Challenges in quantitative single molecule localization microscopy

Single molecule localization microscopy (SMLM), which can provide up to an order of magnitude improvement in spatial resolution over conventional fluorescence microscopy, has the potential to be a highly useful tool for quantitative biological experiments. It has already been used for this purpose in varied fields in biology, ranging from molecular biology to neuroscience. In this review article, we briefly review the applications of SMLM in quantitative biology, and also the challenges involved and some of the solutions that have been proposed. Due to its advantages in labeling specificity and the relatively low overcounting caused by photoblinking when photo-activable fluorescent proteins (PA-FPs) are used as labels, we focus specifically on Photo-Activated Localization Microscopy (PALM), even though the ideas presented might be applicable to SMLM in general. Also, we focus on the following three quantitative measurements: single molecule counting, analysis of protein spatial distribution heterogeneity and co-localization analysis.     

Quantitative photo activated localization microscopy: unraveling the effects of photoblinking

In this work we discuss how to use photophysical information for improved quantitative measurements using Photo Activated Localization Microscopy (PALM) imaging. We introduce a method that reliably estimates the number of photoblinking molecules present in a biological sample and gives a robust way to quantify proteins at the single-cell level from PALM images. We apply this method to determine the amount of β2 adrenergic receptor, a prototypical G Protein Coupled Receptor, expressed on the plasma membrane of HeLa cells.     

Progress in quantitative single-molecule localization microscopy

With the advent of single-molecule localization microscopy (SMLM) techniques, intracellular proteins can be imaged at unprecedented resolution with high specificity and contrast. These techniques can lead to a better understanding of cell functioning, as they allow, among other applications, counting the number of molecules of a protein specie in a single cell, studying the heterogeneity in protein spatial organization, and probing the spatial interactions between different protein species. However, the use of these techniques for accurate quantitative measurements requires corrections for multiple inherent sources of error, including: overcounting due to multiple localizations of a single fluorophore (i.e., photoblinking), undercounting caused by incomplete photoconversion, uncertainty in the localization of single molecules, sample drift during the long imaging time, and inaccurate image registration in the case of dual-color imaging. In this paper, we review recent efforts that address some of these sources of error in quantitative SMLM and give examples in the context of photoactivated localization microscopy (PALM).     

Super-Resolution Imaging Strategies for Cell Biologists Using a Spinning Disk Microscope

In this study we use a spinning disk confocal microscope (SD) to generate super-resolution images of multiple cellular features from any plane in the cell. We obtain super-resolution images by using stochastic intensity fluctuations of biological probes, combining Photoactivation Light-Microscopy (PALM)/Stochastic Optical Reconstruction Microscopy (STORM) methodologies. We compared different image analysis algorithms for processing super-resolution data to identify the most suitable for analysis of particular cell structures. SOFI was chosen for X and Y and was able to achieve a resolution of ca. 80 nm; however higher resolution was possible >30 nm, dependant on the super-resolution image analysis algorithm used. Our method uses low laser power and fluorescent probes which are available either commercially or through the scientific community, and therefore it is gentle enough for biological imaging. Through comparative studies with structured illumination microscopy (SIM) and widefield epifluorescence imaging we identified that our methodology was advantageous for imaging cellular structures which are not immediately at the cell-substrate interface, which include the nuclear architecture and mitochondria. We have shown that it was possible to obtain two coloured images, which highlights the potential this technique has for high-content screening, imaging of multiple epitopes and live cell imaging.     

Superresolution microscopy for microbiology

This review provides a practical introduction to superresolution microscopy from the perspective of microbiological research. Because of the small sizes of bacterial cells, superresolution methods are particularly powerful and suitable for revealing details of cellular structures that are not resolvable under conventional fluorescence light microscopy. Here we describe the methodological concepts behind three major categories of superresolution light microscopy: photoactivated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM), structured illumination microscopy (SIM) and stimulated emission‐depletion (STED) microscopy. We then present recent applications of each of these techniques to microbial systems, which have revealed novel conformations of cellular structures and described new properties of in vivo protein function and interactions. Finally, we discuss the unique issues related to implementing each of these superresolution techniques with bacterial specimens and suggest avenues for future development. The goal of this review is to provide the necessary technical background for interested microbiologists to choose the appropriate superresolution method for their biological systems, and to introduce the practical considerations required for designing and analysing superresolution imaging experiments.     

High-Density 3D Single Molecular Analysis Based on Compressed Sensing

Single molecule fitting-based superresolution microscopy achieves sub-diffraction-limit image resolution but suffers from a need for long acquisition times to gather enough molecules. Several methods have recently been developed that analyze high molecule density images but most are only applicable to two dimensions. In this study, we implemented a high-density superresolution localization algorithm based on compressed sensing and a biplane approach that provides three-dimensional information about molecules, achieving super-resolution imaging at higher molecule densities than those achieved using the conventional single molecule fitting method.     

High-Density 3D Single Molecular Analysis Based on Compressed Sensing

Single molecule fitting-based superresolution microscopy achieves sub-diffraction-limit image resolution but suffers from a need for long acquisition times to gather enough molecules. Several methods have recently been developed that analyze high molecule density images but most are only applicable to two dimensions. In this study, we implemented a high-density superresolution localization algorithm based on compressed sensing and a biplane approach that provides three-dimensional information about molecules, achieving super-resolution imaging at higher molecule densities than those achieved using the conventional single molecule fitting method.     

Photoactivated Localization Microscopy with Bimolecular Fluorescence Complementation (BiFC-PALM)

Protein-protein interactions (PPIs) are key molecular events to biology. However, it remains a challenge to visualize PPIs with sufficient resolution and sensitivity in cells because the resolution of conventional light microscopy is diffraction-limited to ~250 nm. By combining bimolecular fluorescence complementation (BiFC) with photoactivated localization microscopy (PALM), PPIs can be visualized in cells with single molecule sensitivity and nanometer spatial resolution. BiFC is a commonly used technique for visualizing PPIs with fluorescence contrast, which involves splitting of a fluorescent protein into two non-fluorescent fragments. PALM is a recent superresolution microscopy technique for imaging biological samples at the nanometer and single molecule scales, which uses phototransformable fluorescent probes such as photoactivatable fluorescent proteins (PA-FPs). BiFC-PALM was demonstrated by splitting PAmCherry1, a PA-FP compatible with PALM, for its monomeric nature, good single molecule brightness, high contrast ratio, and utility for stoichiometry measurements. When split between amino acids 159 and 160, PAmCherry1 can be made into a BiFC probe that reconstitutes efficiently at 37 °C with high specificity to PPIs and low non-specific reconstitution. Ras-Raf interaction is used as an example to show how BiFC-PALM helps to probe interactions at the nanometer scale and with single molecule resolution. Their diffusion can also be tracked in live cells using single molecule tracking (smt-) PALM. In this protocol, factors to consider when designing the fusion proteins for BiFC-PALM are discussed, sample preparation, image acquisition, and data analysis steps are explained, and a few exemplary results are showcased. Providing high spatial resolution, specificity, and sensitivity, BiFC-PALM is a useful tool for studying PPIs in intact biological samples.     

PALM成像中激光强度和定位精度相关性的研究

近十年来,基于单分子定位的PALM成像技术快速发展,将显微镜的分辨率提高到了2-25nm。本文发现PALM成像过程中采用的激发光强度与成像的定位精度之间有密切的联系。我们分别选择了PALM成像使用的光激活荧光蛋白、光转换荧光蛋白和光开关荧光蛋白中最常用的荧光蛋白进行验证。实验发现伴随激光强度的增加,大部分荧光蛋白的光子数先升高然后趋于饱和,背景噪声几乎线性升高。进一步分析发现荧光蛋白的定位误差随着激光强度增强先降低后升高,因此选用合适的激光强度在PALM成像实验中至关重要。如何提高PALM成像的分辨率一直是科学家研究的热点,本研究内容可以指导研究人员在PALM成像中选用合适的激发光强度,从而得到高分辨率的图像。     

Self-Organization of the Escherichia coli Chemotaxis Network Imaged with Super-Resolution Light Microscopy

The Escherichia coli chemotaxis network is a model system for biological signal processing. In E. coli, transmembrane receptors responsible for signal transduction assemble into large clusters containing several thousand proteins. These sensory clusters have been observed at cell poles and future division sites. Despite extensive study, it remains unclear how chemotaxis clusters form, what controls cluster size and density, and how the cellular location of clusters is robustly maintained in growing and dividing cells. Here, we use photoactivated localization microscopy (PALM) to map the cellular locations of three proteins central to bacterial chemotaxis (the Tar receptor, CheY, and CheW) with a precision of 15 nm. We find that cluster sizes are approximately exponentially distributed, with no characteristic cluster size. One-third of Tar receptors are part of smaller lateral clusters and not of the large polar clusters. Analysis of the relative cellular locations of 1.1 million individual proteins (from 326 cells) suggests that clusters form via stochastic self-assembly. The super-resolution PALM maps of E. coli receptors support the notion that stochastic self-assembly can create and maintain approximately periodic structures in biological membranes, without direct cytoskeletal involvement or active transport.     

Super-resolution Imaging of Neuronal Dense-core Vesicles

Detection of fluorescence provides the foundation for many widely utilized and rapidly advancing microscopy techniques employed in modern biological and medical applications. Strengths of fluorescence include its sensitivity, specificity, and compatibility with live imaging. Unfortunately, conventional forms of fluorescence microscopy suffer from one major weakness, diffraction-limited resolution in the imaging plane, which hampers studies of structures with dimensions smaller than ~250 nm. Recently, this limitation has been overcome with the introduction of super-resolution fluorescence microscopy techniques, such as photoactivated localization microscopy (PALM). Unlike its conventional counterparts, PALM can produce images with a lateral resolution of tens of nanometers. It is thus now possible to use fluorescence, with its myriad strengths, to elucidate a spectrum of previously inaccessible attributes of cellular structure and organization.Unfortunately, PALM is not trivial to implement, and successful strategies often must be tailored to the type of system under study. In this article, we show how to implement single-color PALM studies of vesicular structures in fixed, cultured neurons. PALM is ideally suited to the study of vesicles, which have dimensions that typically range from ~50-250 nm. Key steps in our approach include labeling neurons with photoconvertible (green to red) chimeras of vesicle cargo, collecting sparsely sampled raw images with a super-resolution microscopy system, and processing the raw images to produce a high-resolution PALM image. We also demonstrate the efficacy of our approach by presenting exceptionally well-resolved images of dense-core vesicles (DCVs) in cultured hippocampal neurons, which refute the hypothesis that extrasynaptic trafficking of DCVs is mediated largely by DCV clusters.     

Quantitative analysis of photoactivated localization microscopy (PALM) datasets using pair-correlation analysis

Pointillistic based super-resolution techniques, such as photoactivated localization microscopy (PALM), involve multiple cycles of sequential activation, imaging, and precise localization of single fluorescent molecules. A super-resolution image, having nanoscopic structural information, is then constructed by compiling all the image sequences. Because the final image resolution is determined by the localization precision of detected single molecules and their density, accurate image reconstruction requires imaging of biological structures labeled with fluorescent molecules at high density. In such image datasets, stochastic variations in photon emission and intervening dark states lead to uncertainties in identification of single molecules. This, in turn, prevents the proper utilization of the wealth of information on molecular distribution and quantity. A recent strategy for overcoming this problem is pair-correlation analysis applied to PALM. Using rigorous statistical algorithms to estimate the number of detected proteins, this approach allows the spatial organization of molecules to be quantitatively described.     

Reconstruction of low-resolution molecular structures from simulated atomic force microscopy images

BackgroundAtomic Force Microscopy (AFM) is an experimental technique to study structure-function relationship of biomolecules. AFM provides images of biomolecules at nanometer resolution. High-speed AFM experiments produce a series of images following dynamics of biomolecules. To further understand biomolecular functions, information on three-dimensional (3D) structures is beneficial.MethodWe aim to recover 3D information from an AFM image by computational modeling. The AFM image includes only low-resolution representation of a molecule; therefore we represent the structures by a coarse grained model (Gaussian mixture model). Using Monte-Carlo sampling, candidate models are generated to increase similarity between AFM images simulated from the models and target AFM image.ResultsThe algorithm was tested on two proteins to model their conformational transitions. Using a simulated AFM image as reference, the algorithm can produce a low-resolution 3D model of the target molecule. Effect of molecular orientations captured in AFM images on the 3D modeling performance was also examined and it is shown that similar accuracy can be obtained for many orientations.ConclusionsThe proposed algorithm can generate 3D low-resolution protein models, from which conformational transitions observed in AFM images can be interpreted in more detail.General significanceHigh-speed AFM experiments allow us to directly observe biomolecules in action, which provides insights on biomolecular function through dynamics. However, as only partial structural information can be obtained from AFM data, this new AFM based hybrid modeling method would be useful to retrieve 3D information of the entire biomolecule.     

From microscopy data to in silico environments for in vivo-oriented simulations

In our previous study, we introduced a combination methodology of Fluorescence Correlation Spectroscopy (FCS) and Transmission Electron Microscopy (TEM), which is powerful to investigate the effect of intracellular environment to biochemical reaction processes. Now, we developed a reconstruction method of realistic simulation spaces based on our TEM images. Interactive raytracing visualization of this space allows the perception of the overall 3D structure, which is not directly accessible from 2D TEM images. Simulation results show that the diffusion in such generated structures strongly depends on image post-processing. Frayed structures corresponding to noisy images hinder the diffusion much stronger than smooth surfaces from denoised images. This means that the correct identification of noise or structure is significant to reconstruct appropriate reaction environment in silico in order to estimate realistic behaviors of reactants in vivo. Static structures lead to anomalous diffusion due to the partial confinement. In contrast, mobile crowding agents do not lead to anomalous diffusion at moderate crowding levels. By varying the mobility of these non-reactive obstacles (NRO), we estimated the relationship between NRO diffusion coefficient (D_nro) and the anomaly in the tracer diffusion (α). For D_nro=21.96 to 44.49 μm²/s, the simulation results match the anomaly obtained from FCS measurements. This range of the diffusion coefficient from simulations is compatible with the range of the diffusion coefficient of structural proteins in the cytoplasm. In addition, we investigated the relationship between the radius of NRO and anomalous diffusion coefficient of tracers by the comparison between different simulations. The radius of NRO has to be 58 nm when the polymer moves with the same diffusion speed as a reactant, which is close to the radius of functional protein complexes in a cell.     

Crystal Structure Prediction based on Statistical Potentials

Organic molecule crystals are becoming more and more important in applications like piezoelectricity, ferroelectricity and pigments. These properties depend on the molecule and on the crystal structure. For this reason much effort is being made to predict the crystal structure of organic molecules. We have developed a new algorithm differing mainly in three features from other approaches (simulated annealing, Monte Carlo etc.). First, we analyze just one molecule for proper symmetry operations building up the crystal; second, the program works in a discrete space; and finally the scoring function (energy function) is derived statistically from known crystal structures and tabulated. Our program computes a list of crystal structures weighted according to our scoring function. The new algorithm FlexCryst is currently implemented for the four space groups P1, Pbar1, P2₁, and P2₁2₁2₁. The three latter space groups are widespread in nature. The algorithm computes structural models of acceptable quality and shows excellent time performance. During our validation we found the experimental structure among the structures proposed by the algorithm in 123 of 129 cases for P1, in 66 of 95 cases for Pbar1, in 73 of 100 cases for P2₁, and in 94 of 98 cases for P2₁2₁2₁. The performance depends on the space group. In the case of P1 the run time per molecule is about two minutes and increases up to roughly one hour for the space group P2₁.     

Characterizing locus specific chromatin structure and dynamics with correlative conventional and super-resolution imaging in living cells

The dynamic rearrangement of chromatin is critical for gene regulation, but mapping both the spatial organization of chromatin and its dynamics remains a challenge. Many structural conformations are too small to be resolved via conventional fluorescence microscopy and the long acquisition time of super-resolution photoactivated localization microscopy (PALM) precludes the structural characterization of chromatin below the optical diffraction limit in living cells due to chromatin motion. Here we develop a correlative conventional fluorescence and PALM imaging approach to quantitatively map time-averaged chromatin structure and dynamics below the optical diffraction limit in living cells. By assigning localizations to a locus as it moves, we reliably discriminate between bound and unbound dCas9 molecules, whose mobilities overlap. Our approach accounts for changes in DNA mobility and relates local chromatin motion to larger scale domain movement. In our experimental system, we show that compacted telomeres move faster and have a higher density of bound dCas9 molecules, but the relative motion of those molecules is more restricted than in less compacted telomeres. Correlative conventional and PALM imaging therefore improves the ability to analyze the mobility and time-averaged nanoscopic structural features of locus specific chromatin with single molecule sensitivity and yields unprecedented insights across length and time scales.     

ProDomAs,protein domain assignment algorithm using center‐based clustering and independent dominating set

Decomposition of structural domains is an essential task in classifying protein structures, predicting protein function, and many other proteomics problems. As the number of known protein structures in PDB grows exponentially, the need for accurate automatic domain decomposition methods becomes more essential. In this article, we introduce a bottom‐up algorithm for assigning protein domains using a graph theoretical approach. This algorithm is based on a center‐based clustering approach. For constructing initial clusters, members of an independent dominating set for the graph representation of a protein are considered as the centers. A distance matrix is then defined for these clusters. To obtain final domains, these clusters are merged using the compactness principle of domains and a method similar to the neighbor‐joining algorithm considering some thresholds. The thresholds are computed using a training set consisting of 50 protein chains. The algorithm is implemented using C++ language and is named ProDomAs. To assess the performance of ProDomAs, its results are compared with seven automatic methods, against five publicly available benchmarks. The results show that ProDomAs outperforms other methods applied on the mentioned benchmarks. The performance of ProDomAs is also evaluated against 6342 chains obtained from ASTRAL SCOP 1.71. ProDomAs is freely available at http://www.bioinf.cs.ipm.ir/software/prodomas . Proteins 2014; 82:1937–1946. © 2014 Wiley Periodicals, Inc.     

Live-cell photoactivated localization microscopy of nanoscale adhesion dynamics

We demonstrate live-cell super-resolution imaging using photoactivated localization microscopy (PALM). The use of photon-tolerant cell lines in combination with the high resolution and molecular sensitivity of PALM permitted us to investigate the nanoscale dynamics within individual adhesion complexes (ACs) in living cells under physiological conditions for as long as 25 min, with half of the time spent collecting the PALM images at spatial resolutions down to approximately 60 nm and frame rates as short as 25 s. We visualized the formation of ACs and measured the fractional gain and loss of individual paxillin molecules as each AC evolved. By allowing observation of a wide variety of nanoscale dynamics, live-cell PALM provides insights into molecular assembly during the initiation, maturation and dissolution of cellular processes.     

Condensed Mitotic Chromosome Structure at Nanometer Resolution Using PALM and EGFP- Histones

Photoactivated localization microscopy (PALM) and related fluorescent biological imaging methods are capable of providing very high spatial resolutions (up to 20 nm). Two major demands limit its widespread use on biological samples: requirements for photoactivatable/photoconvertible fluorescent molecules, which are sometimes difficult to incorporate, and high background signals from autofluorescence or fluorophores in adjacent focal planes in three-dimensional imaging which reduces PALM resolution significantly. We present here a high-resolution PALM method utilizing conventional EGFP as the photoconvertible fluorophore, improved algorithms to deal with high levels of biological background noise, and apply this to imaging higher order chromatin structure. We found that the emission wavelength of EGFP is efficiently converted from green to red when exposed to blue light in the presence of reduced riboflavin. The photon yield of red-converted EGFP using riboflavin is comparable to other bright photoconvertible fluorescent proteins that allow <20 nm resolution. We further found that image pre-processing using a combination of denoising and deconvolution of the raw PALM images substantially improved the spatial resolution of the reconstruction from noisy images. Performing PALM on Drosophila mitotic chromosomes labeled with H2AvD-EGFP, a histone H2A variant, revealed filamentous components of ∼70 nm. This is the first observation of fine chromatin filaments specific for one histone variant at a resolution approximating that of conventional electron microscope images (10–30 nm). As demonstrated by modeling and experiments on a challenging specimen, the techniques described here facilitate super-resolution fluorescent imaging with common biological samples.