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  • In vivo engineering of CAR natural killer cells for immunotherapy using ligand-directed lipid nanoparticles

    In vivo engineering of CAR natural killer cells for immunotherapy using ligand-directed lipid nanoparticles

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    All data that support the findings of this study are provided within the paper and its Supplementary Information.

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    References

    1. Irvine, D. J., Maus, M. V., Mooney, D. J. & Wong, W. W. The future of engineered immune cell therapies. Science 378, 853–858 (2022).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    2. Li, Y. R., Zhu, Y., Halladay, T. & Yang, L. In vivo CAR engineering for immunotherapy. Nat. Rev. Immunol. 25, 725–744 (2025).

      Article  CAS  PubMed  Google Scholar 

    3. June, C. H., O’Connor, R. S., Kawalekar, O. U., Ghassemi, S. & Milone, M. C. CAR T cell immunotherapy for human cancer. Science 359, 1361–1365 (2018).

      Article  CAS  PubMed  Google Scholar 

    4. Maude, S. L. et al. Chimeric antigen receptor T cells for sustained remissions in leukemia. N. Engl. J. Med. 371, 1507–1517 (2014).

      Article  PubMed  PubMed Central  Google Scholar 

    5. Wang, V., Gauthier, M., Decot, V., Reppel, L. & Bensoussan, D. Systematic review on CAR-T cell clinical trials up to 2022: academic center input. Cancers (Basel) 15, 1003 (2023).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    6. Brudno, J. N., Maus, M. V. & Hinrichs, C. S. CAR T cells and T-cell therapies for cancer: a translational science review. JAMA 332, 1924–1935 (2024).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    7. Biederstadt, A. & Rezvani, K. Engineered natural killer cells for cancer therapy. Cancer Cell 43, 1987–2013 (2025).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    8. Liu, E. et al. Use of CAR-transduced natural killer cells in CD19-positive lymphoid tumors. N. Engl. J. Med. 382, 545–553 (2020).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    9. Marin, D. et al. Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial. Nat. Med. 30, 772–784 (2024).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    10. Ghobadi, A. et al. Induced pluripotent stem-cell-derived CD19-directed chimeric antigen receptor natural killer cells in B-cell lymphoma: a phase 1, first-in-human trial. Lancet 405, 127–136 (2025).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    11. Sterner, R. C. & Sterner, R. M. CAR-T cell therapy: current limitations and potential strategies. Blood Cancer J. 11, 69 (2021).

      Article  PubMed  PubMed Central  Google Scholar 

    12. Laskowski, T. J., Biederstadt, A. & Rezvani, K. Natural killer cells in antitumour adoptive cell immunotherapy. Nat. Rev. Cancer 22, 557–575 (2022).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    13. Wolf, N. K., Kissiov, D. U. & Raulet, D. H. Roles of natural killer cells in immunity to cancer, and applications to immunotherapy. Nat. Rev. Immunol. 23, 90–105 (2023).

      Article  CAS  PubMed  Google Scholar 

    14. Schett, G., Mackensen, A. & Mougiakakos, D. CAR T-cell therapy in autoimmune diseases. Lancet 402, 2034–2044 (2023).

      Article  CAS  PubMed  Google Scholar 

    15. Blache, U., Popp, G., Dunkel, A., Koehl, U. & Fricke, S. Potential solutions for manufacture of CAR T cells in cancer immunotherapy. Nat. Commun. 13, 5225 (2022).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    16. Cliff, E. R. S. et al. High cost of chimeric antigen receptor T-cells: challenges and solutions. Am. Soc. Clin. Oncol. Educ. Book 43, e397912 (2023).

      Article  PubMed  Google Scholar 

    17. Rurik, J. G. et al. CAR T cells produced in vivo to treat cardiac injury. Science 375, 91–96 (2022).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    18. Billingsley, M. M. et al. In vivo mRNA CAR T cell engineering via targeted ionizable lipid nanoparticles with extrahepatic tropism. Small 20, e2304378 (2024).

      Article  PubMed  Google Scholar 

    19. Hunter, T. L. et al. In vivo CAR T cell generation to treat cancer and autoimmune disease. Science 388, 1311–1317 (2025).

      Article  CAS  PubMed  Google Scholar 

    20. Mullard, A. In vivo CAR T cells move into clinical trials. Nat. Rev. Drug Discov. 23, 727–730 (2024).

      Article  CAS  PubMed  Google Scholar 

    21. Schoenmaker, L. et al. mRNA–lipid nanoparticle COVID-19 vaccines: structure and stability. Int. J. Pharm. 601, 120586 (2021).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    22. Carrasco, M. J. et al. Ionization and structural properties of mRNA lipid nanoparticles influence expression in intramuscular and intravascular administration. Commun. Biol. 4, 956 (2021).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    23. Matosevic, S. Viral and nonviral engineering of natural killer cells as emerging adoptive cancer immunotherapies. J. Immunol. Res. 2018, 4054815 (2018).

      Article  PubMed  PubMed Central  Google Scholar 

    24. Schmidt, P., Raftery, M. J. & Pecher, G. Engineering NK cells for CAR therapy—recent advances in gene transfer methodology. Front. Immunol. 11, 611163 (2020).

      Article  CAS  PubMed  Google Scholar 

    25. Robbins, G. M., Wang, M., Pomeroy, E. J. & Moriarity, B. S. Nonviral genome engineering of natural killer cells. Stem Cell Res. Ther. 12, 350 (2021).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    26. Bi, J. & Tian, Z. NK cell exhaustion. Front. Immunol. 8, 760 (2017).

      Article  PubMed  PubMed Central  Google Scholar 

    27. Borst, L., van der Burg, S. H. & van Hall, T. The NKG2A–HLA-E axis as a novel checkpoint in the tumor microenvironment. Clin. Cancer Res. 26, 5549–5556 (2020).

      Article  CAS  PubMed  Google Scholar 

    28. Vivier, E. et al. Natural killer cell therapies. Nature 626, 727–736 (2024).

      Article  CAS  PubMed  Google Scholar 

    29. Sun, C. et al. High NKG2A expression contributes to NK cell exhaustion and predicts a poor prognosis of patients with liver cancer. Oncoimmunology 6, e1264562 (2017).

      Article  PubMed  Google Scholar 

    30. Bournazos, S., Gupta, A. & Ravetch, J. V. The role of IgG Fc receptors in antibody-dependent enhancement. Nat. Rev. Immunol. 20, 633–643 (2020).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    31. Hassett, K. J. et al. Optimization of lipid nanoparticles for intramuscular administration of mRNA vaccines. Mol. Ther. Nucleic Acids 15, 1–11 (2019).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    32. Hagemeyer, C. E. et al. Particle generation, functionalization and sortase A-mediated modification with targeting of single-chain antibodies for diagnostic and therapeutic use. Nat. Protoc. 10, 90–105 (2015).

      Article  CAS  PubMed  Google Scholar 

    33. Beerli, R. R., Hell, T., Merkel, A. S. & Grawunder, U. Sortase enzyme-mediated generation of site-specifically conjugated antibody drug conjugates with high in vitro and in vivo potency. PLoS ONE 10, e0131177 (2015).

      Article  PubMed  PubMed Central  Google Scholar 

    34. Waggoner, L. E., Miyasaki, K. F. & Kwon, E. J. Analysis of PEG-lipid anchor length on lipid nanoparticle pharmacokinetics and activity in a mouse model of traumatic brain injury. Biomater. Sci. 11, 4238–4253 (2023).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    35. Madisen, L. et al. A robust and high-throughput Cre reporting and characterization system for the whole mouse brain. Nat. Neurosci. 13, 133–140 (2010).

      Article  CAS  PubMed  Google Scholar 

    36. Cheng, Q. et al. Selective organ targeting (SORT) nanoparticles for tissue-specific mRNA delivery and CRISPR–Cas gene editing. Nat. Nanotechnol. 15, 313–320 (2020).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    37. Dilliard, S. A., Cheng, Q. & Siegwart, D. J. On the mechanism of tissue-specific mRNA delivery by selective organ targeting nanoparticles. Proc. Natl Acad. Sci. USA 118, e2109256118 (2021).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    38. Chiesa, R. et al. Base-edited CAR7 T cells for relapsed T-cell acute lymphoblastic leukemia. N. Engl. J. Med. 389, 899–910 (2023).

      Article  CAS  PubMed  Google Scholar 

    39. Cwynarski, K. et al. TRBC1-CAR T cell therapy in peripheral T cell lymphoma: a phase 1/2 trial. Nat. Med. 31, 137–143 (2025).

      Article  CAS  PubMed  Google Scholar 

    40. Terziroli Beretta-Piccoli, B., Mieli-Vergani, G. & Vergani, D. Autoimmune hepatitis. Cell. Mol. Immunol. 19, 158–176 (2022).

    41. Dubrot, J. et al. In vivo CRISPR screens reveal the landscape of immune evasion pathways across cancer. Nat. Immunol. 23, 1495–1506 (2022).

      Article  CAS  PubMed  Google Scholar 

    42. Zhang, X. et al. Qa-1b modulates resistance to anti-PD-1 immune checkpoint blockade in tumors with defects in antigen processing. Mol. Cancer Res. 19, 1076–1084 (2021).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    43. Li, P., McDermott, G. & Strong, R. K. Crystal structures of RAE-1β and its complex with the activating immunoreceptor NKG2D. Immunity 16, 77–86 (2002).

      Article  CAS  PubMed  Google Scholar 

    44. Jencks, W. P. On the attribution and additivity of binding energies. Proc. Natl Acad. Sci. USA 78, 4046–4050 (1981).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    45. Aryee, K. E. et al. Enhanced development of functional human NK cells in NOD-scid-IL2rgnull mice expressing human IL15. FASEB J. 36, e22476 (2022).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    46. Board, N. L. et al. Bispecific antibodies promote natural killer cell-mediated elimination of HIV-1 reservoir cells. Nat. Immunol. 25, 462–470 (2024).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    47. Hervier, B., Russick, J., Cremer, I. & Vieillard, V. NK cells in the human lungs. Front. Immunol. 10, 1263 (2019).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    48. Li, J., Xiao, C., Li, C. & He, J. Tissue-resident immune cells: from defining characteristics to roles in diseases. Signal Transduct. Target. Ther. 10, 12 (2025).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    49. Basar, R., Daher, M. & Rezvani, K. Next-generation cell therapies: the emerging role of CAR-NK cells. Blood Adv. 4, 5868–5876 (2020).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    50. Ran, G. H. et al. Natural killer cell homing and trafficking in tissues and tumors: from biology to application. Signal Transduct. Target. Ther. 7, 205 (2022).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    51. Sun, L., Su, Y., Jiao, A., Wang, X. & Zhang, B. T cells in health and disease. Signal Transduct. Target. Ther. 8, 235 (2023).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    52. Kroeze, E., Loeffen, J. L. C., Poort, V. M. & Meijerink, J. P. P. T-cell lymphoblastic lymphoma and leukemia: different diseases from a common premalignant progenitor? Blood Adv. 4, 3466–3473 (2020).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    53. Aljobaily, N. et al. Autoimmune CD4+ T cells fine-tune TCF1 expression to maintain function and survive persistent antigen exposure during diabetes. Immunity 57, 2583–2596 (2024).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    54. Arnold, F. et al. Tissue-resident memory T cells break tolerance to renal autoantigens and orchestrate immune-mediated nephritis. Cell. Mol. Immunol. 21, 1066–1081 (2024).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    55. Liu, E. et al. Cord blood NK cells engineered to express IL-15 and a CD19-targeted CAR show long-term persistence and potent antitumor activity. Leukemia 32, 520–531 (2018).

      Article  CAS  PubMed  Google Scholar 

    56. Myers, J. A. & Miller, J. S. Exploring the NK cell platform for cancer immunotherapy. Nat. Rev. Clin. Oncol. 18, 85–100 (2021).

      Article  PubMed  Google Scholar 

    57. Holdener, M. et al. Breaking tolerance to the natural human liver autoantigen cytochrome P450 2D6 by virus infection. J. Exp. Med. 205, 1409–1422 (2008).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    58. Guimaraes, C. P. et al. Site-specific C-terminal and internal loop labeling of proteins using sortase-mediated reactions. Nat. Protoc. 8, 1787–1799 (2013).

      Article  PubMed  PubMed Central  Google Scholar 

    59. Ta, H. T. et al. Enzymatic single-chain antibody tagging: a universal approach to targeted molecular imaging and cell homing in cardiovascular disease. Circ. Res. 109, 365–373 (2011).

      Article  CAS  PubMed  Google Scholar 

    60. Guo, X., Wu, Z. & Guo, Z. New method for site-specific modification of liposomes with proteins using sortase A-mediated transpeptidation. Bioconjug. Chem. 23, 650–655 (2012).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    61. Chen, Y. et al. Targeting Xkr8 via nanoparticle-mediated in situ co-delivery of siRNA and chemotherapy drugs for cancer immunochemotherapy. Nat. Nanotechnol. 18, 193–204 (2023).

      Article  PubMed  Google Scholar 

    62. Hintermann, E., Ehser, J. & Christen, U. The CYP2D6 animal model: how to induce autoimmune hepatitis in mice. J. Vis. Exp. (60), 3644 (2012).

    63. Chen, Y. et al. An oncolytic virus–T cell chimera for cancer immunotherapy. Nat. Biotechnol. 42, 1876–1887 (2024).

      Article  CAS  PubMed  Google Scholar 

    64. Eastman, P. et al. OpenMM 8: molecular dynamics simulation with machine learning potentials. J. Phys. Chem. B 128, 109–116 (2024).

      Article  CAS  PubMed  Google Scholar 

    65. Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    66. Shaw, D. E. et al. Atomic-level characterization of the structural dynamics of proteins. Science 330, 341–346 (2010).

      Article  CAS  PubMed  Google Scholar 

    67. Michaud-Agrawal, N., Denning, E. J., Woolf, T. B. & Beckstein, O. MDAnalysis: a toolkit for the analysis of molecular dynamics simulations. J. Comput. Chem. 32, 2319–2327 (2011).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    68. Thadhani, E. NK-MD-Analysis: MD interface analysis notebooks, v1.0.0. Zenodo https://doi.org/10.5281/zenodo.21983910 (2026).

    69. Shan, L., Flavell, R. A. & Herndler-Brandstetter, D. Development of humanized mouse models for studying human NK cells in health and disease. Methods Mol. Biol. 2463, 53–66 (2022).

      Article  PubMed  PubMed Central  Google Scholar 

    70. Cooper, M. L. et al. An ‘off-the-shelf’ fratricide-resistant CAR-T for the treatment of T cell hematologic malignancies. Leukemia 32, 1970–1983 (2018).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

    71. Lei, W. et al. Safety and feasibility of 4-1BB co-stimulated CD19-specific CAR-NK cell therapy in refractory/relapsed large B cell lymphoma: a phase 1 trial. Nat. Cancer 6, 786–800 (2025).

      Article  CAS  PubMed  PubMed Central  Google Scholar 

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    Acknowledgements

    We thank the Koch Institute’s Robert A. Swanson (1969) Biotechnology Center for technical support, specifically the Flow Cytometry Core, the Hope Babette Tang (1983) Histology Core and the Animal Imaging and Preclinical Testing Core.

    Funding

    This work was funded by Sanofi (formerly Translate Bio) (D.G.A.), the Marble Center for Cancer Nanomedicine (to D.G.A. and R.L.) and Koch Institute Support (core) grant 5P30-CA014051 from the National Cancer Institute.

    Author information

    Author notes

    1. These authors contributed equally: Yuang Chen, Julia Monkovic, Haseeb Mughal.

    Authors and Affiliations

    1. David H. Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA

      Yuang Chen, Julia Monkovic, Haseeb Mughal, Binlong Chen, Elina Thadhani, Patrick McMullen, Ava Self, Yuxuan Chen, Yizong Hu, Samuel Detmer, Min Chen, Anna Lapteva, Ria Kamat, Ashley Vernon, Robert Langer & Daniel G. Anderson

    2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

      Yuang Chen, Julia Monkovic, Binlong Chen, Patrick McMullen, Ava Self, Yuxuan Chen, Yizong Hu, Min Chen, Robert Langer & Daniel G. Anderson

    3. Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA

      Haseeb Mughal, Samuel Detmer & Robert Langer

    4. Harvard and MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA

      Elina Thadhani, Robert Langer & Daniel G. Anderson

    5. Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children’s Hospital, Boston, MA, USA

      Patrick McMullen, Robert Langer & Daniel G. Anderson

    6. Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

      Anna Lapteva, Ria Kamat & Robert Langer

    7. Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

      Robert Langer & Daniel G. Anderson

    Authors

    1. Yuang Chen
    2. Julia Monkovic
    3. Haseeb Mughal
    4. Binlong Chen
    5. Elina Thadhani
    6. Patrick McMullen
    7. Ava Self
    8. Yuxuan Chen
    9. Yizong Hu
    10. Samuel Detmer
    11. Min Chen
    12. Anna Lapteva
    13. Ria Kamat
    14. Ashley Vernon
    15. Robert Langer
    16. Daniel G. Anderson

    Contributions

    Yuang Chen, J.M., H.M., B.C., E.T., P.M., A.S., Yuxuan Chen, Y.H., S.D., M.C., A.L., R.K. and A.V. performed the experiments and analyzed data. Yuang Chen, J.M., H.M. and D.G.A. discussed the results and wrote the paper with input from all authors. R.L. and D.G.A. acquired funding and supervised the project.

    Corresponding author

    Correspondence to Daniel G. Anderson.

    Ethics declarations

    Competing interests

    D.G.A. discloses potential competing interests because of his advising (past and present) for Alnylam, Sanofi, Translate Bio, CRISPR Tx, oRNA Therapeutics, Soufflé Therapeutics and Combined Therapeutics. R.L. discloses potential competing interests because of his advising (past and present) for Alnylam, Moderna, Soufflé Therapeutics and Combined Therapeutics. A list of entities with which R.L. is involved with, compensated or uncompensated, is available on Dropbox (www.dropbox.com/s/yc3xqb5s8s94v7x/Rev%20Langer%20COI.pdf?dl=0). A patent application covering aspects of this work has been filed by D.G.A., Yuang C., J.M., H.M. and R.L. (application no. 64/059,548) and is currently pending. The other authors declare no competing interests.

    Peer review

    Peer review information

    Nature Biotechnology thanks Rafet Basar, Yizhou Dong, Katy Rezvani and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

    Additional information

    Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

    Extended data

    Extended Data Fig. 1 Qa-1b-dependent enhancement of NK cell function by NKG2A targeting.

    a, Quantification of Qa-1b expression on resting, activated B cell, T cell and B16F10 tumor cell line. b,c, In vitro co-culture of NK cells with B16F10 melanoma cells (Qa-1b high) after pretreatment with unmodified LNP, BiNKG2A-LNP, or BiNKG2D-LNP encapsulating luciferase mRNA. Frequencies of NK cells’ degranulation (CD107a+) and corresponding tumor cell killing are shown. d,e, Co-culture of NK cells with Qa-1b knockout B16F10 cells (B16F10Qa-1b−/−) under the same treatment conditions. f, In vivo growth of parental B16F10 and engineered B16F10-hCD19 tumors after subcutaneous inoculation. g, Quantification and representative flow cytometry plots of CD69 expression on CAR NK cells following treatment of mice bearing parental B16F10 or B16F10-hCD19 tumors. h,i, Individual tumor growth curves of mice under the indicated treatment conditions. Each line represents one mouse. n = 3 for in vitro experiment. Data are mean ± s.d. Statistical significance was assessed by one-way and two-way ANOVA followed by Dunnett’s multiple comparisons test. n = 5 biologically independent mice per group. Data are mean ± s.d. Statistical significance was assessed by one-way ANOVA followed by Dunnett’s multiple comparisons test.

    Extended Data Fig. 2 Safety and immunogenicity profile of repeated intravenous NtLNP administration.

    a, Serum cytokine profiling collected at acute (6 h) time point after repeated intravenous treatment with PBS, unmodified LNP, BiNKG2A-LNP, or BiNKG2D-LNP. b, Longitudinal body weight monitoring during the treatment course. c, Blood chemistry analysis of liver and kidney function collected at short (24 h) and long-term (14 d) time points after repeated treatment. d, Hepatocyte-tumor cell binding was quantified as PB+ APC+ events within a doublet-enriched flow cytometry gate. e, Representative H&E staining of major organs, including heart, liver, spleen, lung, and kidney, collected at short and long-term time points after repeated treatment. f, Anti-scFv antibody titers in serum collected after repeated administration of BiNKG2A-LNP or BiNKG2D-LNP. g,h, Functional evaluation of serum neutralizing activity after repeated NtLNP administration, measured by MFI of scFv binding to primary NK cells. i,j, Functional evaluation of serum dissociated NKG2D scFv on NKG2D-dependent NK cell cytotoxicity after repeated NtLNP administration, measured by CD107a. n = 5 biologically independent mice per group. Data are mean ± s.d. Statistical significance was assessed by one-way ANOVA followed by Dunnett’s multiple comparisons test or by two-tailed Student’s t-test for comparison.

    Extended Data Fig. 3 Structural features and cross-species activity of NtLNPs.

    a, Pairwise human-murine sequence identity, alignment score, and normalized score for the NKG2D homodimer and NKG2A:CD94 heterodimer. b, Conservation maps across receptor domains (dark blue = high, gray = low) show continuous conservation for NKG2D versus fragmented conservation in NKG2A and CD94. c, RAE-1β:NKG2D and BiNKG2D:NKG2D MD structures aligned on murine NKG2D (mNKG2D); side (top), top (bottom) views. d, Residue-level H-bond occupancy across RAE-1β, BiNKG2D, and the NKG2DE2 (single-epitope scFv):NKG2D complexes (avg. frequency, 1 μs trajectory). e, Epitope mapping of RAE-1β (left) vs BiNKG2D (right) on NKG2D. f, Representative H-bond networks at the NKG2D interface; BiNKG2D engages more residues, likely stabilizing binding. g,h, Average H-bond occupancy for NKG2D residues implicated in native binding (g) or unique to engineered ligands (h). n = 3 independent MD simulation replicates per complex, each initiated from the same starting structure with a distinct random seed for initial atomic velocities; mean ± s.d., one-way ANOVA with Dunnett’s test. i, Structural alignment of BiNKG2D with human (purple) and murine (teal) NKG2D. j, Close-up of conserved BiNKG2D interface residues (H174, D173, Y154, G152) through hydrogen bonds, salt bridges, and cation–π interactions. k, MD-structure of the human MICA-NKG2D complex. l, Close-up of the native MICA:hNKG2D interface: salt bridges (A.K197-D66/D164) and hydrogen bonding (A.Y199-D164). m, Residue-level H-bond occupancy heat map for hNKG2D with MICA or BiNKG2D. n,o, Average H-bond occupancy for hNKG2D residues implicated in MICA binding (n) or newly engaged by BiNKG2D (o). n = 3 independent MD simulation replicates per complex, each initiated from the same starting structure with a distinct random seed for initial atomic velocities; mean ± s.d., one-way ANOVA with Dunnett’s test.

    Extended Data Fig. 4 Extended assessment of tumor recurrence, CAR NK kinetics and T cell depletion in humanized xenograft models.

    a, Continued bioluminescence imaging of Raji-luciferase-GFP tumor burden from day 36 to day 48 in NSG-IL-15 mice treated with ex vivo hCD19-hCAR NK cells or the indicated LNP formulations. b, Percentages of hCD19-hCAR+ human NK cells in blood at 1, 6, 24, 48, 72 and 96 h after intravenous administration of BiNKG2D-LNP. c, Representative spleen and liver images at day 50. PBS-treated mice inoculated with Raji-luciferase-GFP cells on day 30 served as tumor-bearing positive control. d,e, Flow cytometric analysis of GFP+ Raji tumor cells in spleen, liver, bone marrow and blood at day 50. f, Continued bioluminescence imaging of CCRF-CEM-luciferase-GFP tumor burden from day 36 to day 48 in NSG-IL-15 mice treated with the indicated LNP formulations. g, CAR positivity of infused ex vivo CAR NK cells before each treatment. h,i, Validation of human T cell depletion in the Raji model. Representative flow cytometry plots and quantification of human T cells and NK cells among live hCD45+ peripheral immune cells in mice treated with BiNKG2D-LNP or BiNKG2D-LNP with T cell depletion. n = 5 biologically independent mice per group. Data are mean ± s.d.

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    Chen, Y., Monkovic, J., Mughal, H. et al. In vivo engineering of CAR natural killer cells for immunotherapy using ligand-directed lipid nanoparticles. Nat Biotechnol (2026). https://doi.org/10.1038/s41587-026-03310-1

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  • Engineering smart delivery for gene editors

    Engineering smart delivery for gene editors

    Modern genome editing techniques, including CRISPR systems, hold great potential for treating genetic diseases. However, delivering these molecular tools reliably to their target cells remains a significant challenge.

    “Previous viral and non-viral delivery systems such as adeno-associated viruses (AAVs), lipid nanoparticles (LNPs), and other virus-like particles (VLPs), have been valuable but face limitations,” says Dr. Dong-Jiunn Jeffery Truong, last author of the study and group leader at the Institute for Synthetic Biomedicine at Helmholtz Munich. “Challenges include the increased persistence of gene editors potentially causing immune reactions, or simply their limited efficiency. ENVLPE directly addresses these issues while its modular design maintains compatibility with future gene-editing advancements.”

    ENVLPE is based on modified, non-infectious virus-derived shells. These act as carriers for molecular gene editors such as base or prime editors — specialized CRISPR tools that can chemically change single DNA bases in the genome and remove or insert new DNA sequences. ENVLPE’s design solves the logistics challenge of previous methods during the production of the VLPs by hijacking the intracellular transport mechanism so that all components come together at the right time and place.

    Prior methods often included partially assembled, non-functional gene editors, reducing delivery effectiveness. “ENVLPE now not only ensures the packaging of fully assembled gene editors but also contains an extra molecular shield that protects the most vulnerable part of the editor from degradation during transport,” explains Truong. “This allows the genetic tools to be safely delivered into target cells where the intended DNA edit can take place.”

    Restoring Vision: Gene Editing in Action

    In close collaboration with a team led by Prof. Krzysztof Palczewski, a professor of ophthalmology at UC Irvine, the scientists tested the ENVLPE system in a mouse model of inherited blindness. “The mice carry a disabling mutation in the Rpe65 gene, which is essential for producing light-sensitive molecules in the retina, and therefore are fully blind and unresponsive to light,” explains Samuel W. Du, a co-author and MD/PhD candidate at UC Irvine. After injecting ENVLPE into the subretinal space (the area between the retinal pigment epithelium and photoreceptors) to correct the mutation, the animals began to respond to light stimuli again. “The extent of restoration was astounding,” says Julian Geilenkeuser, co-first author of the study and a doctoral researcher at the Institute for Synthetic Biomedicine. “It showed us that our particles have real therapeutic potential in a living animal.”

    Compared to established systems, ENVLPE achieved significantly better results: a competing system required more than 10 times the dose to reach similar effects. “Our goal was to build a tool that is both useful for researchers and suitable for real-world applications,” says Niklas Armbrust, also co-first author and a doctoral researcher at the Institute for Synthetic Biomedicine. “We resolved critical bottlenecks and achieved a much more efficient packaging by the delivery agents.”

    Advancing Cancer Therapy with Universal T Cells

    ENVLPE could also open up new possibilities for adoptive T cell therapies, where immune cells taken from the patient are genetically modified in the lab so that they can specifically recognize and attack tumor cells. In collaboration with Dr. Andrea Schmidts’ laboratory at TUM University Hospital, ENVLPE facilitated the targeted removal of specific surface molecules that could trigger an immune response when the cells are administered to a recipient different from the donor. This could lead to the development of so-called “universal” T cells that do not need to be customized for individual patients, making treatments more accessible and cost-effective.

    These innovations address critical challenges in both in vivo gene therapies for genetically inherited diseases and ex vivo cell therapies for cancer, paving the way for important translational advancements. “The highly modular ENVLPE system brings us substantially closer to on-demand and precise genetic modifications of complex cellular models,” says Prof. Gil Westmeyer, Director of the Institute for Synthetic Biomedicine and Professor for Neurobiological Engineering at TUM and co-senior author of the study. “It is an example of how synthetic biology can help drive medical innovation.”

    Moving Toward Clinical Use

    Having now achieved highly efficient delivery of the most common gene-editing tools, the team now seeks to use the diversity found in nature, along with the recent advancements in AI-assisted protein design, to increase targeting precision by restricting the delivery of these tools to specific cell or tissue types only. To move ENVLPE toward clinical application, the research team is pursuing follow-up funding from translational grants and partnerships in the pharmaceutical industry. The goal is to optimize the technology for various therapeutic applications and ultimately make it available to patients.

    What is ENVLPE?

    ENVLPE stands for “Engineered Nucleocytosolic Vehicles for Loading of Programmable Editors.” These are non-infectious virus-like particles that can efficiently transport gene-editing tools such as base or prime editors into target cells. ENVLPE overcomes two major limitations of earlier systems: the instability of the guide RNA payload and inefficient packaging of only functional gene editors within the production cells. By adjusting intracellular transport mechanisms, the researchers significantly increased the system’s efficiency and safety.