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.