Egbon Osafu Augustine, Hickey John W, Anchang Benedict
Biostatistics and Computational Biology Branch, National Institute of Environmental Health Sciences, 111 T W Alexander Dr Rall Building, Research Triangle Park, 27709, Durham, NC, United States.
Center for Cancer Research, National Cancer Institute, Bethesda, MD 20892, United States.
Brief Bioinform. 2025 May 1;26(3). doi: 10.1093/bib/bbaf277.
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.
了解细胞如何随时间推移以及在不同组织中对生物扰动做出反应，是识别可为个性化医疗提供信息的调节因子和网络的关键。当前的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，尤其是使用具有空间和时间分辨率的单细胞数据时。为了解决这一问题，我们引入了Perturb-STNet，这是一个新颖的框架，它利用基于网络的时空模型对由于驱动发育和疾病过程的扰动（pSTDERs）而产生的空间和时间差异表达调节因子进行排序。Perturb-STNet可识别显著的pSTDERs，估计动态调节网络，并提供调节因子、细胞和邻域相互作用的详细可视化，这对于理解疾病进展和治疗反应至关重要。我们使用合成数据和上皮-间质转化肺癌数据对Perturb-STNet进行了验证，结果表明其性能优于标准方法。此外，我们将其应用于来自小鼠黑色素瘤模型的CODEX单细胞成像时间数据，以研究CD8+ T细胞疗法的效果，并应用于MERFISH空间转录组学时间数据，以探索结肠炎中的炎症和组织修复。在黑色素瘤中，Perturb-STNet发现了如KLRG1和CD79b等调节因子，以及介导对和三元组（IgD-H2kb、PDL1-H2kb、NKP46-CD117和FOXP3-CD5-CD25），揭示了治疗策略，包括通过靶向PDL1-H2kb恢复CD8+ T细胞功能的检查点抑制、通过抑制FOXP3-CD5-CD25轴清除调节性T细胞以及通过增强NKP46-CD117相互作用激活NK细胞。在结肠炎中，Perturb-STNet识别出参与免疫调节、基质重塑和上皮修复的关键基因（Csf1r、Col6a1、Lgr4、Myc和Fzd5）和介导对（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的单细胞扰动数据中稳健地识别时空调节网络。
