Engineering an in vivo charging station for CAR-redirected invariant natural killer T cells to enhance cancer therapy

Invariant natural killer T (iNKT) cells are a distinct subset of T lymphocytes that possess unique properties making them highly suitable for addressing the challenges of solid tumor immunotherapy. Unlike conventional T cells, which are restricted by polymorphic major histocompatibility complex (MHC) molecules and recognize peptide antigens, iNKT cells are restricted by the non-polymorphic CD1d molecule and respond to lipid antigens. Chimeric antigen receptor (CAR)-redirected iNKT (CAR-iNKT) cells represent a significant advancement in cancer immunotherapy. However, optimizing sustained activation and long-term persistence of CAR-iNKT cells remains a critical need for effective solid tumor treatment. To address these limitations, we develop the iNKT cell-targeted microparticle recruitment and activation system (iMRAS), a biomimetic platform designed to enhance iNKT cell functionality through localized immunostimulation in vivo. This biomimetic platform is designed to function as an in vivo “charging station” containing chemotactic and activation signals for the recruitment, activation, and expansion of CAR-iNKT cells, leading to more effective tumor killing and longer persistence of CAR-iNKT cells, as demonstrated in the therapy of lymphoma and melanoma. Through its biomimetic design and localized immunostimulatory effects, iMRAS helps overcome the limitations of current therapies for solid tumors, establishing a robust platform for enhancing systemic CAR-iNKT cell-mediated immunotherapy. Overall design: Human CAR-iNKT cell samples were FACS sorted (identified as iNKT TCR+CD3+ cells) and delivered to the UCLA TCGB Core for library construction and scRNA-seq. Cells were quantified using a Cell Countess II automated cell counter (Invitrogen/Thermo Fisher Scientific). A total of 10,000 cells from each experimental group were loaded on the Chromium platform (10X Genomics), and libraries were constructed using the Chromium Next GEM Single Cell 3' Kit v3.1 and the Chromium Next GEM Chip G Single Cell Kit (10X Genomics), according to the manufacturer's instructions. Library quality was assessed using the D1000 ScreenTape on a 4200 TapeStation System (Agilent Technologies). Libraries were sequenced on an Illumina NovaSeq using the NovaSeq S4 Reagent Kit (100 cycles; Illumina). For cell clustering and annotation, the merged digital expression matrix generated by Cellranger was analyzed using an R package Seurat (v.4.0.0) following the official website guidelines. Briefly, after filtering the low-quality cells, the expression matrix was normalized using NormalizeData function, followed by selecting top 2,000 most variable genes across datasets using FindVariableFeatures and SelectIntegrationFeatures functions. To correct the batch effect, FindIntegrrationAnchors and IntegrateData functions were used based on the selected feature genes. The corrected dataset was subjected to standard Seurat workflow for dimension reduction and clustering. In this study, clusters of therapeutic cells were manually merged and annotated based on gene signatures reported from Human Protein Atlas (proteinatlas.org) and previous studies, and clusters of mouse immune cells were merged and annotated based on the immune lineage markers. AddModuleScore was used to calculate module scores of each list of gene signatures, and FeaturePlot function was used to visualize the expression of each signature in the UMAP plots. For gene set enrichment analysis (GSEA), clusterProfiler packages were used to calculate the enrichment scores of each cluster in the signature gene list.