Single-cell technologies are emerging as powerful tools for cancer research. These technologies characterize the molecular state of each cell within a tumor, enabling new exploration of tumor heterogeneity, microenvironment cell-type composition, and cell state transitions that affect therapeutic response, particularly in the context of immunotherapy. Analyzing clinical samples has great promise for precision medicine but is technically challenging. Successfully identifying predictors of response requires well-coordinated, multi-disciplinary teams to ensure adequate sample processing for high-quality data generation and computational analysis for data interpretation. Here, we review current approaches to sample processing and computational analysis regarding their application to translational cancer immunotherapy research.

单细胞技术正在成为癌症研究的有力工具。这些技术表征了肿瘤内每个细胞的分子状态，从而能够对影响治疗反应的肿瘤异质性、微环境细胞类型组成和细胞状态转变进行新的探索，特别是在免疫治疗的背景下。分析临床样本对精准医学有很大的希望，但在技术上具有挑战性。成功识别响应预测因子需要协调良好的多学科团队，以确保为高质量数据生成和数据解释的计算分析进行充分的样本处理。在这里，我们回顾了当前的样本处理和计算分析方法，以及它们在转化癌症免疫治疗研究中的应用。