DNA-barcoded labeling probes for highly multiplexed Exchange-PAINT imaging† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc05420j Click here for additional data file.

Recent advances in super-resolution fluorescence imaging allow researchers to overcome the classical diffraction limit of light, and are already starting to make an impact in biology. However, a key challenge for traditional super-resolution methods is their limited multiplexing capability, which prevents a systematic understanding of multi-protein interactions on the nanoscale. Exchange-PAINT, a recently developed DNA-based multiplexing approach, in theory facilitates spectrally-unlimited multiplexing by sequentially imaging target molecules using orthogonal dye-labeled ‘imager’ strands. While this approach holds great promise for the bioimaging community, its widespread application has been hampered by the availability of DNA-conjugated ligands for protein labeling. Herein, we report a universal approach for the creation of DNA-barcoded labeling probes for highly multiplexed Exchange-PAINT imaging, using a variety of affinity reagents such as primary and secondary antibodies, nanobodies, and small molecule binders. Furthermore, we extend the availability of orthogonal imager strands for Exchange-PAINT to over 50 and assay their orthogonality in a novel DNA origami-based crosstalk assay. Using our optimized conjugation and labeling strategies, we demonstrate nine-color super-resolution imaging in situ in fixed cells

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model

Principles of RNA recruitment to viral ribonucleoprotein condensates in a segmented dsRNA virus.

Funder: Max Planck Institute for BiochemistryRotaviruses transcribe 11 distinct RNAs that must be co-packaged prior to their replication to make an infectious virion. During infection, nontranslating rotavirus transcripts accumulate in cytoplasmic protein-RNA granules known as viroplasms that support segmented genome assembly and replication via a poorly understood mechanism. Here, we analysed the RV transcriptome by combining DNA-barcoded smFISH of rotavirus-infected cells. Rotavirus RNA stoichiometry in viroplasms appears to be distinct from the cytoplasmic transcript distribution, with the largest transcript being the most enriched in viroplasms, suggesting a selective RNA enrichment mechanism. While all 11 types of transcripts accumulate in viroplasms, their stoichiometry significantly varied between individual viroplasms. Accumulation of transcripts requires the presence of 3' untranslated terminal regions and viroplasmic localisation of the viral polymerase VP1, consistent with the observed lack of polyadenylated transcripts in viroplasms. Our observations reveal similarities between viroplasms and other cytoplasmic RNP granules and identify viroplasmic proteins as drivers of viral RNA assembly during viroplasm formation

Principles of RNA recruitment to viral ribonucleoprotein condensates in a segmented dsRNA virus

Rotaviruses transcribe 11 distinct RNAs that must be co-packaged prior to their replication to make an infectious virion. During infection, nontranslating rotavirus transcripts accumulate in cytoplasmic protein-RNA granules known as viroplasms that support segmented genome assembly and replication via a poorly understood mechanism. Here, we analysed the RV transcriptome by combining DNA-barcoded smFISH of rotavirus-infected cells. Rotavirus RNA stoichiometry in viroplasms appears to be distinct from the cytoplasmic transcript distribution, with the largest transcript being the most enriched in viroplasms, suggesting a selective RNA enrichment mechanism. While all 11 types of transcripts accumulate in viroplasms, their stoichiometry significantly varied between individual viroplasms. Accumulation of transcripts requires the presence of 3’ untranslated terminal regions and viroplasmic localisation of the viral polymerase VP1, consistent with the observed lack of polyadenylated transcripts in viroplasms. Our observations reveal similarities between viroplasms and other cytoplasmic RNP granules and identify viroplasmic proteins as drivers of viral RNA assembly during viroplasm formation

Dynamic Supramolecular Interaction in Cucurbit[7]uril Host-Guest Complex Enables Autonomous Single Molecule Blinking and Super-Resolution Imaging in Cells and Tissues

Synthetic supramolecular host-guest complexes are inherently dynamic as they employ weak and reversible noncovalent interactions for their recognition process. This dynamic behavior allows host-guest chemistry to be employed for various state of the art applications. Herein, we demonstrate the use of the dynamic supramolecular interaction to enable nanoscopic imaging inside cells and tissues. This imaging method exploits repetitive and transient binding of fluorescently labeled hexamethylenediamine (HMD) guest molecule to complementary cucurbit[7]uril (CB[7]) host to obtain stochastic switching between fluorescence ON- and OFF-states. Through connecting CB[7] hosts to targeting ligands (e.g., antibodies and small molecules), we show that this autonomous blinking enables two-dimensional (2D) and 3D super-resolution imaging of proteins in fixed cells and tissues. Finally, we exploited the capability of host-guest molecules to maintain their interaction specificity in the complexity of the live intracellular environment to obtain super-resolution actin imaging in living HeLa cell

Nanobodies combined with DNA-PAINT super-resolution reveal a staggered titin nano-architecture in flight muscles

Abstract Sarcomeres are the force producing units of all striated muscles. Their nanoarchitecture critically depends on the large titin protein, which in vertebrates spans from the sarcomeric Z-disc to the M-band and hence links actin and myosin filaments stably together. This ensures sarcomeric integrity and determines the length of vertebrate sarcomeres. However, the instructive role of titins for sarcomeric architecture outside of vertebrates is not as well understood. Here, we used a series of nanobodies, the Drosophila titin nanobody toolbox, recognising specific domains of the two Drosophila titin homologs Sallimus and Projectin to determine their precise location in intact flight muscles. By combining nanobodies with DNA-PAINT super- resolution microscopy, we found that, similar to vertebrate titin, Sallimus bridges across the flight muscle I-band, whereas Projectin is located at the beginning of the A- band. Interestingly, the ends of both proteins overlap at the I-band/A-band border, revealing a staggered organisation of the two Drosophila titin homologs. This architecture may help to stably anchor Sallimus at the myosin filament and hence ensure efficient force transduction during flight

Detecting structural heterogeneity in single-molecule localization microscopy data

Particle fusion for single molecule localization microscopy improves signal-to-noise ratio and overcomes underlabeling, but ignores structural heterogeneity or conformational variability. We present a-priori knowledge-free unsupervised classification of structurally different particles employing the Bhattacharya cost function as dissimilarity metric. We achieve 96% classification accuracy on mixtures of up to four different DNA-origami structures, detect rare classes of origami occuring at 2% rate, and capture variation in ellipticity of nuclear pore complexes.ImPhys/Computational ImagingImPhys/Imaging Physic

Single-molecule localization microscopy

International audienceSingle-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM

High-precision estimation of emitter positions using Bayesian grouping of localizations

Single-molecule localization microscopy super-resolution methods rely on stochastic blinking/binding events, which often occur multiple times from each emitter over the course of data acquisition. Typically, the blinking/binding events from each emitter are treated as independent events, without an attempt to assign them to a particular emitter. Here, we describe a Bayesian method of inferring the positions of the tagged molecules by exploring the possible grouping and combination of localizations from multiple blinking/binding events. The results are position estimates of the tagged molecules that have improved localization precision and facilitate nanoscale structural insights. The Bayesian framework uses the localization precisions to learn the statistical distribution of the number of blinking/binding events per emitter and infer the number and position of emitters. We demonstrate the method on a range of synthetic data with various emitter densities, DNA origami constructs and biological structures using DNA-PAINT and dSTORM data. We show that under some experimental conditions it is possible to achieve sub-nanometer precision.</p