Efficient super‐resolution volumetric imaging by radial fluctuation Bayesian analysis light‐sheet microscopy

Various computational super‐resolution methods are available based on the analysis of fluorescence fluctuation behind acquired frames. However, dilemmas often exist in the balance of fluorophore characteristics, computation cost, and achievable resolution. Here we present an approach that uses a super‐resolution radial fluctuations (SRRF) image to guide the Bayesian analysis of fluorophore blinking and bleaching (3B) events, allowing greatly accelerated localization of overlapping fluorophores with high accuracy. This radial fluctuation Bayesian analysis (RFBA) approach is also extended to three dimensions for the first time and combined with light‐sheet fluorescence microscopy, to achieve super‐resolution volumetric imaging of thick samples densely labeled with common fluorophores. For example, a 700‐nm thin Bessel plane illumination is developed to optically section the Drosophila brain, providing a high‐contrast 3D image of rhythmic neurons. RFBA analyzes 30 serial volumes to reconstruct a super‐resolved 3D image at 4‐times higher resolutions (~70 and 170 nm), and precisely resolve the axon terminals. The computation is over 2‐orders faster than conventional 3B analysis microscopy. The capability of RFBA is also verified through dual‐color imaging of cell nucleus in live Drosophila brain. The spatial co‐localization patterns of the nuclear envelope and DNA in a neuron deep inside the brain can be precisely extracted by our approach.     

Sub-diffraction-limit imaging by stochastic optical reconstruction microscopy (STORM)

We have developed a high-resolution fluorescence microscopy method based on high-accuracy localization of photoswitchable fluorophores. In each imaging cycle, only a fraction of the fluorophores were turned on, allowing their positions to be determined with nanometer accuracy. The fluorophore positions obtained from a series of imaging cycles were used to reconstruct the overall image. We demonstrated an imaging resolution of 20 nm. This technique can, in principle, reach molecular-scale resolution.     

Mitigating Unwanted Photophysical Processes for Improved Single-Molecule Fluorescence Imaging

Organic fluorophores common to fluorescence-based investigations suffer from unwanted photophysical properties, including blinking and photobleaching, which limit their overall experimental performance. Methods to control such processes are particularly important for single-molecule fluorescence and fluorescence resonance energy transfer imaging where uninterrupted, stable fluorescence is paramount. Fluorescence and FRET-based assays have been carried out on dye-labeled DNA and RNA-based systems to quantify the effect of including small-molecule solution additives on the fluorescence and FRET behaviors of both cyanine and Alexa fluorophores. A detailed dwell time analysis of the fluorescence and FRET trajectories of more than 200,000 individual molecules showed that two compounds identified previously as triplet state quenchers, cyclooctatetraene, and Trolox, as well as 4-nitrobenzyl alcohol, act to favorably attenuate blinking, photobleaching, and influence the rate of photoresurrection in a concentration-dependent and context-dependent manner. In both biochemical systems examined, a unique cocktail of compounds was shown to be optimal for imaging performance. By simultaneously providing the most rapid and direct access to multiple photophysical kinetic parameters, smFRET imaging provides a powerful avenue for future investigations aimed at discovering new compounds, and effective combinations thereof. These efforts may ultimately facilitate tuning organic dye molecule performance according to each specific experimental demand.     

Quantitative Localization Microscopy: Effects of Photophysics and Labeling Stoichiometry

Quantification in localization microscopy with reversibly switchable fluorophores is severely hampered by the unknown number of switching cycles a fluorophore undergoes and the unknown stoichiometry of fluorophores on a marker such as an antibody. We overcome this problem by measuring the average number of localizations per fluorophore, or generally per fluorescently labeled site from the build-up of spatial image correlation during acquisition. To this end we employ a model for the interplay between the statistics of activation, bleaching, and labeling stoichiometry. We validated our method using single fluorophore labeled DNA oligomers and multiple-labeled neutravidin tetramers where we find a counting error of less than 17% without any calibration of transition rates. Furthermore, we demonstrated our quantification method on nanobody- and antibody-labeled biological specimens.     

Bayesian inference for improved single molecule fluorescence tracking

Single molecule tracking is widely used to monitor the change in position of lipids and proteins in living cells. In many experiments in which molecules are tagged with a single or small number of fluorophores, the signal/noise ratio may be limiting, the number of molecules is not known, and fluorophore blinking and photobleaching can occur. All these factors make accurate tracking over long trajectories difficult and hence there is still a pressing need to develop better algorithms to extract the maximum information from a sequence of fluorescence images. We describe here a Bayesian-based inference approach, based on a trans-dimensional sequential Monte Carlo method that utilizes both the spatial and temporal information present in the image sequences. We show, using model data, where the real trajectory of the molecule is known, that our method allows accurate tracking of molecules over long trajectories even with low signal/noise ratio and in the presence of fluorescence blinking and photobleaching. The method is then applied to real experimental data.     

Nonblinking and long-lasting single-molecule fluorescence imaging

Photobleaching and blinking of fluorophores pose fundamental limitations on the information content of single-molecule fluorescence measurements. Photoinduced blinking of Cy5 has hampered many previous investigations using this popular fluorophore. Here we show that Trolox in combination with the enzymatic oxygen-scavenging system eliminates Cy5 blinking, dramatically reduces photobleaching and improves the signal linearity at high excitation rates, significantly extending the applicability of single-molecule fluorescence techniques.     

PLPD: reliable protein localization prediction from imbalanced and overlapped datasets

Subcellular localization is one of the key functional characteristics of proteins. An automatic and efficient prediction method for the protein subcellular localization is highly required owing to the need for large-scale genome analysis. From a machine learning point of view, a dataset of protein localization has several characteristics: the dataset has too many classes (there are more than 10 localizations in a cell), it is a multi-label dataset (a protein may occur in several different subcellular locations), and it is too imbalanced (the number of proteins in each localization is remarkably different). Even though many previous works have been done for the prediction of protein subcellular localization, none of them tackles effectively these characteristics at the same time. Thus, a new computational method for protein localization is eventually needed for more reliable outcomes. To address the issue, we present a protein localization predictor based on D-SVDD (PLPD) for the prediction of protein localization, which can find the likelihood of a specific localization of a protein more easily and more correctly. Moreover, we introduce three measurements for the more precise evaluation of a protein localization predictor. As the results of various datasets which are made from the experiments of Huh et al. (2003), the proposed PLPD method represents a different approach that might play a complimentary role to the existing methods, such as Nearest Neighbor method and discriminate covariant method. Finally, after finding a good boundary for each localization using the 5184 classified proteins as training data, we predicted 138 proteins whose subcellular localizations could not be clearly observed by the experiments of Huh et al. (2003).     

On Optical Detection of Densely Labeled Synapses in Neuropil and Mapping Connectivity with Combinatorially Multiplexed Fluorescent Synaptic Markers

We propose a new method for mapping neural connectivity optically, by utilizing Cre/Lox system Brainbow to tag synapses of different neurons with random mixtures of different fluorophores, such as GFP, YFP, etc., and then detecting patterns of fluorophores at different synapses using light microscopy (LM). Such patterns will immediately report the pre- and post-synaptic cells at each synaptic connection, without tracing neural projections from individual synapses to corresponding cell bodies. We simulate fluorescence from a population of densely labeled synapses in a block of hippocampal neuropil, completely reconstructed from electron microscopy data, and show that high-end LM is able to detect such patterns with over 95% accuracy. We conclude, therefore, that with the described approach neural connectivity in macroscopically large neural circuits can be mapped with great accuracy, in scalable manner, using fast optical tools, and straightforward image processing. Relying on an electron microscopy dataset, we also derive and explicitly enumerate the conditions that should be met to allow synaptic connectivity studies with high-resolution optical tools.     

Estimating the dynamic range of quantitative single-molecule localization microscopy

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

Statistical deconvolution for superresolution fluorescence microscopy

Superresolution microscopy techniques based on the sequential activation of fluorophores can achieve image resolution of ～10 nm but require a sparse distribution of simultaneously activated fluorophores in the field of view. Image analysis procedures for this approach typically discard data from crowded molecules with overlapping images, wasting valuable image information that is only partly degraded by overlap. A data analysis method that exploits all available fluorescence data, regardless of overlap, could increase the number of molecules processed per frame and thereby accelerate superresolution imaging speed, enabling the study of fast, dynamic biological processes. Here, we present a computational method, referred to as deconvolution-STORM (deconSTORM), which uses iterative image deconvolution in place of single- or multiemitter localization to estimate the sample. DeconSTORM approximates the maximum likelihood sample estimate under a realistic statistical model of fluorescence microscopy movies comprising numerous frames. The model incorporates Poisson-distributed photon-detection noise, the sparse spatial distribution of activated fluorophores, and temporal correlations between consecutive movie frames arising from intermittent fluorophore activation. We first quantitatively validated this approach with simulated fluorescence data and showed that deconSTORM accurately estimates superresolution images even at high densities of activated fluorophores where analysis by single- or multiemitter localization methods fails. We then applied the method to experimental data of cellular structures and demonstrated that deconSTORM enables an approximately fivefold or greater increase in imaging speed by allowing a higher density of activated fluorophores/frame.     

Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling

Flow cytometry allows high-content, multiparameter analysis of single cells, making it a promising tool for drug discovery and profiling of intracellular signaling. To add high-throughput capacity to flow cytometry, we developed a cell-based multiplexing technique called fluorescent cell barcoding (FCB). In FCB, each sample is labeled with a different signature, or barcode, of fluorescence intensity and emission wavelengths, and mixed with other samples before antibody staining and analysis by flow cytometry. Using three FCB fluorophores, we were able to barcode and combine entire 96-well plates, reducing antibody consumption 100-fold and acquisition time to 5-15 min per plate. Using FCB and phospho-specific flow cytometry, we screened a small-molecule library for inhibitors of T cell-receptor and cytokine signaling, simultaneously determining compound efficacy and selectivity. We also analyzed IFN-gamma signaling in multiple cell types from primary mouse splenocytes, revealing differences in sensitivity and kinetics between B cells, CD4+ and CD4- T cells and CD11b-hi cells.     

A photoprotection strategy for microsecond-resolution single-molecule fluorescence spectroscopy

Time resolution of current single-molecule fluorescence techniques is limited to milliseconds because of dye blinking and bleaching. Here we introduce a photoprotection strategy that affords microsecond resolution by combining efficient triplet quenching by oxygen and Trolox with minimized bleaching via the oxygen radical scavenger cysteamine. Using this approach we resolved the single-molecule microsecond conformational fluctuations of two proteins: the two-state folder α-spectrin SH3 domain and the ultrafast downhill folder BBL.     

AAIndexLoc: predicting subcellular localization of proteins based on a new representation of sequences using amino acid indices

Identifying a protein's subcellular localization is an important step to understand its function. However, the involved experimental work is usually laborious, time consuming and costly. Computational prediction hence becomes valuable to reduce the inefficiency. Here we provide a method to predict protein subcellular localization by using amino acid composition and physicochemical properties. The method concatenates the information extracted from a protein's N-terminal, middle and full sequence. Each part is represented by amino acid composition, weighted amino acid composition, five-level grouping composition and five-level dipeptide composition. We divided our dataset into training and testing set. The training set is used to determine the best performing amino acid index by using five-fold cross validation, whereas the testing set acts as the independent dataset to evaluate the performance of our model. With the novel representation method, we achieve an accuracy of approximately 75% on independent dataset. We conclude that this new representation indeed performs well and is able to extract the protein sequence information. We have developed a web server for predicting protein subcellular localization. The web server is available at http://aaindexloc.bii.a-star.edu.sg .     

Polarized fluorescence photobleaching recovery for measuring rotational diffusion in solutions and membranes.

A variation of fluorescence photobleaching recovery (FPR) suitable for measuring the rate of rotational molecular diffusion in solution and cell membranes is presented in theory and experimental practice for epi-illumination microscopy. In this technique, a brief flash of polarized laser light creates an anisotropic distribution of unbleached fluorophores which relaxes by rotational diffusion, leading to a time-dependent postbleach fluorescence. Polarized FPR (PFPR) is applicable to any time scales from seconds to microseconds. However, at fast (microsecond) time scales, a partial recovery independent of molecular orientation tends to obscure rotational effects. The theory here presents a method for overcoming this reversible photobleaching, and includes explicit results for practical geometries, fast wobble of fluorophores, and arbitrary bleaching depth. This variation of a polarized luminescence "pump-and-probe" technique is compared with prior ones and with "pump-only" time-resolved luminescence anisotropy decay methods. The technique is experimentally verified on small latex beads with a variety of diameters, common fluorophore labels, and solvent viscosities. Preliminary measurements on a protein (acetylcholine receptor) in the membrane of nondeoxygenated cells in live culture (rat myotubes) show a difference in rotational diffusion between clustered and nonclustered receptors. In most experiments, signal averaging, high laser power, and automated sample translation must be employed to achieve adequate statistical accuracy.     

Double labeling of KNOTTED1 mRNA and protein reveals multiple potential sites of protein trafficking in the shoot apex

Recent reports indicate that several plant mRNAs and proteins are able to traffic intercellularly through plasmodesmata. Localization studies can reveal differences between mRNA and protein localization that would be indicative of such a process. However, subtle differences could be missed when comparing localization in adjacent sections, especially in developmental studies where adjacent sections through immature apical regions may be one or more cells removed from each other. Therefore, we have developed a novel method for double localization of KNOTTED1 mRNA and protein in sections through the maize (Zea mays) shoot apex. The advantage of double labeling is revealed in our demonstration of novel potential sites of cell-to-cell trafficking of KNOTTED1 protein in the shoot apical region. The technique should be applicable to any gene products where the appropriate probes are available and will, therefore, help to determine the extent of protein and/or mRNA trafficking in plants.     

Two‐photon laser‐scanning fluorescence microscopy applied for studies of human skin

Two‐photon laser scanning fluorescence microscopy (TPM) has been shown to be advantageous for imaging optically turbid media such as human skin. The ability of performing three‐dimensional imaging without presectioning of the samples makes the technique not only suitable for noninvasive diagnostics but also for studies of topical delivery of xenobiotics. Here, TPM is used as a method to visualize both autofluorescent and exogenous fluorophores in skin. Samples exposed to sulforhodamine B have been scanned from two directions to investigate attenuation effects. It is shown that optical effects play a major role. Thus, TPM is excellent for visualizing the localization and distribution of fluorophores in human skin, although quantification might be difficult. Furthermore, an image‐analysis algorithm has been implemented to facilitate interpretation of TPM images of autofluorescent features of nonmelanoma skin cancer obtained ex vivo. The algorithm was designed to detect cell nuclei and currently has a sensitivity and specificity of 82% and 78% to single cell nuclei. However, in order to detect multinucleated cells, the algorithm needs further development. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Inferring subunit stoichiometry from single molecule photobleaching

Single molecule photobleaching is a powerful tool for determining the stoichiometry of protein complexes. By attaching fluorophores to proteins of interest, the number of associated subunits in a complex can be deduced by imaging single molecules and counting fluorophore photobleaching steps. Because some bleaching steps might be unobserved, the ensemble of steps will be binomially distributed. In this work, it is shown that inferring the true composition of a complex from such data is nontrivial because binomially distributed observations present an ill-posed inference problem. That is, a unique and optimal estimate of the relevant parameters cannot be extracted from the observations. Because of this, a method has not been firmly established to quantify confidence when using this technique. This paper presents a general inference model for interpreting such data and provides methods for accurately estimating parameter confidence. The formalization and methods presented here provide a rigorous analytical basis for this pervasive experimental tool.     

Dual-color time-integrated fluorescence cumulant analysis

We introduce dual-color time-integrated fluorescence cumulant analysis (TIFCA) to analyze fluorescence fluctuation spectroscopy data. Dual-color TIFCA utilizes the bivariate cumulants of the integrated fluorescent intensity from two detection channels to extract the brightness in each channel, the occupation number, and the diffusion time of fluorophores simultaneously. Detecting the fluorescence in two detector channels introduces the possibility of differentiating fluorophores based on their fluorescence spectrum. We derive an analytical expression for the bivariate factorial cumulants of photon counts for arbitrary sampling times. The statistical accuracy of each cumulant is described by its variance, which we calculate by the moments-of-moments technique. A method that takes nonideal detector effects such as dead-time and afterpulsing into account is developed and experimentally verified. We perform dual-color TIFCA analysis on simple dye solutions and a mixture of dyes to characterize the performance and accuracy of our theory. We demonstrate the robustness of dual-color TIFCA by measuring fluorescent proteins over a wide concentration range inside cells. Finally we demonstrate the sensitivity of dual-color TIFCA by resolving EGFP/EYFP binary mixtures in living cells with a single measurement.     

Bayesian localization microscopy based on intensity distribution of fluorophores

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data (3B) method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fluorophores in the cell to fit the data. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensity at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution (FID3B). We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the performance of our method using both simulated data and experimental data from cellular structures. The results confirm the effectiveness of our method.     

Towards defining the nuclear proteome

Background

The nucleus is a complex cellular organelle and accurately defining its protein content is essential before any systematic characterization can be considered.

Results

We report direct evidence for 2,568 mammalian proteins within the nuclear proteome: the nuclear subcellular localization of 1,529 proteins based on a high-throughput subcellular localization protocol of full-length proteins and an additional 1,039 proteins for which clear experimental evidence is documented in published literature. This is direct evidence that the nuclear proteome consists of at least 14% of the entire proteome. This dataset was used to evaluate computational approaches designed to identify additional nuclear proteins.

Conclusion

This represents direct experimental evidence that the nuclear proteome consists of at least 14% of the entire proteome. This high-quality nuclear proteome dataset was used to evaluate computational approaches designed to identify additional nuclear proteins. Based on this analysis, researchers can determine the stringency and types of lines of evidence they consider to infer the size and complement of the nuclear proteome.