RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Single cell RNAseq signatures refined with combiroc enhance identification of NK cells in blood and solid tissues.
Single cell RNAseq signatures refined with combiroc enhance identification of NK cells in blood and solid tissues.
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细胞毒性CD8 T淋巴细胞(CTLs)和自然杀伤(NK)细胞尽管通过不同的靶标识别机制,但具有控制感染以及检测和清除肿瘤细胞的共同目标。虽然CTLs属于适应性免疫系统,而NK细胞是固有淋巴样细胞,但它们在分子表型上经常表现出相当大的重叠。这种重叠,如同细胞生物学中的许多其他重叠一样,在单细胞转录组学的背景下对区分细胞构成了挑战。在先前ROC驱动的组合方法基础上,我们使用combiroc R包开发了一个新的单细胞RNA-seq计算框架,并在本研究中表明,它可以在外周血单核细胞数据集中识别NK细胞的非经典标志物组合。这些组合标志物与人类蛋白质图谱一致,我们通过流式细胞术染色和功能实验对其进行了验证。使用combiroc选择的标志物在血液和实体肿瘤组织中均表现出卓越的NK细胞识别区分能力。除此发现外,我们还表明我们的方法极大地优化了标准差异表达特征:它降低了维度,同时提高了可解释性和向诊断应用的可迁移性,为在复杂转录组数据集中精炼免疫细胞身份提供了实用解决方案。
Cytotoxic CD8 T lymphocytes (CTLs) and natural killer (NK) cells share the common objective of controlling infections and detecting and removing tumor cells, albeit through distinct target recognition mechanisms. Although CTLs belong to the adaptive immune system and NK cells are innate lymphoid cells, they frequently exhibit considerable overlap in their molecular phenotypes. This overlap, as with many others in cell biology, poses challenges for distinguishing cells in the context of single cell transcriptomics. Building on a previous ROC-driven combinatorial approach, we developed a new computational framework for single-cell RNA-seq with the combiroc R package, and in this study we showed that it can identify non-canonical marker combinations for NK cells in Peripheral Blood Mononuclear Cell datasets.
These combinatorial markers were in line with the Human Protein Atlas and we validated them through cytometry staining and functional assays. Markers selected with combiroc exhibit exceptional discriminatory power for identifying NK cells, both in blood and solid tumoral tissues.
Besides this finding, we showed that our approach vastly optimizes standard differential expression signatures: it reduces dimensionality while improving interpretability and transferability to diagnostic applications, offering a practical solution for refining immune cell identities in complex transcriptomic datasets.
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