Biology subjects

Bolis, G.

Publications and source records attributed to Bolis, G..

3 recordsLinked to original sources

bioRxiv2026

Rapid and reliable quantification of cytosolic mRNA escape (RNASCAPE)

Endosomal escape of mRNA remains a critical bottleneck in oligonucleotide therapeutics, with typically less than 5% of delivered mRNA reaching the cytosol. Precise quantification of this escape is hindered by stochastic variability in uptake, release, and expression, while existing methods lack scalability and accuracy. Here we introduce RNASCAPE, a deep learning framework trained on biologically realistic simulations that estimates unlabelled mRNA cytosolic escape efficiency using only three timepoints of EGFP reporter expression and four lipid nanoparticle (LNP) meta-parameters. Benchmarking on synthetic data shows RNASCAPE achieves a mean absolute percentage accuracy of 78%. RNASCAPE accurately predicts escape efficiencies around 5-9% across diverse cell types and revealed that exchanging cholesterol to {beta}-sitosterol drastically reduces mRNA loading and enhances twofold its functional release. By enabling robust, microscopy-agnostic quantification without requiring specialized cell lines or labeled cargo, RNASCAPE provides a scalable framework for benchmarking and rational design of LNP formulations, advancing nucleic acid therapeutic delivery.

biophysics↗

bioRxiv2026

Tuning siRNA packing order in lipid nanoparticles modulates oligonucleotide functional delivery

Efficient siRNA delivery by lipid nanoparticles (LNPs) is widely attributed to carrier composition, yet how intraparticle packing governs function remains unclear. Here, we developed a single-particle fluorescence microscopy assay that simultaneously quantifies size and siRNA loading of individual, chromophore-labeled LNPs. Imaging ~0.5M particles per condition per hour uncovered two major packing modes: a high and a low order corroborated by cryo-EM. Quantitative live cell imaging combined with systematic variation of LNPs lipid composition and N/P ratio allowed deconvolution of siRNA packing, internalization, and silencing and its dependance on lipid composition and electrostatics. Surprisingly, low-order particles while encapsulating modest RNA mediate more efficient knockdown of a fluorescent reporter than their high-order counterparts. Guided by these findings we predicted and experimentally validated that tuning composition and N/P ratio to favor less compact siRNA packing enhances silencing potency. This framework offers actionable guidance for the rational optimization of LNP formulations for RNA therapeutics.

biophysics↗

bioRxiv2025

Polymer functionalized liposomes as universal nanocarriers for drug delivery: Single particle insights on size-dependent performance and intracellular behavior

Nanomedicine requires smart delivery systems that are precise, robust, and universal. While liposomes are established vehicles in drug delivery, their full potential is challenged by limited stability, leakage, insufficient response and limited insight into particle size dependent performance. Here, we provide polymer-modified liposomes (PMLs), engineered for high structural integrity, broad cargo compatibility, and stimuli-responsive cargo release. We thoroughly characterize PMLs at the single particle level shedding light on key structure-function relationships within polydisperse formulations revealing that small vesicles (<100 nm) displayed significantly higher cargo packing densities, while release was independent of vesicle size. PMLs display high versatility effectively encapsulating cargo types ranging from positively or negatively charged small molecules, to oligonucleotides, and proteins. Studies on PMLs interaction with cell membrane show that PMLs maintain high internalization rate in HeLa and hCMEC/D3 brain cells and achieve [~]50% reduction in cell viability within 24 hours when loaded with the anticancer drug 5-fluorouracil. Finaly, PMLs successfully deliver siRNA targeting eGFP in HEK293-d2eGFP cells, achieving a 10-12% knockdown of eGFP expression, as resolved by machine learning-driven single-cell analysis. This work establishes a framework for PMLs high-resolution functional profiling and opens the way for the next generation rational design of tunable PMLs for drug delivery.

biophysics↗