76 Spatial mapping of T cell receptors and transcriptomes in renal cell carcinoma following immune checkpoint inhibitor therapy (2021)

<h3>Background</h3> Because conventional single-cell strategies rely on dissociating tissues into suspensions that lose spatial context,<sup>1</sup> we developed Slide-TCR-seq to sequence both whole transcriptomes and TCRs with 10µm-spatial resolution, &amp; applied it to renal cell carcinoma (ccRCC) treated with immune checkpoint inhibitors (ICI). <h3>Methods</h3> Slide-TCR-seq combines Slide-seqV2<sup>2 3</sup>—a 10µm-resolution spatial approach utilizing mRNA capture and DNA-barcoded beads—with sensitive targeted capture of TCR sequences (rhTCRseq,<sup>4</sup> previously developed by our group), thereby enabling amplification of segments extending from upstream of CDR3 to the 3’-end of the TCR transcript (figure 1A). We tested Slide-TCR-seq first on OT-I murine spleen and then applied this methodology to 3 patients‘ pre-αPD-1 ccRCC samples<sup>5</sup> and a post-αPD-1 metastasis to investigate the spatial, functional, and clonotypic organization of T cells in relationship to tumor using RCTD,<sup>6</sup> spatial enrichment, and spatial expression analyses. <h3>Results</h3> Using Slide-TCR-seq, we first recapitulated native spatial structure of OT-I mouse spleen (figure 1B-G). TCRα/β CDR3 sequences were detected on 37.1% of beads with Trac/Trbc2 constant sequences—comparable to other scTCRseq methods. Because the clonal and spatial context of TILs have been increasingly implicated in immunotherapy resistance, we used Slide-TCR-seq to analyze a lung ccRCC metastasis following αPD-1 therapy. We employed unsupervised clustering to delineate the tumor, intervening boundary, and lung compartments, and RCTD analyses to spatially map individual cell types; together recapitulating the architecture observed in corresponding histology (figure 2). We identified 1,132 unique clonotypes, with distinct spatial distributions spanning the tissue compartments. Eight clonotypes were significantly enriched in tumor, whereas 5 were depleted (all p&lt;0.05) (figure 3). We then analyzed the relationships between the T cells’ clonotype, gene expression, and tumor infiltration depth among clonotypes. Using a T-cell geneset associated with poor response to ICI,<sup>7</sup> we dichotomized T-clonotype beads by geneset expression, and found spatial segregation of this geneset’s expression both within and across clonotypes (figure 4). TCR-4—the most significantly tumor-enriched clonotype—and TCR-2 displayed high expression of the poor ICI response geneset near the tumor’s edge, but low expression deeper in the tumor compartment; indicating that there are transcriptionally distinct subpopulations of these clonotypes, which depended on the extent of their tumor infiltration. <h3>Conclusions</h3> Slide-TCR-seq effectively integrates spatial transcriptomics with TCR detection at 10µm resolution, thereby relating T cells’ clonality and gene expression to their spatial organization in tumors. Our findings suggest that a clonotype’s T cells may exhibit mixed responses to ICI depending on their spatial localization. The heterogeneity among clonotypes, in both gene expression and organization, underscores the importance of studying the TCR repertoire with spatial resolution. <h3>Acknowledgements</h3> We are grateful to Irving A. Barrera-Lopez, Zoe N. 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