Ultra-high resolution imaging by fluorescence photoactivation localization microscopy

Biological structures span many orders of magnitude in size, but far-field visible light microscopy suffers from limited resolution. A new method for fluorescence imaging has been developed that can obtain spatial distributions of large numbers of fluorescent molecules on length scales shorter than the classical diffraction limit. Fluorescence photoactivation localization microscopy (FPALM) analyzes thousands of single fluorophores per acquisition, localizing small numbers of them at a time, at low excitation intensity. To control the number of visible fluorophores in the field of view and ensure that optically active molecules are separated by much more than the width of the point spread function, photoactivatable fluorescent molecules are used, in this case the photoactivatable green fluorescent protein (PA-GFP). For these photoactivatable molecules, the activation rate is controlled by the activation illumination intensity; nonfluorescent inactive molecules are activated by a high-frequency (405-nm) laser and are then fluorescent when excited at a lower frequency. The fluorescence is imaged by a CCD camera, and then the molecules are either reversibly inactivated or irreversibly photobleached to remove them from the field of view. The rate of photobleaching is controlled by the intensity of the laser used to excite the fluorescence, in this case an Ar+ ion laser. Because only a small number of molecules are visible at a given time, their positions can be determined precisely; with only approximately 100 detected photons per molecule, the localization precision can be as much as 10-fold better than the resolution, depending on background levels. Heterogeneities on length scales of the order of tens of nanometers are observed by FPALM of PA-GFP on glass. FPALM images are compared with images of the same molecules by widefield fluorescence. FPALM images of PA-GFP on a terraced sapphire crystal surface were compared with atomic force microscopy and show that the full width at half-maximum of features approximately 86 +/- 4 nm is significantly better than the expected diffraction-limited optical resolution. The number of fluorescent molecules and their brightness distribution have also been determined using FPALM. This new method suggests a means to address a significant number of biological questions that had previously been limited by microscope resolution.     

Poisson-Gaussian Noise Reduction Using the Hidden Markov Model in Contourlet Domain for Fluorescence Microscopy Images

In certain image acquisitions processes, like in fluorescence microscopy or astronomy, only a limited number of photons can be collected due to various physical constraints. The resulting images suffer from signal dependent noise, which can be modeled as a Poisson distribution, and a low signal-to-noise ratio. However, the majority of research on noise reduction algorithms focuses on signal independent Gaussian noise. In this paper, we model noise as a combination of Poisson and Gaussian probability distributions to construct a more accurate model and adopt the contourlet transform which provides a sparse representation of the directional components in images. We also apply hidden Markov models with a framework that neatly describes the spatial and interscale dependencies which are the properties of transformation coefficients of natural images. In this paper, an effective denoising algorithm for Poisson-Gaussian noise is proposed using the contourlet transform, hidden Markov models and noise estimation in the transform domain. We supplement the algorithm by cycle spinning and Wiener filtering for further improvements. We finally show experimental results with simulations and fluorescence microscopy images which demonstrate the improved performance of the proposed approach.     

Sampling effects, noise, and photobleaching in temporal image correlation spectroscopy

We present an extensive investigation of the accuracy and precision of temporal image correlation spectroscopy (TICS). Using simulations of laser scanning microscopy image time series, we investigate the effect of spatiotemporal sampling, particle density, noise, sampling frequency, and photobleaching of fluorophores on the recovery of transport coefficients and number densities by TICS. We show that the recovery of transport coefficients is usually limited by spatial sampling, while the measurement of accurate number densities is restricted by background noise in an image series. We also demonstrate that photobleaching of the fluorophore causes a consistent overestimation of diffusion coefficients and flow rates, and a severe underestimation of number densities. We derive a bleaching correction equation that removes both of these biases when used to fit temporal autocorrelation functions, without increasing the number of fit parameters. Finally, we image the basal membrane of a CHO cell with EGFP/alpha-actinin, using two-photon microscopy, and analyze a subregion of this series using TICS and apply the bleaching correction. We show that the photobleaching correction can be determined simply by using the average image intensities from the time series, and we use the simulations to provide good estimates of the accuracy and precision of the number density and transport coefficients measured with TICS.     

Localization Precision in Stepwise Photobleaching Experiments

The precise determination of the position of fluorescent labels is essential for the quantitative study of biomolecular structures by various localization microscopy techniques. Localization by stepwise photobleaching is especially suited for measuring nanometer-scale distances between two labels; however, the precision of this method has remained elusive. Here, we show that shot noise from other emitters and error propagation compromise the localization precision in stepwise photobleaching. Incorporation of point spread function-shaped shot noise into the variance term in the Fisher matrix yielded fundamental Cràmer-Rao lower bounds (CRLBs) that were in general anisotropic and depended on emitter intensity and position. We performed simulations to benchmark the extent to which different analysis procedures reached these ideal CRLBs. The accumulation of noise from several images accounted for the worse localization precision in image subtraction. Propagation of fitting errors compromised the CRLBs in sequential fitting using fixed parameters. Global fitting of all images was also governed by error propagation, but made optimal use of the available information. The precision of individual distance measurements depended critically on the exact bleaching kinetics and was correctly quantified by the CRLBs. The methods presented here provide a consistent framework for quantitatively analyzing stepwise photobleaching experiments and shed light on the localization precision in some other bleaching- or blinking-assisted techniques.     

Localization Precision in Stepwise Photobleaching Experiments

The precise determination of the position of fluorescent labels is essential for the quantitative study of biomolecular structures by various localization microscopy techniques. Localization by stepwise photobleaching is especially suited for measuring nanometer-scale distances between two labels; however, the precision of this method has remained elusive. Here, we show that shot noise from other emitters and error propagation compromise the localization precision in stepwise photobleaching. Incorporation of point spread function-shaped shot noise into the variance term in the Fisher matrix yielded fundamental Cràmer-Rao lower bounds (CRLBs) that were in general anisotropic and depended on emitter intensity and position. We performed simulations to benchmark the extent to which different analysis procedures reached these ideal CRLBs. The accumulation of noise from several images accounted for the worse localization precision in image subtraction. Propagation of fitting errors compromised the CRLBs in sequential fitting using fixed parameters. Global fitting of all images was also governed by error propagation, but made optimal use of the available information. The precision of individual distance measurements depended critically on the exact bleaching kinetics and was correctly quantified by the CRLBs. The methods presented here provide a consistent framework for quantitatively analyzing stepwise photobleaching experiments and shed light on the localization precision in some other bleaching- or blinking-assisted techniques.     

Development of a green reversibly photoswitchable variant of Eos fluorescent protein with fixation resistance

Superresolution microscopy determines the localization of fluorescent proteins with high precision, beyond the diffraction limit of light. Superresolution microscopic techniques include photoactivated localization microscopy (PALM), which can localize a single protein by the stochastic activation of its fluorescence. In the determination of single-molecule localization by PALM, the number of molecules that can be analyzed per image is limited. Thus, many images are required to reconstruct the localization of numerous molecules in the cell. However, most fluorescent proteins lose their fluorescence upon fixation. Here, we combined the amino acid substitutions of two Eos protein derivatives, Skylan-S and mEos4b, which are a green reversibly photoswitchable fluorescent protein (RSFP) and a fixation-resistant green-to-red photoconvertible fluorescent protein, respectively, resulting in the fixation-resistant Skylan-S (frSkylan-S), a green RSFP. The frSkylan-S protein is inactivated by excitation light and reactivated by irradiation with violet light, and retained more fluorescence after aldehyde fixation than Skylan-S. The qualities of the frSkylan-S fusion proteins were sufficiently high in PALM observations, as examined using α-tubulin and clathrin light chain. Furthermore, frSkylan-S can be combined with antibody staining for multicolor imaging. Therefore, frSkylan-S is a green fluorescent protein suitable for PALM imaging under aldehyde-fixation conditions.     

Precision in iterative modulation enhanced single-molecule localization microscopy

Modulation enhanced single-molecule localization microscopy (meSMLM) methods improve the localization precision by using patterned illumination to encode additional position information. Iterative meSMLM (imeSMLM) methods iteratively generate prior information on emitter positions, used to locally improve the localization precision during subsequent iterations. The Cramér-Rao lower bound cannot incorporate prior information to bound the best achievable localization precision because it requires estimators to be unbiased. By treating estimands as random variables with a known prior distribution, the Van Trees inequality (VTI) can be used to bound the best possible localization precision of imeSMLM methods. An imeSMLM method is considered, where the positions of in-plane standing-wave illumination patterns are controlled over the course of multiple iterations. Using the VTI, we analytically approximate a lower bound on the maximum localization precision of imeSMLM methods that make use of standing-wave illumination patterns. In addition, we evaluate the maximally achievable localization precision for different illumination pattern placement strategies using Monte Carlo simulations. We show that in the absence of background and under perfect modulation, the information content of signal photons increases exponentially as a function of the iteration count. However, the information increase is no longer exponential as a function of the iteration count under non-zero background, imperfect modulation, or limited mechanical resolution of the illumination positioning system. As a result, imeSMLM with two iterations reaches at most a fivefold improvement over SMLM at 8 expected background photons per pixel and 95% modulation contrast. Moreover, the information increase from imeSMLM is balanced by a reduced signal photon rate. Therefore, SMLM outperforms imeSMLM when considering an equal measurement time and illumination power per iteration. Finally, the VTI is an excellent tool for the assessment of the performance of illumination control and is therefore the method of choice for optimal design and control of imeSMLM methods.     

Estimating the localization spread function of static single-molecule localization microscopy images

Single-molecule localization microscopy (SMLM) permits the visualization of cellular structures an order of magnitude smaller than the diffraction limit of visible light, and an accurate, objective evaluation of the resolution of an SMLM data set is an essential aspect of the image processing and analysis pipeline. Here, we present a simple method to estimate the localization spread function (LSF) of a static SMLM data set directly from acquired localizations, exploiting the correlated dynamics of individual emitters and properties of the pair autocorrelation function evaluated in both time and space. The method is demonstrated on simulated localizations, DNA origami rulers, and cellular structures labeled by dye-conjugated antibodies, DNA-PAINT, or fluorescent fusion proteins. We show that experimentally obtained images have LSFs that are broader than expected from the localization precision alone, due to additional uncertainty accrued when localizing molecules imaged over time.     

SimpleSTORM: a fast,self-calibrating reconstruction algorithm for localization microscopy

Although there are many reconstruction algorithms for localization microscopy, their use is hampered by the difficulty to adjust a possibly large number of parameters correctly. We propose SimpleSTORM, an algorithm that determines appropriate parameter settings directly from the data in an initial self-calibration phase. The algorithm is based on a carefully designed yet simple model of the image acquisition process which allows us to standardize each image such that the background has zero mean and unit variance. This standardization makes it possible to detect spots by a true statistical test (instead of hand-tuned thresholds) and to de-noise the images with an efficient matched filter. By reducing the strength of the matched filter, SimpleSTORM also performs reasonably on data with high-spot density, trading off localization accuracy for improved detection performance. Extensive validation experiments on the ISBI Localization Challenge Dataset, as well as real image reconstructions, demonstrate the good performance of our algorithm.     

Accuracy and dynamic range of spatial image correlation and cross-correlation spectroscopy

We present a comprehensive study of the accuracy and dynamic range of spatial image correlation spectroscopy (ICS) and image cross-correlation spectroscopy (ICCS). We use simulations to model laser scanning microscopy imaging of static subdiffraction limit fluorescent proteins or protein clusters in a cell membrane. The simulation programs allow us to control the spatial imaging sampling variables and the particle population densities and interactions and introduce and vary background and counting noise typical of what is encountered in digital optical microscopy. We systematically calculate how the accuracy of both image correlation methods depends on practical experimental collection parameters and characteristics of the sample. The results of this study provide a guide to appropriately plan spatial image correlation measurements on proteins in biological membranes in real cells. The data presented map regimes where the spatial ICS and ICCS provide accurate results as well as clearly showing the conditions where they systematically deviate from acceptable accuracy. Finally, we compare the simulated data with standard confocal microscopy using live CHO cells expressing the epidermal growth factor receptor fused with green fluorescent protein (GFP/EGFR) to obtain typical values for the experimental variables that were investigated in our study. We used our simulation results to estimate a relative precision of 20% for the ICS measured receptor density of 64 microm(-2) within a 121 x 98 pixel subregion of a single cell.     

Evaluating the Performance of Time-Gated Live-Cell Microscopy with Lanthanide Probes

Probes and biosensors that incorporate luminescent Tb(III) or Eu(III) complexes are promising for cellular imaging because time-gated microscopes can detect their long-lifetime (approximately milliseconds) emission without interference from short-lifetime (approximately nanoseconds) fluorescence background. Moreover, the discrete, narrow emission bands of Tb(III) complexes make them uniquely suited for multiplexed imaging applications because they can serve as Förster resonance energy transfer (FRET) donors to two or more differently colored acceptors. However, lanthanide complexes have low photon emission rates that can limit the image signal/noise ratio, which has a square-root dependence on photon counts. This work describes the performance of a wide-field, time-gated microscope with respect to its ability to image Tb(III) luminescence and Tb(III)-mediated FRET in cultured mammalian cells. The system employed a UV-emitting LED for low-power, pulsed excitation and an intensified CCD camera for gated detection. Exposure times of ∼1 s were needed to collect 5–25 photons per pixel from cells that contained micromolar concentrations of a Tb(III) complex. The observed photon counts matched those predicted by a theoretical model that incorporated the photophysical properties of the Tb(III) probe and the instrument’s light-collection characteristics. Despite low photon counts, images of Tb(III)/green fluorescent protein FRET with a signal/noise ratio ≥ 7 were acquired, and a 90% change in the ratiometric FRET signal was measured. This study shows that the sensitivity and precision of lanthanide-based cellular microscopy can approach that of conventional FRET microscopy with fluorescent proteins. The results should encourage further development of lanthanide biosensors that can measure analyte concentration, enzyme activation, and protein-protein interactions in live cells.     

3D multicolor super-resolution imaging offers improved accuracy in neuron tracing

The connectivity among neurons holds the key to understanding brain function. Mapping neural connectivity in brain circuits requires imaging techniques with high spatial resolution to facilitate neuron tracing and high molecular specificity to mark different cellular and molecular populations. Here, we tested a three-dimensional (3D), multicolor super-resolution imaging method, stochastic optical reconstruction microscopy (STORM), for tracing neural connectivity using cultured hippocampal neurons obtained from wild-type neonatal rat embryos as a model system. Using a membrane specific labeling approach that improves labeling density compared to cytoplasmic labeling, we imaged neural processes at 44 nm 2D and 116 nm 3D resolution as determined by considering both the localization precision of the fluorescent probes and the Nyquist criterion based on label density. Comparison with confocal images showed that, with the currently achieved resolution, we could distinguish and trace substantially more neuronal processes in the super-resolution images. The accuracy of tracing was further improved by using multicolor super-resolution imaging. The resolution obtained here was largely limited by the label density and not by the localization precision of the fluorescent probes. Therefore, higher image resolution, and thus higher tracing accuracy, can in principle be achieved by further improving the label density.     

Single-molecule detection with total internal reflection excitation: comparing signal-to-background and total signals in different geometries.

Excitation of fluorescence with total internal reflection (TIR) excitation yields very low background scattered light and good signal-to-background contrast. The background and its associated noise can be made low enough to detect single fluorescent molecules under ambient conditions. In this paper, different TIR geometries were compared for excitation and detection of single rhodamine 6G (R6G) molecules at air-silica interfaces and single B-phycoerythrin proteins at water-silica interfaces. Through-objective, objective-coverslip, and prism-based TIR geometries were investigated. The signal-to-background ratio (SBR) and the number of photons detected before photobleaching (Nb) were optimum in different geometries. The greatest image contrast was obtained when using prism-TIR (SBR = 11.5), but the largest number of detected signal photoelectrons was obtained by using through-objective TIR for R6G-air-silica ( = 10(4)). The results were discussed in terms of the TIR field enhancements and the modified dipole emission pattern near a dielectric interface. The SBR and total detected photons are important parameters for designing photon-limited experiments.     

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.     

Monte-Carlo simulation of a slot-scanning X-ray imaging system

We present a method for simulating slot-scanning X-ray imaging using the general-purpose Monte Carlo simulation package PENELOPE and penEasy Imaging. Different phantoms can be defined with the PENGEOM package, which defines bodies as combinations of volumes limited by quadric surfaces. The source-detector geometry, the position of the object, the collimator, the X-ray tube properties, the detector material and the pixel dimensions are defined. The output of the time-delay integration detector is simulated using sequential slot outputs derived from penEasy Imaging. The simulations are validated using tungsten and aluminium test objects, which are both simulated and imaged. The simulations are compared to the X-ray images using standard image quality metrics. The MTF, NPS and DQE curves show that the real and simulated X-ray images are comparable in terms of spatial resolution, noise and frequency information. The implementation can be modified to suit alterations in the system being simulated.     

Sequential Superresolution Imaging of Multiple Targets Using a Single Fluorophore

Fluorescence superresolution (SR) microscopy, or fluorescence nanoscopy, provides nanometer scale detail of cellular structures and allows for imaging of biological processes at the molecular level. Specific SR imaging methods, such as localization-based imaging, rely on stochastic transitions between on (fluorescent) and off (dark) states of fluorophores. Imaging multiple cellular structures using multi-color imaging is complicated and limited by the differing properties of various organic dyes including their fluorescent state duty cycle, photons per switching event, number of fluorescent cycles before irreversible photobleaching, and overall sensitivity to buffer conditions. In addition, multiple color imaging requires consideration of multiple optical paths or chromatic aberration that can lead to differential aberrations that are important at the nanometer scale. Here, we report a method for sequential labeling and imaging that allows for SR imaging of multiple targets using a single fluorophore with negligible cross-talk between images. Using brightfield image correlation to register and overlay multiple image acquisitions with ~10 nm overlay precision in the x-y imaging plane, we have exploited the optimal properties of AlexaFluor647 for dSTORM to image four distinct cellular proteins. We also visualize the changes in co-localization of the epidermal growth factor (EGF) receptor and clathrin upon EGF addition that are consistent with clathrin-mediated endocytosis. These results are the first to demonstrate sequential SR (s-SR) imaging using direct stochastic reconstruction microscopy (dSTORM), and this method for sequential imaging can be applied to any superresolution technique.     

Automated Analysis of Single-Molecule Photobleaching Data by Statistical Modeling of Spot Populations

The number of fluorophores within a molecule complex can be revealed by single-molecule photobleaching imaging. A widely applied strategy to analyze intensity traces over time is the quantification of photobleaching step counts. However, several factors can limit and bias the detection of photobleaching steps, including noise, high numbers of fluorophores, and the possibility that several photobleaching events occur almost simultaneously. In this study, we propose a new approach, to our knowledge, to determine the fluorophore number that correlates the intensity decay of a population of molecule complexes with the decay of the number of visible complexes. We validated our approach using single and fourfold Atto-labeled DNA strands. As an example we estimated the subunit stoichiometry of soluble CD95L using GFP fusion proteins. To assess the precision of our method we performed in silico experiments showing that the estimates are not biased for experimentally observed intensity fluctuations and that the relative precision remains constant with increasing number of fluorophores. In case of fractional fluorescent labeling, our simulations predicted that the fluorophore number estimate corresponds to the product of the true fluorophore number with the labeling fraction. Our method, denoted by spot number and intensity correlation (SONIC), is fully automated, robust to noise, and does not require the counting of photobleaching events.     

Evaluation of fluorophores for optimal performance in localization-based super-resolution imaging

One approach to super-resolution fluorescence imaging uses sequential activation and localization of individual fluorophores to achieve high spatial resolution. Essential to this technique is the choice of fluorescent probes; the properties of the probes, including photons per switching event, on-off duty cycle, photostability and number of switching cycles, largely dictate the quality of super-resolution images. Although many probes have been reported, a systematic characterization of the properties of these probes and their impact on super-resolution image quality has been described in only a few cases. Here we quantitatively characterized the switching properties of 26 organic dyes and directly related these properties to the quality of super-resolution images. This analysis provides guidelines for characterization of super-resolution probes and a resource for selecting probes based on performance. Our evaluation identified several photoswitchable dyes with good to excellent performance in four independent spectral ranges, with which we demonstrated low-cross-talk, four-color super-resolution imaging.     

MATtrack: A MATLAB-Based Quantitative Image Analysis Platform for Investigating Real-Time Photo-Converted Fluorescent Signals in Live Cells

We introduce here MATtrack, an open source MATLAB-based computational platform developed to process multi-Tiff files produced by a photo-conversion time lapse protocol for live cell fluorescent microscopy. MATtrack automatically performs a series of steps required for image processing, including extraction and import of numerical values from Multi-Tiff files, red/green image classification using gating parameters, noise filtering, background extraction, contrast stretching and temporal smoothing. MATtrack also integrates a series of algorithms for quantitative image analysis enabling the construction of mean and standard deviation images, clustering and classification of subcellular regions and injection point approximation. In addition, MATtrack features a simple user interface, which enables monitoring of Fluorescent Signal Intensity in multiple Regions of Interest, over time. The latter encapsulates a region growing method to automatically delineate the contours of Regions of Interest selected by the user, and performs background and regional Average Fluorescence Tracking, and automatic plotting. Finally, MATtrack computes convenient visualization and exploration tools including a migration map, which provides an overview of the protein intracellular trajectories and accumulation areas. In conclusion, MATtrack is an open source MATLAB-based software package tailored to facilitate the analysis and visualization of large data files derived from real-time live cell fluorescent microscopy using photoconvertible proteins. It is flexible, user friendly, compatible with Windows, Mac, and Linux, and a wide range of data acquisition software. MATtrack is freely available for download at eleceng.dit.ie/courtney/MATtrack.zip.     

Information bounds and optimal analysis of dynamic single molecule measurements

Time-resolved single molecule fluorescence measurements may be used to probe the conformational dynamics of biological macromolecules. The best time resolution in such techniques will only be achieved by measuring the arrival times of individual photons at the detector. A general approach to the estimation of molecular parameters based on individual photon arrival times is presented. The amount of information present in a data set is quantified by the Fisher information, thereby providing a guide to deriving the basic equations relating measurement uncertainties and time resolution. Based on these information-theoretical considerations, a data analysis algorithm is presented that details the optimal analysis of single-molecule data. This method natively accounts and corrects for background photons and cross talk, and can scale to an arbitrary number of channels. By construction, and with corroboration from computer simulations, we show that this algorithm reaches the theoretical limit, extracting the maximal information out of the data. The bias inherent in the algorithm is considered and its implications for experimental design are discussed. The ideas underlying this approach are general and are expected to be applicable to any information-limited measurement.