Cavendish Laboratory, Department of Physics, University of Cambridge, J J Thomson Ave, Cambridge, UK; Division of Preclinical Innovation, National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
Laboratory of Cancer Biology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Cell Syst. 2023 Jun 21;14(6):464-481.e7. doi: 10.1016/j.cels.2023.05.003.
Single-cell RNA sequencing (scRNA-seq) is a powerful technique for describing cell states. Identifying the spatial arrangement of these states in tissues remains challenging, with the existing methods requiring niche methodologies and expertise. Here, we describe segmentation by exogenous perfusion (SEEP), a rapid and integrated method to link surface proximity and environment accessibility to transcriptional identity within three-dimensional (3D) disease models. The method utilizes the steady-state diffusion kinetics of a fluorescent dye to establish a gradient along the radial axis of disease models. Classification of sample layers based on dye accessibility enables dissociated and sorted cells to be characterized by transcriptomic and regional identities. Using SEEP, we analyze spheroid, organoid, and in vivo tumor models of high-grade serous ovarian cancer (HGSOC). The results validate long-standing beliefs about the relationship between cell state and position while revealing new concepts regarding how spatially unique microenvironments influence the identity of individual cells within tumors.
单细胞 RNA 测序 (scRNA-seq) 是描述细胞状态的强大技术。确定组织中这些状态的空间排列仍然具有挑战性，现有的方法需要特定的方法和专业知识。在这里，我们描述了通过外源性灌注进行分割 (SEEP)，这是一种快速集成的方法，可将表面接近度和环境可及性与三维 (3D) 疾病模型中的转录身份联系起来。该方法利用荧光染料的稳态扩散动力学沿疾病模型的径向轴建立梯度。基于染料可及性对样本层进行分类，使分离和分选的细胞能够通过转录组和区域身份进行表征。使用 SEEP，我们分析了高级别浆液性卵巢癌 (HGSOC) 的球体、类器官和体内肿瘤模型。结果验证了关于细胞状态和位置之间关系的长期观点，同时揭示了关于空间独特微环境如何影响肿瘤内单个细胞身份的新概念。
