Measuring molecular motions inside single cells with improved analysis of single-particle trajectories

Robust Quantification of Live-Cell Single-Molecule Tracking Data for Fluorophores with Different Photophysical Properties

High-speed single-molecule tracking in live cells is becoming an increasingly popular method for quantifying the spatiotemporal behavior of proteins in vivo. The method provides a wealth of quantitative information, but users need to be aware of biases that can skew estimates of molecular mobilities. The range of suitable fluorophores for live-cell single-molecule imaging has grown substantially over the past few years, but it remains unclear to what extent differences in photophysical properties introduce biases. Here, we tested two fluorophores with entirely different photophysical properties, one that photoswitches frequently between bright and dark states (TMR) and one that shows exceptional photostability without photoswitching (JFX650). We used a fusion of the Escherichia coli DNA repair enzyme MutS to the HaloTag and optimized sample preparation and imaging conditions for both types of fluorophore. We then assessed the reliability of two common data analysis algorithms, mean-square displacement (MSD) analysis and Hidden Markov Modeling (HMM), to estimate the diffusion coefficients and fractions of MutS molecules in different states of motion. We introduce a simple approach that removes discrepancies in the data analyses and show that both algorithms yield consistent results, regardless of the fluorophore used. Nevertheless, each dye has its own strengths and weaknesses, with TMR being more suitable for sampling the diffusive behavior of many molecules, while JFX650 enables prolonged observation of only a few molecules per cell. These characterizations and recommendations should help to standardize measurements for increased reproducibility and comparability across studies.

Epigenetic memory is governed by an effector recruitment specificity toggle in Heterochromatin Protein 1

HP1 proteins are essential for establishing and maintaining transcriptionally silent heterochromatin. They dimerize, forming a binding interface to recruit diverse chromatin-associated factors. HP1 proteins are specialized and rapidly evolve, but the extent of variation required to achieve functional specialization is unknown. To investigate how changes in amino acid sequence impacts epigenetic inheritance, we performed a targeted mutagenesis screen of the S. pombe HP1 homolog, Swi6. Substitutions within an auxiliary surface adjacent to the HP1 dimerization interface produced Swi6 variants with divergent maintenance properties. Remarkably, substitutions at a single amino acid position led to the persistent gain or loss of epigenetic inheritance. These substitutions increased Swi6 chromatin occupancy in vivo and altered Swi6-protein interactions that reprogram H3K9me maintenance. We show that relatively minor changes in Swi6 amino acid composition can lead to profound changes in epigenetic inheritance which provides a redundant mechanism to evolve novel effector specificity. .

Single-molecule dynamics of surface lipoproteins in bacteroides indicate similarities and cooperativity

The gut microbiota comprises hundreds of species with a composition shaped by the available glycans. The well-studied starch utilization system (Sus) is a prototype for glycan uptake in the human gut bacterium Bacteroides thetaiotaomicron (Bt). Each Sus-like system includes outer-membrane proteins, which translocate glycan into the periplasm, and one or more cell-surface glycoside hydrolases, which break down a specific (cognate) polymer substrate. Although the molecular mechanisms of the Sus system are known, how the Sus and Sus-like proteins cooperate remains elusive. Previously, we used single-molecule and super-resolution fluorescence microscopy to show that SusG is mobile on the outer membrane and slows down in the presence of starch. Here, we compare the dynamics of three glycoside hydrolases: SusG, Bt4668, and Bt1760, which target starch, galactan, and levan, respectively. We characterized the diffusion of each surface hydrolase in the presence of its cognate glycan and found that all three enzymes are mostly immobile in the presence of the polysaccharide, consistent with carbohydrate binding. Moreover, experiments in glucose versus oligosaccharides suggest that the enzyme dynamics depend on their expression level. Furthermore, we characterized enzyme diffusion in a mixture of glycans and found that noncognate polysaccharides modify the dynamics of SusG and Bt1760 but not Bt4668. We investigated these systems with polysaccharide mixtures and genetic knockouts and found that noncognate polysaccharides modify hydrolase dynamics through some combination of nonspecific protein interactions and downregulation of the hydrolase. Overall, these experiments extend our understanding of how Sus-like lipoprotein dynamics can be modified by changing carbohydrate conditions and the expression level of the enzyme.

HP1 oligomerization compensates for low-affinity H3K9me recognition and provides a tunable mechanism for heterochromatin-specific localization

HP1 proteins traverse a complex and crowded chromatin landscape to bind with low affinity but high specificity to histone H3K9 methylation (H3K9me) and form transcriptionally inactive genomic compartments called heterochromatin. Here, we visualize single-molecule dynamics of an HP1 homolog, the fission yeast Swi6, in its native chromatin environment. By tracking single Swi6 molecules, we identify mobility states that map to discrete biochemical intermediates. Using Swi6 mutants that perturb H3K9me recognition, oligomerization, or nucleic acid binding, we determine how each biochemical property affects protein dynamics. We estimate that Swi6 recognizes H3K9me3 with ~94-fold specificity relative to unmodified nucleosomes in living cells. While nucleic acid binding competes with Swi6 oligomerization, as few as four tandem chromodomains can overcome these inhibitory effects to facilitate Swi6 localization at heterochromatin formation sites. Our studies indicate that HP1 oligomerization is essential to form dynamic, higher-order complexes that outcompete nucleic acid binding to enable specific H3K9me recognition.

NOBIAS: Analyzing Anomalous Diffusion in Single-Molecule Tracks With Nonparametric Bayesian Inference

Single particle tracking (SPT) enables the investigation of biomolecular dynamics at a high temporal and spatial resolution in living cells, and the analysis of these SPT datasets can reveal biochemical interactions and mechanisms. Still, how to make the best use of these tracking data for a broad set of experimental conditions remains an analysis challenge in the field. Here, we develop a new SPT analysis framework: NOBIAS (NOnparametric Bayesian Inference for Anomalous Diffusion in Single-Molecule Tracking), which applies nonparametric Bayesian statistics and deep learning approaches to thoroughly analyze SPT datasets. In particular, NOBIAS handles complicated live-cell SPT data for which: the number of diffusive states is unknown, mixtures of different diffusive populations may exist within single trajectories, symmetry cannot be assumed between the x and y directions, and anomalous diffusion is possible. NOBIAS provides the number of diffusive states without manual supervision, it quantifies the dynamics and relative populations of each diffusive state, it provides the transition probabilities between states, and it assesses the anomalous diffusion behavior for each state. We validate the performance of NOBIAS with simulated datasets and apply it to the diffusion of single outer-membrane proteins in Bacteroides thetaiotaomicron. Furthermore, we compare NOBIAS with other SPT analysis methods and find that, in addition to these advantages, NOBIAS is robust and has high computational efficiency and is particularly advantageous due to its ability to treat experimental trajectories with asymmetry and anomalous diffusion.

Single-molecule observation of diffusion and catalysis in nanoporous solids

Nanoporous solids, including microporous, mesoporous and hierarchically structured porous materials, are of scientific and technological interest because of their high surface-to-volume ratio and ability to impose shape- and size-selectivity on molecules diffusing through them. Enormous efforts have been put in the mechanistic understanding of diffusion–reaction relationships of nanoporous solids, with the ultimate goal of developing materials with improved catalytic performance. Single-molecule localization microscopy can be used to explore the pore space via the trajectories of individual molecules. This ensemble-free perspective directly reveals heterogeneities in diffusion and diffusion-related reactivity of individual molecules, which would have been obscured in bulk measurements. In this article, we review developments in the spatial and temporal characterization of nanoporous solids using single-molecule localization microscopy. We illustrate various aspects of this approach, and showcase how it can be used to follow molecular diffusion and reaction behaviors in nanoporous solids.

SMAUG: Analyzing single-molecule tracks with nonparametric Bayesian statistics

Single-molecule fluorescence microscopy probes nanoscale, subcellular biology in real time. Existing methods for analyzing single-particle tracking data provide dynamical information, but can suffer from supervisory biases and high uncertainties. Here, we develop a method for the case of multiple interconverting species undergoing free diffusion and introduce a new approach to analyzing single-molecule trajectories: the Single-Molecule Analysis by Unsupervised Gibbs sampling (SMAUG) algorithm, which uses nonparametric Bayesian statistics to uncover the whole range of information contained within a single-particle trajectory dataset. Even in complex systems where multiple biological states lead to a number of observed mobility states, SMAUG provides the number of mobility states, the average diffusion coefficient of single molecules in that state, the fraction of single molecules in that state, the localization noise, and the probability of transitioning between two different states. In this paper, we provide the theoretical background for the SMAUG analysis and then we validate the method using realistic simulations of single-particle trajectory datasets as well as experiments on a controlled in vitro system. Finally, we demonstrate SMAUG on real experimental systems in both prokaryotes and eukaryotes to measure the motions of the regulatory protein TcpP in Vibrio cholerae and the dynamics of the B-cell receptor antigen response pathway in lymphocytes. Overall, SMAUG provides a mathematically rigorous approach to measuring the real-time dynamics of molecular interactions in living cells.

SMALL-LABS: Measuring Single-Molecule Intensity and Position in Obscuring Backgrounds

Single-molecule and super-resolution imaging relies on successful, sensitive, and accurate detection of the emission from fluorescent molecules. Yet, despite the widespread adoption of super-resolution microscopies, single-molecule data processing algorithms can fail to provide accurate measurements of the brightness and position of molecules in the presence of backgrounds that fluctuate significantly over time and space. Thus, samples or experiments that include obscuring backgrounds can severely, or even completely, hinder this process. To date, no general data analysis approach to this problem has been introduced that is capable of removing obscuring backgrounds for a wide variety of experimental modalities. To address this need, we present the Single-Molecule Accurate LocaLization by LocAl Background Subtraction (SMALL-LABS) algorithm, which can be incorporated into existing single-molecule and super-resolution analysis packages to accurately locate and measure the intensity of single molecules, regardless of the shape or brightness of the background. Accurate background subtraction is enabled by separating the foreground from the background based on differences in the temporal variations of the foreground and the background (i.e., fluorophore blinking, bleaching, or moving). We detail the function of SMALL-LABS here, and we validate the SMALL-LABS algorithm on simulated data as well as real data from single-molecule imaging in living cells.