Shrinking gate fluorescence correlation spectroscopy yields equilibrium constants and separates photophysics from structural dynamics

Engineering modular and tunable single-molecule sensors by decoupling sensing from signal output

Biosensors play key roles in medical research and diagnostics. However, the development of biosensors for new biomolecular targets of interest often involves tedious optimization steps to ensure a high signal response at the analyte concentration of interest. Here we show a modular nanosensor platform that facilitates these steps by offering ways to decouple and independently tune the signal output as well as the response window. Our approach utilizes a dynamic DNA origami nanostructure to engineer a high optical signal response based on fluorescence resonance energy transfer. We demonstrate mechanisms to tune the sensor's response window, specificity and cooperativity as well as highlight the modularity of the proposed platform by extending it to different biomolecular targets including more complex sensing schemes. This versatile nanosensor platform offers a promising starting point for the rapid development of biosensors with tailored properties.

Single fluorogen imaging reveals distinct environmental and structural features of biomolecular condensates

Biomolecular condensates are viscoelastic materials. Simulations predict that fluid-like condensations are defined by spatially inhomogeneous organization of the underlying molecules. Here, we test these predictions using single-fluorogen tracking and super-resolution imaging. Specifically, we leverage the localization and orientational preferences of freely diffusing fluorogens and the solvatochromic effect whereby specific fluorogens are turned on in response to condensate microenvironments. We deployed three different fluorogens to probe the microenvironments and molecular organization of different protein-based condensates. The spatiotemporal resolution and environmental sensitivity afforded by single-fluorogen imaging shows that the internal environments of condensates are more hydrophobic than coexisting dilute phases. Molecules within condensates are organized in a spatially inhomogeneous manner, and this gives rise to slow-moving nanoscale molecular clusters that coexist with fast-moving molecules. Fluorogens that localize preferentially to the interface help us map their distinct features. Our findings provide a structural and dynamical basis for the viscoelasticity of condensates.

Understanding associative polymer self-assembly with shrinking gate fluorescence correlation spectroscopy

The self-assembly of polymers is integral to their role in liquid formulations. In this study, we combine a dye whose lifetime is sensitive to the nanoviscosity of its local environment with shrinking gate fluorescence correlation spectroscopy (sgFCS) to study the self-assembly of a model telechelic polymer, hydrophobically modified ethoxylated urethane (HEUR). Fluorescence lifetime measurements show a monotonic increase in average lifetime with increasing HEUR concentration driven by a small fraction of dye (<1%) with long lifetimes strongly bound to HEUR. Despite this small fraction, sgFCS isolates the diffusional dynamics of the bound fraction with no a priori assumptions as to the distribution of lifetimes. Sensitivity is greatly enhanced compared to standard FCS, revealing micellar aggregates forming between 0.2 and 1 wt% followed by formation of a percolated network. This sgFCS approach, which we apply for the first time to polymers in this work, is readily extendable to any dye that changes lifetime on binding.

Closing the Gap between Experiment and Simulation─A Holistic Study on the Complexation of Small Interfering RNAs with Polyethylenimine

Rational design is pivotal in the modern development of nucleic acid nanocarrier systems. With the rising prominence of polymeric materials as alternatives to lipid-based carriers, understanding their structure-function relationships becomes paramount. Here, we introduce a newly developed coarse-grained model of polyethylenimine (PEI) based on the Martini 3 force field. This model facilitates molecular dynamics simulations of true-sized PEI molecules, exemplified by molecules with molecular weights of 1.3, 5, 10, and 25 kDa, with degrees of branching between 50.0 and 61.5%. We employed this model to investigate the thermodynamics of small interfering RNA (siRNA) complexation with PEI. Our simulations underscore the pivotal role of electrostatic interactions in the complexation process. Thermodynamic analyses revealed a stronger binding affinity with increased protonation, notably in acidic (endosomal) pH, compared to neutral conditions. Furthermore, the molecular weight of PEI was found to be a critical determinant of binding dynamics: smaller PEI molecules closely enveloped the siRNA, whereas larger ones extended outward, facilitating the formation of complexes with multiple RNA molecules. Experimental validations, encompassing isothermal titration calorimetry and single-molecule fluorescence spectroscopy, aligned well with our computational predictions. Our findings not only validate the fidelity of our PEI model but also accentuate the importance of in silico data in the rational design of polymeric drug carriers. The synergy between computational predictions and experimental validations, as showcased here, signals a refined and precise approach to drug carrier design.

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures

Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite powered by deep neural networks to rapidly analyze single-, two- and three-color single-molecule data, especially from single-molecule Förster Resonance Energy Transfer (smFRET) experiments. Deep-LASI automatically sorts recorded traces, determines FRET correction factors and classifies the state transitions of dynamic traces all in ~20-100 ms per trajectory. We benchmarked Deep-LASI using ground truth simulations as well as experimental data analyzed manually by an expert user and compared the results with a conventional Hidden Markov Model analysis. We illustrate the capabilities of the technique using a highly tunable L-shaped DNA origami structure and use Deep-LASI to perform titrations, analyze protein conformational dynamics and demonstrate its versatility for analyzing both total internal reflection fluorescence microscopy and confocal smFRET data.

Exploring the Synergies of Single-Molecule Fluorescence and 2D Materials Coupled by DNA

The world of 2D materials is steadily growing, with numerous researchers attempting to discover, elucidate, and exploit their properties. Approaches relying on the detection of single fluorescent molecules offer a set of advantages, for instance, high sensitivity and specificity, that allow the drawing of conclusions with unprecedented precision. Herein, it is argued how the study of 2D materials benefits from fluorescence-based single-molecule modalities, and vice versa. A special focus is placed on DNA, serving as a versatile adaptor when anchoring single dye molecules to 2D materials. The existing literature on the fruitful combination of the two fields is reviewed, and an outlook on the additional synergies that can be created between them provided.