Nature biotechnology最新文献

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.

Therapeutic efficacy and safety of adeno-associated virus (AAV) liver gene therapy depend on capsid choice. To predict AAV capsid performance under near-clinical conditions, we established side-by-side comparison at single-cell resolution in human livers maintained by normothermic machine perfusion. AAV-LK03 transduced hepatocytes much more efficiently and specifically than AAV5, AAV8 and AAV6, which are most commonly used clinically, and AAV-NP59, which is better at transducing human hepatocytes engrafted in immune-deficient mice. AAV-LK03 preferentially transduced periportal hepatocytes in normal liver, whereas AAV5 targeted pericentral hepatocytes in steatotic liver. AAV5 and AAV8 transduced liver sinusoidal endothelial cells as efficiently as hepatocytes. AAV capsid and steatosis influenced vector episome formation, which determines gene therapy durability, with AAV5 delaying concatemerization. Our findings inform capsid choice in clinical AAV liver gene therapy, including consideration of disease-relevant hepatocyte zonation and effects of steatosis, and facilitate the development of AAV capsids that transduce hepatocytes or other therapeutically relevant cell types in the human liver with maximum efficiency and specificity.

Understanding a small molecule’s mode of action (MoA) is essential to guide the selection, optimization and clinical development of lead compounds. In this study, we used high-throughput non-targeted metabolomics to profile changes in 2,269 putative metabolites induced by 1,520 drugs in A549 lung cancer cells. Although only 26% of the drugs inhibited cell growth, 86% caused intracellular metabolic changes, which were largely conserved in two additional cancer cell lines. By testing more than 3.4 million drug–metabolite dependencies, we generated a lookup table of drug interference with metabolism, enabling high-throughput characterization of compounds across drug therapeutic classes in a single-pass screen. The identified metabolic changes revealed previously unknown effects of drugs, expanding their MoA annotations and potential therapeutic applications. We confirmed metabolome-based predictions for four new glucocorticoid receptor agonists, two unconventional 3-hydroxy-3-methylglutaryl-CoA (HMGCR) inhibitors and two dihydroorotate dehydrogenase (DHODH) inhibitors. Furthermore, we demonstrated that metabolome profiling complements other phenotypic and molecular profiling technologies, opening opportunities to increase the efficiency, scale and accuracy of preclinical drug discovery.

Extending single-cell analysis to intact tissues while maintaining organ-scale spatial information poses a major challenge due to unequal chemical processing of densely packed cells. Here we introduce Continuous Redispersion of Volumetric Equilibrium (CuRVE) in nanoporous matrices, a framework to address this challenge. CuRVE ensures uniform processing of all cells in organ-scale tissues by perpetually maintaining dynamic equilibrium of the tissue’s gradually shifting chemical environment. The tissue chemical reaction environment changes at a continuous, slow rate, allowing redispersion of unevenly distributed chemicals and preserving chemical equilibrium tissue wide at any given moment. We implemented CuRVE to immunologically label whole mouse and rat brains and marmoset and human tissue blocks within 1 day. We discovered highly variable regionalized reduction of parvalbumin immunoreactive cells in wild-type adult mice, a phenotype missed by the commonly used genetic labeling. We envision that our platform will advance volumetric single-cell processing and analysis, facilitating comprehensive single-cell level investigations within their spatial context in organ-scale tissues.