High-sensitive spatially resolved T cell receptor sequencing with SPTCR-seq

Understanding the spatial distribution of T cells is pivotal to decrypting immune dysfunction in cancer. Current spatially resolved transcriptomics fall short in directly annotating T cell receptors (TCRs), limiting the comprehension of anti-cancer immunity. We introduce a novel technology, Spatially Resolved T Cell Receptor Sequencing (SPTCR-seq), integrating target enrichment and long-read sequencing for highly sensitive TCR sequencing. This approach yields an on-target rate of ~85%, and via a bespoke computational pipeline, it provides meticulous spatial mapping, error correction, and UMI refinement. SPTCR-seq outperforms PCR-based methods, offering superior reconstruction of the complete TCR architecture, inclusive of V, D, J regions and the vital complementarity-determining region 3 (CDR3). Applying SPTCR-seq, we reveal local T cell diversity, clonal expansion, and transcriptional evolution across spatially distinct niches in glioblastoma, identifying critical involvement of NK and B cells in spatial T cell adaptation. SPTCR-seq, by bridging spatially resolved omics and TCR sequencing, stands as a robust tool for exploring T cell dysfunction in cancers and beyond. Overall design: The Spatial Transcriptomics-based T Cell Receptor sequencing (SPTCR) approach begins by generating full-length cDNA from fresh frozen glioblastoma tissue samples. This cDNA is enriched for T cell receptor (TCR) sequences using custom probes, then undergoes long-read nanopore sequencing for a thorough interpretation of TCR diversity. Following sequencing, computational processing is employed for TCR annotation, error correction, and clonality analysis. SPTCR combines this information with spatial transcriptomics data, mapping TCR sequences to their original spatial context within the tumor. Finally, integration with imaging mass spectrometry metabolomics data allows for a unique exploration of TCR clonal expansion, exhaustion, and metabolism within the tumor microenvironment. The spatial transcriptomics data related to this study has been deposited on DataDryad and is accessible to the public (DOI: 10.5061/dryad.h70rxwdmj). The spatial metabolomics data can be found on the OSF platform using this link: https://osf.io/8qbdz/?view_only=5287d7f6263e4ba680ca8c396aeefeee. Further processed files and detailed steps of our analysis have also been made available on OSF: https://osf.io/65y3t/?view_only=6571f0c374ce4bf294b9cbd10ade62cf.

解析T细胞的空间分布，对于阐明癌症中的免疫功能异常至关重要。当前的空间转录组技术无法直接注释T细胞受体（T cell receptors, TCRs），这限制了人们对抗肿瘤免疫的认知。本研究提出一种全新技术——空间分辨T细胞受体测序（Spatially Resolved T Cell Receptor Sequencing, SPTCR-seq），该技术整合靶向富集与长读长测序技术，实现高灵敏度的TCR测序。该技术的靶向捕获效率约为85%，并通过定制化计算流程，实现精细的空间定位、错误校正以及唯一分子标识符（unique molecular identifier, UMI）的优化处理。SPTCR-seq 性能优于基于PCR的方法，可更完整地重建TCR结构，涵盖V、D、J基因区段以及关键的互补决定区3（complementarity-determining region 3, CDR3）。利用SPTCR-seq技术，本研究在胶质母细胞瘤的不同空间微环境中揭示了局部T细胞多样性、克隆扩增以及转录组演化特征，并发现NK细胞与B细胞在空间T细胞适应性过程中发挥关键作用。SPTCR-seq 通过打通空间分辨组学与TCR测序之间的壁垒，成为研究癌症乃至其他疾病中T细胞功能异常的可靠工具。

实验整体设计：基于空间转录组的T细胞受体测序（SPTCR）技术流程，首先从新鲜冷冻的胶质母细胞瘤组织样本中提取全长cDNA。随后通过定制探针富集T细胞受体（TCR）序列，再利用长读长纳米孔测序技术全面解析T细胞受体多样性。测序完成后，通过计算处理完成TCR注释、错误校正以及克隆性分析。SPTCR技术将上述信息与空间转录组数据相结合，将TCR序列定位至肿瘤内的原始空间位置。最后，结合成像质谱代谢组学数据，可在肿瘤微环境中独特地解析TCR克隆扩增、耗竭以及代谢状态。

本研究相关的空间转录组数据已上传至DataDryad数据库并公开可获取（DOI: 10.5061/dryad.h70rxwdmj）。空间代谢组学数据可通过以下链接在OSF平台获取：https://osf.io/8qbdz/?view_only=5287d7f6263e4ba680ca8c396aeefeee。进一步的处理文件以及分析的详细步骤也已上传至OSF平台：https://osf.io/65y3t/?view_only=6571f0c374ce4bf294b9cbd10ade62cf。