Divide-and-conquer analysis reveals hidden immune cell influencers across the placenta in preeclampsia.

IF 6.1 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Hyojung Paik, Tae Lyun Ko, Myungsun Park, Jong-Eun Park, Daniel Bunis, Marina Sirota, Byung Soo Lee, Hyoung-Sam Heo, Sung Ki Lee
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引用次数: 0

Abstract

Background: Placenta is a focal point of cellular interactions of maternal and fetal immune systems, fostering immune tolerance essential for a successful pregnancy. However, the mechanisms underlying immune dysregulation in disorders such as preeclampsia remain poorly understood.

Methods: We constructed a multilayered atlas of the human placenta by performing single-cell RNA sequencing across distinct placental layers from both normal and severe preeclamptic pregnancies. Rather than focusing on specific regions of placenta, such as the villi and decidua, we explore placental architecture by collecting pair-matched tissues from individual pregnancies to suggest appropriate placental-wide immune atlas. To interpret intercellular communication in those unexplored placental layers, we designed two conceptual models: one based on immune interaction frequency (IIF) and another on immune tolerance influencers (IT). We applied machine learning classifiers to identify gene signatures associated with preeclampsia.

Results: We observed extensive admixture of semi-allogeneic fetal and maternal cells across all placental layers, regardless of disease status. This contradicts the IIF-based model, which is premised on that such intermixing frequency is a pathologic feature specific to preeclampsia. Instead, analysis under the IT framework revealed key molecular determinants of preeclampsia. Notably, classifier-prioritized genes associated with preeclampsia were enriched for ligands and receptors supporting a role for intercellular immune interactions. Among them, the ligand-receptor pair SPP1-CD44 between fetus and maternal immune cells emerged as peculiarly associated influencers of preeclampsia. Spatial image analysis confirmed co-localization of SPP1-CD44 expression within immune cell populations in preeclamptic placental tissue. Our study provides a comprehensive map of the human placenta and identifies disease-specific immune signaling pathways in preeclampsia using the divide and rule approach. The findings highlight SPP1-CD44 as a putative target of immune dysregulation, offering new insight into the cellular basis of maternal-fetal tolerance and its breakdown in pregnancy-related disorders.

Clinical trial number: Not applicable.

分而治之的分析揭示了子痫前期胎盘中隐藏的免疫细胞影响因素。
背景:胎盘是母体和胎儿免疫系统细胞相互作用的焦点,促进免疫耐受对成功妊娠至关重要。然而,在诸如子痫前期等疾病中免疫失调的机制仍然知之甚少。方法:我们通过对正常和严重子痫前期妊娠的不同胎盘层进行单细胞RNA测序,构建了人类胎盘的多层图谱。我们不是关注胎盘的特定区域,如绒毛和蜕膜,而是通过收集来自个体妊娠的配对组织来探索胎盘结构,以建议适当的全胎盘免疫图谱。为了解释这些未被探索的胎盘层中的细胞间通讯,我们设计了两个概念模型:一个基于免疫相互作用频率(IIF),另一个基于免疫耐受影响者(IT)。我们应用机器学习分类器来识别与子痫前期相关的基因特征。结果:我们在所有胎盘层观察到广泛的半同种异体胎儿和母体细胞混合,无论疾病状态如何。这与基于iif的模型相矛盾,该模型的前提是这种混合频率是子痫前期特有的病理特征。相反,在IT框架下的分析揭示了子痫前期的关键分子决定因素。值得注意的是,与先兆子痫相关的分类优先基因富集了支持细胞间免疫相互作用的配体和受体。其中,胎儿和母体免疫细胞之间的配体受体对SPP1-CD44是子痫前期的特殊相关影响因素。空间图像分析证实了SPP1-CD44在子痫前期胎盘组织免疫细胞群中的共定位表达。我们的研究提供了人类胎盘的全面地图,并使用分而治之方法确定了子痫前期疾病特异性免疫信号通路。这些发现强调SPP1-CD44可能是免疫失调的靶点,为母胎耐受性的细胞基础及其在妊娠相关疾病中的分解提供了新的见解。临床试验号:不适用。
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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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