FluoSim: simulator of single molecule dynamics for fluorescence live-cell and super-resolution imaging of membrane proteins

Development of AI-assisted microscopy frameworks through realistic simulation with pySTED

The integration of artificial intelligence into microscopy systems significantly enhances performance, optimizing both image acquisition and analysis phases. Development of artificial intelligence-assisted super-resolution microscopy is often limited by access to large biological datasets, as well as by difficulties to benchmark and compare approaches on heterogeneous samples. We demonstrate the benefits of a realistic stimulated emission depletion microscopy simulation platform, pySTED, for the development and deployment of artificial intelligence strategies for super-resolution microscopy. pySTED integrates theoretically and empirically validated models for photobleaching and point spread function generation in stimulated emission depletion microscopy, as well as simulating realistic point-scanning dynamics and using a deep learning model to replicate the underlying structures of real images. This simulation environment can be used for data augmentation to train deep neural networks, for the development of online optimization strategies and to train reinforcement learning models. Using pySTED as a training environment allows the reinforcement learning models to bridge the gap between simulation and reality, as showcased by its successful deployment on a real microscope system without fine tuning.

Single molecule dynamics in a virtual cell combining a 3-dimensional matrix model with random walks

Recent advances in light microscopy have enabled single molecules to be imaged and tracked within living cells and this approach is impacting our understanding of cell biology. Computer modeling and simulation are important adjuncts to the experimental cycle since they aid interpretation of experimental results and help refine, test and generate hypotheses. Object-oriented computer modeling is particularly well-suited for simulating random, thermal, movements of individual molecules as they interact with other molecules and subcellular structures, but current models are often limited to idealized systems consisting of unit volumes or planar surfaces. Here, a simulation tool is described that combines a 3-dimensional, voxelated, representation of the cell consisting of subcellular structures (e.g. nucleus, endoplasmic reticulum, cytoskeleton, vesicles, and filopodia) combined with numerical floating-point precision simulation of thousands of individual molecules moving and interacting within the 3-dimensional space. Simulations produce realistic time-series video sequences comprising single fluorophore intensities and realistic background noise which can be directly compared to experimental fluorescence video microscopy data sets.

Fraping: A computational tool for detecting slight differences in fluorescence recovery after photobleaching (FRAP) data for actin polymerization analysis

Fluorescence recovery after photobleaching (FRAP) is a laser method of light microscopy to evaluate the rapid movement of fluorescent molecules. To have a more reliable approach to analyze data from FRAP, we designed Fraping, a free access R library to data analysis obtained from FRAP. Unlike other programs, Fraping has a new form of analyzing curves of FRAP using statistical analysis based on the average curve difference. To evaluate our library, we analyzed the differences of actin polymerization in real time between dendrites and secondary neurites of cultured neuron transfected with LifeAct to track F-actin changes of neurites. We found that Fraping provided greater sensitivity than the conventional model using mobile fraction analysis. Likewise, this approach allowed us to normalize the fluorescence to the size area of interest and adjust data curves choosing the best parametric model. In addition, this library was supplemented with data simulation to have a more significant enrichment for the analysis behavior. We concluded that Fraping is a method that reduces bias when analyzing two data groups as compared with the conventional methods. This method also allows the users to choose a more suitable analysis approach according to their requirements. RESEARCH HIGHLIGHTS: Fraping is a new programming tool to analyze FRAP data to normalize fluorescence recovery curves. The conventional method uses one-point analysis, and the new one compares all the points to define the similarity of the fluorescence recovery.

Sub-membrane actin rings compartmentalize the plasma membrane

The compartmentalization of the plasma membrane (PM) is a fundamental feature of cells. The diffusivity of membrane proteins is significantly lower in biological than in artificial membranes. This is likely due to actin filaments, but assays to prove a direct dependence remain elusive. We recently showed that periodic actin rings in the neuronal axon initial segment (AIS) confine membrane protein motion between them. Still, the local enrichment of ion channels offers an alternative explanation. Here we show, using computational modeling, that in contrast to actin rings, ion channels in the AIS cannot mediate confinement. Furthermore, we show, employing a combinatorial approach of single particle tracking and super-resolution microscopy, that actin rings are close to the PM and that they confine membrane proteins in several neuronal cell types. Finally, we show that actin disruption leads to loss of compartmentalization. Taken together, we here develop a system for the investigation of membrane compartmentalization and show that actin rings compartmentalize the PM.

Physically informed Monte Carlo simulation of dual-wedge prism-based spectroscopic single-molecule localization microscopy

Significance

The dual-wedge prism (DWP)-based spectroscopic single-molecule localization microscopy (sSMLM) system offers improved localization precision and adjustable spectral or localization performance, but its nonlinear spectral dispersion presents a challenge. A systematic method can help understand the challenges and thereafter optimize the DWP system's performance by customizing the system parameters to maximize the spectral or localization performance for various molecular labels.

Aim

We developed a Monte Carlo (MC)-based model that predicts the imaging output of the DWP-based sSMLM system given different system parameters.

Approach

We assessed our MC model's localization and spectral precisions by comparing our simulation against theoretical equations and fluorescent microspheres. Furthermore, we simulated the DWP-based system using beamsplitters (BSs) with a reflectance (R):transmittance (T) of R50:T50 and R30:T70 and their tradeoffs.

Results

Our MC simulation showed average deviations of 2.5 and 2.1 nm for localization and spectral precisions against theoretical equations and 2.3 and 1.0 nm against fluorescent microspheres. An R30:T70 BS improved the spectral precision by 8% but worsened the localization precision by 35% on average compared with an R50:T50 BS.

Conclusions

The MC model accurately predicted the localization precision, spectral precision, spectral peaks, and spectral widths of fluorescent microspheres, as validated by experimental data. Our work enhances the theoretical understanding of DWP-based sSMLM for multiplexed imaging, enabling performance optimization.

A generative model to simulate spatiotemporal dynamics of biomolecules in cells

Generators of space-time dynamics in bioimaging have become essential to build ground truth datasets for image processing algorithm evaluation such as biomolecule detectors and trackers, as well as to generate training datasets for deep learning algorithms. In this contribution, we leverage a stochastic model, called birth-death-move (BDM) point process, in order to generate joint dynamics of biomolecules in cells. This particle-based stochastic simulation method is very flexible and can be seen as a generalization of well-established standard particle-based generators. In comparison, our approach allows us: (1) to model a system of particles in motion, possibly in interaction, that can each possibly switch from a motion regime (e.g., Brownian) to another (e.g., a directed motion); (2) to take into account finely the appearance over time of new trajectories and their disappearance, these events possibly depending on the cell regions but also on the current spatial configuration of all existing particles. This flexibility enables to generate more realistic dynamics than standard particle-based simulation procedures, by for example accounting for the colocalization phenomena often observed between intracellular vesicles. We explain how to specify all characteristics of a BDM model, with many practical examples that are relevant for bioimaging applications. As an illustration, based on real fluorescence microscopy datasets, we finally calibrate our model to mimic the joint dynamics of Langerin and Rab11 proteins near the plasma membrane, including the well-known colocalization occurrence between these two types of vesicles. We show that the resulting synthetic sequences exhibit comparable features as those observed in real microscopy image sequences.

What's past is prologue: FRAP keeps delivering 50 years later

Fluorescence recovery after photobleaching (FRAP) has emerged as one of the most widely utilized techniques to quantify binding and diffusion kinetics of biomolecules in biophysics. Since its inception in the mid-1970s, FRAP has been used to address an enormous array of questions including the characteristic features of lipid rafts, how cells regulate the viscosity of their cytoplasm, and the dynamics of biomolecules inside condensates formed by liquid-liquid phase separation. In this perspective, I briefly summarize the history of the field and discuss why FRAP has proven to be so incredibly versatile and popular. Next, I provide an overview of the extensive body of knowledge that has emerged on best practices for quantitative FRAP data analysis, followed by some recent examples of biological lessons learned using this powerful approach. Finally, I touch on new directions and opportunities for biophysicists to contribute to the continued development of this still-relevant research tool.

Machine learning framework to segment sarcomeric structures in SMLM data

Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network's internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data.

Single molecule imaging simulations with advanced fluorophore photophysics

Advanced fluorescence imaging techniques such as single-molecule localization microscopy (SMLM) fundamentally rely on the photophysical behavior of the employed fluorophores. This behavior is generally complex and impacts data quality in a subtle manner. A simulation software named Single-Molecule Imaging Simulator (SMIS) is introduced that simulates a widefield microscope and incorporates fluorophores with their spectral and photophysical properties. With SMIS, data collection schemes combining 3D, multicolor, single-particle-tracking or quantitative SMLM can be implemented. The influence of advanced fluorophore characteristics, imaging conditions, and environmental parameters can be evaluated, facilitating the design of real experiments and their proper interpretation.

Precise measurement of nanoscopic septin ring structures with deep learning-assisted quantitative superresolution microscopy

The combination of image analysis and superresolution microscopy methods allows for unprecedented insight into the organization of macromolecular assemblies in cells. Advances in deep learning (DL)-based object recognition enable the automated processing of large amounts of data, resulting in high accuracy through averaging. However, while the analysis of highly symmetric structures of constant size allows for a resolution approaching the dimensions of structural biology, DL-based image recognition may introduce bias. This prohibits the development of readouts for processes that involve significant changes in size or shape of amorphous macromolecular complexes. Here we address this problem by using changes of septin ring structures in single molecule localization-based superresolution microscopy data as a paradigm. We identify potential sources of bias resulting from different training approaches by rigorous testing of trained models using real or simulated data covering a wide range of possible results. In a quantitative comparison of our models, we find that a trade-off exists between measurement accuracy and the range of recognized phenotypes. Using our thus verified models, we find that septin ring size can be explained by the number of subunits they are assembled from alone. Furthermore, we provide a new experimental system for the investigation of septin polymerization.

In vitro reconstitution of Escherichia coli divisome activation

The actin-homologue FtsA is essential for E. coli cell division, as it links FtsZ filaments in the Z-ring to transmembrane proteins. FtsA is thought to initiate cell constriction by switching from an inactive polymeric to an active monomeric conformation, which recruits downstream proteins and stabilizes the Z-ring. However, direct biochemical evidence for this mechanism is missing. Here, we use reconstitution experiments and quantitative fluorescence microscopy to study divisome activation in vitro. By comparing wild-type FtsA with FtsA R286W, we find that this hyperactive mutant outperforms FtsA WT in replicating FtsZ treadmilling dynamics, FtsZ filament stabilization and recruitment of FtsN. We could attribute these differences to a faster exchange and denser packing of FtsA R286W below FtsZ filaments. Using FRET microscopy, we also find that FtsN binding promotes FtsA self-interaction. We propose that in the active divisome FtsA and FtsN exist as a dynamic copolymer that follows treadmilling filaments of FtsZ.

MDGAs are fast-diffusing molecules that delay excitatory synapse development by altering neuroligin behavior

MDGA molecules can bind neuroligins and interfere with trans-synaptic interactions to neurexins, thereby impairing synapse development. However, the subcellular localization and dynamics of MDGAs, or their specific action mode in neurons remain unclear. Here, surface immunostaining of endogenous MDGAs and single molecule tracking of recombinant MDGAs in dissociated hippocampal neurons reveal that MDGAs are homogeneously distributed and exhibit fast membrane diffusion, with a small reduction in mobility across neuronal maturation. Knocking-down/out MDGAs using shRNAs and CRISPR/Cas9 strategies increases the density of excitatory synapses, the membrane confinement of neuroligin-1, and the phosphotyrosine level of neuroligins associated with excitatory post-synaptic differentiation. Finally, MDGA silencing reduces the mobility of AMPA receptors, increases the frequency of miniature EPSCs (but not IPSCs), and selectively enhances evoked AMPA-receptor-mediated EPSCs in CA1 pyramidal neurons. Overall, our results support a mechanism by which interactions between MDGAs and neuroligin-1 delays the assembly of functional excitatory synapses containing AMPA receptors.

High-Resolution Fluorescence Imaging Combined With Computer Simulations to Quantitate Surface Dynamics and Nanoscale Organization of Neuroligin-1 at Synapses

Neuroligins (NLGNs) form a family of cell adhesion molecules implicated in synapse development, but the mechanisms that retain these proteins at synapses are still incompletely understood. Recent studies indicate that surface-associated NLGN1 is diffusionally trapped at synapses, where it interacts with quasi-static scaffolding elements of the post-synaptic density. Whereas single molecule tracking reveals rapid diffusion and transient immobilization of NLGN1 at synapses within seconds, fluorescence recovery after photobleaching experiments indicate instead a long-term turnover of NLGN1 at synapse, in the hour time range. To gain insight into the mechanisms supporting NLGN1 anchorage at post-synapses and try to reconcile those experimental paradigms, we quantitatively analyzed here live-cell and super-resolution imaging experiments performed on NLGN1 using a newly released simulator of membrane protein dynamics for fluorescence microscopy, FluoSim. Based on a small set of parameters including diffusion coefficients, binding constants, and photophysical rates, the framework describes fairly well the dynamic behavior of extra-synaptic and synaptic NLGN1 over both short and long time ranges, and provides an estimate of NLGN1 copy numbers in post-synaptic densities at steady-state (around 50 dimers). One striking result is that the residence time of NLGN1 at synapses is much longer than what can be expected from extracellular interactions with pre-synaptic neurexins only, suggesting that NLGN1 is stabilized at synapses through multivalent interactions with intracellular post-synaptic scaffolding proteins.

Emerin self-assembly and nucleoskeletal coupling regulate nuclear envelope mechanics against stress

Emerin is an integral nuclear envelope protein participating in the maintenance of nuclear shape. When mutated or absent, emerin causes X-linked Emery-Dreifuss muscular dystrophy (EDMD). To define how emerin takes parts in molecular scaffolding at the nuclear envelope and helps protect the nucleus against mechanical stress, we established its nanoscale organization using single molecule tracking and super-resolution microscopy. We show that emerin monomers form localized oligomeric nanoclusters stabilized by both lamin A/C and SUN1 LINC complex. Interactions of emerin with nuclear actin and BAF additionally modulate its membrane mobility and its ability to oligomerize. In nuclei subjected to mechanical challenges, the mechanotransducing functions of emerin are coupled to changes in its oligomeric state, and the incremental self-assembly of emerin determines nuclear shape adaptation against forces. We also show that the abnormal nuclear envelope deformations induced by EDMD emerin mutants stem from an improper formation of lamin A/C and LINC complex-stabilized emerin oligomers. These findings place emerin at the center of the molecular processes that regulate nuclear shape remodeling in response to mechanical challenges.

An Efficient GUI-Based Clustering Software for Simulation and Bayesian Cluster Analysis of Single-Molecule Localization Microscopy Data

Ligand binding of membrane proteins triggers many important cellular signaling events by the lateral aggregation of ligand-bound and other membrane proteins in the plane of the plasma membrane. This local clustering can lead to the co-enrichment of molecules that create an intracellular signal or bring sufficient amounts of activity together to shift an existing equilibrium towards the execution of a signaling event. In this way, clustering can serve as a cellular switch. The underlying uneven distribution and local enrichment of the signaling cluster’s constituting membrane proteins can be used as a functional readout. This information is obtained by combining single-molecule fluorescence microscopy with cluster algorithms that can reliably and reproducibly distinguish clusters from fluctuations in the background noise to generate quantitative data on this complex process. Cluster analysis of single-molecule fluorescence microscopy data has emerged as a proliferative field, and several algorithms and software solutions have been put forward. However, in most cases, such cluster algorithms require multiple analysis parameters to be defined by the user, which may lead to biased results. Furthermore, most cluster algorithms neglect the individual localization precision connected to every localized molecule, leading to imprecise results. Bayesian cluster analysis has been put forward to overcome these problems, but so far, it has entailed high computational cost, increasing runtime drastically. Finally, most software is challenging to use as they require advanced technical knowledge to operate. Here we combined three advanced cluster algorithms with the Bayesian approach and parallelization in a user-friendly GUI and achieved up to an order of magnitude faster processing than for previous approaches. Our work will simplify access to a well-controlled analysis of clustering data generated by SMLM and significantly accelerate data processing. The inclusion of a simulation mode aids in the design of well-controlled experimental assays.

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.