Programmable RNA Loading of Extracellular Vesicles with Toehold-Release Purification.

Synthetic nanoparticles as lipid nanoparticles (LNPs) are widely used as drug delivery vesicles. However, they hold several drawbacks, including low biocompatibility and unfavorable immune responses. Naturally occurring extracellular vesicles (EVs) hold the potential as native, safe, and multifunctional nanovesicle carriers. However, loading of EVs with large biomolecules remains a challenge. Here, we present a controlled loading methodology using DNA-mediated and programmed fusion between EVs and messenger RNA (mRNA)-loaded liposomes. The fusion efficiency is characterized at the single-particle level by real-time microscopy through EV surface immobilization via lipidated biotin-DNA handles. Subsequently, fused EV-liposome particles (EVLs) can be collected by employing a DNA strand-replacement reaction. Transferring the fusion reaction to magnetic beads enables us to scale up the production of EVLs one million times. Finally, we demonstrated encapsulation of mCherry mRNA, transfection, and improved translation using the EVLs compared to liposomes or LNPs in HEK293-H cells. We envision this as an important tool for the EV-mediated delivery of RNA therapeutics.

Deconvoluting the Effect of Cell-Penetrating Peptides for Enhanced and Controlled Insertion of Large-Scale DNA Nanopores.

DNA nanopores have emerged as powerful tools for molecular sensing, but the efficient insertion of large DNA nanopores into lipid membranes remains challenging. In this study, we investigate the potential of cell-penetrating peptides (CPPs), specifically SynB1 and GALA, to enhance the insertion efficiency of large DNA nanopores. We constructed SynB1- or GALA-functionalized DNA nanopores with an 11 nm inner diameter and visualized and quantified their membrane insertion using a TIRF microscopy-based single-liposome assay. The results demonstrated that incorporating an increasing number of SynB1 or GALA peptides into the DNA nanopore significantly enhanced the membrane perforation. Kinetic analysis revealed that the DNA nanopore scaffold played a role in prearranging the CPPs, which facilitated membrane interaction and pore formation. Notably, the use of pH-responsive GALA peptides allowed highly efficient and pH-controlled insertion of large DNA pores. Furthermore, single-channel recording elucidated that the insertion process of single GALA-modified nanopores into planar lipid bilayers was dynamic, likely forming transient large toroidal pores. Overall, our study highlights the potential of CPPs as insertion enhancers for DNA nanopores, which opens avenues for improved molecule sensing and the controlled release of cargo molecules.

The dopamine transporter antiports potassium to increase the uptake of dopamine.

The dopamine transporter facilitates dopamine reuptake from the extracellular space to terminate neurotransmission. The transporter belongs to the neurotransmitter:sodium symporter family, which includes transporters for serotonin, norepinephrine, and GABA that utilize the Na+ gradient to drive the uptake of substrate. Decades ago, it was shown that the serotonin transporter also antiports K+, but investigations of K+-coupled transport in other neurotransmitter:sodium symporters have been inconclusive. Here, we show that ligand binding to the Drosophila- and human dopamine transporters are inhibited by K+, and the conformational dynamics of the Drosophila dopamine transporter in K+ are divergent from the apo- and Na+-states. Furthermore, we find that K+ increases dopamine uptake by the Drosophila dopamine transporter in liposomes, and visualize Na+ and K+ fluxes in single proteoliposomes using fluorescent ion indicators. Our results expand on the fundamentals of dopamine transport and prompt a reevaluation of the impact of K+ on other transporters in this pharmacologically important family.

Single-particle combinatorial multiplexed liposome fusion mediated by DNA.

Combinatorial high-throughput methodologies are central for both screening and discovery in synthetic biochemistry and biomedical sciences. They are, however, often reliant on large-scale analyses and thus limited by a long running time and excessive materials cost. We here present a single-particle combinatorial multiplexed liposome fusion mediated by DNA for parallelized multistep and non-deterministic fusion of individual subattolitre nanocontainers. We observed directly the efficient (>93%) and leakage free stochastic fusion sequences for arrays of surface-tethered target liposomes with six freely diffusing populations of cargo liposomes, each functionalized with individual lipidated single-stranded DNA and fluorescently barcoded by a distinct ratio of chromophores. The stochastic fusion resulted in a distinct permutation of fusion sequences for each autonomous nanocontainer. Real-time total internal reflection imaging allowed the direct observation of >16,000 fusions and 566 distinct fusion sequences accurately classified using machine learning. The high-density arrays of surface-tethered target nanocontainers (~42,000 containers per mm2) offers entire combinatorial multiplex screens using only picograms of material.

DeepFRET, a software for rapid and automated single-molecule FRET data classification using deep learning.

Single-molecule Förster Resonance energy transfer (smFRET) is an adaptable method for studying the structure and dynamics of biomolecules. The development of high throughput methodologies and the growth of commercial instrumentation have outpaced the development of rapid, standardized, and automated methodologies to objectively analyze the wealth of produced data. Here we present DeepFRET, an automated, open-source standalone solution based on deep learning, where the only crucial human intervention in transiting from raw microscope images to histograms of biomolecule behavior, is a user-adjustable quality threshold. Integrating standard features of smFRET analysis, DeepFRET consequently outputs the common kinetic information metrics. Its classification accuracy on ground truth data reached >95% outperforming human operators and commonly used threshold, only requiring ~1% of the time. Its precise and rapid operation on real data demonstrates DeepFRET's capacity to objectively quantify biomolecular dynamics and the potential to contribute to benchmarking smFRET for dynamic structural biology.

Proteins are folded into particular shapes in order to carry out their roles in the cell. However, their structures are not rigid: proteins bend and rotate in response to their environment. Identifying these movements is an important part of understanding how proteins work and interact with each other. Unfortunately, when researchers study the structures of proteins, they often look at the 'average' shape a protein takes, missing out on other conformations the protein might only be in temporarily. An important technique for studying protein flexibility is known as single molecule Förster resonance energy transfer (FRET). In this technique, two light-sensitive tags are attached to the same protein molecule and give off a signal when they come into close contact. This nano-scale sensor allows structural biologists to get information from individual protein movements that can be lost when looking at the average conformations of proteins. Advances in the instruments used to perform FRET have made observing the motion of individual proteins more widely accessible to non-specialists, but the analysis of the data that these instruments produce still requires a high level of expertise. To lower the barrier for non-specialists to use the technology, and to ensure that experiments can be reproduced on different instruments and by different researchers, Thomsen et al. have developed a new way to automate the data analysis. They used machine learning technology to recognize, filter and characterize data so as to produce reliable results, with the user only needing to perform a couple of steps. This new analysis approach could help expand the use of single-molecule FRET to different fields , allowing researchers to investigate the importance of protein flexibility for certain diseases, or to better understand the roles that proteins have in a cell.

A large size-selective DNA nanopore with sensing applications.

Transmembrane nanostructures like ion channels and transporters perform key biological functions by controlling flow of molecules across lipid bilayers. Much work has gone into engineering artificial nanopores and applications in selective gating of molecules, label-free detection/sensing of biomolecules and DNA sequencing have shown promise. Here, we use DNA origami to create a synthetic 9 nm wide DNA nanopore, controlled by programmable, lipidated flaps and equipped with a size-selective gating system for the translocation of macromolecules. Successful assembly and insertion of the nanopore into lipid bilayers are validated by transmission electron microscopy (TEM), while selective translocation of cargo and the pore mechanosensitivity are studied using optical methods, including single-molecule, total internal reflection fluorescence (TIRF) microscopy. Size-specific cargo translocation and oligonucleotide-triggered opening of the pore are demonstrated showing that the DNA nanopore can function as a real-time detection system for external signals, offering potential for a variety of highly parallelized sensing applications.