Nature biotechnology最新文献

The instability of mRNA-lipid nanoparticles (LNPs) necessitates ultra-cold storage, limiting global distribution and their broader application in advanced delivery systems. Solid-state, water-free formulations enhance thermostability and enable integration into emerging delivery modalities such as microneedle patches. Previous efforts to stabilize mRNA-LNPs have been constrained by narrow formulation scope and low-throughput screening methods. Here we introduce Algorithm-Guided Experimental design for lipid Nanoparticle Thermostabilization (AGENT), an artificial intelligence (AI)-driven framework that couples high-throughput experimentation with Bayesian optimization to identify thermostable mRNA-LNP formulations. AGENT extracts maximal information from sparse experimental datasets, enabling efficient formulation optimization in six iterations completed within 1 month. We stabilized mRNA vaccines with two clinically relevant LNPs representative of the Moderna (SM-102-based) and Pfizer-BioNTech (ALC-0315-based) compositions into solid-state formulations that retained 100% bioactivity after storage at 37 °C for more than 2 months. In rodents and non-human primates, thermostable, solid-state vaccine formulations induced antigen-specific immune responses non-inferior to those elicited by intramuscular delivery of freshly prepared soluble vaccines.

Although lipid nanoparticles are clinically validated for RNA delivery, their potency often declines as transcript size increases. Here we report a large-cargo-informed lipid discovery strategy that incorporates mRNA size into ionizable lipid screening. A combinatorial library of 384 lipids was screened using a 5.7-kb ABE-NanoLuc reporter mRNA, identifying large-cargo-optimized lipids led by LC-1. Following intravenous, intrathecal and intratracheal administration, LC-1 achieved up to 79% Cas9-mediated knockout in liver and 48% and 27% in brain and lung, up to fourfold higher than LP-01 and ALC-0315, and produced higher ABE-mediated reporter correction across all three routes. LC-1 supported base editing of PCSK9, CFTR^R55X and Ube3a-ATS. Mechanistic studies showed that, unlike the benchmarks, LC-1 forms stronger lipid-RNA interactions and retains an ordered, fusogenic inverted-hexagonal structure and pH-responsive membrane disruption as mRNA size increases, rather than becoming disordered. These findings establish cargo size as a key design parameter for ionizable lipid discovery.

Simultaneous characterization of proteins, RNAs and glycans with a nanopore remains a substantial challenge. To advance toward this goal, we introduce a multifunctional adaptor, maleimido-C2-formylphenylboronic acid (maleimido-C2-FPBA), into the Mycobacterium smegmatis porin A (MspA) nanopore at the pore constriction site. Here we demonstrate that the engineered nanopore, MspA-FPBA, enables the simultaneous identification of a diverse set of analytes, including 21 proteinogenic amino acids, three post-translationally modified (PTM) amino acids, four canonical nucleoside monophosphates (NMPs), three epigenetically modified NMPs, four monosaccharides and five peptides. Combined with machine learning, this sensor achieves an overall accuracy of 98.7%. Furthermore, it generates predictable event signatures for specific analyte types, enabling the machine learning model to identify analytes without strictly relying on prearchived event features, as demonstrated by the nanopore analysis of yeast cell extract. Lastly, we apply MspA-FPBA for compositional analysis of G2 glycopeptides. Future integration of hydrolase with MspA-FPBA may further improve MspA-FPBA utility as a single system to identify diverse biomolecules.