Nature biotechnology最新文献

The value of lipid nanoparticles (LNPs) for delivery of messenger RNA (mRNA) was demonstrated by the coronavirus disease 2019 (COVID-19) mRNA vaccines, but the ability to use LNPs to deliver plasmid DNA (pDNA) would provide additional advantages, such as longer-term expression and availability of promoter sequences. However, pDNA-LNPs face substantial challenges, such as toxicity and low delivery efficiency. Here we show that pDNA-LNPs induce acute inflammation in naive mice that is primarily driven by the cGAS–STING pathway. Inspired by DNA viruses that inhibit this pathway for replication, we loaded endogenous lipids that inhibit STING into pDNA-LNPs. Loading nitro-oleic acid (NOA) into pDNA-LNPs (NOA-pDNA-LNPs) ameliorated serious inflammatory responses in vivo, enabling safer, prolonged transgene expression—11.5 times greater than that of mRNA-LNPs at day 32. Additionally, we performed a small LNP formulation screen to iteratively optimize transgene expression and increase expression 50-fold in vitro. pDNA-LNPs loaded with NOA and other bioactive molecules should advance genetic medicine by enabling longer-term and promoter-controlled transgene expression.

Engineering T cell specificity and function at multiple loci can generate more effective cellular therapies, but current manufacturing methods produce heterogenous mixtures of partially engineered cells. Here we develop a one-step process to enrich unlabeled cells containing knock-ins at multiple target loci using a family of repair templates named synthetic exon expression disruptors (SEEDs). SEEDs associate transgene integration with the disruption of a paired target endogenous surface protein while preserving target expression in nonmodified and partially edited cells to enable their removal (SEED-Selection). We design SEEDs to modify three critical loci encoding T cell specificity, coreceptor expression and major histocompatibility complex expression. The results demonstrate up to 98% purity after selection for individual modifications and up to 90% purity for six simultaneous edits (three knock-ins and three knockouts). This method is compatible with existing clinical manufacturing workflows and can be readily adapted to other loci to facilitate production of complex gene-edited cell therapies.

Tumors exhibit an increased ability to obtain and metabolize nutrients. Here, we implant engineered adipocytes that outcompete tumors for nutrients and show that they can substantially reduce cancer progression, a technology termed adipose manipulation transplantation (AMT). Adipocytes engineered to use increased amounts of glucose and fatty acids by upregulating UCP1 were placed alongside cancer cells or xenografts, leading to significant cancer suppression. Transplanting modulated adipose organoids in pancreatic or breast cancer genetic mouse models suppressed their growth and decreased angiogenesis and hypoxia. Co-culturing patient-derived engineered adipocytes with tumor organoids from dissected human breast cancers significantly suppressed cancer progression and proliferation. In addition, cancer growth was impaired by inducing engineered adipose organoids to outcompete tumors using tetracycline or placing them in an integrated cell-scaffold delivery platform and implanting them next to the tumor. Finally, we show that upregulating UPP1 in adipose organoids can outcompete a uridine-dependent pancreatic ductal adenocarcinoma for uridine and suppress its growth, demonstrating the potential customization of AMT.

Here we report a method, smol-seq (small-molecule sequencing), using structure-switching aptamers (SSAs) and DNA sequencing to quantify metabolites. In smol-seq, each SSA detects a single target molecule and releases a unique DNA barcode on target binding. Sequencing the released barcodes can, thus, read out metabolite levels. We show that SSAs are highly specific and can be multiplexed to detect multiple targets in parallel, bringing the power of DNA sequencing to metabolomics.

Correction to: Nature Biotechnology https://doi.org/10.1038/s41587-024-02535-2, published online 16 January 2025.

Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network. DPA-TISR adaptively enhances the cross-frame alignment in the phase domain and outperforms existing state-of-the-art SISR and TISR models. We also develop Bayesian DPA-TISR and design an expected calibration error minimization framework that reliably infers inference confidence. We demonstrate multicolor live-cell SR imaging for more than 10,000 time points of various biological specimens with high fidelity, temporal consistency and accurate confidence quantification.