Nature biotechnology最新文献

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.

We introduce a quantum–classical generative model for small-molecule design, specifically targeting KRAS inhibitors for cancer therapy. We apply the method to design, select and synthesize 15 proposed molecules that could notably engage with KRAS for cancer therapy, with two holding promise for future development as inhibitors. This work showcases the potential of quantum computing to generate experimentally validated hits that compare favorably against classical models.