Development of hematopoietic stem cell-targeted lipid nanoparticles through lipid composition optimization

Area of Science:

• Biomedical Engineering
• Molecular Biology
• Hematology

Background:

• Hematopoietic stem cell (HSC)-targeted gene editing offers potential for hereditary blood disorders.
• Efficient and safe delivery of gene editing tools into HSCs remains a significant challenge.
• Lipid nanoparticles (LNPs) are promising for nucleic acid delivery but face transfection efficiency hurdles in HSCs.

Purpose of the Study:

• To develop and optimize HSC-targeted LNPs for enhanced gene editing delivery.
• To improve transfection efficiency and cell viability in HSCs using machine learning-guided LNP design.

Main Methods:

• Integrated Bayesian optimization with functional amino lipids to design HSC-targeted LNPs.
• Evaluated LNP transfection efficiency and cell viability in HSCs.
• Performed ex vivo TP53 gene editing in cord blood (CB) CD34+ cells.
• Assessed RNA delivery into primary human monocytic leukemia cells.

Main Results:

• Optimized LNPs demonstrated significantly improved transfection efficiency and preserved cell viability compared to previous formulations.
• Achieved up to 40% on-target TP53 gene editing efficiency in ex vivo CB CD34+ cells.
• One LNP formulation showed efficient RNA delivery in human monocytic leukemia cells.

Conclusions:

• Machine learning-guided LNP design is a powerful strategy for advancing HSC-targeted therapies.
• LNP-based gene editing platforms show significant promise for treating hereditary and malignant hematopoietic disorders.
• Optimized LNPs represent a viable tool for efficient gene editing in HSCs.