Data availability
All data are available within the Article, Supplementary Information or Source Data file. The corresponding authors (L.Y. and S.L.) will make raw data and step-by-step protocols available upon request. All data generated in this study, including source data for figures, are available via figshare at (ref. 91). Source data are provided with this paper.
Code availability
The scRNA-seq data generated in this study have been deposited in the public repository Gene Expression Omnibus Database under accession GSE291443.
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Acknowledgements
We thank the University of California Los Angeles (UCLA) animal facility for providing animal support; the UCLA Translational Pathology Core Laboratory (TPCL) for providing histology support; the UCLA Technology Centre for Genomics and Bioinformatics (TCGB) facility for providing RNA-seq services; the UCLA CFAR Virology Core for providing human cells; and the UCLA BSCRC Flow Cytometry Core Facility for cell sorting support. This work was supported by a seed grant from UCLA Jonsson Comprehensive Cancer Center (to S.L. and L.Y.), an innovation award from UCLA Technology Development Group (to S.L. and L.Y.), a Partnering Opportunity for Discovery Stage Research Projects Award and a Partnering Opportunity for Translational Research Projects Award from the California Institute for Regenerative Medicine (CIRM) (DISC2-11157 and TRAN1-12250, to L.Y.), a CIRM Discovery grant (DISC2-14169 to S.L.), an NIH grant (R01GM143485 to S.L.), a Department of Defense CDMRP PRCRP Impact Award (CA200456 to L.Y.), a UCLA BSCRC Innovation Award (to L.Y.), and an Ablon Scholars Award (to L.Y.). L.Y. is a member of the UCLA Parker Institute for Cancer Immunotherapy. Y.-R.L. is a postdoctoral fellow supported by a UCLA MIMG M. John Pickett Post-Doctoral Fellow Award, a CIRM-BSCRC Postdoctoral Fellowship, a UCLA Sydney Finegold Postdoctoral Award, a UCLA Chancellor’s Award for Postdoctoral Research, and a Goodman–Luskin Microbiome Center Collaborative Research Fellowship award. E.Z. is a postdoctoral fellow supported by a T32 UCLA/Caltech Integrated Cardiometabolic Medicine for Bioengineers fellowship (NIH/NHLBI T32HL144449). We also thank the NIH Tetramer Facility for providing the tetramers.
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Competing interests
L.Y. is a scientific advisor to AlzChem and Amberstone Biosciences, and a co-founder, stockholder and advisory board member of Appia Bio. None of the declared companies contributed to or directed any of the research reported in this Article. Y.-R.L., H.N., Z. Liu, L.Y. and S.L. filed a patent application related to this work through UCLA Technology Development Group (UCLA TDG), identified as UCLA CASE NO. 2025-209-1, titled ‘Recruitment and activation of car-redirected invariant natural killer T cells’. The remaining authors declare no competing interests.
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Extended data
Extended Data Fig. 1 Mechanical characterization of iMRAS.
a, AFM indentation technique was used to characterize the mechanical properties of human APC, iMRAS, bare iMRAS, and iMRAS without nanoparticles. Representative stress relaxation curves of each sample are shown. b, Swelling of iMRAS with different components and PDMS elastomer (as a negative control) after 1, 7, and 15 days of DMEM immersion, quantified by the ratio of the volume of gels at day 1, day 7 or day 15 to their volumes before immersion (n = 6). Data are presented as the mean ± s.e.m. NS, not significant, by one-way ANOVA (b). Source data are provided as a Source Data file.
Extended Data Fig. 2 Fabrication and characterization of PLGA nanoparticles for iMRAS.
a, Schematic representation of PLGA nanoparticles fabrication steps using solvent evaporation and ultrasonic homogenization. Created in BioRender. Liu, Z. (2025) https://BioRender.com/jnuo8z1b, PLGA nanoparticles size distribution at different ultrasonic homogenization amplitude at 50%, 25%, and 10% (frequency fixed at 20 kHz) (n = 3). c, Size distribution of PLGA nanoparticles prepared with varying PLGA concentrations at 1, 5, 10, 20, and 40 mg/ml (n = 3). d, Comparison of nanoparticle size when different organic solvents dichloromethane (DCM), acetonitrile (MeCN), and tetrahydrofuran (THF) are used (n = 3). e, Encapsulation efficiency of αGC under different PLGA-to-αGC mass ratios. Based on optimal encapsulation efficiency and drug release kinetics, a ratio of 20:1 was selected for subsequent experiments (n = 4). f, Encapsulation efficiency of PLGA nanoparticles at different molecular weight (n = 4). g, Encapsulation efficiency and cumulative release profiles of αGC from PLGA nanoparticles with different lactic acid (LA) to glycolic acid (GA) ratios (n = 4). Data are presented as the mean ± s.e.m. NS, not significant; **P < 0.01, ***P < 0.001, by one-way ANOVA (d–g). In e, the center line represents the median, the box shows the 25% and 75% percentiles, and the whiskers extend to the minimum and maximum values. Source data are provided as a Source Data file.
Extended Data Fig. 3 Gene Set Enrichment Analysis (GSEA) of CAR-iNKT cell samples.
GSEA plots show the enrichment of gene signatures of proliferating, naïve, effector, cytotoxic, memory, and exhausted cells in the indicated cell clusters. Normal p value calculated as two-tailed t-test.
Extended Data Fig. 4 Longitudinal immune profiling of NKT, NK, monocyte, B and T cells following iMRAS treatment in C57BL/6 mice.
Middle-term (15 days) and long-term (45 days) effects of IMRAS loaded with or without αGC on leukocyte populations in blood (a), liver (b) and spleen (c) of C57BL/6 mice (n = 4). Data are presented as the mean ± s.e.m. NS, not significant; **P < 0.01, ***P < 0.001, ****P < 0.0001, by one-way ANOVA. Source data are provided as a Source Data file.
Extended Data Fig. 5 CAR-iNKT cell infiltration into A375-CD19 solid tumors.
a, Immunohistochemical (IHC) staining shows infiltration of human CD3⁺ CAR-iNKT or CAR-T cells into the A375-CD19 solid tumors. The tumor border and core are delineated, and the arrow indicates the direction of CAR-iNKT or CAR-T cell infiltration from the periphery toward the tumor core. The experimental design is illustrated in Fig. 5f. Tumor tissues were harvested on Day 60, at which point the tumor size was sufficient for analysis. b, Quantification of a (n = 5 from five experimental mice). Images were collected from five mice in one experiment, and the in vivo studies were independently repeated three times. Source data are provided as a Source Data file.
Extended Data Fig. 6 Superior antitumor efficacy of CAR-iNKT cells with iMRAS compared to conventional CAR-T cell in blood cancer and solid tumor xenograft models.
a-c, Comparing the in vivo antitumor capacity between CAR-iNKT + iMRAS and conventional CAR-T cells using an A375 human melanoma xenograft mouse model. a, Experimental design. b, Tumor volume measurements over time (n = 5). c, Tumor weight measurements at Day 30 (n = 5). Note that the data for the Vehicle and CAR-iNKT + iMRAS groups were also presented in the main Fig. 5e-g. d-g, Comparing the in vivo antitumor capacity between CAR-iNKT + iMRAS and conventional CAR-T cells using a Raji human lymphoma xenograft mouse model. d, Experimental design. e, BLI images showing the presence of tumor cells in CAR-T cell-treated experimental mice over time. f, Quantification of e (n = 5). g, Kaplan-Meier survival curves of experimental mice over time (n = 5). Note that the data for the Vehicle and CAR-iNKT + iMRAS groups were also presented in the main Fig. 6b-f. a and d, Created in BioRender. Liu, Z. (2025) https://BioRender.com/jnuo8z1 Data are presented as the mean ± s.e.m. NS, not significant; *P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001, by one-way ANOVA (c), or by two-way ANOVA (b and f). In all figures, n represents the number of individual mice. Source data are provided as a Source Data file.
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Li, YR., Nan, H., Liu, Z. et al. Engineering an in vivo charging station for CAR-redirected invariant natural killer T cells to enhance cancer therapy. Nat. Biomed. Eng (2026). https://doi.org/10.1038/s41551-026-01629-3
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DOI: https://doi.org/10.1038/s41551-026-01629-3
