Single Cell Transcriptomics of Traditional Chinese Medicine Bioactives: Mapping Immune, Neural and Hepatic Responses.

Q4 Medicine
Shubham Singh, Sakshi Singh, Sayli Saw, Sanjesh Rathi, Bhawna Sharma
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引用次数: 0

Abstract

Traditional Chinese medicine (TCM) bioactives display wide pharmacological effects, yet bulk transcriptomic studies mask their cell-specific actions. This study applied single-cell RNA sequencing to delineate immune, neural and hepatic responses to chemically characterized TCM constituents. Murine splenic, hippocampal and hepatic tissues were dissociated into single-cell suspensions, processed on a 10× Genomics platform, sequenced at high depth and analyzed with Seurat for clustering, marker annotation, differential expression and pathway enrichment; ligand-receptor interactions were inferred by CellChat, with RT-qPCR and immunofluorescence validation. From over 42,000 high-quality cells, we resolved 15 immune, 12 neural and 10 hepatic subtypes. TCM exposure induced NF-κB modulators in macrophages, neurotrophic gene expression in astrocytes and xenobiotic-metabolism programs in hepatocytes, while pathway analysis highlighted coordinated immune-hepatic detoxification and neuroprotective signaling. Ligand-receptor mapping revealed strengthened IL-10-STAT3 and hepatocyte-Kupffer cross-talk. These findings demonstrate that single-cell transcriptomics exposes previously hidden, cell-type-specific pharmacodynamics of TCM bioactives, laying a mechanistic foundation for precision-oriented herbal pharmacology and rational drug development.

中药生物活性成分的单细胞转录组学:免疫、神经和肝脏反应图谱。
传统中药(TCM)生物活性物质显示出广泛的药理作用,但大量转录组学研究掩盖了它们的细胞特异性作用。本研究应用单细胞RNA测序来描述对化学特征的中药成分的免疫、神经和肝脏反应。小鼠脾、海马和肝脏组织分离成单细胞悬液,在10x Genomics平台上处理,高深度测序,并用Seurat进行聚类、标记注释、差异表达和途径富集分析;通过CellChat通过RT-qPCR和免疫荧光验证推断配体与受体的相互作用。从超过42,000个高质量细胞中,我们分离出15个免疫亚型,12个神经亚型和10个肝脏亚型。中药暴露诱导巨噬细胞中的NF-κB调节因子,星形胶质细胞中的神经营养基因表达和肝细胞中的异种代谢程序,而通路分析强调免疫-肝解毒和神经保护信号的协调。配体-受体定位显示IL-10-STAT3和肝细胞- kupffer互导增强。这些发现表明,单细胞转录组学揭示了以前隐藏的、细胞类型特异性的中药生物活性药效学,为精准的草药药理学和合理的药物开发奠定了机制基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
53
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