肿瘤细胞治疗研究
英文原题：Fusion of spatiotemporal and network models to prioritize multiscale effects in single-cell perturbations.
理解细胞如何随时间推移及跨不同组织响应生物学扰动，是识别可为个性化医疗提供依据的调控因子和调控网络的关键。
理解细胞如何随时间和跨组织响应生物学扰动，是识别能为个性化医疗提供依据的调控因子和调控网络的关键。现有方法难以在复杂的多细胞或多组织系统中量化这些动态影响，尤其是在使用具有空间和时间分辨率的单细胞数据时。为解决这一问题，我们提出了 Perturb-STNet，这是一种新颖的框架，利用基于网络的时空模型对驱动发育和疾病过程的扰动所致空间和时间差异表达调控因子（pSTDERs）进行排序。Perturb-STNet 可识别显著的 pSTDERs、估计动态调控网络，并对理解疾病进展和治疗反应至关重要的调控因子、细胞及邻域相互作用提供详细的可视化。我们使用合成数据和上皮-间质转化肺癌数据验证了 Perturb-STNet，其表现优于标准方法。此外，我们将其应用于小鼠黑色素瘤模型的 CODEX 单细胞成像时间序列数据以研究 CD8+ T 细胞治疗的效应，并应用于 MERFISH 空间转录组学时间序列数据以探索结肠炎中的炎症和组织修复。在黑色素瘤中，Perturb-STNet 发现了 KLRG1 和 CD79b 等调控因子，以及介导性配对和三元组合（IgD-H2kb、PDL1-H2kb、NKP46-CD117 和 FOXP3-CD5-CD25），揭示了多种治疗策略，包括通过靶向 PDL1-H2kb 进行检查点抑制以恢复 CD8+ T 细胞功能、通过抑制 FOXP3-CD5-CD25 轴实现 Treg 耗竭，以及通过增强 NKP46-CD117 相互作用激活 NK 细胞。在结肠炎中，Perturb-STNet 鉴定出参与免疫调节、基质重塑和上皮修复的关键基因（Csf1r、Col6a1、Lgr4、Myc 和 Fzd5）及介导基因对（Itga5-Flnc、Cd68-Csf1r、Csf1r-Cx3cl1 和 Tnfrsf1b-Bmp1），提供了潜在的治疗靶点。总体而言，Perturb-STNet 能够在不同疾病背景下稳健地识别单细胞扰动数据中的时空调控网络。
Understanding how cells respond to biological perturbations over time and across tissues is key to identifying regulators and networks that inform personalized medicine. Current methods struggle to quantify these dynamic influences in complex multicellular or multitissue systems, especially using single-cell data with spatial and temporal resolution. To address this, we introduce Perturb-STNet, a novel framework that leverages network-based spatiotemporal models to rank spatial and temporal differentially expressed regulators due to perturbation (pSTDERs) driving developmental and disease processes. Perturb-STNet identifies significant pSTDERs, estimates dynamic regulatory networks, and provides detailed visualizations of regulator, cell, and neighborhood interactions critical for understanding disease progression and therapeutic responses. We validated Perturb-STNet using synthetic data and epithelial-to-mesenchymal transition lung cancer data, showing superior performance compared to standard methods. Additionally, we applied it to CODEX single-cell imaging temporal data from a murine melanoma model to study CD8+ T-cell therapy effects, and to MERFISH spatial transcriptomics temporal data to explore inflammation and tissue repair in colitis. In melanoma, Perturb-STNet uncovered regulators like KLRG1 and CD79b, along with mediating pairs and triples (IgD-H2kb, PDL1-H2kb, NKP46-CD117, and FOXP3-CD5-CD25), revealing therapeutic strategies including checkpoint inhibition by targeting PDL1-H2kb to restore CD8+ T cell function, Treg depletion through inhibition of FOXP3-CD5-CD25 axis, and NK cell activation by enhancing NKP46-CD117 interactions. In colitis, Perturb-STNet identified key genes (Csf1r, Col6a1, Lgr4, Myc, and Fzd5) and mediator pairs (Itga5-Flnc, Cd68-Csf1r, Csf1r-Cx3cl1, and Tnfrsf1b-Bmp1) involved in immune regulation, matrix remodeling, and epithelial repair, offering potential therapeutic targets. Overall, Perturb-STNet enables robust identification of spatiotemporal regulatory networks in single-cell perturbation data across diverse disease contexts.
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