PO.BCS01.12 · 生物信息与计算
从转录本到细胞：剖析Xenium空间转录组学中的灵敏度、信号污染与特异性
From transcripts to cells: Dissecting sensitivity, signal contamination, and specificity in Xenium spatial transcriptomics
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
理解肿瘤内的细胞状态和相互作用需要精确的空间基因测量。空间转录组学通过在完整组织内直接绘制基因表达提供了这一能力，为研究肿瘤生态系统提供了强大的框架。本研究的目的是系统评估Xenium平台在癌症样本中的性能，并开发一种改进的策略以精细化细胞水平的信号。我们生成了迄今最大的Xenium数据集之一，包含来自27名供体的41个乳腺和肺肿瘤切片，并使用多个靶向panel以及较新的5K panel进行分析。利用配对的snRNA-seq，我们评估了检测特异性、panel性能、分割策略以及转录本溢出（transcript spillover）的普遍程度——转录本溢出是密集混杂的肿瘤生态系统中技术变异的一个主要来源。我们发现，更宽泛的panel内容提高了生物学覆盖度，但降低了单基因灵敏度。我们进一步表明，来自相邻细胞的转录本溢出显著影响信号特异性，产生混合谱，从而掩盖了癌症组织中关键的免疫程序。基于这些洞见，我们开发了SPLIT（分层细胞内转录本的空间纯化，Spatial Purification of Layered Intracellular Transcripts），一种将snRNA-seq与解卷积相结合以校正溢出并恢复更纯净细胞类型特征的方法。SPLIT改善了背景校正，增强了细胞类型分辨率，并揭示了诸如与局部肿瘤-免疫邻近性相关的T细胞耗竭等在原始受污染数据中无法检测到的特征。总之，我们的研究提供了Xenium在癌症背景下的全面性能评估，并引入了一种可扩展且可解释的信号精细化方法，从而能够更可靠地推断肿瘤微环境内的细胞程序和相互作用。该数据集和方法学为设计、基准测试和解读癌症中的空间转录组学研究提供了重要资源。
查看英文原文 English abstract
Understanding cell states and interactions within tumors requires accurate spatial gene measurements. Spatial transcriptomics provides this capability by mapping gene expression directly within intact tissue, offering a powerful framework for studying tumor ecosystems. The purpose of this study was to systematically evaluate the performance of the Xenium platform in cancer samples and to develop an improved strategy for refining cell-level signals.We generated one of the largest Xenium datasets to date, comprising 41 breast and lung tumor sections from 27 donors and profiled using multiple targeted panels as well as the newer 5K panel. Using matched snRNA-seq, we assessed assay specificity, panel performance, segmentation strategies, and the prevalence of transcript spillover-a major source of technical variability in densely intermixed tumor ecosystems.We found that broader panel content increases biological coverage but reduces per-gene sensitivity. We further show that transcript spillover from adjacent cells significantly affects signal specificity, producing mixed profiles that obscure critical immune programs in cancer tissues. Building on these insights, we developed SPLIT (Spatial Purification of Layered Intracellular Transcripts), a method that combines snRNA-seq with deconvolution to correct spillover and recover cleaner cell-type signatures. SPLIT improved background correction, enhanced cell-type resolution, and revealed features such as T-cell exhaustion linked to local tumor-immune proximity that were not detectable using raw, contaminated data.In conclusion, our study provides a comprehensive performance assessment of Xenium in cancer contexts and introduces a scalable and interpretable approach for signal refinement, enabling more reliable inference of cellular programs and interactions within the tumor microenvironment. This dataset and methodology offer an important resource for designing, benchmarking, and interpreting spatial transcriptomics studies in cancer.
利益披露 Disclosure
M. Bilous, None..
D. Buszta, None..
J. Bac, None..
S. Kang, None..
Y. Dong, None..
S. Renaud-Tissot, None..
S. Andre, None..
M. Alexandre-Gaveta, None..
C. Voize, None..
S. Peters, None..
K. Homicsko, None..
R. Gottardo, None.