人类免疫细胞和血小板中的 G 蛋白偶联受体转录本。

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-09-27 DOI:10.1038/s41597-024-03880-2

Arne Hansen, Daniel Martin, Florian Langer, Kathleen Harrison, John Kehrl, Claudia Cicala, Elena Martinelli, Michael J Brownstein, Eva Mezey

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引用次数: 0

摘要

G 蛋白偶联受体（GPCR）由非丰富的 mRNA 编码，因此很难用常用的高度平行方法可靠地检测它们。因此，我们开发并验证了一种灵敏、特异、半定量的方法来检测这些转录本。我们用这种方法分析了白细胞（WBCs）--B、CD4、CD8、NK 和树突状细胞；单核细胞和经粒细胞-巨噬细胞集落刺激因子处理的巨噬细胞样单核细胞--以及血小板中的 GPCR 转录本。所研究的白细胞平均表达 160 个受体 mRNA（范围为 123-206）。血小板表达 69 个。我们发现的受体中，有些（但远非全部）早先已被检测到。我们相信，我们的数据将促进对受体功能的研究，并有助于药物开发。

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G protein coupled receptor transcripts in human immune cells and platelets.

G-protein coupled receptors (GPCRs) are encoded by nonabundant mRNAs, and it is difficult to detect them reliably with the highly parallel methods that are in general use. Because of this, we developed and validated a sensitive, specific, semi-quantitative method for detecting these transcripts. We have used the method to profile GPCR transcripts in white blood cells (WBCs)-B, CD4, CD8, NK, and dendritic cells; monocytes, and macrophage-like monocytes treated with granulocyte-macrophage colony-stimulating factor-as well as platelets. On average, the white cells studied expressed 160 receptor mRNAs (range, 123-206). Platelets made 69. Some, but far from all, of the receptors we found have been detected earlier. We believe our data should stimulate studies of receptor function and contribute to drug development.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.