bioRxivBioengineeringPreprint
Therapeutic immune cell engineering with an mRNA : AAV-Sleeping Beauty composite system
L. Ye, S. Z. Lam, L. Yang, K. Suzuki, Y. Zou, Q. Lin, Y. Zhang, P. Clark, L. Peng, S. Chen
Abstract
Adoptive cell therapy has shown clinical success in patients with hematological malignancies. Immune cell engineering is critical for production, research, and development of cell therapy; however, current approaches for generation of therapeutic immune cells face various limitations. Here, we establish a composite gene delivery system for the highly efficient engineering of therapeutic immune cells. This system, termed MAJESTIC (mRNA AAV-Sleeping-Beauty Joint Engineering of Stable Therapeutic Immune Cells), combines the merits of mRNA, AAV vector, and transposon into one composite system. In MAJESTIC, the transient mRNA component encodes a transposase that mediates permanent genomic integration of the Sleeping Beauty (SB) transposon, which carries the gene-of-interest and is embedded within the AAV vector. This system can transduce diverse immune cell types with low cellular toxicity and achieve highly efficient and stable therapeutic cargo delivery. Compared with conventional gene delivery systems, such as lentiviral vector, DNA transposon plasmid, or minicircle electroporation, MAJESTIC shows higher cell viability, chimeric antigen receptor (CAR) transgene expression, therapeutic cell yield, as well as prolonged transgene expression. CAR-T cells generated by MAJESTIC are functional and have strong anti-tumor activity in vivo. This system also demonstrates versatility for engineering different cell therapy constructs such as canonical CAR, bi-specific CAR, kill switch CAR, and synthetic TCR; and for CAR delivery into various immune cells, including T cells, natural killer cells, myeloid cells, and induced pluripotent stem cells.
- organism: human
- assay: CRISPR screen
- assay: proteomics
- research focus: cell type annotation
- research focus: drug response / drug discovery
- research focus: gene regulatory network inference
- approach: deep learning
- approach: experimental assay
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