Optimization of lipid nanoparticles for gene editing of the liver via intraduodenal delivery.

Lipid nanoparticles (LNPs) have recently emerged as successful gene delivery platforms for a diverse array of disease treatments. Efforts to optimize their design for common administration methods such as intravenous injection, intramuscular injection, or inhalation, revolve primarily around the addition of targeting ligands or the choice of ionizable lipid. Here, we employed a multi-step screening method to optimize the type of helper lipid and component ratios in a plasmid DNA (pDNA) LNP library to efficiently deliver pDNA through intraduodenal delivery as an indicative route for oral administration. By addressing different physiological barriers in a stepwise manner, we down-selected effective LNP candidates from a library of over 1000 formulations. Beyond reporter protein expression, we assessed the efficiency in non-viral gene editing in mouse liver mediated by LNPs to knockdown PCSK9 and ANGPTL3 expression, thereby lowering low-density lipoprotein (LDL) cholesterol levels. Utilizing an all-in-one pDNA construct with Strep. pyogenes Cas9 and gRNAs, our results showcased that intraduodenal administration of selected LNPs facilitated targeted gene knockdown in the liver, resulting in a 27% reduction in the serum LDL cholesterol level. This LNP-based all-in-one pDNA-mediated gene editing strategy highlights its potential as an oral therapeutic approach for hypercholesterolemia, opening up new possibilities for DNA-based gene medicine applications.

Screening for lipid nanoparticles that modulate the immune activity of helper T cells towards enhanced antitumour activity.

Lipid nanoparticles (LNPs) can be designed to potentiate cancer immunotherapy by promoting their uptake by antigen-presenting cells, stimulating the maturation of these cells and modulating the activity of adjuvants. Here we report an LNP-screening method for the optimization of the type of helper lipid and of lipid-component ratios to enhance the delivery of tumour-antigen-encoding mRNA to dendritic cells and their immune-activation profile towards enhanced antitumour activity. The method involves screening for LNPs that enhance the maturation of bone-marrow-derived dendritic cells and antigen presentation in vitro, followed by assessing immune activation and tumour-growth suppression in a mouse model of melanoma after subcutaneous or intramuscular delivery of the LNPs. We found that the most potent antitumour activity, especially when combined with immune checkpoint inhibitors, resulted from a coordinated attack by T cells and NK cells, triggered by LNPs that elicited strong immune activity in both type-1 and type-2 T helper cells. Our findings highlight the importance of optimizing the LNP composition of mRNA-based cancer vaccines to tailor antigen-specific immune-activation profiles.

Machine Learning Elucidates Design Features of Plasmid DNA Lipid Nanoparticles for Cell Type-Preferential Transfection.

For cell and gene therapies to become more broadly accessible, it is critical to develop and optimize non-viral cell type-preferential gene carriers such as lipid nanoparticles (LNPs). Despite the effectiveness of high throughput screening (HTS) approaches in expediting LNP discovery, they are often costly, labor-intensive, and often do not provide actionable LNP design rules that focus screening efforts on the most relevant chemical and formulation parameters. Here we employed a machine learning (ML) workflow using well-curated plasmid DNA LNP transfection datasets across six cell types to maximize chemical insights from HTS studies and has achieved predictions with 5-9% error on average depending on cell type. By applying Shapley additive explanations to our ML models, we unveiled composition-function relationships dictating cell type-preferential LNP transfection efficiency. Notably, we identified consistent LNP composition parameters that enhance in vitro transfection efficiency across diverse cell types, such as ionizable to helper lipid ratios near 1:1 or 10:1 and the incorporation of cationic/zwitterionic helper lipids. In addition, several parameters were found to modulate cell type-preferentiality, including the ionizable and helper lipid total molar percentage, N/P ratio, cholesterol to PEGylated lipid ratio, and the chemical identity of the helper lipid. This study leverages HTS of compositionally diverse LNP libraries and ML analysis to understand the interactions between lipid components in LNP formulations; and offers fundamental insights that contribute to the establishment of unique sets of LNP compositions tailored for cell type-preferential transfection.

Engineering chimeric antigen receptor T cells for solid tumour therapy.

Cell-based immunotherapy, for example, chimeric antigen receptor T (CAR-T) cell immunotherapy, has revolutionized cancer treatment, particularly for blood cancers. However, factors such as insufficient T cell tracking, tumour heterogeneity, inhibitory tumour microenvironment (TME) and T cell exhaustion limit the broad application of CAR-based immunotherapy for solid tumours. In particular, the TME is a complex and evolving entity, which is composed of cells of different types (e.g., cancer cells, immune cells and stromal cells), vasculature, soluble factors and extracellular matrix (ECM), with each component playing a critical role in CAR-T immunotherapy. Thus, developing approaches to mitigate the inhibitory TME factors is critical for future success in applying CAR-T cells for solid tumour treatment. Accordingly, understanding the bilateral interaction of CAR-T cells with the TME is in pressing need to pave the way for more efficient therapeutics. In the following review, we will discuss TME-associated aspects with an emphasis on T cell trafficking, ECM barriers, abnormal vasculature, solid tumour heterogenicity and immune suppressive microenvironment. We will then summarize current engineering strategies to overcome the challenges posed by the TME-associated factors. Lastly, the future directions for engineering efficient CAR-T cells for solid tumour therapy will be discussed.

Tracking the Dynamic Histone Methylation of H3K27 in Live Cancer Cells.

Histone methylations play a crucial role in chromatin remodeling and genome regulations. However, there is a lack of tools to visualize these histone modifications with high spatiotemporal resolutions in live cells. We have developed a biosensor based on fluorescence resonance energy transfer (FRET) and incorporated it into nucleosomes, capable of monitoring the trimethylation of H3K27 (H3K27me3) in live cells. We also revealed that the performance of the FRET biosensor can be significantly improved by adjusting the linkers within the biosensor. An improved biosensor enables the live-cell imaging of different histone methylation status, induced by the suppressive H3.3K27M or existing in breast cancer cells with varying genetic backgrounds. We have further applied the biosensor to reveal the dynamic coupling between H3K27me3 changes and caspase activity representing the initiation of apoptosis in cancer cells by imaging both H3K27me3 and caspase activity simultaneously in the same live cells. Thus, this new FRET biosensor can provide a powerful tool to visualize the epigenetic regulation in live cells with high spatial temporal resolutions.