Extracting physics of life at the molecular level: A review of single-molecule data analyses

a-SiC heteromorphic immersion nanocavities enabling wide-field real-time single-molecule detection

Real-time single-molecule detection via fluorescence exhibits advantages of non-contact and specificity, especially in illustrating the dynamic heterogeneity of living substances. However, wide-field view and signal-to-noise ratio (SNR) are always contradictory in real-time single-molecule detection with fluorescence labels, owing to the limitation of the omnidirectional radiation characteristics of fluorophores. Herein, we propose a nano optical sensing device based on a-SiC heteromorphic immersion nanocavities (aHINCs), enabling wide-field real-time single-molecule imaging without sacrificing SNR. The characteristics of architectural aHINCs for far-field imaging are investigated, and the designed sensing device help adjust the emission direction of fluorescence in gold hot spots of nanocavities, allowing the fluorescence divergence angle to be adjusted to ±10°. The experimental results show the SNR reached 22, markedly strengthening the fluorescence collection efficiency. The simultaneous observation of nanocavity sites, within the same field of view of the chip, is 488 times the number of sites observed with classic detection method. Furthermore, the wide-field real-time detection of the single molecule-specific binding process of the oligo DNA complementary chain is successfully realized. The nano optical sensing device based on the aHINC shows potential for parallel real-time single-molecule detection applications.

RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

Revealing the Binding Events of Single Proteins on Exosomes Using Nanocavity Antennas beyond Zero-Mode Waveguides

Exosomes (EXOs) play a crucial role in biological action mechanisms. Understanding the biological process of single-molecule interactions on the surface of the EXO membrane is essential for elucidating the precise function of the EXO receptor. However, due to dimensional incompatibility, monitoring the binding events between EXOs of tens to hundreds of nanometers and biomolecules of nanometers using existing nanostructure antennas is difficult. Unlike the typical zero-mode waveguides (ZMWs), this work presents a nanocavity antenna (λvNAs) formed by nanocavities with diameters close to the visible light wavelength dimensions. Effective excitation volumes suitable for observing single-molecule fluorescence were generated in nanocavities of larger diameters than typical ZMWs; the optimal signal-to-noise ratio obtained was 19.5 when the diameter was 300 nm and the incident angle was ∼50°. EXOs with a size of 50-150 nm were loaded into λvNAs with an optimized diameter of 300-500 nm, resulting in appreciable occupancy rates that overcame the nanocavity size limitation for large-volume biomaterial loading. Additionally, this method identified the binding events between the single transmembrane CD9 proteins on the EXO surface and their monoclonal antibody anti-CD9, demonstrating that λvNAs expanded the application range beyond subwavelength ZMWs. Furthermore, the λvNAs provide a platform for obtaining in-depth knowledge of the interactions of single molecules with biomaterials ranging in size from tens to hundreds of nanometers.

Trends in Single-Molecule Total Internal Reflection Fluorescence Imaging and Their Biological Applications with Lab-on-a-Chip Technology

Single-molecule imaging technologies, especially those based on fluorescence, have been developed to probe both the equilibrium and dynamic properties of biomolecules at the single-molecular and quantitative levels. In this review, we provide an overview of the state-of-the-art advancements in single-molecule fluorescence imaging techniques. We systematically explore the advanced implementations of in vitro single-molecule imaging techniques using total internal reflection fluorescence (TIRF) microscopy, which is widely accessible. This includes discussions on sample preparation, passivation techniques, data collection and analysis, and biological applications. Furthermore, we delve into the compatibility of microfluidic technology for single-molecule fluorescence imaging, highlighting its potential benefits and challenges. Finally, we summarize the current challenges and prospects of fluorescence-based single-molecule imaging techniques, paving the way for further advancements in this rapidly evolving field.

AutoStepfinder: A fast and automated step detection method for single-molecule analysis

Single-molecule techniques allow the visualization of the molecular dynamics of nucleic acids and proteins with high spatiotemporal resolution. Valuable kinetic information of biomolecules can be obtained when the discrete states within single-molecule time trajectories are determined. Here, we present a fast, automated, and bias-free step detection method, AutoStepfinder, that determines steps in large datasets without requiring prior knowledge on the noise contributions and location of steps. The analysis is based on a series of partition events that minimize the difference between the data and the fit. A dual-pass strategy determines the optimal fit and allows AutoStepfinder to detect steps of a wide variety of sizes. We demonstrate step detection for a broad variety of experimental traces. The user-friendly interface and the automated detection of AutoStepfinder provides a robust analysis procedure that enables anyone without programming knowledge to generate step fits and informative plots in less than an hour.

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Fast, automated, and bias-free detection of steps within single-molecule trajectories

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Robust step detection without any prior knowledge on the data

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A dual-pass strategy for the detection of steps over a wide variety of scales

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A user-friendly interface for a simplified step fitting procedure

Single-molecule techniques have made it possible to track individual protein complexes in real time with a nanometer spatial resolution and a millisecond timescale. Accurate determination of the dynamic states within single-molecule time traces provides valuable kinetic information that underlie the function of biological macromolecules. Here, we present a new automated step detection method called AutoStepfinder, a versatile, robust, and easy-to-use algorithm that allows researchers to determine the kinetic states within single-molecule time trajectories without any bias.

Coming Together: RNAs and Proteins Assemble under the Single-Molecule Fluorescence Microscope

RNAs, across their numerous classes, often work in concert with proteins in RNA-protein complexes (RNPs) to execute critical cellular functions. Ensemble-averaging methods have been instrumental in revealing many important aspects of these RNA-protein interactions, yet are insufficiently sensitive to much of the dynamics at the heart of RNP function. Single-molecule fluorescence microscopy (SMFM) offers complementary, versatile tools to probe RNP conformational and compositional changes in detail. In this review, we first outline the basic principles of SMFM as applied to RNPs, describing key considerations for labeling, imaging, and quantitative analysis. We then sample applications of in vitro and in vivo single-molecule visualization using the case studies of pre-messenger RNA (mRNA) splicing and RNA silencing, respectively. After discussing specific insights single-molecule fluorescence methods have yielded, we briefly review recent developments in the field and highlight areas of anticipated growth.

Plasmon-Enhanced Single-Molecule Enzymology

We present a numerical study on plasmon-enhanced single-molecule enzymology. We combine Brownian dynamics and electromagnetic simulations to calculate the enhancement of fluorescence signals of fluorogenic substrate converted by an enzyme conjugated to a plasmonic particle. We simulate the Brownian motion of a fluorescent product away from the active site of the enzyme, and calculate the photon detection rate taking into account modifications of the excitation and emission processes by coupling to the plasmon. We show that plasmon enhancement can boost the signal-to-noise ratio (SNR) of single turnovers by up to 100 fold compared to confocal microscopy. This enhancement factor is a trade-off between the reduced residence time in the near-field of the particle, and the enhanced emission intensity due to coupling to the plasmon. The enhancement depends on the size, shape and material of the particle and the photophysical properties of the fluorescent product. Our study provides guidelines on how to enhance the SNR of single-molecule enzyme studies and may aid in further understanding and quantifying static and dynamic heterogeneity.

Lipogels for Encapsulation of Hydrophilic Proteins and Hydrophobic Small Molecules

Lipid-polymer hybrid materials have the potential to exhibit enhanced stability and loading capabilities in comparison to parent liposome or polymer materials. However, complexities lie in formulating and characterizing such complex nanomaterials. Here we describe a lipid-coated polymer gel (lipogel) formulated using a single-pot methodology, where self-assembling liposomes template a UV-curable polymer gel core. Using fluorescently labeled lipids, protein, and hydrophobic molecules, we characterized their formation, purification, stability, and encapsulation efficiency via common instrumentation methods such as dynamic light scattering (DLS), matrix-assisted laser desorption ionization-mass spectrometry (MALDI-MS), UV-vis spectroscopy, fluorescence spectroscopy, and single-particle total internal reflection fluorescence (TIRF) microscopy. In addition, we confirmed that these dual-guest-loaded lipogels are stable in solution for several months. The simplicity of this complete aqueous formation and noncovalent dual-guest encapsulation holds potential as a tunable nanomaterial scaffold.

Noise slows the rate of Michaelis-Menten reactions

Microscopic randomness and the small volumes of living cells combine to generate random fluctuations in molecule concentrations called "noise". Here I investigate the effect of noise on biochemical reactions obeying Michaelis-Menten kinetics, concluding that substrate noise causes these reactions to slow. I derive a general expression for the time evolution of the joint probability density of chemical species in arbitrarily connected networks of non-linear chemical reactions in small volumes. This equation is a generalization of the chemical master equation (CME), a common tool for investigating stochastic chemical kinetics, extended to reaction networks occurring in small volumes, such as living cells. I apply this equation to a generalized Michaelis-Menten reaction in an open system, deriving the following general result: 〈p〉≤p¯ and 〈s〉≥s¯, where s¯ and p¯ denote the deterministic steady-state concentration of reactant and product species, respectively, and 〈s〉 and 〈p〉 denote the steady-state ensemble average over independent realizations of a stochastic reaction. Under biologically realistic conditions, namely when substrate is degraded or diluted by cell division, 〈p〉≤p¯. Consequently, noise slows the rate of in vivo Michaelis-Menten reactions. These predictions are validated by extensive stochastic simulations using Gillespie's exact stochastic simulation algorithm. I specify the conditions under which these effects occur and when they vanish, therefore reconciling discrepancies among previous theoretical investigations of stochastic biochemical reactions. Stochastic slowdown of reaction flux caused by molecular noise in living cells may have functional consequences, which the present theory may be used to quantify.

Estimation of microscope drift using fluorescent nanodiamonds as fiducial markers

Fiducial markers are used to correct the microscope drift and should be photostable, be usable at multiple wavelengths and be compatible for multimodal imaging. Fiducial markers such as beads, gold nanoparticles, microfabricated patterns and organic fluorophores lack one or more of these criteria. Moreover, the localization accuracy and drift correction can be degraded by other fluorophores, instrument noise and artefacts due to image processing and tracking algorithms. Estimating mechanical drift by assuming Gaussian distributed noise is not suitable under these circumstances. Here we present a method that uses fluorescent nanodiamonds as fiducial markers and uses an improved maximum likelihood algorithm to estimate the drift with both accuracy and precision within the range 1.55-5.75 nm.