pseudotime. We identified genetic variants regulating gene expression mean and variability, including treatment-specific effects, and showed widespread genetic regulation of the transcriptional dynamics of the gene expression response.
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