Simulated single molecule microscopy with SMeagol

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.

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.

3D GAN image synthesis and dataset quality assessment for bacterial biofilm

MOTIVATION

Data-driven deep learning techniques usually require a large quantity of labeled training data to achieve reliable solutions in bioimage analysis. However, noisy image conditions and high cell density in bacterial biofilm images make 3D cell annotations difficult to obtain. Alternatively, data augmentation via synthetic data generation is attempted, but current methods fail to produce realistic images.

RESULTS

This article presents a bioimage synthesis and assessment workflow with application to augment bacterial biofilm images. 3D cyclic generative adversarial networks (GAN) with unbalanced cycle consistency loss functions are exploited in order to synthesize 3D biofilm images from binary cell labels. Then, a stochastic synthetic dataset quality assessment (SSQA) measure that compares statistical appearance similarity between random patches from random images in two datasets is proposed. Both SSQA scores and other existing image quality measures indicate that the proposed 3D Cyclic GAN, along with the unbalanced loss function, provides a reliably realistic (as measured by mean opinion score) 3D synthetic biofilm image. In 3D cell segmentation experiments, a GAN-augmented training model also presents more realistic signal-to-background intensity ratio and improved cell counting accuracy.

AVAILABILITY AND IMPLEMENTATION

https://github.com/jwang-c/DeepBiofilm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

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.

Sequence specificity in DNA binding is mainly governed by association

Sequence-specific binding of proteins to DNA is essential for accessing genetic information. We derive a model that predicts an anticorrelation between the macroscopic association and dissociation rates of DNA binding proteins. We tested the model for thousands of different lac operator sequences with a protein binding microarray and by observing kinetics for individual lac repressor molecules in single-molecule experiments. We found that sequence specificity is mainly governed by the efficiency with which the protein recognizes different targets. The variation in probability of recognizing different targets is at least 1.7 times as large as the variation in microscopic dissociation rates. Modulating the rate of binding instead of the rate of dissociation effectively reduces the risk of the protein being retained on nontarget sequences while searching.

Cega: a single particle segmentation algorithm to identify moving particles in a noisy system

Improvements to particle tracking algorithms are required to effectively analyze the motility of biological molecules in complex or noisy systems. A typical single particle tracking (SPT) algorithm detects particle coordinates for trajectory assembly. However, particle detection filters fail for data sets with low signal-to-noise levels. When tracking molecular motors in complex systems, standard techniques often fail to separate the fluorescent signatures of moving particles from background signal. We developed an approach to analyze the motility of kinesin motor proteins moving along the microtubule cytoskeleton of extracted neurons using the Kullback-Leibler divergence to identify regions where there are significant differences between models of moving particles and background signal. We tested our software on both simulated and experimental data and found a noticeable improvement in SPT capability and a higher identification rate of motors as compared with current methods. This algorithm, called Cega, for “find the object,” produces data amenable to conventional blob detection techniques that can then be used to obtain coordinates for downstream SPT processing. We anticipate that this algorithm will be useful for those interested in tracking moving particles in complex in vitro or in vivo environments.

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10-20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

A Model for the Spatiotemporal Design of Gene Regulatory Circuits †

Mathematical modeling assists the design of synthetic regulatory networks by providing a detailed mechanistic understanding of biological systems. Models that can predict the performance of a design are fundamental for synthetic biology since they minimize iterations along the design-build-test lifecycle. Such predictability depends crucially on what assumptions (i.e., biological simplifications) the model considers. Here, we challenge a common assumption when it comes to the modeling of bacterial-based gene regulation: considering negligible the effects of intracellular physical space. It is commonly assumed that molecules, such as transcription factors (TF), are homogeneously distributed inside a cell, so there is no need to model their diffusion. We describe a mathematical model that accounts for molecular diffusion and show how simulations of network performance are decisively affected by the distance between its components. Specifically, the model focuses on the search by a TF for its target promoter. The combination of local searches, via one-dimensional sliding along the chromosome, and global searches, via three-dimensional diffusion through the cytoplasm, determine TF-promoter interplay. Previous experimental results with engineered bacteria in which the distance between TF source and target was minimized or enlarged were successfully reproduced by the spatially resolved model we introduce here. This suggests that the spatial specification of the circuit alone can be exploited as a design parameter in synthetic biology to select programmable output levels.

Simulation of live-cell imaging system reveals hidden uncertainties in cooperative binding measurements

We propose a computational method to quantitatively evaluate the systematic uncertainties that arise from undetectable sources in biological measurements using live-cell imaging techniques. We then demonstrate this method in measuring the biological cooperativity of molecular binding networks, in particular, ligand molecules binding to cell-surface receptor proteins. Our results show how the nonstatistical uncertainties lead to invalid identifications of the measured cooperativity. Through this computational scheme, the biological interpretation can be more objectively evaluated and understood under a specific experimental configuration of interest.

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.

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.

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.

Pointwise error estimates in localization microscopy

Pointwise localization of individual fluorophores is a critical step in super-resolution localization microscopy and single particle tracking. Although the methods are limited by the localization errors of individual fluorophores, the pointwise localization precision has so far been estimated using theoretical best case approximations that disregard, for example, motion blur, defocus effects and variations in fluorescence intensity. Here, we show that pointwise localization precision can be accurately estimated directly from imaging data using the Bayesian posterior density constrained by simple microscope properties. We further demonstrate that the estimated localization precision can be used to improve downstream quantitative analysis, such as estimation of diffusion constants and detection of changes in molecular motion patterns. Finally, the quality of actual point localizations in live cell super-resolution microscopy can be improved beyond the information theoretic lower bound for localization errors in individual images, by modelling the movement of fluorophores and accounting for their pointwise localization uncertainty.