CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Systematic mapping of emergent transcriptional states in interacting single-cell dyads by Cell-Cell-seq.
Systematic mapping of emergent transcriptional states in interacting single-cell dyads by Cell-Cell-seq.
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细胞间相互作用会驱动基因表达快速且异质性变化,但大多数转录组方法要么解离细胞,丢失配对身份和相互作用时序;要么根据配体-受体共表达间接推断细胞通讯。本文介绍Cell-Cell-seq,一种可扩展的工作流程,能够以单细胞转录组分辨率分析定义明确的细胞对(“二联体”)。Cell-Cell-seq使用带腔室的水凝胶微粒(Nanovials)限制两个细胞,使接触起始时间同步,在操作和分选期间保护脆弱的细胞结合体,并可直接连接液滴式RNA测序。研究以抗原匹配的前列腺肿瘤细胞和工程化T细胞为模型,捕获数千个肿瘤-T细胞二联体,揭示不同相互作用间广泛的功能和转录异质性。二联体分析揭示了标准孔板共培养中被掩盖的短暂活化程序,这与整体检测中不同步的细胞接触相符。为区分相互作用诱导的程序和二联体转录组的复合特征,研究开发了一种伪混合框架,可生成计算机模拟伪二联体,构建“无相互作用”条件下的经验零分布,从而稳健地统计鉴定新出现的基因,并按相互作用伙伴解析反应来源。二联体分辨分析进一步揭示了协调的跨细胞程序,包括符合双向旁分泌信号的耦合趋化因子表达,以及肿瘤免疫调节程序与T细胞活化之间的反向耦合。
最后,研究介绍ccRepair,用于校正混合转录组中的组成稀释,从而提高可解释性,同时保留真实的跨细胞协调信号。总之,Cell-Cell-seq提供了一个通用平台,可解析免疫突触生物学并绘制异质细胞群中的相互作用依赖程序,可用于分析肿瘤-免疫通讯及功能性筛选免疫疗法。
Cell-cell interactions drive rapid and heterogeneous changes in gene expression, yet most transcriptomic methods either dissociate cells, losing pair identity and interaction timing, or infer communication indirectly from ligand-receptor co-expression.
Here we present Cell-Cell-seq, a scalable workflow for profiling defined cell pairs ("dyads") with single-cell transcriptomic resolution. Cell-Cell-seq uses cavity-containing hydrogel microparticles (Nanovials) to confine two cells, synchronize contact onset, protect fragile conjugates during handling and sorting, and interface directly with droplet-based RNA sequencing. Using antigen-matched prostate tumor cells and engineered T cells as a model system, Cell-Cell-seq captured thousands of tumor-T cell dyads and revealed broad functional and transcriptional heterogeneity across interactions.
Dyads unmasked transient activation programs that were obscured in standard well-plate co-culture, consistent with asynchronous contact in bulk assays. To distinguish interaction-induced programs from the composite nature of dyad transcriptomes, we developed a pseudo-mixing framework that generates in silico pseudo-dyads to construct an empirical null distribution under "no interaction," enabling statistically robust identification of emergent genes and partner-resolved attribution of responses.
Dyad-resolved analysis further revealed coordinated cross-cell programs, including coupled chemokine expression consistent with bidirectional paracrine signaling and inverse coupling between tumor immunoregulatory programs and T cell activation.
Finally, we introduce ccRepair to correct compositional dilution in mixed transcriptomes, improving interpretability while preserving genuine cross-cell coordination.
Together, Cell-Cell-seq provides a generalizable platform for dissecting immune synapse biology and mapping interaction-dependent programs across heterogeneous cell populations, with applications in profiling tumor-immune communication and functionally screening immunotherapies.
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