Perinatology Research Branch, Division of Obstetrics and Maternal-Fetal Medicine, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, U.S. Department of Health and Human Services, Bethesda, MD, and, Detroit, Michigan48201, United States.
Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, Michigan48201, United States.
J Proteome Res. 2022 Nov 4;21(11):2687-2702. doi: 10.1021/acs.jproteome.2c00391. Epub 2022 Sep 26.
The human plasma proteome is underexplored despite its potential value for monitoring health and disease. Herein, using a recently developed aptamer-based platform, we profiled 7288 proteins in 528 plasma samples from 91 normal pregnancies (Gene Expression Omnibus identifier GSE206454). The coefficient of variation was <20% for 93% of analytes (median 7%), and a cross-platform correlation for selected key angiogenic and anti-angiogenic proteins was significant. Gestational age was associated with changes in 953 proteins, including highly modulated placenta- and decidua-specific proteins, and they were enriched in biological processes including regulation of growth, angiogenesis, immunity, and inflammation. The abundance of proteins corresponding to RNAs specific to populations of cells previously described by single-cell RNA-Seq analysis of the placenta was highly modulated throughout gestation. Furthermore, machine learning-based prediction of gestational age and of time from sampling to term delivery compared favorably with transcriptomic models (mean absolute error of 2 weeks). These results suggested that the plasma proteome may provide a non-invasive readout of placental cellular dynamics and serve as a blueprint for investigating obstetrical disease.
尽管人类血浆蛋白质组具有监测健康和疾病的潜在价值,但对其的研究仍不够充分。在此,我们使用一种新开发的基于适配体的平台,对 91 例正常妊娠(基因表达综合数据库标识符 GSE206454)的 528 个血浆样本中的 7288 种蛋白质进行了分析。93%的分析物(中位数为 7%)的变异系数<20%,选定的关键血管生成和抗血管生成蛋白的跨平台相关性具有显著性。妊娠年龄与 953 种蛋白质的变化相关,包括高度调节的胎盘和蜕膜特异性蛋白质,它们富集于包括生长调节、血管生成、免疫和炎症在内的生物学过程。与胎盘单细胞 RNA-Seq 分析之前描述的细胞群体相对应的蛋白质的丰度在整个妊娠期间高度调节。此外,基于机器学习的妊娠年龄和从采样到足月分娩时间的预测与转录组模型相比表现良好(平均绝对误差为 2 周)。这些结果表明,血浆蛋白质组可能为胎盘细胞动力学提供一种非侵入性的检测手段,并为研究产科疾病提供一个蓝图。