Mapping Gene Expression in Kidney Cancer Using Single-Cell Analysis
Summary
The intricate behavior of tumors is fundamentally reliant on the oncogenic properties of cancer cells, and their multifarious intercellular interactions. To gain deeper insights into these dependencies within the broader microenvironment, the present study undertook an extensive investigation, encompassing over 270,000 single-cell transcriptomes and 100 microdissected whole exomes, derived from 12 patients afflicted with kidney tumors. Prior to validation using spatial transcriptomics, the tissues were sampled from multiple regions, including the tumor core, the tumor-normal interface, normal surrounding tissues, and peripheral blood. The findings of this study indicate that the exhaustion state of CD8+ T cell clonotypes is predominantly dictated by their tissue-type location, and intra-tumoral spatial heterogeneity cannot be fully explicated by somatic heterogeneity. Furthermore, the researchers conducted de novo mutation calling using single-cell RNA-sequencing data, which facilitated the broad inference of the clonality of stromal cells and lineage-trace myeloid cell development. Remarkably, the present investigation reports six conserved meta-programs that effectively differentiate tumor cell function. The researchers identified an epithelial-mesenchymal transition meta-program, which is highly enriched at the tumor-normal interface, and co-localizes with IL1B-expressing macrophages, thereby offering a potential therapeutic target.
Fig.1 Graphical abstract. (Li, 2022)
Research Criteria
The absence of an adequate understanding of the spatial heterogeneity and evolution of Renal Cell Carcinoma (RCC) concerning tumor, immune, and stromal cells, and their interactions in the wider tumor microenvironment (TME) represents a significant gap in current knowledge. To address this limitation, the present study conducted multi-region-based single-cell RNA-sequencing from 12 patients, with tissue samples obtained from multiple sources, including peripheral blood, normal kidney, four different spatial regions of the tumor core, and the tumor-normal interface, alongside exhaustive exome sequencing of laser-capture microdissection (LCM)-derived tumor samples. Furthermore, the researchers validated crucial regional transcriptomic differences at finer resolution through the use of spatial transcriptomics, enabling a comparison of cellular profiles across the tumor-normal interface with the tumor core.
Fig.2 Experimental design. (Li, 2022)
Sample Type
For scRNA-seq: cells from human renal tumor tissue.
For spatial gene expression: tissue section of renal tumor tissue.
Result—Multi-Region-Based Single-Cell Transcriptomic Profiling of RCC
Using scRNA-seq, they captured transcriptomes from approximately 270,000 cells after stringent quality control, which can be broadly categorized into 12 major cell types based on the expression of canonical marker genes. As a result of their singe-cell isolation protocol, T cells were most abundant in their data. Tumor cells were identified within clusters that specifically expressed CA9 and harbored extensive copy-number variations (CNVs) across their genomes, as inferred from scRNA-seq data. Next, they investigated the tissue of origin of the 12 major cell types and observed different tissue distributions. They further conducted sub-clustering analyses for the major cell compartments, leading to the identification of 105 cell subsets with various tissue distribution preferences.
Fig.3 Overall tissue distribution of the major cell types in RCC. (Li, 2022)
Result—Expansion of CD8+ T Cell Clonotypes and the Influence of Tissue Localization on Exhaustion
Following the sub-clustering of the CD8+ T cell compartment, the present investigation identified typical CD8+ T cell clusters that represented diverse T cell functional states, including naive, effector, memory, pre-dysfunction, and dysfunction, based on the expression of canonical marker genes. Furthermore, the researchers discovered two gdT cell clusters, gdT_Vd1 (expressing TRDV1) and gdT_Vd2 (expressing TRDV2), that were not reported in the previous four RCC studies, in addition to the conventional CD8+ T cell clusters. The subsequent pseudo-time trajectory analysis conducted on CD8+ T cells, excluding gdT cells and cycling clusters, revealed that cytotoxicity-related genes were gradually downregulated, while dysfunction-related genes were gradually upregulated. Furthermore, the typical T cell pre-dysfunction-related genes were initially upregulated and then decreased along the pseudo-time trajectory. Moreover, the projection of the top ten expanded TCR clonotypes onto the trajectory led to an observation that individual TCR lineages were usually restricted to a similar phenotypic state, rather than distributed across the entire trajectory.
Fig.4 CD8+ T cell characterization, clonality, exhaustion, and regional enrichment. (Li, 2022)
Result—Spatial Correlation of IL1B-Expressing Macrophages with High EMT-Expressing RCC Cells
Earlier studies revealed that IL1B was specifically expressed by Mac.2, which, in turn, was enriched at the tumor-normal interface. To validate this finding, the present investigation employed spatial transcriptomics at the tumor-normal interface and tumor core. The researchers observed a consistent inverse correlation between signals derived from PT and EMT genes in RCC cells. To formally quantify this correlation across all of their tissue sections, the researchers compared the location of IL1B macrophages with all of the RCC cell subsets. The findings indicated that, in several tissue sections, IL1B-expressing macrophages were most strongly correlated with EMThigh RCC cells.
Fig.5 Cellular interactions in the ccRCC microenvironment. (Li, 2022)
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Reference
- Li, R.Y.; et al. Mapping single-cell transcriptomes in the intra-tumoral and associated territories of kidney cancer. Cancer Cell. 2022, 40(12): 1583-1599.
