Pointwise error estimates in localization microscopy

The Escherichia coli chromosome moves to the replisome

In Escherichia coli, it is debated whether the two replisomes move independently along the two chromosome arms during replication or if they remain spatially confined. Here, we use high-throughput fluorescence microscopy to simultaneously determine the location and short-time-scale (1 s) movement of the replisome and a chromosomal locus throughout the cell cycle. The assay is performed for several loci. We find that (i) the two replisomes are confined to a region of ~250 nm and ~120 nm along the cell's long and short axis, respectively, (ii) the chromosomal loci move to and through this region sequentially based on their distance from the origin of replication, and (iii) when a locus is being replicated, its short time-scale movement slows down. This behavior is the same at different growth rates. In conclusion, our data supports a model with DNA moving towards spatially confined replisomes at replication.

Single-molecule tracking to determine the abundances and stoichiometries of freely-diffusing protein complexes in living cells: Past applications and future prospects

Most biological processes in living cells rely on interactions between proteins. Live-cell compatible approaches that can quantify to what extent a given protein participates in homo- and hetero-oligomeric complexes of different size and subunit composition are therefore critical to advance our understanding of how cellular physiology is governed by these molecular interactions. Biomolecular complex formation changes the diffusion coefficient of constituent proteins, and these changes can be measured using fluorescence microscopy-based approaches, such as single-molecule tracking, fluorescence correlation spectroscopy, and fluorescence recovery after photobleaching. In this review, we focus on the use of single-molecule tracking to identify, resolve, and quantify the presence of freely-diffusing proteins and protein complexes in living cells. We compare and contrast different data analysis methods that are currently employed in the field and discuss experimental designs that can aid the interpretation of the obtained results. Comparisons of diffusion rates for different proteins and protein complexes in intracellular aqueous environments reported in the recent literature reveal a clear and systematic deviation from the Stokes-Einstein diffusion theory. While a complete and quantitative theoretical explanation of why such deviations manifest is missing, the available data suggest the possibility of weighing freely-diffusing proteins and protein complexes in living cells by measuring their diffusion coefficients. Mapping individual diffusive states to protein complexes of defined molecular weight, subunit stoichiometry, and structure promises to provide key new insights into how protein-protein interactions regulate protein conformational, translational, and rotational dynamics, and ultimately protein function.

ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

Single-particle tracking microscopy is a powerful technique to investigate how proteins dynamically interact with their environment in live cells. However, the analysis of tracks is confounded by noisy molecule localization, short tracks, and rapid transitions between different motion states, notably between immobile and diffusive states. Here, we propose a probabilistic method termed ExTrack that uses the full spatio-temporal information of tracks to extract global model parameters, to calculate state probabilities at every time point, to reveal distributions of state durations, and to refine the positions of bound molecules. ExTrack works for a wide range of diffusion coefficients and transition rates, even if experimental data deviate from model assumptions. We demonstrate its capacity by applying it to slowly diffusing and rapidly transitioning bacterial envelope proteins. ExTrack greatly increases the regime of computationally analyzable noisy single-particle tracks. The ExTrack package is available in ImageJ and Python.

ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks

Single-particle tracking microscopy is a powerful technique to investigate how proteins dynamically interact with their environment in live cells. However, the analysis of tracks is confounded by noisy molecule localization, short tracks, and rapid transitions between different motion states, notably between immobile and diffusive states. Here, we propose a probabilistic method termed ExTrack that uses the full spatio-temporal information of tracks to extract global model parameters, to calculate state probabilities at every time point, to reveal distributions of state durations, and to refine the positions of bound molecules. ExTrack works for a wide range of diffusion coefficients and transition rates, even if experimental data deviate from model assumptions. We demonstrate its capacity by applying it to slowly diffusing and rapidly transitioning bacterial envelope proteins. ExTrack greatly increases the regime of computationally analyzable noisy single-particle tracks. The ExTrack package is available in ImageJ and Python.

Error-Correction Method for High-Throughput Sizing of Nanoscale Vesicles with Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) allows super-resolution imaging, mapping, counting, and sizing of biological nanostructures such as cell organelles and extracellular vesicles (EVs), but sizing structures smaller than ∼100 nm can be inaccurate due to single-molecule localization error caused by distortion of the point spread function and limited photon number. Here we demonstrate a method to correct localization error when sizing vesicles and other spherical nanoparticles with SMLM and compare sizing results using two vesicle labeling schemes. We use mean approximation theory to derive a simple equation using full width at half-maximum (FWHM) for correcting particle sizes measured by two-dimensional SMLM, validate the method by sizing streptavidin-coated polystyrene nanobeads with the SMLM technique dSTORM with and without error correction, using transmission electron microscopy (TEM) for comparison, and then apply the method to sizing small seminal EVs. Nanobead sizes measured by dSTORM became increasingly less accurate (larger than TEM values) for beads smaller than 50 nm. The error-correction method reduced the size difference versus TEM from 15% without error correction to 7% with error correction for 40 nm beads, from 44% to 9% for 30 nm beads, and from 66% to 15% for 20 nm beads. Seminal EVs were labeled with a lipophilic membrane dye (MemBright 700) and with an Alexa Fluor 488-anti-CD63 antibody conjugate, and were sized separately using both dyes by dSTORM. Error-corrected exosome diameters were smaller than uncorrected values: 72 nm vs 79 nm mean diameter with membrane dyes; 84 nm vs 97 nm with the antibody-conjugated dyes. The mean error-corrected diameter was 12 nm smaller when using the membrane dye than when using the antibody-conjugated dye likely due to the large size of the antibody. Thus, both the error-correction method and the compact membrane labeling scheme reduce overestimation of vesicle size by SMLM. This error-correction method has a low computational cost as it does not require correction of individual blinking events, and it is compatible with all SMLM techniques (e.g., PALM, STORM, and DNA-PAINT).

Spatiotemporal kinetics of the SRP pathway in live E. coli cells

Mechanistic details of the signal recognition particle (SRP)-mediated insertion of membrane proteins have been described from decades of in vitro biochemical studies. However, the dynamics of the pathway inside the living cell remain obscure. By combining in vivo single-molecule tracking with numerical modeling and simulated microscopy, we have constructed a quantitative reaction-diffusion model of the SRP cycle. Our results suggest that the SRP-ribosome complex finds its target, the membrane-bound translocon, through a combination of three-dimensional (3D) and 2D diffusional search, together taking on average 750 ms. During this time, the nascent peptide is expected to be elongated only 12 or 13 amino acids, which explains why, in Escherichia coli, no translation arrest is needed to prevent incorrect folding of the polypeptide in the cytosol. We also found that a remarkably high proportion (75%) of SRP bindings to ribosomes occur in the cytosol, suggesting that the majority of target ribosomes bind SRP before reaching the membrane. In combination with the average SRP cycling time, 2.2 s, this result further shows that the SRP pathway is capable of targeting all substrate ribosomes to translocons.

Recovering mixtures of fast-diffusing states from short single-particle trajectories

Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.

Direct measurements of mRNA translation kinetics in living cells

Ribosome mediated mRNA translation is central to life. The cycle of translation, however, has been characterized mostly using reconstituted systems, with only few techniques applicable for studies in the living cell. Here we describe a live-cell ribosome-labeling method, which allows us to characterize the whole processes of finding and translating an mRNA, using single-molecule tracking techniques. We find that more than 90% of both bacterial ribosomal subunits are engaged in translation at any particular time, and that the 30S and 50S ribosomal subunits spend the same average time bound to an mRNA, revealing that 30S re-initiation on poly-cistronic mRNAs is not prevalent in E. coli. Instead, our results are best explained by substantial 70S re-initiation of translation of poly-cistronic mRNAs, which is further corroborated by experiments with translation initiation inhibitors. Finally, we find that a variety of previously described orthogonal ribosomes, with altered anti-Shine-Dalgarno sequences, show significant binding to endogenous mRNAs.

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.

Real-time measurements of aminoglycoside effects on protein synthesis in live cells

The spread of antibiotic resistance is turning many of the currently used antibiotics less effective against common infections. To address this public health challenge, it is critical to enhance our understanding of the mechanisms of action of these compounds. Aminoglycoside drugs bind the bacterial ribosome, and decades of results from in vitro biochemical and structural approaches suggest that these drugs disrupt protein synthesis by inhibiting the ribosome's translocation on the messenger RNA, as well as by inducing miscoding errors. So far, however, we have sparse information about the dynamic effects of these compounds on protein synthesis inside the cell. In the present study, we measured the effect of the aminoglycosides apramycin, gentamicin, and paromomycin on ongoing protein synthesis directly in live Escherichia coli cells by tracking the binding of dye-labeled transfer RNAs to ribosomes. Our results suggest that the drugs slow down translation elongation two- to fourfold in general, and the number of elongation cycles per initiation event seems to decrease to the same extent. Hence, our results imply that none of the drugs used in this study cause severe inhibition of translocation.

Direct Measurements of Erythromycin's Effect on Protein Synthesis Kinetics in Living Bacterial Cells

Macrolide antibiotics, such as erythromycin, bind to the nascent peptide exit tunnel (NPET) of the bacterial ribosome and modulate protein synthesis depending on the nascent peptide sequence. Whereas in vitro biochemical and structural methods have been instrumental in dissecting and explaining the molecular details of macrolide-induced peptidyl-tRNA drop-off and ribosome stalling, the dynamic effects of the drugs on ongoing protein synthesis inside live bacterial cells are far less explored. In the present study, we used single-particle tracking of dye-labeled tRNAs to study the kinetics of mRNA translation in the presence of erythromycin, directly inside live Escherichia coli cells. In erythromycin-treated cells, we find that the dwells of elongator tRNA^Phe on ribosomes extend significantly, but they occur much more seldom. In contrast, the drug barely affects the ribosome binding events of the initiator tRNA^fMet. By overexpressing specific short peptides, we further find context-specific ribosome binding dynamics of tRNA^Phe, underscoring the complexity of erythromycin's effect on protein synthesis in bacterial cells.

Blind Deconvolution Based on Compressed Sensing with bi-l0-l2-norm Regularization in Light Microscopy Image

Blind deconvolution of light microscopy images could improve the ability of distinguishing cell-level substances. In this study, we investigated the blind deconvolution framework for a light microscope image, which combines the benefits of bi-l₀-l₂-norm regularization with compressed sensing and conjugated gradient algorithms. Several existing regularization approaches were limited by staircase artifacts (or cartooned artifacts) and noise amplification. Thus, we implemented our strategy to overcome these problems using the bi-l₀-l₂-norm regularization proposed. It was investigated through simulations and experiments using optical microscopy images including the background noise. The sharpness was improved through the successful image restoration while minimizing the noise amplification. In addition, quantitative factors of the restored images, including the intensity profile, root-mean-square error (RMSE), edge preservation index (EPI), structural similarity index measure (SSIM), and normalized noise power spectrum, were improved compared to those of existing or comparative images. In particular, the results of using the proposed method showed RMSE, EPI, and SSIM values of approximately 0.12, 0.81, and 0.88 when compared with the reference. In addition, RMSE, EPI, and SSIM values in the restored image were proven to be improved by about 5.97, 1.26, and 1.61 times compared with the degraded image. Consequently, the proposed method is expected to be effective for image restoration and to reduce the cost of a high-performance light microscope.

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.

Bayesian Inference: The Comprehensive Approach to Analyzing Single-Molecule Experiments

Biophysics experiments performed at single-molecule resolution provide exceptional insight into the structural details and dynamic behavior of biological systems. However, extracting this information from the corresponding experimental data unequivocally requires applying a biophysical model. In this review, we discuss how to use probability theory to apply these models to single-molecule data. Many current single-molecule data analysis methods apply parts of probability theory, sometimes unknowingly, and thus miss out on the full set of benefits provided by this self-consistent framework. The full application of probability theory involves a process called Bayesian inference that fully accounts for the uncertainties inherent to single-molecule experiments. Additionally, using Bayesian inference provides a scientifically rigorous method of incorporating information from multiple experiments into a single analysis and finding the best biophysical model for an experiment without the risk of overfitting the data. These benefits make the Bayesian approach ideal for analyzing any type of single-molecule experiment.

Quantifying accuracy and heterogeneity in single-molecule super-resolution microscopy

The resolution and accuracy of single-molecule localization microscopes (SMLMs) are routinely benchmarked using simulated data, calibration rulers, or comparisons to secondary imaging modalities. However, these methods cannot quantify the nanoscale accuracy of an arbitrary SMLM dataset. Here, we show that by computing localization stability under a well-chosen perturbation with accurate knowledge of the imaging system, we can robustly measure the confidence of individual localizations without ground-truth knowledge of the sample. We demonstrate that our method, termed Wasserstein-induced flux (WIF), measures the accuracy of various reconstruction algorithms directly on experimental 2D and 3D data of microtubules and amyloid fibrils. We further show that WIF confidences can be used to evaluate the mismatch between computational models and imaging data, enhance the accuracy and resolution of reconstructed structures, and discover hidden molecular heterogeneities. As a computational methodology, WIF is broadly applicable to any SMLM dataset, imaging system, and localization algorithm.

Standard benchmarking of single-molecule localization microscopy cannot quantify nanoscale accuracy of arbitrary datasets. Here, the authors present Wasserstein-induced flux, a method using a chosen perturbation and knowledge of the imaging system to measure confidence of individual localizations.

Ultrasound Microvascular Imaging Based on Super-Resolution Radial Fluctuations

OBJECTIVES

Super-resolution ultrasound (SRUS) has become a tool for in vivo microvascular imaging. Most of the SRUS methods are based on microbubble localization: namely, ultrasound localization microscopy (ULM). The aim of this study was to develop a nonlocalization SRUS method and verify its feasibility in microvascular imaging.

METHODS

We introduce a new super-resolution strategy based on the postprocessing of contrast-enhanced ultrasound. The proposed method, which is termed ultrasound diffraction attenuation microscopy (UDAM), uses super-resolution radial fluctuations instead of microbubble localization to overcome acoustic diffraction limits. Biceps of Japanese long-ear white rabbits were adopted to validate its feasibility on muscle vascular imaging, using a clinical accessible ultrasound system at a frame rate of 30 Hz under a single bolus injection of SonoVue (Bracco SpA, Milan, Italy). The super-resolution image was compared with the maximum-intensity projection and ULM.

RESULTS

The animal study illustrates that the proposed UDAM can obtain super-resolution microvascular images of rabbits' muscles under a single bolus injection of SonoVue with a 150-second contrast-enhanced ultrasound video. Both ULM and UDAM can achieve a very similar vascular structure with the maximum-intensity projection but much higher spatial resolution. The measurement of 1-dimensional signals shows that UDAM can distinguish the subwavelength structures and substantial reduce the full width at half-maximum of microvessels.

CONCLUSIONS

We conclude UDAM provides a noninvasive tool for in vivo super-resolution microvascular imaging.

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.

Analysis and refinement of 2D single-particle tracking experiments

In recent decades, single particle tracking (SPT) has been developed into a sophisticated analytical approach involving complex instruments and data analysis schemes to extract information from time-resolved particle trajectories. Very often, mobility-related properties are extracted from these particle trajectories, as they often contain information about local interactions experienced by the particles while moving through the sample. This tutorial aims to provide a comprehensive overview about the accuracies that can be achieved when extracting mobility-related properties from 2D particle trajectories and how these accuracies depend on experimental parameters. Proper interpretation of SPT data requires an assessment of whether the obtained accuracies are sufficient to resolve the effect under investigation. This is demonstrated by calculating mean square displacement curves that show an apparent super- or subdiffusive behavior due to poor measurement statistics instead of the presence of true anomalous diffusion. Furthermore, the refinement of parameters involved in the design or analysis of SPT experiments is discussed and an approach is proposed in which square displacement distributions are inspected to evaluate the quality of SPT data and to extract information about the maximum distance over which particles should be tracked during the linking process.

Single-Molecule Tracking Approaches to Protein Synthesis Kinetics in Living Cells

Decades of traditional biochemistry, structural approaches, and, more recently, single-molecule-based in vitro techniques have provided us with an astonishingly detailed understanding of the molecular mechanism of ribosome-catalyzed protein synthesis. However, in order to understand these details in the context of cell physiology and population biology, new techniques to probe the dynamics of molecular processes inside the cell are needed. Recent years' development in super-resolved fluorescence microscopy has revolutionized imaging of intracellular processes, and we now have the possibility to directly peek into the microcosm of biomolecules in their native environment. In this Perspective, we discuss how these methods are currently being applied and further developed to study the kinetics of protein synthesis directly inside living cells.

Single-Molecule Kinetics in Living Cells

In the past decades, advances in microscopy have made it possible to study the dynamics of individual biomolecules in vitro and resolve intramolecular kinetics that would otherwise be hidden in ensemble averages. More recently, single-molecule methods have been used to image, localize, and track individually labeled macromolecules in the cytoplasm of living cells, allowing investigations of intermolecular kinetics under physiologically relevant conditions. In this review, we illuminate the particular advantages of single-molecule techniques when studying kinetics in living cells and discuss solutions to specific challenges associated with these methods.

Subnanometer localization accuracy in widefield optical microscopy

The common assumption that precision is the limit of accuracy in localization microscopy and the typical absence of comprehensive calibration of optical microscopes lead to a widespread issue-overconfidence in measurement results with nanoscale statistical uncertainties that can be invalid due to microscale systematic errors. In this article, we report a comprehensive solution to this underappreciated problem. We develop arrays of subresolution apertures into the first reference materials that enable localization errors approaching the atomic scale across a submillimeter field. We present novel methods for calibrating our microscope system using aperture arrays and develop aberration corrections that reach the precision limit of our reference materials. We correct and register localization data from multiple colors and test different sources of light emission with equal accuracy, indicating the general applicability of our reference materials and calibration methods. In a first application of our new measurement capability, we introduce the concept of critical-dimension localization microscopy, facilitating tests of nanofabrication processes and quality control of aperture arrays. In a second application, we apply these stable reference materials to answer open questions about the apparent instability of fluorescent nanoparticles that commonly serve as fiducial markers. Our study establishes a foundation for subnanometer localization accuracy in widefield optical microscopy.

Variational Algorithms for Analyzing Noisy Multistate Diffusion Trajectories

Single-particle tracking offers a noninvasive high-resolution probe of biomolecular reactions inside living cells. However, efficient data analysis methods that correctly account for various noise sources are needed to realize the full quantitative potential of the method. We report algorithms for hidden Markov-based analysis of single-particle tracking data, which incorporate most sources of experimental noise, including heterogeneous localization errors and missing positions. Compared to previous implementations, the algorithms offer significant speedups, support for a wider range of inference methods, and a simple user interface. This will enable more advanced and exploratory quantitative analysis of single-particle tracking data.

tRNA tracking for direct measurements of protein synthesis kinetics in live cells

Our ability to directly relate results from test tube biochemical experiments to the kinetics in living cells is very limited. Here we present experimental and analytical tools to directly study the kinetics of fast biochemical reactions in live cells. Dye-labeled molecules are electroporated into bacterial cells and tracked using super-resolved single-molecule microscopy. Trajectories are analyzed by machine-learning algorithms to directly monitor transitions between bound and free states. In particular, we measure the dwell-time of tRNAs on ribosomes, and hence achieve direct measurements of translation rates inside living cells at codon resolution. We find elongation rates with tRNA^Phe in perfect agreement with previous indirect estimates, and that once fMet-tRNA^fMet has bound to the 30S ribosomal subunit, initiation of translation is surprisingly fast and does not limit the overall rate of protein synthesis. The experimental and analytical tools for direct kinetics measurements in live cells have applications far beyond bacterial protein synthesis.

Robust model-based analysis of single-particle tracking experiments with Spot-On

Single-particle tracking (SPT) has become an important method to bridge biochemistry and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT studies is challenging due to biases in both data analysis and experimental design. To address analysis bias, we introduce 'Spot-On', an intuitive web-interface. Spot-On implements a kinetic modeling framework that accounts for known biases, including molecules moving out-of-focus, and robustly infers diffusion constants and subpopulations from pooled single-molecule trajectories. To minimize inherent experimental biases, we implement and validate stroboscopic photo-activation SPT (spaSPT), which minimizes motion-blur bias and tracking errors. We validate Spot-On using experimentally realistic simulations and show that Spot-On outperforms other methods. We then apply Spot-On to spaSPT data from live mammalian cells spanning a wide range of nuclear dynamics and demonstrate that Spot-On consistently and robustly infers subpopulation fractions and diffusion constants.

Kinetics of dCas9 target search in Escherichia coli

How fast can a cell locate a specific chromosomal DNA sequence specified by a single-stranded oligonucleotide? To address this question, we investigate the intracellular search processes of the Cas9 protein, which can be programmed by a guide RNA to bind essentially any DNA sequence. This targeting flexibility requires Cas9 to unwind the DNA double helix to test for correct base pairing to the guide RNA. Here we study the search mechanisms of the catalytically inactive Cas9 (dCas9) in living Escherichia coli by combining single-molecule fluorescence microscopy and bulk restriction-protection assays. We find that it takes a single fluorescently labeled dCas9 6 hours to find the correct target sequence, which implies that each potential target is bound for less than 30 milliseconds. Once bound, dCas9 remains associated until replication. To achieve fast targeting, both Cas9 and its guide RNA have to be present at high concentrations.