Quantitative localization microscopy: effects of photophysics and labeling stoichiometry

Phase-separated ribosome-nascent chain complexes in genotoxic stress response

Assemblysomes are EDTA- and RNase-resistant ribonucleoprotein (RNP) complexes of paused ribosomes with protruding nascent polypeptide chains. They have been described in yeast and human cells for the proteasome subunit Rpt1, and the disordered amino-terminal part of the nascent chain was found to be indispensable for the accumulation of the Rpt1-RNP into assemblysomes. Motivated by this, to find other assemblysome-associated RNPs we used bioinformatics to rank subunits of Saccharomyces cerevisiae protein complexes according to their amino-terminal disorder propensity. The results revealed that gene products involved in DNA repair are enriched among the top candidates. The Sgs1 DNA helicase was chosen for experimental validation. We found that indeed nascent chains of Sgs1 form EDTA-resistant RNP condensates, assemblysomes by definition. Moreover, upon exposure to UV, SGS1 mRNA shifted from assemblysomes to polysomes, suggesting that external stimuli are regulators of assemblysome dynamics. We extended our studies to human cell lines. The BLM helicase, ortholog of yeast Sgs1, was identified upon sequencing assemblysome-associated RNAs from the MCF7 human breast cancer cell line, and mRNAs encoding DNA repair proteins were overall enriched. Using the radiation-resistant A549 cell line, we observed by transmission electron microscopy that 1,6-hexanediol, an agent known to disrupt phase-separated condensates, depletes ring ribosome structures compatible with assemblysomes from the cytoplasm of cells and makes the cells more sensitive to X-ray treatment. Taken together, these findings suggest that assemblysomes may be a component of the DNA damage response from yeast to human.

Biotinylated Fluorescent Polymeric Nanoparticles for Enhanced Immunostaining

The performance of fluorescence immunostaining is physically limited by the brightness of organic dyes, whereas fluorescence labeling with multiple dyes per antibody can lead to dye self-quenching. The present work reports a methodology of antibody labeling by biotinylated zwitterionic dye-loaded polymeric nanoparticles (NPs). A rationally designed hydrophobic polymer, poly(ethyl methacrylate) bearing charged, zwitterionic and biotin groups (PEMA-ZI-biotin), enables preparation of small (14 nm) and bright fluorescent biotinylated NPs loaded with large quantities of cationic rhodamine dye with bulky hydrophobic counterion (fluorinated tetraphenylborate). The biotin exposure at the particle surface is confirmed by Förster resonance energy transfer with dye-streptavidin conjugate. Single-particle microscopy validates specific binding to biotinylated surfaces, with particle brightness 21-fold higher than quantum dot-585 (QD-585) at 550 nm excitation. The nanoimmunostaining method, which couples biotinylated antibody (cetuximab) with bright biotinylated zwitterionic NPs through streptavidin, significantly improves fluorescence imaging of target epidermal growth factor receptors (EGFR) on the cell surface compared to a dye-based labeling. Importantly, cetuximab labeled with PEMA-ZI-biotin NPs can differentiate cells with distinct expression levels of EGFR cancer marker. The developed nanoprobes can greatly amplify the signal from labeled antibodies, and thus become a useful tool in the high-sensitivity detection of disease biomarkers.

Single-molecule counting applied to the study of GPCR oligomerization

Single-molecule counting techniques enable a precise determination of the intracellular abundance and stoichiometry of proteins and macromolecular complexes. These details are often challenging to quantitatively assess yet are essential for our understanding of cellular function. Consider G-protein-coupled receptors-an expansive class of transmembrane signaling proteins that participate in many vital physiological functions making them a popular target for drug development. While early evidence for the role of oligomerization in receptor signaling came from ensemble biochemical and biophysical assays, innovations in single-molecule measurements are now driving a paradigm shift in our understanding of its relevance. Here, we review recent developments in single-molecule counting with a focus on photobleaching step counting and the emerging technique of quantitative single-molecule localization microscopy-with a particular emphasis on the potential for these techniques to advance our understanding of the role of oligomerization in G-protein-coupled receptor signaling.

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.

Quantitative comparison of spinning disk geometries for PAINT based super-resolution microscopy

PAINT methods that use DNA- or protein- based exchangeable probes have become popular for super-resolution imaging and have been combined with spinning disk confocal microscopy for imaging thicker samples. However, the widely available spinning disks used for routine biological imaging are not optimized for PAINT-based applications and may compromise resolution and imaging speed. Here, we use Drosophila egg chambers in the presence of the actin-binding peptide Lifeact to study the performance of four different spinning disk geometries. We find that disk geometries with higher light collection efficiency perform better for PAINT-based super-resolution imaging due to increased photon numbers and, subsequently, detection of more blinking events.

Analysis of Ionizing Radiation Induced DNA Damage by Superresolution dSTORM Microscopy

The quantitative detection of radiation caused DNA double-strand breaks (DSB) by immunostained γ-H2AX foci using direct stochastic optical reconstruction microscopy (dSTORM) provides a deeper insight into the DNA repair process at nanoscale in a time-dependent manner. Glioblastoma (U251) cells were irradiated with 250 keV X-ray at 0, 2, 5, 8 Gy dose levels. Cell cycle phase distribution and apoptosis of U251 cells upon irradiation was assayed by flow cytometry. We studied the density, topology and volume of the γ-H2AX foci with 3D confocal microscopy and the dSTORM superresolution method. A pronounced increase in γ-H2AX foci and cluster density was detected by 3D confocal microscopy after 2 Gy, at 30 min postirradiation, but both returned to the control level at 24 h. Meanwhile, at 24 h a considerable amount of residual foci could be measured from 5 Gy, which returned to the normal level 48 h later. The dSTORM based γ-H2AX analysis revealed that the micron-sized γ-H2AX foci are composed of distinct smaller units with a few tens of nanometers. The density of these clusters, the epitope number and the dynamics of γ-H2AX foci loss could be analyzed. Our findings suggest a discrete level of repair enzyme capacity and the restart of the repair process for the residual DSBs, even beyond 24 h. The dSTORM superresolution technique provides a higher precision over 3D confocal microscopy to study radiation induced γ-H2AX foci and molecular rearrangements during the repair process, opening a novel perspective for radiation research.

Substrate rigidity modulates traction forces and stoichiometry of cell–matrix adhesions

In cell–matrix adhesions, integrin receptors and associated proteins provide a dynamic coupling of the extracellular matrix (ECM) to the cytoskeleton. This allows bidirectional transmission of forces between the ECM and the cytoskeleton, which tunes intracellular signaling cascades that control survival, proliferation, differentiation, and motility. The quantitative relationships between recruitment of distinct cell–matrix adhesion proteins and local cellular traction forces are not known. Here, we applied quantitative super-resolution microscopy to cell–matrix adhesions formed on fibronectin-stamped elastomeric pillars and developed an approach to relate the number of talin, vinculin, paxillin, and focal adhesion kinase (FAK) molecules to the local cellular traction force. We find that FAK recruitment does not show an association with traction-force application, whereas a ∼60 pN force increase is associated with the recruitment of one talin, two vinculin, and two paxillin molecules on a substrate with an effective stiffness of 47 kPa. On a substrate with a fourfold lower effective stiffness, the stoichiometry of talin:vinculin:paxillin changes to 2:12:6 for the same ∼60 pN traction force. The relative change in force-related vinculin recruitment indicates a stiffness-dependent switch in vinculin function in cell–matrix adhesions. Our results reveal a substrate-stiffness-dependent modulation of the relationship between cellular traction-force and the molecular stoichiometry of cell–matrix adhesions.

Super-Resolution Microscopy for Structural Cell Biology

Super-resolution microscopy techniques, and specifically single-molecule localization microscopy (SMLM), are approaching nanometer resolution inside cells and thus have great potential to complement structural biology techniques such as electron microscopy for structural cell biology. In this review, we introduce the different flavors of super-resolution microscopy, with a special emphasis on SMLM and MINFLUX (minimal photon flux). We summarize recent technical developments that pushed these localization-based techniques to structural scales and review the experimental conditions that are key to obtaining data of the highest quality. Furthermore, we give an overview of different analysis methods and highlight studies that used SMLM to gain structural insights into biologically relevant molecular machines. Ultimately, we give our perspective on what is needed to push the resolution of these techniques even further and to apply them to investigating dynamic structural rearrangements in living cells. Expected final online publication date for the Annual Review of Biophysics, Volume 51 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Protein-PAINT: Superresolution microscopy with signaling proteins

Superresolution techniques have advanced our understanding of complex cellular structures and processes but require the attachment of fluorophores to targets through tags or antibodies, which can be bulky and result in underlabeling. To overcome these limitations, we developed a technique to visualize the nanoscale binding locations of signaling proteins by taking advantage of their native interaction domains. Here, we demonstrated that pPAINT (protein point accumulation in nanoscale topography) is a new, single-molecule localization microscopy (SMLM) technique and used it to investigate T cell signaling by visualizing the Src homology 2 (SH2) domain, which is common in signaling molecules. When SH2 domain-containing proteins relocate to the plasma membrane, the domains selectively, transiently, and reversibly bind to preferred phosphorylated tyrosine residues on receptors. This transient binding yields the stochastic blinking events necessary for SMLM when observed with total internal reflection microscopy and enables quantification of binding coefficients in intact cells. We used pPAINT to reveal the binding sites of several T cell receptor-proximal signaling molecules, including Zap70, PI3K, Grb2, Syk, Eat2, and SHP2, and showed that the probes could be multiplexed. We showed that the binding half-life of the tandem SH2 domain of PI3K correlated with binding site cluster size at the immunological synapses of T cells, but that longer binding lifetimes were associated with smaller clusters for the monovalent SH2 domain of Eat2. These results demonstrate the potential of pPAINT for investigating phosphotyrosine-mediated signaling processes at the plasma membrane.

An expectation-maximization approach to quantifying protein stoichiometry with single-molecule imaging

Motivation

Single-molecule localization microscopy (SMLM) is a super-resolution technique capable of rendering nanometer scale images of cellular structures. Recently, much effort has gone into developing algorithms for extracting quantitative features from SMLM datasets, such as the abundance and stoichiometry of macromolecular complexes. These algorithms often require knowledge of the complicated photophysical properties of photoswitchable fluorophores.

Results

Here, we develop a calibration-free approach to quantitative SMLM built upon the observation that most photoswitchable fluorophores emit a geometrically distributed number of blinks before photobleaching. From a statistical model of a mixture of monomers, dimers and trimers, the method employs an adapted expectation-maximization algorithm to learn the protomer fractions while simultaneously determining the single-fluorophore blinking distribution. To illustrate the utility of our approach, we benchmark it on both simulated datasets and experimental datasets assembled from SMLM images of fluorescently labeled DNA nanostructures.

Availability and implementation

An implementation of our algorithm written in Python is available at: https://www.utm.utoronto.ca/milsteinlab/resources/Software/MMCode/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

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.

Blinking Statistics and Molecular Counting in direct Stochastic Reconstruction Microscopy (dSTORM)

MOTIVATION

Many recent advancements in single molecule localisation microscopy exploit the stochastic photo-switching of fluorophores to reveal complex cellular structures beyond the classical diffraction limit. However, this same stochasticity makes counting the number of molecules to high precision extremely challenging, preventing key insight into the cellular structures and processes under observation.

RESULTS

Modelling the photo-switching behaviour of a fluorophore as an unobserved continuous time Markov process transitioning between a single fluorescent and multiple dark states, and fully mitigating for missed blinks and false positives, we present a method for computing the exact probability distribution for the number of observed localisations from a single photo-switching fluorophore. This is then extended to provide the probability distribution for the number of localisations in a dSTORM experiment involving an arbitrary number of molecules. We demonstrate that when training data is available to estimate photoswitching rates, the unknown number of molecules can be accurately recovered from the posterior mode of the number of molecules given the number of localisations. Finally, we demonstrate the method on experimental data by quantifying the number of adapter protein Linker for Activation of T cells (LAT) on the cell surface of the T cell immunological synapse.

AVAILABILITY

Software available at https://github.com/lp1611/mol_count_dstorm.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-color Molecular Visualization of Signaling Proteins Reveals How C-Terminal Src Kinase Nanoclusters Regulate T Cell Receptor Activation

Elucidating the mechanisms that controlled T cell activation requires visualization of the spatial organization of multiple proteins on the submicron scale. Here, we use stoichiometrically accurate, multiplexed, single-molecule super-resolution microscopy (DNA-PAINT) to image the nanoscale spatial architecture of the primary inhibitor of the T cell signaling pathway, Csk, and two binding partners implicated in its membrane association, PAG and TRAF3. Combined with a newly developed co-clustering analysis framework, we find that Csk forms nanoscale clusters proximal to the plasma membrane that are lost post-stimulation and are re-recruited at later time points. Unexpectedly, these clusters do not co-localize with PAG at the membrane but instead provide a ready pool of monomers to downregulate signaling. By generating CRISPR-Cas9 knockout T cells, our data also identify that a major risk factor for autoimmune diseases, the protein tyrosine phosphatase non-receptor type 22 (PTPN22) locus, is essential for Csk nanocluster re-recruitment and for maintenance of the synaptic PAG population.

Spatially compartmentalized phase regulation of a Ca2+-cAMP-PKA oscillatory circuit

Signaling networks are spatiotemporally organized to sense diverse inputs, process information, and carry out specific cellular tasks. In β cells, Ca2+, cyclic adenosine monophosphate (cAMP), and Protein Kinase A (PKA) exist in an oscillatory circuit characterized by a high degree of feedback. Here, we describe a mode of regulation within this circuit involving a spatial dependence of the relative phase between cAMP, PKA, and Ca2+. We show that in mouse MIN6 β cells, nanodomain clustering of Ca2+-sensitive adenylyl cyclases (ACs) drives oscillations of local cAMP levels to be precisely in-phase with Ca2+ oscillations, whereas Ca2+-sensitive phosphodiesterases maintain out-of-phase oscillations outside of the nanodomain. Disruption of this precise phase relationship perturbs Ca2+ oscillations, suggesting the relative phase within an oscillatory circuit can encode specific functional information. This work unveils a novel mechanism of cAMP compartmentation utilized for localized tuning of an oscillatory circuit and has broad implications for the spatiotemporal regulation of signaling networks.

Nanobody-Based Probes for Subcellular Protein Identification and Visualization

Understanding how building blocks of life contribute to physiology is greatly aided by protein identification and cellular localization. The two main labeling approaches developed over the past decades are labeling with antibodies such as immunoglobulin G (IgGs) or use of genetically encoded tags such as fluorescent proteins. However, IgGs are large proteins (150 kDa), which limits penetration depth and uncertainty of target position caused by up to ∼25 nm distance of the label created by the chosen targeting approach. Additionally, IgGs cannot be easily recombinantly modulated and engineered as part of fusion proteins because they consist of multiple independent translated chains. In the last decade single domain antigen binding proteins are being explored in bioscience as a tool in revealing molecular identity and localization to overcome limitations by IgGs. These nanobodies have several potential benefits over routine applications. Because of their small size (15 kDa), nanobodies better penetrate during labeling procedures and improve resolution. Moreover, nanobodies cDNA can easily be fused with other cDNA. Multidomain proteins can thus be easily engineered consisting of domains for targeting (nanobodies) and visualization by fluorescence microscopy (fluorescent proteins) or electron microscopy (based on certain enzymes). Additional modules for e.g., purification are also easily added. These nanobody-based probes can be applied in cells for live-cell endogenous protein detection or may be purified prior to use on molecules, cells or tissues. Here, we present the current state of nanobody-based probes and their implementation in microscopy, including pitfalls and potential future opportunities.

Unscrambling fluorophore blinking for comprehensive cluster detection via photoactivated localization microscopy

Determining nanoscale protein distribution via Photoactivated Localization Microscopy (PALM) mandates precise knowledge of the applied fluorophore's blinking properties to counteract overcounting artifacts that distort the resulting biomolecular distributions. Here, we present a readily applicable methodology to determine, optimize and quantitatively account for the blinking behavior of any PALM-compatible fluorophore. Using a custom-designed platform, we reveal complex blinking of two photoswitchable fluorescence proteins (PS-CFP2 and mEOS3.2) and two photoactivatable organic fluorophores (PA Janelia Fluor 549 and Abberior CAGE 635) with blinking cycles on time scales of several seconds. Incorporating such detailed information in our simulation-based analysis package allows for robust evaluation of molecular clustering based on individually recorded single molecule localization maps.

Quo vadis FRET? Förster's method in the era of superresolution

Although the theoretical foundations of Förster resonance energy transfer (FRET) were laid in the 1940s as part of the quantum physical revolution of the 20th century, it was only in the 1970s that it made its way to biology as a result of the availability of suitable measuring and labeling technologies. Thanks to its ease of application, FRET became widely used for studying molecular associations on the nanometer scale. The development of superresolution techniques at the turn of the millennium promised an unprecedented insight into the structure and function of molecular complexes. Without downplaying the significance of superresolution microscopies this review expresses our view that FRET is still a legitimate tool in the armamentarium of biologists for studying molecular associations since it offers distinct advantages and overcomes certain limitations of superresolution approaches.

Bayesian analysis of data from segmented super-resolution images for quantifying protein clustering

Super-resolution imaging techniques have largely improved our capabilities to visualize nanometric structures in biological systems. Their application further permits the quantitation relevant parameters to determine the molecular organization and stoichiometry in cells. However, the inherently stochastic nature of fluorescence emission and labeling strategies imposes the use of dedicated methods to accurately estimate these parameters. Here, we describe a Bayesian approach to precisely quantitate the relative abundance of molecular aggregates of different stoichiometry from segmented images. The distribution of proxies for the number of molecules in a cluster, such as the number of localizations or the fluorescence intensity, is fitted via a nested sampling algorithm to compare mixture models of increasing complexity and thus determine the optimum number of mixture components and their weights. We test the performance of the algorithm on in silico data as a function of the number of data points, threshold, and distribution shape. We compare these results to those obtained with other statistical methods, showing the improved performance of our approach. Our method provides a robust tool for model selection in fitting data extracted from fluorescence imaging, thus improving the precision of parameter determination. Importantly, the largest benefit of this method occurs for small-statistics or incomplete datasets, enabling an accurate analysis at the single image level. We further present the results of its application to experimental data obtained from the super-resolution imaging of dynein in HeLa cells, confirming the presence of a mixed population of cytoplasmic single motors and higher-order structures.

A HIDDEN MARKOV MODEL APPROACH TO CHARACTERIZING THE PHOTO-SWITCHING BEHAVIOR OF FLUOROPHORES

Fluorescing molecules (fluorophores) that stochastically switch between photon-emitting and dark states underpin some of the most celebrated advancements in super-resolution microscopy. While this stochastic behavior has been heavily exploited, full characterization of the underlying models can potentially drive forward further imaging methodologies. Under the assumption that fluorophores move between fluorescing and dark states as continuous time Markov processes, the goal is to use a sequence of images to select a model and estimate the transition rates. We use a hidden Markov model to relate the observed discrete time signal to the hidden continuous time process. With imaging involving several repeat exposures of the fluorophore, we show the observed signal depends on both the current and past states of the hidden process, producing emission probabilities that depend on the transition rate parameters to be estimated. To tackle this unusual coupling of the transition and emission probabilities, we conceive transmission (transition-emission) matrices that capture all dependencies of the model. We provide a scheme of computing these matrices and adapt the forward-backward algorithm to compute a likelihood which is readily optimized to provide rate estimates. When confronted with several model proposals, combining this procedure with the Bayesian Information Criterion provides accurate model selection.

Quantification of DNA damage induced repair focus formation via super-resolution dSTORM localization microscopy

In eukaryotic cells, each process, in which DNA is involved, should take place in the context of a chromatin structure. DNA double-strand breaks (DSBs) are one of the most deleterious lesions often leading to chromosomal rearrangement. In response to environmental stresses, cells have developed repair mechanisms to eliminate the DSBs. Upon DSB induction, several factors play roles in chromatin relaxation by catalysing the appropriate histone posttranslational modification (PTM) steps, therefore promoting the access of the repair factors to the DSBs. Among these PTMs, the phosphorylation of the histone variant H2AX at its Ser139 residue (also known as γH2AX) could be observed at the break sites. The structure of a DNA double-strand break induced repair focus has to be organized during the repair as it contributes to the accessibility of specific repair proteins to the damaged site. Our aim was to develop a quantitative approach to analyse the morphology of single repair foci by super-resolution dSTORM microscopy to gain insight into chromatin organization in DNA repair. We have established a specific dSTORM measurement process by developing a new analytical algorithm for gaining quantitative information about chromatin morphology and repair foci topology at an individual γH2AX enriched repair focus. Using this method we quantified single repair foci to show the distribution of γH2AX. The image of individual γH2AX referred to as the Single target Molecule response scatter Plot (SMPlot) was obtained by using high lateral resolution dSTORM images. Determination of the average localization numbers in an SMPlot was one of the key steps of quantitative dSTORM. A repair focus is made up of nanofoci. Such a substructure of repair foci can only be resolved and detected with super-resolution microscopy. Determination of the number of γH2AXs in the nanofoci was another key step of quantitative dSTORM. Additionally, based on our new analysis method, we were able to show the number of nucleosomes in each nanofocus that could allow us to define the possible chromatin structure and the nucleosome density around the break sites. This method is one of the first demonstrations of a single-cell based quantitative measurement of a discrete repair focus, which could provide new opportunities to categorize the spatial organization of nanofoci by parametric determination of topological similarity.

Super-resolution modularity analysis shows polyhedral caveolin-1 oligomers combine to form scaffolds and caveolae

Caveolin-1 (Cav1), the coat protein for caveolae, also forms non-caveolar Cav1 scaffolds. Single molecule Cav1 super-resolution microscopy analysis previously identified caveolae and three distinct scaffold domains: smaller S1A and S2B scaffolds and larger hemispherical S2 scaffolds. Application here of network modularity analysis of SMLM data for endogenous Cav1 labeling in HeLa cells shows that small scaffolds combine to form larger scaffolds and caveolae. We find modules within Cav1 blobs by maximizing the intra-connectivity between Cav1 molecules within a module and minimizing the inter-connectivity between Cav1 molecules across modules, which is achieved via spectral decomposition of the localizations adjacency matrix. Features of modules are then matched with intact blobs to find the similarity between the module-blob pairs of group centers. Our results show that smaller S1A and S1B scaffolds are made up of small polygons, that S1B scaffolds correspond to S1A scaffold dimers and that caveolae and hemispherical S2 scaffolds are complex, modular structures formed from S1B and S1A scaffolds, respectively. Polyhedral interactions of Cav1 oligomers, therefore, leads progressively to the formation of larger and more complex scaffold domains and the biogenesis of caveolae.

What we talk about when we talk about nanoclusters

Superresolution microscopy results have sparked the idea that many membrane proteins are not randomly distributed across the plasma membrane but are instead arranged in nanoclusters. Frequently, these new results seemed to confirm older data based on biochemical and electron microscopy experiments. Recently, however, it was recognized that multiple countings of the very same fluorescently labeled protein molecule can be easily confused with true protein clusters. Various strategies have been developed, which are intended to solve the problem of discriminating true protein clusters from imaging artifacts. We believe that there is currently no perfect algorithm for this problem; instead, different approaches have different strengths and weaknesses. In this review, we discuss single molecule localization microscopy in view of its ability to detect nanoclusters of membrane proteins. To capture the different views on nanoclustering, we chose an unconventional style for this article: we placed its scientific content in the setting of a fictive conference, where five researchers from different fields discuss the problem of detecting and quantifying nanoclusters. Using this style, we feel that the different approaches common for different research areas can be well illustrated. Similarities to a short story by Raymond Carver are not unintentional.

A Platform To Enhance Quantitative Single Molecule Localization Microscopy

Quantitative single molecule localization microscopy (qSMLM) is a powerful approach to study in situ protein organization. However, uncertainty regarding the photophysical properties of fluorescent reporters can bias the interpretation of detected localizations and subsequent quantification. Furthermore, strategies to efficiently detect endogenous proteins are often constrained by label heterogeneity and reporter size. Here, a new surface assay for molecular isolation (SAMI) was developed for qSMLM and used to characterize photophysical properties of fluorescent proteins and dyes. SAMI-qSMLM afforded robust quantification. To efficiently detect endogenous proteins, we used fluorescent ligands that bind to a specific site on engineered antibody fragments. Both the density and nano-organization of membrane-bound epidermal growth factor receptors (EGFR, HER2, and HER3) were determined by a combination of SAMI, antibody engineering, and pair-correlation analysis. In breast cancer cell lines, we detected distinct differences in receptor density and nano-organization upon treatment with therapeutic agents. This new platform can improve molecular quantification and can be developed to study the local protein environment of intact cells.

Super Resolution Network Analysis Defines the Molecular Architecture of Caveolae and Caveolin-1 Scaffolds

Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.

The Effect of Fluorophore Conjugation on Antibody Affinity and the Photophysical Properties of Dyes

Because the degree of labeling (DOL) of cell-bound antibodies, often required in quantitative fluorescence measurements, is largely unknown, we investigated the effect of labeling with two different fluorophores (AlexaFluor546, AlexaFluor647) in a systematic way using antibody stock solutions with different DOLs. Here, we show that the mean DOL of the cell-bound antibody fraction is lower than that of the stock using single molecule fluorescence measurements. The effect is so pronounced that the mean DOL levels off at approximately two fluorophores/IgG for some antibodies. We developed a method for comparing the average DOL of antibody stocks to that of the isolated, cell-bound fraction based on fluorescence anisotropy measurements confirming the aforementioned conclusions. We created a model in which individual antibody species with different DOLs, present in an antibody stock solution, were assumed to have distinct affinities and quantum yields. The model calculations confirmed that a calibration curve constructed from the anisotropy of antibody stocks can be used for determining the DOL of the bound fraction. The fluorescence intensity of the cell-bound antibody fractions and of the antibody stocks exhibited distinctly different dependence on the DOL. The behavior of the two dyes was systematically different in this respect. Fitting of the model to these data revealed that labeling with each dye affects quantum yield and antibody affinity differentially. These measurements also implied that fluorophores in multiply labeled antibodies exhibit self-quenching and lead to decreased antibody affinity, conclusions directly confirmed by steady-state intensity measurements and competitive binding assays. Although the fluorescence lifetime of antibodies labeled with multiple fluorophores decreased, the magnitude of this change was not sufficient to account for self-quenching indicating that both dynamic and static quenching processes occur involving H-aggregate formation. Our results reveal multiple effects of fluorophore conjugation, which must not be overlooked in quantitative cell biological measurements.

CRISPR-Cas9 Mediated Labelling Allows for Single Molecule Imaging and Resolution

Single molecule imaging approaches like dSTORM and PALM resolve structures at 10–20 nm, and allow for unique insights into protein stoichiometry and spatial relationships. However, key obstacles remain in developing highly accurate quantitative single molecule approaches. The genomic tagging of PALM fluorophores through CRISPR-Cas9 offers an excellent opportunity for generating stable cell lines expressing a defined single molecule probe at endogenous levels, without the biological disruption and variability inherent to transfection. A fundamental question is whether these comparatively low levels of expression can successfully satisfy the stringent labelling demands of super-resolution SMLM. Here we apply CRISPR-Cas9 gene editing to tag a cytoskeletal protein (α-tubulin) and demonstrate a relationship between expression level and the subsequent quality of PALM imaging, and that spatial resolutions comparable to dSTORM can be achieved with CRISPR-PALM. Our approach shows a relationship between choice of tag and the total expression of labelled protein, which has important implications for the development of future PALM tags. CRISPR-PALM allows for nanoscopic spatial resolution and the unique quantitative benefits of single molecule localization microscopy through endogenous expression, as well as the capacity for super-resolved live cell imaging.

Molecular Counting with Localization Microscopy: A Bayesian Estimate Based on Fluorophore Statistics

Superresolved localization microscopy has the potential to serve as an accurate, single-cell technique for counting the abundance of intracellular molecules. However, the stochastic blinking of single fluorophores can introduce large uncertainties into the final count. Here we provide a theoretical foundation for applying superresolved localization microscopy to the problem of molecular counting based on the distribution of blinking events from a single fluorophore. We also show that by redundantly tagging single molecules with multiple, blinking fluorophores, the accuracy of the technique can be enhanced by harnessing the central limit theorem. The coefficient of variation then, for the number of molecules M estimated from a given number of blinks B, scales like ∼1/N_l, where N_l is the mean number of labels on a target. As an example, we apply our theory to the challenging problem of quantifying the cell-to-cell variability of plasmid copy number in bacteria.

Quantitative microscopy based on single-molecule fluorescence

Quantitative microscopy is needed to understand reactions or phenomena carried out by biological molecules such as enzymes, receptors, and membrane-localized proteins. Counting the biomolecules of interest in single organelles or cellular compartments is critical in these approaches. In this brief perspective, we focus on the development of quantitative fluorescence microscopies that measure the precise copy numbers of proteins in cellular organelles or purified samples. We introduce recent improvements in quantitative microscopies to overcome undercounting or overcounting errors in certain conditions. We conclude by discussing biological applications.

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.

Effect of probe diffusion on the SOFI imaging accuracy

Live-cell super-resolution fluorescence imaging is becoming commonplace for exploring biological systems, though sample dynamics can affect the imaging quality. In this work we evaluate the effect of probe diffusion on super-resolution optical fluctuation imaging (SOFI), using a theoretical model and numerical simulations based on the imaging of live cells labelled with photochromic fluorescent proteins. We find that, over a range of physiological conditions, fluorophore diffusion results in a change in the amplitude of the SOFI signal. The magnitude of this change is approximately proportional to the on-time ratio of the fluorophores. However, for photochromic fluorescent proteins this effect is unlikely to present a significant distortion in practical experiments in biological systems. Due to this lack of distortions, probe diffusion strongly enhances the SOFI imaging by avoiding spatial undersampling caused by the limited labeling density.

Single-Molecule Localization Microscopy in Eukaryotes

Super-resolution fluorescence imaging by photoactivation or photoswitching of single fluorophores and position determination (single-molecule localization microscopy, SMLM) provides microscopic images with subdiffraction spatial resolution. This technology has enabled new insights into how proteins are organized in a cellular context, with a spatial resolution approaching virtually the molecular level. A unique strength of SMLM is that it delivers molecule-resolved information, along with super-resolved images of cellular structures. This allows quantitative access to cellular structures, for example, how proteins are distributed and organized and how they interact with other biomolecules. Ultimately, it is even possible to determine protein numbers in cells and the number of subunits in a protein complex. SMLM thus has the potential to pave the way toward a better understanding of how cells function at the molecular level. In this review, we describe how SMLM has contributed new knowledge in eukaryotic biology, and we specifically focus on quantitative biological data extracted from SMLM images.

Efficient homogeneous illumination and optical sectioning for quantitative single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) relies on the switching of fluorescent molecules between a fluorescent and a dark state to achieve super resolution. This process is inherently dependent on the intensity distribution of the laser light used for both activation from the dark state and excitation of the bright state. Typically, laser light is coupled directly or via a single-mode fiber into the microscope, which leads to a Gaussian intensity profile in total internal reflection (TIR) or epi illumination. As a result, switching dynamics and brightness of the fluorescent molecules vary strongly across the field of view, impacting their localization precision and impeding quantitative analysis. Here we present a simple illumination scheme based on the use of a multimode fiber and a laser speckle-reducer, which results in a flat, homogeneous and speckle-free illumination across the entire field of view. In addition, we combined homogeneous multimode excitation of the sample with single-mode based TIR activation to simultaneously obtain the advantages of both approaches: uniform brightness of single fluorophores and TIR-like optical sectioning.

Probing the Spatial Organization of Molecular Complexes Using Triple-Pair-Correlation

Super-resolution microscopy coupled with multiplexing techniques can resolve specific spatial arrangements of different components within molecular complexes. However, reliable quantification and analysis of such specific organization is extremely problematic because it is frequently obstructed by random co-localization incidents between crowded molecular species and the intrinsic heterogeneity of molecular complexes. To address this, we present a Triple-Pair-Correlation (TPC) analysis approach for unbiased interpretation of the spatial organization of molecular assemblies in crowded three-color super-resolution (SR) images. We validate this approach using simulated data, as well as SR images of DNA replication foci in human cells. This demonstrates the applicability of TPC in deciphering the specific spatial organization of molecular complexes hidden in dense multi-color super-resolution images.

Quantitative super-resolution imaging with qPAINT

Counting molecules in complexes is challenging, even with super-resolution microscopy. Here, we use the programmable and specific binding of dye-labeled DNA probes to count integer numbers of targets. This method, called quantitative points accumulation in nanoscale topography (qPAINT), works independently of dye photophysics for robust counting with high precision and accuracy over a wide dynamic range. qPAINT was benchmarked on DNA nanostructures and demonstrated for cellular applications by quantifying proteins in situ and the number of single-molecule FISH probes bound to an mRNA target.