A novel method to accurately locate and count large numbers of steps by photobleaching

Bayesian analysis and efficient algorithms for single-molecule fluorescence data and step counting

With the growing adoption of single-molecule fluorescence experiments, there is an increasing demand for efficient statistical methodologies and accurate analysis of the acquired measurements. Existing analysis frameworks, such as those that use kinetic models, often rely on strong assumptions on the dynamics of the molecules and fluorophores under study that render them inappropriate for general purpose step-counting applications, especially when the systems of study exhibit uncharac-terized dynamics. Here, we propose a novel Bayesian nonparametric framework to analyze single-molecule fluorescence data that is kinetic model independent. For the evaluation of our methods, we develop four MCMC samplers, ranging from elemental to highly sophisticated, and demonstrate that the added complexity is essential for accurate data analysis. We apply our methods to experimental data obtained from TIRF photobleaching assays of the EphA2 receptor tagged with GFP. In addition, we validate our approach with synthetic data mimicking realistic conditions and demonstrate its ability to recover ground truth under high- and low-signal-to-noise data, establishing it as a versatile tool for fluorescence data analysis.

FluoroTensor: Identification and tracking of colocalised molecules and their stoichiometries in multi-colour single molecule imaging via deep learning

The identification of photobleaching steps in single molecule fluorescence imaging is a well-established procedure for analysing the stoichiometries of molecular complexes. Nonetheless, the method is challenging with protein fluorophores because of the high levels of noise, rapid bleaching and highly variable signal intensities, all of which complicate methods based on statistical analyses of intensities to identify bleaching steps. It has recently been shown that deep learning by convolutional neural networks can yield an accurate analysis with a relatively short computational time. We describe here an improved use of such an approach that detects bleaching events even in the first time point of observation, and we have included this within an integrated software package incorporating fluorescence spot detection, colocalisation, tracking, FRET and photobleaching step analyses of single molecules or complexes. This package, known as FluoroTensor, is written in Python with a self-explanatory user interface.

Foundation model for efficient biological discovery in single-molecule data

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

Foundation model for efficient biological discovery in single-molecule data

Modern data-intensive techniques offer ever deeper insights into biology, but render the process of discovery increasingly complex. For example, exploiting the unique ability of single-molecule fluorescence microscopy (SMFM)1-5. to uncover rare but critical intermediates often demands manual inspection of time traces and iterative ad hoc approaches that are difficult to systematize. To facilitate systematic and efficient discovery from SMFM data, we introduce META-SiM, a transformer-based foundation model pre-trained on diverse SMFM analysis tasks. META-SiM achieves high performance-rivaling best-in-class algorithms-on a broad range of analysis tasks including trace selection, classification, segmentation, idealization, and stepwise photobleaching analysis. Additionally, the model produces high-dimensional embedding vectors that encapsulate detailed information about each trace, which the web-based META-SiM Projector (https://www.simol-projector.org) casts into lower-dimensional space for efficient whole-dataset visualization, labeling, comparison, and sharing. Combining this Projector with the objective metric of Local Shannon Entropy enables rapid identification of condition-specific behaviors, even if rare or subtle. As a result, by applying META-SiM to an existing single-molecule Förster resonance energy transfer (smFRET) dataset6, we discover a previously unobserved intermediate state in pre-mRNA splicing. META-SiM thus removes bottlenecks, improves objectivity, and both systematizes and accelerates biological discovery in complex single-molecule data.

Measuring protein stoichiometry with single-molecule imaging in Xenopus egg extracts

In human cells, DNA double-strand breaks are rapidly bound by the highly abundant non-homologous end joining (NHEJ) factor Ku70/Ku80 (Ku). Cellular imaging and structural data revealed a single Ku molecule is bound to a free DNA end and yet the mechanism regulating Ku remains unclear. Here, we describe how to utilize the cell-free Xenopus laevis egg extract system in conjunction with single-molecule microscopy to investigate regulation of Ku stoichiometry during non-homologous end joining. Egg extract is an excellent model system to study DNA repair as it contains the soluble proteome including core and accessory NHEJ factors, and efficiently repairs double-strand breaks in an NHEJ-dependent manner. To examine the Ku stoichiometry in the extract system, we developed a single-molecule photobleaching assay, which reports on the number of stable associated Ku molecules by monitoring the intensity of fluorescently labeled Ku molecules bound to double-stranded DNA over time. Photobleaching is distinguishable as step decreases in fluorescence intensity and the number of photobleaching events indicate fluorophore stoichiometry. In this paper we describe sample preparation, experimental methodology, and data analysis to discern Ku stoichiometry and the regulatory mechanism controlling its loading. These approaches can be readily adopted to determine stoichiometry of molecular factors within other macromolecular complexes.

Single-molecule imaging reveals a direct role of CTCF's zinc fingers in SA interaction and cluster-dependent RNA recruitment

CTCF is a zinc finger protein associated with transcription regulation that also acts as a barrier factor for topologically associated domains (TADs) generated by cohesin via loop extrusion. These processes require different properties of CTCF-DNA interaction, and it is still unclear how CTCF's structural features may modulate its diverse roles. Here, we employ single-molecule imaging to study both full-length CTCF and truncation mutants. We show that CTCF enriches at CTCF binding sites (CBSs), displaying a longer lifetime than observed previously. We demonstrate that the zinc finger domains mediate CTCF clustering and that clustering enables RNA recruitment, possibly creating a scaffold for interaction with RNA-binding proteins like cohesin's subunit SA. We further reveal a direct recruitment and an increase of SA residence time by CTCF bound at CBSs, suggesting that CTCF-SA interactions are crucial for cohesin stability on chromatin at TAD borders. Furthermore, we establish a single-molecule T7 transcription assay and show that although a transcribing polymerase can remove CTCF from CBSs, transcription is impaired. Our study shows that context-dependent nucleic acid binding determines the multifaceted CTCF roles in genome organization and transcription regulation.

Single Effective Complex Loading into Zero-Mode Waveguides Optimized with Fluorescence Evaluation at Quenching and Accumulation Checkpoints

Single-molecule detection with high accuracy and specialty plays an important role in biomedical diagnosis and screening. Zero-mode waveguides (ZMWs) enable the possibility of single biological molecule detection in real time. Nevertheless, the absence of a reliable assessment for single effective complex loading has constrained further applications of ZMWs in complex interaction. Both the quantity and activity of the complex loaded into ZMWs have a critical effect on the efficiency of detection. Herein, a fluorescence evaluation at quenching and accumulation checkpoints was established to assess and optimize single effective complex loading into ZMWs. A primer-template-enzyme ternary complex was designed, and then an evaluation for quantity statistics at the quenching checkpoint and functional activity at the accumulation checkpoint was used to validate the effectiveness of complexes loaded into ZMWs. By optimizing the parameters such as loading time, procedures, and enzyme amount, the single-molecule effective occupancy was increased to 25.48%, achieving 68.86% of the theoretical maximum value (37%) according to Poisson statistics. It is of great significance to provide effective complex-loading validation for improving the sample-loading efficiency of single-molecule assays or sequencing in the future.

A Biophysics Toolbox for Reliable Data Acquisition and Processing in Integrated Force-Confocal Fluorescence Microscopy

Integrated single-molecule force-fluorescence spectroscopy setups allow for simultaneous fluorescence imaging and mechanical force manipulation and measurements on individual molecules, providing comprehensive dynamic and spatiotemporal information. Dual-beam optical tweezers (OT) combined with a confocal scanning microscope form a force-fluorescence spectroscopy apparatus broadly used to investigate various biological processes, in particular, protein:DNA interactions. Such experiments typically involve imaging of fluorescently labeled proteins bound to DNA and force spectroscopy measurements of trapped individual DNA molecules. Here, we present a versatile state-of-the-art toolbox including the preparation of protein:DNA complex samples, design of a microfluidic flow cell incorporated with OT, automation of OT-confocal scanning measurements, and the development and implementation of a streamlined data analysis package for force and fluorescence spectroscopy data processing. Its components can be adapted to any commercialized or home-built dual-beam OT setup equipped with a confocal scanning microscope, which will facilitate single-molecule force-fluorescence spectroscopy studies on a large variety of biological systems.

Fluorescence Microscopy: a statistics-optics perspective

Fundamental properties of light unavoidably impose features on images collected using fluorescence microscopes. Modeling these features is ever more important in quantitatively interpreting microscopy images collected at scales on par or smaller than light's wavelength. Here we review the optics responsible for generating fluorescent images, fluorophore properties, microscopy modalities leveraging properties of both light and fluorophores, in addition to the necessarily probabilistic modeling tools imposed by the stochastic nature of light and measurement.

MaxCal can infer models from coupled stochastic trajectories of gene expression and cell division

Gene expression is inherently noisy due to small numbers of proteins and nucleic acids inside a cell. Likewise, cell division is stochastic, particularly when tracking at the level of a single cell. The two can be coupled when gene expression affects the rate of cell division. Single-cell time-lapse experiments can measure both fluctuations by simultaneously recording protein levels inside a cell and its stochastic division. These information-rich noisy trajectory data sets can be harnessed to learn about the underlying molecular and cellular details that are often not known a priori. A critical question is: How can we infer a model given data where fluctuations at two levels-gene expression and cell division-are intricately convoluted? We show the principle of maximum caliber (MaxCal)-integrated within a Bayesian framework-can be used to infer several cellular and molecular details (division rates, protein production, and degradation rates) from these coupled stochastic trajectories (CSTs). We demonstrate this proof of concept using synthetic data generated from a known model. An additional challenge in data analysis is that trajectories are often not in protein numbers, but in noisy fluorescence that depends on protein number in a probabilistic manner. We again show that MaxCal can infer important molecular and cellular rates even when data are in fluorescence, another example of CST with three confounding factors-gene expression noise, cell division noise, and fluorescence distortion-all coupled. Our approach will provide guidance to build models in synthetic biology experiments as well as general biological systems where examples of CSTs are abundant.

Single-molecule localization microscopy goes quantitative

In the last few years, single-molecule localization (SMLM) techniques have been used to address biological questions in different research fields. More recently, super-resolution has also been proposed as a quantitative tool for quantifying protein copy numbers at the nanoscale level. In this scenario, quantitative approaches, mainly based on stepwise photobleaching and quantitative SMLM assisted by calibration standards, offer an exquisite tool for investigating protein complexes. This primer focuses on the basic concepts behind quantitative super-resolution microscopy, also providing strategies to overcome the technical hurdles that could limit their application.

Selective recognition of the amyloid marker single thioflavin T using DNA origami-based gold nanobipyramid nanoantennas

The development of effective methods for the detection of protein misfolding is highly beneficial for early stage medical diagnosis and the prevention of many neurodegenerative diseases. Self-assembled plasmonic nanoantennas with precisely tunable nanogaps show extraordinary electromagnetic enhancement, generating extreme signal amplification imperative for the design of ultrasensitive biosensors for point of care applications. Herein, we report the custom arrangement of Au nanobipyramid (Au NBP) monomer and dimer nanoantennas engineered precisely based on the DNA origami technique. Furthermore, we demonstrate the SERS based detection of thioflavin T (ThT), a well-established marker for the detection of amyloid fibril formation, where G-Quadruplexes govern the site-specific attachment of ThT in the plasmonic hotspot. This is the first study for the SERS based detection of the ThT dye attached specifically using a G-Quadruplex complex. The spectroscopic signals of ThT were greatly enhanced due to the designed nanoantennas demonstrating their potential as superior SERS substrates. This study paves the way for boosting the design of next-generation diagnostic tools for the specific and precise detection of various target disease biomarkers using molecular probes.

Improved estimation of the ratio of detection efficiencies of excited acceptors and donors for FRET measurements

Förster resonance energy transfer (FRET) is a radiationless interaction between a donor and an acceptor whose distance dependence makes it a sensitive tool for studying the oligomerization and the structure of proteins. When FRET is determined by measuring the sensitized emission of the acceptor, a parameter characterizing the ratio of detection efficiencies of an excited acceptor versus an excited donor is invariably involved in the formalism. For FRET measurements involving fluorescent antibodies or other external labels, this parameter, designated by α, is usually determined by comparing the intensity of a known number of donors and acceptors in two independent samples leading to a large statistical variability if the sample size is small. Here, we present a method that improves precision by applying microbeads with a calibrated number of antibody binding sites and a donor-acceptor mixture in which donors and acceptors are present in a certain, experimentally determined ratio. A formalism is developed for determining α and the superior reproducibility of the proposed method compared to the conventional approach is demonstrated. Since the novel methodology does not require sophisticated calibration samples or special instrumentation, it can be widely applied for the quantification of FRET experiments in biological research.

Single-molecule counting applied to the study of GPCR oligomerization

Single-molecule counting techniques enable a precise determination of the intracellular abundance and stoichiometry of proteins and macromolecular complexes. These details are often challenging to quantitatively assess yet are essential for our understanding of cellular function. Consider G-protein-coupled receptors-an expansive class of transmembrane signaling proteins that participate in many vital physiological functions making them a popular target for drug development. While early evidence for the role of oligomerization in receptor signaling came from ensemble biochemical and biophysical assays, innovations in single-molecule measurements are now driving a paradigm shift in our understanding of its relevance. Here, we review recent developments in single-molecule counting with a focus on photobleaching step counting and the emerging technique of quantitative single-molecule localization microscopy-with a particular emphasis on the potential for these techniques to advance our understanding of the role of oligomerization in G-protein-coupled receptor signaling.

Modeling Non-additive Effects in Neighboring Chemically Identical Fluorophores

Quantitative fluorescence analysis is often used to derive chemical properties, including stoichiometries, of biomolecular complexes. One fundamental underlying assumption in the analysis of fluorescence data─whether it be the determination of protein complex stoichiometry by super-resolution, or step-counting by photobleaching, or the determination of RNA counts in diffraction-limited spots in RNA fluorescence in situ hybridization (RNA-FISH) experiments─is that fluorophores behave identically and do not interact. However, recent experiments on fluorophore-labeled DNA origami structures such as fluorocubes have shed light on the nature of the interactions between identical fluorophores as these are brought closer together, thereby raising questions on the validity of the modeling assumption that fluorophores do not interact. Here, we analyze photon arrival data under pulsed illumination from fluorocubes where distances between dyes range from 2 to 10 nm. We discuss the implications of non-additivity of brightness on quantitative fluorescence analysis.

Gold Ion Beam Milled Gold Zero-Mode Waveguides

Zero-mode waveguides (ZMWs) are widely used in single molecule fluorescence microscopy for their enhancement of emitted light and the ability to study samples at physiological concentrations. ZMWs are typically produced using photo or electron beam lithography. We report a new method of ZMW production using focused ion beam (FIB) milling with gold ions. We demonstrate that ion-milled gold ZMWs with 200 nm apertures exhibit similar plasmon-enhanced fluorescence seen with ZMWs fabricated with traditional techniques such as electron beam lithography.

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics

Life's fundamental processes involve multiple molecules operating in close proximity within cells. To probe the composition and kinetics of molecular clusters confined within small (diffraction-limited) regions, experiments often report on the total fluorescence intensity simultaneously emitted from labeled molecules confined to such regions. Methods exist to enumerate total fluorophore numbers (e.g., step counting by photobleaching). However, methods aimed at step counting by photobleaching cannot treat photophysical dynamics in counting nor learn their associated kinetic rates. Here we propose a method to simultaneously enumerate fluorophores and determine their individual photophysical state trajectories. As the number of active (fluorescent) molecules at any given time is unknown, we rely on Bayesian nonparametrics and use specialized Monte Carlo algorithms to derive our estimates. Our formulation is benchmarked on synthetic and real data sets. While our focus here is on photophysical dynamics (in which labels transition between active and inactive states), such dynamics can also serve as a proxy for other types of dynamics such as assembly and disassembly kinetics of clusters. Similarly, while we focus on the case where all labels are initially fluorescent, other regimes, more appropriate to photoactivated localization microscopy, where fluorophores are instantiated in a non-fluorescent state, fall within the scope of the framework. As such, we provide a complete and versatile framework for the interpretation of complex time traces arising from the simultaneous activity of up to 100 fluorophores.

Simultaneous, Single-Particle Measurements of Size and Loading Give Insights into the Structure of Drug-Delivery Nanoparticles

Nanoparticles are a promising solution for delivery of a wide range of medicines and vaccines. Optimizing their design depends on being able to resolve, understand, and predict biophysical and therapeutic properties, as a function of design parameters. While existing tools have made great progress, gaps in understanding remain because of the inability to make detailed measurements of multiple correlated properties. Typically, an average measurement is made across a heterogeneous population, obscuring potentially important information. In this work, we develop and apply a method for characterizing nanoparticles with single-particle resolution. We use convex lens-induced confinement (CLiC) microscopy to isolate and quantify the diffusive trajectories and fluorescent intensities of individual nanoparticles trapped in microwells for long times. First, we benchmark detailed measurements of fluorescent polystyrene nanoparticles against prior data to validate our approach. Second, we apply our method to investigate the size and loading properties of lipid nanoparticle (LNP) vehicles containing silencing RNA (siRNA), as a function of lipid formulation, solution pH, and drug-loading. By taking a comprehensive look at the correlation between the intensity and size measurements, we gain insights into LNP structure and how the siRNA is distributed in the LNP. Beyond introducing an analytic for size and loading, this work allows for future studies of dynamics with single-particle resolution, such as LNP fusion and drug-release kinetics. The prime contribution of this work is to better understand the connections between microscopic and macroscopic properties of drug-delivery vehicles, enabling and accelerating their discovery and development.

Photobleaching step analysis for robust determination of protein complex stoichiometries

The counting of discrete photobleaching steps in fluorescence microscopy is ideally suited to study protein complex stoichiometry in situ. The counting range of photobleaching step analysis has been significantly improved with more-sophisticated algorithms for step detection, albeit at an increasing computational cost and with the necessity for high-quality data. Here, we address concerns regarding robustness, automation, and experimental validation, optimizing both data acquisition and analysis. To make full use of the potential of photobleaching step analysis, we evaluate various labeling strategies with respect to their molecular brightness, photostability, and photoblinking. The developed analysis algorithm focuses on automation and computational efficiency. Moreover, we validate the developed methods with experimental data acquired on DNA origami labeled with defined fluorophore numbers, demonstrating counting of up to 35 fluorophores. Finally, we show the power of the combination of optimized trace acquisition and automated data analysis by counting labeled nucleoporin 107 in nuclear pore complexes of intact U2OS cells. The successful in situ application promotes this framework as a new resource enabling cell biologists to robustly determine the stoichiometries of molecular assemblies at the single-molecule level in an automated manner.

Estimating the dynamic range of quantitative single-molecule localization microscopy

In recent years, there have been significant advances in quantifying molecule copy number and protein stoichiometry with single-molecule localization microscopy (SMLM). However, as the density of fluorophores per diffraction-limited spot increases, distinguishing between detection events from different fluorophores becomes progressively more difficult, affecting the accuracy of such measurements. Although essential to the design of quantitative experiments, the dynamic range of SMLM counting techniques has not yet been studied in detail. Here, we provide a working definition of the dynamic range for quantitative SMLM in terms of the relative number of missed localizations or blinks and explore the photophysical and experimental parameters that affect it. We begin with a simple two-state model of blinking fluorophores, then extend the model to incorporate photobleaching and temporal binning by the detection camera. From these models, we first show that our estimates of the dynamic range agree with realistic simulations of the photoswitching. We find that the dynamic range scales inversely with the duty cycle when counting both blinks and localizations. Finally, we validate our theoretical approach on direct stochastic optical reconstruction microscopy (dSTORM) data sets of photoswitching Alexa Fluor 647 dyes. Our results should help guide researchers in designing and implementing SMLM-based molecular counting experiments.

PySTACHIO: Python Single-molecule TrAcking stoiCHiometry Intensity and simulatiOn, a flexible, extensible, beginner-friendly and optimized program for analysis of single-molecule microscopy data

As camera pixel arrays have grown larger and faster, and optical microscopy techniques ever more refined, there has been an explosion in the quantity of data acquired during routine light microscopy. At the single-molecule level, analysis involves multiple steps and can rapidly become computationally expensive, in some cases intractable on office workstations. Complex bespoke software can present high activation barriers to entry for new users. Here, we redevelop our quantitative single-molecule analysis routines into an optimized and extensible Python program, with GUI and command-line implementations to facilitate use on local machines and remote clusters, by beginners and advanced users alike. We demonstrate that its performance is on par with previous MATLAB implementations but runs an order of magnitude faster. We tested it against challenge data and demonstrate its performance is comparable to state-of-the-art analysis platforms. We show the code can extract fluorescence intensity values for single reporter dye molecules and, using these, estimate molecular stoichiometries and cellular copy numbers of fluorescently-labeled biomolecules. It can evaluate 2D diffusion coefficients for the characteristically short single-particle tracking data. To facilitate benchmarking we include data simulation routines to compare different analysis programs. Finally, we show that it works with 2-color data and enables colocalization analysis based on overlap integration, to infer interactions between differently labelled biomolecules. By making this freely available we aim to make complex light microscopy single-molecule analysis more democratized.

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.

Regulating DNA Self-Assembly Dynamics with Controlled Nucleation

Controlling the nucleation step of a self-assembly system is essential for engineering structural complexity and dynamic behaviors. Here, we design a "frame-filling" model system that comprises one type of self-complementary DNA tile and a hosting DNA origami frame to investigate the inherent dynamics of three general nucleation modes in nucleated self-assembly: unseeded, facet, and seeded nucleation. Guided by kinetic simulation, which suggested an optimal temperature range to differentiate the individual nucleation modes, and complemented by single-molecule observations, the transition of tiles from a metastable, monomeric state to a stable, polymerized state through the three nucleation pathways was monitored by Mg²⁺-triggered kinetic measurements. The temperature-dependent kinetics for all three nucleation modes were correlated by a "nucleation-growth" model, which quantified the tendency of nucleation using an empirical nucleation number. Moreover, taking advantage of the temperature dependence of nucleation, tile assembly can be regulated externally by the hosting frame. An ultraviolet (UV)-responsive trigger was integrated into the frame to simultaneously control "when" and "where" nucleation started. Our results reveal the dynamic mechanisms of the distinct nucleation modes in DNA tile-based self-assembly and provide a general strategy for controlling the self-assembly process.

Dynamics of Ku and bacterial non-homologous end-joining characterized using single DNA molecule analysis

We use single-molecule techniques to characterize the dynamics of prokaryotic DNA repair by non-homologous end-joining (NHEJ), a system comprised only of the dimeric Ku and Ligase D (LigD). The Ku homodimer alone forms a ∼2 s synapsis between blunt DNA ends that is increased to ∼18 s upon addition of LigD, in a manner dependent on the C-terminal arms of Ku. The synapsis lifetime increases drastically for 4 nt complementary DNA overhangs, independently of the C-terminal arms of Ku. These observations are in contrast to human Ku, which is unable to bridge either of the two DNA substrates. We also demonstrate that bacterial Ku binds the DNA ends in a cooperative manner for synapsis initiation and remains stably bound at DNA junctions for several hours after ligation is completed, indicating that a system for removal of the proteins is active in vivo. Together these experiments shed light on the dynamics of bacterial NHEJ in DNA end recognition and processing. We speculate on the evolutionary similarities between bacterial and eukaryotic NHEJ and discuss how an increased understanding of bacterial NHEJ can open the door for future antibiotic therapies targeting this mechanism.

Stoichiometry of Receptors at the Plasma Membrane During Their Endocytosis Using Total Internal Reflection Fluorescent (TIRF) Microscopy Live Imaging and Single-Molecule Tracking

Determination of protein stoichiometry in living cells is key to understanding basic biological processes. This is particularly important for receptor-mediated endocytosis, a highly regulated mechanism that requires the sequential assembly of numerous factors. Here, we describe a quantitative approach to analyze receptor clustering dynamics at the plasma membrane. Our workflow combines TIRF live imaging of a CRISPR-Cas9-edited cell line expressing a GFP-tagged receptor in a physiological relevant environment, a calibration technique for single-molecule analysis of GFP, and detection and tracking with an open-source software. This method allows to determine the number of receptor molecules at the plasma membrane in real time.

Analyzing protein dynamics from fluorescence intensity traces using unsupervised deep learning network

We propose an unsupervised deep learning network to analyze the dynamics of membrane proteins from the fluorescence intensity traces. This system was trained in an unsupervised manner with the raw experimental time traces and synthesized ones, so neither predefined state number nor pre-labelling were required. With the bidirectional Long Short-Term Memory (biLSTM) networks as the hidden layers, both the past and future context can be used fully to improve the prediction results and can even extract information from the noise distribution. The method was validated with the synthetic dataset and the experimental dataset of monomeric fluorophore Cy5, and then applied to extract the membrane protein interaction dynamics from experimental data successfully.

Live cell imaging of single RNA molecules with fluorogenic Mango II arrays

RNA molecules play vital roles in many cellular processes. Visualising their dynamics in live cells at single-molecule resolution is essential to elucidate their role in RNA metabolism. RNA aptamers, such as Spinach and Mango, have recently emerged as a powerful background-free technology for live-cell RNA imaging due to their fluorogenic properties upon ligand binding. Here, we report a novel array of Mango II aptamers for RNA imaging in live and fixed cells with high contrast and single-molecule sensitivity. Direct comparison of Mango II and MS2-tdMCP-mCherry dual-labelled mRNAs show marked improvements in signal to noise ratio using the fluorogenic Mango aptamers. Using both coding (β-actin mRNA) and long non-coding (NEAT1) RNAs, we show that the Mango array does not affect cellular localisation. Additionally, we can track single mRNAs for extended time periods, likely due to bleached fluorophore replacement. This property makes the arrays readily compatible with structured illumination super-resolution microscopy.

Stoichiometric Analyses of Soluble CD4 to Native-like HIV-1 Envelope by Single-Molecule Fluorescence Spectroscopy

Analyses of HIV-1 envelope (Env) binding to CD4, and the conformational changes the interactions induce, inform the molecular mechanisms and factors governing HIV-1 infection. To address these questions, we used a single-molecule detection (SMD) approach to study the nature of reactions between soluble CD4 (sCD4) and soluble HIV-1 trimers. SMD of these reactions distinguished a mixture of one, two, or three CD4-bound trimer species. Single-ligand trimers were favored at early reaction times and ligand-saturated trimers later. Furthermore, some trimers occupied by one sCD4 molecule did not bind additional ligands, whereas the majority of two ligand-bound species rapidly transitioned to the saturated state. Quantification of liganded trimers observed in reactions with various sCD4 concentrations reflected an overall negative cooperativity in ligand binding. Collectively, our results highlight the general utility of SMD in studying protein interactions and provide critical insights on the nature of sCD4-HIV-1 Env interactions.

Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport

The liver performs critical physiological functions, including metabolizing and removing substances, such as toxins and drugs, from the bloodstream. Hepatotoxicity itself is intimately linked to abnormal hepatic transport, and hepatotoxicity remains the primary reason drugs in development fail and approved drugs are withdrawn from the market. For this reason, we propose to analyze, across liver compartments, the transport kinetics of fluorescein-a fluorescent marker used as a proxy for drug molecules-using intravital microscopy data. To resolve the transport kinetics quantitatively from fluorescence data, we account for the effect that different liver compartments (with different chemical properties) have on fluorescein's emission rate. To do so, we develop ordinary differential equation transport models from the data where the kinetics is related to the observable fluorescence levels by "measurement parameters" that vary across different liver compartments. On account of the steep non-linearities in the kinetics and stochasticity inherent to the model, we infer kinetic and measurement parameters by generalizing the method of parameter cascades. For this application, the method of parameter cascades ensures fast and precise parameter estimates from noisy time traces.

An alternative framework for fluorescence correlation spectroscopy

Fluorescence correlation spectroscopy (FCS), is a widely used tool routinely exploited for in vivo and in vitro applications. While FCS provides estimates of dynamical quantities, such as diffusion coefficients, it demands high signal to noise ratios and long time traces, typically in the minute range. In principle, the same information can be extracted from microseconds to seconds long time traces; however, an appropriate analysis method is missing. To overcome these limitations, we adapt novel tools inspired by Bayesian non-parametrics, which starts from the direct analysis of the observed photon counts. With this approach, we are able to analyze time traces, which are too short to be analyzed by existing methods, including FCS. Our new analysis extends the capability of single molecule fluorescence confocal microscopy approaches to probe processes several orders of magnitude faster and permits a reduction of photo-toxic effects on living samples induced by long periods of light exposure.

Fluorescence correlation spectroscopy is widely used for in vivo and in vitro applications, yet extracting information from experiments still requires long acquisition times. Here, the authors exploit Bayesian non-parametrics to directly analyze the output of confocal fluorescence experiments thereby probing physical processes on much faster timescales.

Maximum Caliber Can Build and Infer Models of Oscillation in a Three-Gene Feedback Network

Single-cell protein expression time trajectories provide rich temporal data quantifying cellular variability and its role in dictating fitness. However, theoretical models to analyze and fully extract information from these measurements remain limited for three reasons: (i) gene expression profiles are noisy, rendering models of averages inapplicable, (ii) experiments typically measure only a few protein species while leaving other molecular actors-necessary to build traditional bottom-up models-unnoticed, and (iii) measured data are in fluorescence, not particle number. We recently addressed these challenges in an alternate top-down approach using the principle of Maximum Caliber (MaxCal) to model genetic switches with one and two protein species. In the present work we address scalability and broader applicability of MaxCal by extending to a three-gene (A, B, C) feedback network that exhibits oscillation, commonly known as the repressilator. We test MaxCal's inferential power by using synthetic data of noisy protein number time traces-serving as a proxy for experimental data-generated from a known underlying model. We notice that the minimal MaxCal model-accounting for production, degradation, and only one type of symmetric coupling between all three species-reasonably infers several underlying features of the circuit such as the effective production rate, degradation rate, frequency of oscillation, and protein number distribution. Next, we build models of higher complexity including different levels of coupling between A, B, and C and rigorously assess their relative performance. While the minimal model (with four parameters) performs remarkably well, we note that the most complex model (with six parameters) allowing all possible forms of crosstalk between A, B, and C slightly improves prediction of rates, but avoids ad hoc assumption of all the other models. It is also the model of choice based on Bayesian information criteria. We further analyzed time trajectories in arbitrary fluorescence (using synthetic trajectories) to mimic realistic data. We conclude that even with a three-protein system including both fluorescence noise and intrinsic gene expression fluctuations, MaxCal can faithfully infer underlying details of the network, opening future directions to model other network motifs with many species.

Fluorescence Microscopy of Single Liposomes with Incorporated Pigment-Proteins

Reconstitution of transmembrane proteins into liposomes is a widely used method to study their behavior under conditions closely resembling the natural ones. However, this approach does not allow precise control of the liposome size, reconstitution efficiency, and the actual protein-to-lipid ratio in the formed proteoliposomes, which might be critical for some applications and/or interpretation of data acquired during the spectroscopic measurements. Here, we present a novel strategy employing methods of proteoliposome preparation, fluorescent labeling, purification, and surface immobilization that allow us to quantify these properties using fluorescence microscopy at the single-liposome level and for the first time apply it to study photosynthetic pigment-protein complexes LHCII. We show that LHCII proteoliposome samples, even after purification with a density gradient, always contain a fraction of nonreconstituted protein and are extremely heterogeneous in both protein density and liposome sizes. This strategy enables quantitative analysis of the reconstitution efficiency of different protocols and precise fluorescence spectroscopic study of various transmembrane proteins in a controlled nativelike environment.

From "Cellular" RNA to "Smart" RNA: Multiple Roles of RNA in Genome Stability and Beyond

Coding for proteins has been considered the main function of RNA since the "central dogma" of biology was proposed. The discovery of noncoding transcripts shed light on additional roles of RNA, ranging from the support of polypeptide synthesis, to the assembly of subnuclear structures, to gene expression modulation. Cellular RNA has therefore been recognized as a central player in often unanticipated biological processes, including genomic stability. This ever-expanding list of functions inspired us to think of RNA as a "smart" phone, which has replaced the older obsolete "cellular" phone. In this review, we summarize the last two decades of advances in research on the interface between RNA biology and genome stability. We start with an account of the emergence of noncoding RNA, and then we discuss the involvement of RNA in DNA damage signaling and repair, telomere maintenance, and genomic rearrangements. We continue with the depiction of single-molecule RNA detection techniques, and we conclude by illustrating the possibilities of RNA modulation in hopes of creating or improving new therapies. The widespread biological functions of RNA have made this molecule a reoccurring theme in basic and translational research, warranting it the transcendence from classically studied "cellular" RNA to "smart" RNA.

Maximum Caliber Can Characterize Genetic Switches with Multiple Hidden Species

Gene networks with feedback often involve interactions between multiple species of biomolecules, much more than experiments can actually monitor. Coupled with this is the challenge that experiments often measure gene expression in noisy fluorescence instead of protein numbers. How do we infer biophysical information and characterize the underlying circuits from this limited and convoluted data? We address this by building stochastic models using the principle of Maximum Caliber (MaxCal). MaxCal uses the basic information on synthesis, degradation, and feedback-without invoking any other auxiliary species and ad hoc reactions-to generate stochastic trajectories similar to those typically measured in experiments. MaxCal in conjunction with Maximum Likelihood (ML) can infer parameters of the model using fluctuating trajectories of protein expression over time. We demonstrate the success of the MaxCal + ML methodology using synthetic data generated from known circuits of different genetic switches: (i) a single-gene autoactivating circuit involving five species (including mRNA), (ii) a mutually repressing two-gene circuit (toggle switch) with seven species (including mRNA) considering stochastic time traces of two proteins, and (iii) the same toggle switch circuit considering stochastic time traces of only one of the two proteins. To further challenge the MaxCal + ML inference scheme, we repeat our analysis for the second and third scenario with traces expressed in noisy fluorescence instead of protein number to closely mimic typical experiments. We show that, for all of these models with increasing complexity and obfuscation, the minimal model of MaxCal is still able to capture the fluctuations of the trajectory and infer basic underlying rate parameters when benchmarked against the known values used to generate the synthetic data. Importantly, the model also yields an effective feedback parameter that can be used to quantify interactions within these circuits. These applications show the promise of MaxCal's ability to characterize circuits with limited data, and its utility to better understand evolution and advance design strategies for specific functions.

Building Predictive Models of Genetic Circuits Using the Principle of Maximum Caliber

Learning the underlying details of a gene network is a major challenge in cellular and synthetic biology. We address this challenge by building a chemical kinetic model that utilizes information encoded in the stochastic protein expression trajectories typically measured in experiments. The applicability of the proposed method is demonstrated in an auto-activating genetic circuit, a common motif in natural and synthetic gene networks. Our approach is based on the principle of maximum caliber (MaxCal)-a dynamical analog of the principle of maximum entropy-and builds a minimal model using only three constraints: 1) protein synthesis, 2) protein degradation, and 3) positive feedback. The MaxCal-generated model (described with four parameters) was benchmarked against synthetic data generated using a Gillespie algorithm on a known reaction network (with seven parameters). MaxCal accurately predicts underlying rate parameters of protein synthesis and degradation as well as experimental observables such as protein number and dwell-time distributions. Furthermore, MaxCal yields an effective feedback parameter that can be useful for circuit design. We also extend our methodology and demonstrate how to analyze trajectories that are not in protein numbers but in arbitrary fluorescence units, a more typical condition in experiments. This "top-down" methodology based on minimal information-in contrast to traditional "bottom-up" approaches that require ad hoc knowledge of circuit details-provides a powerful tool to accurately infer underlying details of feedback circuits that are not otherwise visible in experiments and to help guide circuit design.

Quantitative microscopy based on single-molecule fluorescence

Quantitative microscopy is needed to understand reactions or phenomena carried out by biological molecules such as enzymes, receptors, and membrane-localized proteins. Counting the biomolecules of interest in single organelles or cellular compartments is critical in these approaches. In this brief perspective, we focus on the development of quantitative fluorescence microscopies that measure the precise copy numbers of proteins in cellular organelles or purified samples. We introduce recent improvements in quantitative microscopies to overcome undercounting or overcounting errors in certain conditions. We conclude by discussing biological applications.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.