Breunig, M. | Search bioRxiv

bioRxiv2026

Hydrogel crosslinking mechanisms influence the release and functional delivery of lipid nanoparticles

Hydrogels have emerged as attractive vaccine delivery platforms because they enable controlled modulation of antigen availability. However, how different hydrogel environments affect the release and functionality of mRNA-loaded lipid nanoparticles (mRNA-LNPs) remains poorly understood. Here, we investigated the release, stability, cellular uptake, and transfection capability of LNPs released from four hydrogel systems representing distinct crosslinking mechanisms: covalently crosslinked poly(ethylene glycol) (PEG), ionically crosslinked alginate, thermoresponsive Poloxamer 407 (P407), and protein-based Matrigel/collagen hydrogels. All hydrogels enabled release of LNPs over days, with kinetics strongly depending on hydrogel composition and polymer concentration. LNPs were quantitatively recovered from all hydrogel types, except from Matrigel/collagen where incomplete matrix dissolution was the limiting step. Lower polymer concentrations generally accelerated nanoparticle release. PEG offered greatest tunability of release kinetics; at the same time the recovery of the LNP-incorporated fluorescent dye DiI was reduced to about 80 %, indicating partial dye leakage. Alginate hydrogels exhibited recovery of DiI below 50 % and broader particle size distributions after release, while P407 hydrogels largely preserved LNP characteristics. Although quantitative recovery from Matrigel/collagen hydrogels was limited, released LNPs remained readily available for cellular uptake. Notably, LNPs released from low- and intermediate-concentration Matrigel/collagen hydrogels achieved approximately 80-90 % of the eGFP expression compared to mRNA-LNP that were not embedded into a hydrogel. Importantly, cellular uptake and transfection experiments demonstrated that all investigated hydrogels released biologically active mRNA-LNPs capable of mediating protein expression. Moreover, our findings show that hydrogel composition is a critical determinant of mRNA-LNP release, stability, and functional delivery. This work provides design principles for the development of hydrogel-based mRNA delivery systems aimed at sustained antigen availability and prolonged vaccine responses. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=156 SRC="FIGDIR/small/741169v1_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@138d9eforg.highwire.dtl.DTLVardef@16c0edaorg.highwire.dtl.DTLVardef@1432dd1org.highwire.dtl.DTLVardef@17511b5_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2026

ViTAMIn-O: Democratizing computer vision-based machine learning for stem cell research

Deep Learning (DL) holds exciting potential in automating the prediction of organoid differentiation results. Nevertheless, current models lack adaptability, openness, and robustness in performance. Additionally, broad employments of predictive models in wet-lab settings necessitate machine learning expertise, often not readily available in biologically oriented laboratories. To offer an intuitive solution, we present ColabViTAMIn-O, a code-free platform together with ViTAMIn-O. ViTAMIn-O is a fully open organoid-specific DL model trained and tested on a total of 34 organoid categories, incorporating annotated images across transmitted light microscopy (TLM) modalities at single-organoid resolution. It is adaptable to downstream prediction tasks of varying dataset sizes and outperforms established models even with linear-probing. It performs reliably within a few-shot framework and is even extensible to human embryo TLM imaging data at single specimen level. By releasing our platform, centralized model hub, and datasets, we hope to encourage broader deployments of specialized DL models in stem-cell laboratories.

bioinformatics↗