Shoemaker, G. | Search bioRxiv

bioRxiv2026

FALCON: Closed-Loop Multi-Objective Optimization of Lipid Nanoparticles for Cell-Selective mRNA Delivery

Efficient, cell type-selective delivery of genetic payloads remains a central challenge in the development of gene and cell therapies. Lipid nanoparticles (LNPs) offer a versatile delivery platform, but their optimization is hindered by reliance on brute-force screening methods that are laborious, resource-intensive, and focus on single targets. Here, we present FALCON (Framework for Active Learning-driven Compositional Optimization of Nanoparticles), a closed-loop pipeline that leverages iterative screening, surrogate modeling, and multi-objective optimization to accelerate LNP compositional design. In B cell-targeted validation experiments, FALCON-optimized LNPs achieved a 1.8-fold increase in splenic B cell transfection in vivo compared with reference compositions. When optimized for selectivity, FALCON LNPs displayed an 84-fold improvement in selective transfection of splenic B cells over off-target liver populations and enabled spleen-tropic behavior across factorial panels of varying ionizable and helper lipid chemistries. In vaccine studies, these LNPs induced higher IgG2c antibody titers and a more Th1-biased immune profile. FALCON was also deployed to optimize LNPs for myeloid cell-selective delivery, achieving enhanced in vivo selectivity following systemic administration both across and within spleen and liver compartments. Our results establish FALCON as a useful tool for data-driven design of LNP compositions for precision gene delivery.

bioengineering↗

bioRxiv2024

Measuring the Stoichiometry of Microbial Parasite Infrapopulations One Cell at a Time using Energy Dispersive Spectroscopy

O_LIProgress in the field of ecological stoichiometry has demonstrated that the outcome of ecological interactions can often be predicted a priori based on the nutrient ratios (e.g., carbon: nitrogen: phosphorus, C:N:P) of interacting organisms. However, the challenges of accurately measuring the nutrient content of active parasites within hosts has limited our ability to rigorously apply ecological stoichiometry to host-parasite systems. Traditional nutrient analyses require high parasite biomasses, often preventing individual-level analyses. This prevents researchers from estimating variation in the nutrient content of individual parasites within a single host infrapopulation, a critical factor that could define how the ecology of the parasite affects the host-parasite interaction. C_LIO_LIHere, we explain how energy dispersive technology, a technique currently used to measure the elemental content of free-living microbes, can be adapted for parasitic microbial infrapopulations. We demonstrate the power of accurately quantifying the biomass stoichiometry of individual microbial parasites sampled directly from individual hosts. C_LIO_LIUsing this approach we show that the stoichiometric composition of two microbial parasites capable of infecting the same host are stoichiometrically distinct and respond to host diet quality differently. We also demonstrate that characteristics of the stoichiometric trait distributions of these infrapopulations were important predictors of host fecundity, a proxy for virulence in this system, and better predictors of parasite load than the mean parasite stoichiometry or our parasite and diet treatments alone. C_LIO_LIEDS provides a rigorous tool for applying ecological stoichiometry to host-parasite systems and enables researchers to explore the nutritional physiology of host-parasite interactions at a scale that is more relevant to the ecology and evolution of the system than traditional nutrient analyses. Here we demonstrate that this level of resolution provides useful insights into the diet-dependent physiology of microbial parasites and their hosts. We anticipate that this improved level of resolution has the potential to elucidate a range of eco-evo interactions in host-parasite systems that were previously unobservable. C_LI

ecology↗