Surwase Sachin S, Zhou Xin Ming M, Luly Kathryn M, Zhu Qingfeng, Talebian Niki, Anders Robert A, Green Jordan J, Tzeng Stephany Y, Sunshine Joel C
Department of Biomedical Engineering, Johns Hopkins University; Translational Tissue Engineering Center, Johns Hopkins University; Johns Hopkins Translational ImmunoEngineering Center, Johns Hopkins University.
Johns Hopkins Translational ImmunoEngineering Center, Johns Hopkins University; Department of Dermatology, Johns Hopkins University.
J Vis Exp. 2025 Jun 6(220). doi: 10.3791/68124.
Presented here is an emerging DNA-barcode-based multiplex imaging technique based on Co-Detection-by-indEXing that analyzes the spatial proteomics of tissue microenvironments. Successful imaging requires a repertoire of well-designed and properly validated antibody panels, but very few currently exist for formalin-fixed paraffin-embedded (FFPE) samples. FFPE offers several advantages over fresh-frozen specimens, such as widespread availability, ease of handling and storage, and the ability to make tissue microarrays (TMAs). Here, we present a protocol to develop an antibody panel for visualizing and analyzing FFPE tissues from a murine melanoma model treated with nanoparticles, which deliver plasmid DNA encoding immunologic signals for tumor microenvironment reprogramming. We also describe an image analysis pipeline using open-source computational tools for annotating tissues, segmenting cells, processing proteomics data, phenotyping cell populations, and quantifying spatial metrics. The protocol offers applications for designing antibody panels in murine FFPE and generating novel insights into the spatial proteomics of complex tissue microenvironments.
本文介绍了一种基于索引共检测的新兴DNA条形码多重成像技术，该技术可分析组织微环境的空间蛋白质组学。成功的成像需要一系列精心设计且经过适当验证的抗体组，但目前针对福尔马林固定石蜡包埋（FFPE）样本的抗体组非常少。与新鲜冷冻样本相比，FFPE具有多种优势，如广泛可得、易于处理和储存，以及能够制作组织微阵列（TMA）。在此，我们展示了一种开发抗体组的方案，用于可视化和分析用纳米颗粒处理的小鼠黑色素瘤模型的FFPE组织，这些纳米颗粒可递送编码用于肿瘤微环境重编程的免疫信号的质粒DNA。我们还描述了一种图像分析流程，使用开源计算工具对组织进行注释、分割细胞、处理蛋白质组学数据、对细胞群体进行表型分析以及量化空间指标。该方案为设计小鼠FFPE中的抗体组以及深入了解复杂组织微环境的空间蛋白质组学提供了应用。
