bioRxivBioinformaticsPreprint
Cytokine-driven cellular polarization in preeclampsia revealed through the "Dictionary of immune responses"
Sheng Biao Wan, Tianfan Zhou, Yingxuan Ma, Ying CheniD, Chen ZhouiD, Jing Yun PengiD, Lin LiiD, Weijia LuoiD, Wei Gu, Zhiwei LiuiD, Xiaolin HuaiD
Abstract
BackgroundPreeclampsia (PE) is a pregnancy-related hypertensive disorder and a leading cause of maternal and perinatal mortality. Current treatments focus primarily on symptom management, as delivery remains the only definitive cure. This underscores the urgent need for innovative therapeutic strategies. Cytokines released by placental immune cells may contribute to the progression of PE and represent promising therapeutic targets. MethodsWe conducted single-cell sequencing on placental tissues obtained via cesarean section from patients with severe PE and cases of non-infectious preterm birth. Machine learning was applied to identify critical immune cells. Immune Response Enrichment Analysis was performed on these key cells to evaluate their polarization states and cytokine responses associated with PE. Additionally, we investigated cell-cell communications, key genes, and their related functions ResultsWe identified cell type-specific alterations in PE, including an increased proportion of CD4+ T cells polarized towards an IL-1- and IL-1{beta} enriched T4-c state. Altered SPP1 signaling between macrophages and CD4+ T cells was observed, indicating immune response dysregulation. Additionally, machine learning algorithms identified four hub genes, and six small-molecule drugs were predicted to hold therapeutic potential for PE. ConclusionsThis study emphasizes the importance of further understanding CD4+ T cell dynamics in preeclampsia, providing potential insights into the principles of placental immunity and contributing to the exploration of immunotherapeutic strategies
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: drug response / drug discovery
- approach: machine learning (classical)
- Citations
- 0
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