A Two-Step Method for smFRET Data Analysis

Deep learning based local feature classification to automatically identify single molecule fluorescence events

Long-term single-molecule fluorescence measurements are widely used powerful tools to study the conformational dynamics of biomolecules in real time to further elucidate their conformational dynamics. Typically, thousands or even more single-molecule traces are analyzed to provide statistically meaningful information, which is labor-intensive and can introduce user bias. Recently, several deep-learning models have been developed to automatically classify single-molecule traces. In this study, we introduce DEBRIS (Deep lEarning Based fRagmentatIon approach for Single-molecule fluorescence event identification), a deep-learning model focusing on classifying local features and capable of automatically identifying steady fluorescence signals and dynamically emerging signals of different patterns. DEBRIS efficiently and accurately identifies both one-color and two-color single-molecule events, including their start and end points. By adjusting user-defined criteria, DEBRIS becomes the pioneer in using a deep learning model to accurately classify four different types of single-molecule fluorescence events using the same trained model, demonstrating its universality and ability to enrich the current toolbox.

Structured Stochastic Curve Fitting without Gradient Calculation

Optimization of parameters and hyperparameters is a general process for any data analysis. Because not all models are mathematically well-behaved, stochastic optimization can be useful in many analyses by randomly choosing parameters in each optimization iteration. Many such algorithms have been reported and applied in chemistry data analysis, but the one reported here is interesting to check out, where a naïve algorithm searches each parameter sequentially and randomly in its bounds. Then it picks the best for the next iteration. Thus, one can ignore irrational solution of the model itself or its gradient in parameter space.

A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

A blind benchmark of analysis tools to infer kinetic rate constants from single-molecule FRET trajectories

Single-molecule FRET (smFRET) is a versatile technique to study the dynamics and function of biomolecules since it makes nanoscale movements detectable as fluorescence signals. The powerful ability to infer quantitative kinetic information from smFRET data is, however, complicated by experimental limitations. Diverse analysis tools have been developed to overcome these hurdles but a systematic comparison is lacking. Here, we report the results of a blind benchmark study assessing eleven analysis tools used to infer kinetic rate constants from smFRET trajectories. We test them against simulated and experimental data containing the most prominent difficulties encountered in analyzing smFRET experiments: different noise levels, varied model complexity, non-equilibrium dynamics, and kinetic heterogeneity. Our results highlight the current strengths and limitations in inferring kinetic information from smFRET trajectories. In addition, we formulate concrete recommendations and identify key targets for future developments, aimed to advance our understanding of biomolecular dynamics through quantitative experiment-derived models.

Single-Molecule Kinetic Studies of Nucleic Acids by Förster Resonance Energy Transfer

Single-molecule microscopy is often used to observe and characterize the conformational dynamics of nucleic acids (NA). Due to the large variety of NA structures and the challenges specific to single-molecule observation techniques, the data recorded in such experiments must be processed via multiple statistical treatments to finally yield a reliable mechanistic view of the NA dynamics. In this chapter, we propose a comprehensive protocol to analyze single-molecule trajectories in the scope of single-molecule Förster resonance energy transfer (FRET) microscopy. The suggested protocol yields the conformational states common to all molecules in the investigated sample, together with the associated conformational transition kinetics. The given model resolves states that are indistinguishable by their observed FRET signals and is estimated with 95% confidence using error calculations on FRET states and transition rate constants. In the end, a step-by-step user guide is given to reproduce the protocol with the Multifunctional Analysis Software to Handle single-molecule FRET data (MASH-FRET).

Recent Efforts and Milestones for Simulating Nucleic Acid FRET Experiments through Computational Methods

Computational methods can greatly aid nucleic acid fluorescence experiments by either offering fully detailed atomic insights into the conformations and interactions present in the studied system or by providing accurate simulations of the fundamental parameters. Fluorescence-based optical biosensors show great potential for clinical diagnosis of life-altering diseases with a very high specificity. Many of the designs for such rely on the concept of Förster resonance energy transfer (FRET). Currently, the methods used experimentally make use of theoretical assumptions which fundamentally affect the results. Having a detailed atomistic overview or significant simulated parameters could improve the understanding of the calculations and provide much more accurate outcomes. However, there are many challenges that need to be addressed before standardized computational protocols can be employed. This review is meant to highlight the progress made for computational methods used to simulate FRET experiments for nucleic acid probes. Recent advances have been made in computational tools, such as force field parametrizations and improved protocols. Complementary simulations to experimental data are also comprised in the this review.

Simulating stochastic adsorption of diluted solute molecules at interfaces

This report uses Monte Carlo simulations to connect stochastic single-molecule and ensemble surface adsorption of molecules from dilute solutions. Monte Carlo simulations often use a fundamental time resolution to simulate each discrete step for each molecule. The adsorption rate obtained from such a simulation surprisingly contains an error compared to the results obtained from the traditional method. Simulating adsorption kinetics is interesting in many processes, such as mass transportation within cells, the kinetics of drug-receptor interactions, membrane filtration, and other general reaction kinetics in diluted solutions. Thus, it is important to understand the origin of the disagreement and find a way to correct the results. This report reviews the traditional model, explains the single-molecule simulations, and introduces a method to correct the results of adsorption rate. For example, one can bin finer time steps into time steps of interest to simulate the fractal diffusion or simply introduce a correction factor for the simulations. Then two model systems, self-assembled monolayer (SAM) and biosensing on the patterned surface, are simulated to check the accuracy of the equations. It is found that the adsorption rate of SAM is highly dependent on the conditions and the uncertainty is large. However, the biosensing system is relatively accurate. This is because the concentration gradient near the interface varies significantly with reaction conditions for SAMs while relatively stable for the biosensing system.

Pushing Camera-Based Single-Molecule Kinetic Measurements to the Frame Acquisition Limit with Stroboscopic smFRET

Single-molecule fluorescence resonance energy transfer (smFRET) experiments permit detailed examination of microscopic dynamics. However, kinetic rate constants determined by smFRET are susceptible to systematic underestimation when the rate constants are comparable to the data acquisition rate. We demonstrate how such systematic errors in camera-based total internal reflection fluorescence microscopy experiments can be greatly reduced by using stroboscopic illumination/detection, allowing accurate rate constant determination up to the data sampling rate and yielding an order of magnitude increase in the dynamic range. Implementation of these stroboscopic smFRET ideas is straightforward, and the stroboscopically obtained data are compatible with multiple trajectory analysis methods, including dwell-time analysis and hidden Markov modeling. Such stroboscopic methods therefore offer a remarkably simple yet valuable addition to the smFRET toolkit, requiring only relatively modest modification to the normal data collection and analysis procedures.

FRET-based dynamic structural biology: Challenges, perspectives and an appeal for open-science practices

Single-molecule FRET (smFRET) has become a mainstream technique for studying biomolecular structural dynamics. The rapid and wide adoption of smFRET experiments by an ever-increasing number of groups has generated significant progress in sample preparation, measurement procedures, data analysis, algorithms and documentation. Several labs that employ smFRET approaches have joined forces to inform the smFRET community about streamlining how to perform experiments and analyze results for obtaining quantitative information on biomolecular structure and dynamics. The recent efforts include blind tests to assess the accuracy and the precision of smFRET experiments among different labs using various procedures. These multi-lab studies have led to the development of smFRET procedures and documentation, which are important when submitting entries into the archiving system for integrative structure models, PDB-Dev. This position paper describes the current 'state of the art' from different perspectives, points to unresolved methodological issues for quantitative structural studies, provides a set of 'soft recommendations' about which an emerging consensus exists, and lists openly available resources for newcomers and seasoned practitioners. To make further progress, we strongly encourage 'open science' practices.

Dissection of Interaction Kinetics through Single-Molecule Interaction Simulation

The ability to extract kinetic interaction parameters from single-molecule fluorescence resonance energy transfer trajectories without the need for solving complex single-molecule differential equations has the potential to address some of the critical biophysical questions. Here, we provide a noise-free single-molecule interaction simulation (SMIS) tool to give the expected dwell-time distributions and relative populations of each FRET level based on the assigned kinetic model and to dissect kinetic interaction parameters from single-molecule FRET trajectories. The method provides the expected dwell-time distributions, average transition rates, and relative populations of each FRET level based on the assigned kinetic model. By comparing with ground truth data and experimental data, we demonstrated that SMIS is useful to quantify the interaction kinetic rate constants without using the traditional single-molecule analytical solution approach.

Time-resolved temperature-jump measurements and steady-state thermal imaging of nanoscale heat transfer of gold nanostructures on AlGaN:Er3+ thin films

For a nanostructure sitting on top of an AlGaN:Er³⁺ thin film, a new thermal imaging technique is presented where dual cameras collect bandpass filtered videos from the H and S bands of Er³⁺ emission. We combine this thermal imaging technique with our newly developed time-resolved temperature measurement technique which relies on luminescence thermometry using Er³⁺ emission. This technique collects time-resolved traces from the H and S bands of Er³⁺ emission. The H and S signal traces are then used to reconstruct the time-resolved temperature transient when a nanostructure is illuminated with a pulsed 532 nm light. Two different types of samples are interrogated with these techniques (drop-casted gold nanosphere cluster and lithographically prepared gold nanodot) on the AlGaN:Er³⁺ film. Steady-state and time-resolved temperature data are collected when the samples are immersed in air and water. The results of time-resolved temperature-jump measurements from a cluster of gold nanospheres show extremely slow heat transfer when the cluster is immersed in water and nearly 200-fold increase when immersed in air. The low thermal diffusivity for the cluster in water suggests poor thermal contact between the cluster and the thermal bath. The lithographically prepared nanodot has much better adhesion to the AlGaN film, resulting in much higher thermal diffusivity in both air and water. This proof-of-concept demonstration opens a new way to measure the dynamics of the local heat generation and dissipation at the nanoparticle-media interface.

Phosphorylation Induces Conformational Rigidity at the C-Terminal Domain of AMPA Receptors

The intracellular C-terminal domain (CTD) of AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) receptor undergoes phosphorylation at specific locations during long-term potentiation. This modification enhances conductance through the AMPA receptor ion channel and thus potentially plays a crucial role in modulating receptor trafficking and signaling. However, because the CTD structure is largely unresolved, it is difficult to establish if phosphorylation induces conformational changes that might play a role in enhancing channel conductance. Herein, we utilize single-molecule Förster resonance energy transfer (smFRET) spectroscopy to probe the conformational changes of a section of the AMPA receptor CTD, under the conditions of point-mutated phosphomimicry. Multiple analysis algorithms fail to identify stable conformational states within the smFRET distributions, consistent with a lack of well-defined secondary structure. Instead, our results show that phosphomimicry induces conformational rigidity to the CTD, and such rigidity is electrostatically tunable.

State transition identification in multivariate time series (STIMTS) applied to rotational jump trajectories from single molecules

Time resolved data from single molecule experiments often suffer from contamination with noise due to a low signal level. Identifying a proper model to describe the data thus requires an approach with sufficient model parameters without misinterpreting the noise as relevant data. Here, we report on a generalized data evaluation process to extract states with piecewise constant signal level from simultaneously recorded multivariate data, typical for multichannel single molecule experiments. The method employs the minimum description length principle to avoid overfitting the data by using an objective function, which is based on a tradeoff between fitting accuracy and model complexity. We validate our method with synthetic data from Monte Carlo simulations modeling fluorescence resonance energy transfer and rotational jumps, respectively. The method is applied to quantify rotational jump dynamics of single terrylene diimide (TDI) molecules deposited on a solid substrate. Depending on the substitution pattern of the TDI molecules and the chosen substrate materials, we find significant differences in time scale and geometry of molecular reorientation. From an additional application of our state transition identification in multivariate time series approach, a significant correlation between shifts of emission spectra and the occurrence of rotational jumps was found.

Reliable State Identification and State Transition Detection in Fluorescence Intensity-Based Single-Molecule Förster Resonance Energy-Transfer Data

Single-molecule Förster resonance energy transfer (smFRET) is a powerful technique to probe biomolecular structure and dynamics. A popular implementation of smFRET consists of recording fluorescence intensity time traces of surface-immobilized, chromophore-tagged molecules. This approach generates large and complex data sets, the analysis of which is to date not standardized. Here, we address a key challenge in smFRET data analysis: the generation of thermodynamic and kinetic models that describe with statistical rigor the behavior of FRET trajectories recorded from surface-tethered biomolecules in terms of the number of FRET states, the corresponding mean FRET values, and the kinetic rates at which they interconvert. For this purpose, we first perform Monte Carlo simulations to generate smFRET trajectories, in which a relevant space of experimental parameters is explored. Then, we provide an account on current strategies to achieve such model selection, as well as a quantitative assessment of their performances. Specifically, we evaluate the performance of each algorithm (change-point analysis, STaSI, HaMMy, vbFRET, and ebFRET) with respect to accuracy, reproducibility, and computing time, which yields a range of algorithm-specific referential benchmarks for various data qualities. Data simulation and analysis were performed with our MATLAB-based multifunctional analysis software for handling smFRET data (MASH-FRET).

Simulations of camera-based single-molecule fluorescence experiments

Single-molecule microscopy has become a widely used technique in (bio)physics and (bio)chemistry. A popular implementation is single-molecule Förster Resonance Energy Transfer (smFRET), for which total internal reflection fluorescence microscopy is frequently combined with camera-based detection of surface-immobilized molecules. Camera-based smFRET experiments generate large and complex datasets and several methods for video processing and analysis have been reported. As these algorithms often address similar aspects in video analysis, there is a growing need for standardized comparison. Here, we present a Matlab-based software (MASH-FRET) that allows for the simulation of camera-based smFRET videos, yielding standardized data sets suitable for benchmarking video processing algorithms. The software permits to vary parameters that are relevant in cameras-based smFRET, such as video quality, and the properties of the system under study. Experimental noise is modeled taking into account photon statistics and camera noise. Finally, we survey how video test sets should be designed to evaluate currently available data analysis strategies in camera-based sm fluorescence experiments. We complement our study by pre-optimizing and evaluating spot detection algorithms using our simulated video test sets.