A spectral demixing method for high-precision multi-color localization microscopy

Single molecule localization microscopy (SMLM) with a dichroic image splitter can provide invaluable multi-color information regarding colocalization of individual molecules, but it often suffers from technical limitations. So far, demixing algorithms give suboptimal results in terms of localization precision and correction of chromatic aberrations. Here we present an image splitter based multi-color SMLM method (splitSMLM) that offers much improved localization precision & drift correction, compensation of chromatic aberrations, and optimized performance of fluorophores in a specific buffer to equalize their reactivation rates for simultaneous imaging. A novel spectral demixing algorithm, SplitViSu, fully preserves localization precision with essentially no data loss and corrects chromatic aberrations at the nanometer scale. Multi-color performance is further improved by using optimized fluorophore and filter combinations. Applied to three-color imaging of the nuclear pore complex (NPC), this method provides a refined positioning of the individual NPC proteins and reveals that Pom121 clusters act as NPC deposition loci, hence illustrating strength and general applicability of the method.

Unraveling the kinetochore nanostructure in Schizosaccharomyces pombe using multi-color single-molecule localization microscopy

The key to ensuring proper chromosome segregation during mitosis is the kinetochore complex. This large and tightly regulated multi-protein complex links the centromeric chromatin to the microtubules attached to the spindle pole body and as such leads the segregation process. Understanding the architecture, function and regulation of this vital complex is therefore essential. However, due to its complexity and dynamics, only its individual subcomplexes could be studied in high-resolution structural detail so far.In this study we construct a nanometer-precise in situ map of the human-like regional kinetochore of Schizosaccharomyces pombe (S. pombe) using multi-color single-molecule localization microscopy (SMLM). We measure each kinetochore protein of interest (POI) in conjunction with two reference proteins, cnp1CENP-A at the centromere and sad1 at the spindle pole. This arrangement allows us to determine the cell cycle and in particularly the mitotic plane, and to visualize individual centromere regions separately. From these data, we determine protein distances within the complex using Bayesian inference, establish the stoichiometry of each POI for individual chromosomes and, consequently, build an in situ kinetochore model for S.pombe with so-far unprecedented precision. Being able to quantify the kinetochore proteins within the full in situ kinetochore structure, we provide valuable new insights in the S.pombe kinetochore architecture.

Nanoscale structural organization and stoichiometry of the budding yeast kinetochore

Proper chromosome segregation is crucial for cell division. In eukaryotes, this is achieved by the kinetochore, an evolutionarily conserved multi-protein complex that physically links the DNA to spindle microtubules, and takes an active role in monitoring and correcting erroneous spindle-chromosome attachments. Our mechanistic understanding of these functions, and how they ensure an error-free outcome of mitosis, is still limited, partly because we lack a comprehensive understanding of the kinetochore structure in the cell. In this study, we use single molecule localization microscopy to visualize individual kinetochore complexes in situ in budding yeast. For all major kinetochore proteins, we measured abundance and position within the metaphase kinetochore. Based on this comprehensive dataset, we propose a quantitative model of the budding yeast kinetochore. While confirming many aspects of previous reports based on bulk imaging of kinetochores, our results present a somewhat different but unifying model of the inner kinetochore. We find that the centromere-specialized histone Cse4 is present in more than two copies per kinetochore along with its binding partner Mif2.

Advanced Data Analysis for Fluorescence-Lifetime Single-Molecule Localization Microscopy

Fluorescence-lifetime single molecule localization microscopy (FL-SMLM) adds the lifetime dimension to the spatial super-resolution provided by SMLM. Independent of intensity and spectrum, this lifetime information can be used, for example, to quantify the energy transfer efficiency in Förster Resonance Energy Transfer (FRET) imaging, to probe the local environment with dyes that change their lifetime in an environment-sensitive manner, or to achieve image multiplexing by using dyes with different lifetimes. We present a thorough theoretical analysis of fluorescence-lifetime determination in the context of FL-SMLM and compare different lifetime-fitting approaches. In particular, we investigate the impact of background and noise, and give clear guidelines for procedures that are optimized for FL-SMLM. We do also present and discuss our public-domain software package “Fluorescence-Lifetime TrackNTrace,” which converts recorded fluorescence microscopy movies into super-resolved FL-SMLM images.

Simultaneous multicolor DNA-PAINT without sequential fluid exchange using spectral demixing

Several variants of multicolor single-molecule localization microscopy (SMLM) have been developed to resolve the spatial relationship of nanoscale structures in biological samples. The oligonucleotide-based SMLM approach DNA-PAINT robustly achieves nanometer localization precision and can be used to count binding sites within nanostructures. However, multicolor DNA-PAINT has primarily been realized by Exchange-PAINT that requires sequential exchange of the imaging solution and thus leads to extended acquisition times. To alleviate the need for fluid exchange and to speed up the acquisition of current multichannel DNA-PAINT, we here present a novel approach that combines DNA-PAINT with simultaneous multicolor acquisition using spectral demixing (SD). By using newly designed probes and a novel multichannel registration procedure we achieve simultaneous multicolor SD-DNA-PAINT with minimal crosstalk. We demonstrate high localization precision (3 - 6 nm) and multicolor registration of dual and triple-color SD-DNA-PAINT by resolving patterns on DNA origami nanostructures and cellular structures.

Improved localization precision via restricting confined biomolecule stochastic motion in single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) plays an irreplaceable role in biological studies, in which nanometer-sized biomolecules are hardly to be resolved due to diffraction limit unless being stochastically activated and accurately located by SMLM. For biological samples preimmobilized for SMLM, most biomolecules are cross-linked and constrained at their immobilizing sites but still expected to undergo confined stochastic motion in regard to their nanometer sizes. However, few lines of direct evidence have been reported about the detectability and influence of confined biomolecule stochastic motion on localization precision in SMLM. Here, we access the potential stochastic motion for each immobilized single biomolecule by calculating the displacements between any two of its localizations at different frames during sequential imaging of Alexa Fluor-647-conjugated oligonucleotides. For most molecules, localization displacements are remarkably larger at random frame intervals than at shortest intervals even after sample drift correction, increase with interval times and then saturate, showing that biomolecule stochastic motion is detected and confined around the immobilizing sizes in SMLM. Moreover, localization precision is inversely proportional to confined biomolecule stochastic motion, whereas it can be deteriorated or improved by enlarging the biomolecules or adding a post-crosslinking step, respectively. Consistently, post-crosslinking of cell samples sparsely stained for tubulin proteins results in a better localization precision. Overall, this study reveals that confined stochastic motion of immobilized biomolecules worsens localization precision in SMLM, and improved localization precision can be achieved via restricting such a motion.

Efficient Cross-Correlation Filtering of One- and Two-Color Single Molecule Localization Microscopy Data

Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.

3D Structure From 2D Microscopy Images Using Deep Learning

Understanding the structure of a protein complex is crucial in determining its function. However, retrieving accurate 3D structures from microscopy images is highly challenging, particularly as many imaging modalities are two-dimensional. Recent advances in Artificial Intelligence have been applied to this problem, primarily using voxel based approaches to analyse sets of electron microscopy images. Here we present a deep learning solution for reconstructing the protein complexes from a number of 2D single molecule localization microscopy images, with the solution being completely unconstrained. Our convolutional neural network coupled with a differentiable renderer predicts pose and derives a single structure. After training, the network is discarded, with the output of this method being a structural model which fits the data-set. We demonstrate the performance of our system on two protein complexes: CEP152 (which comprises part of the proximal toroid of the centriole) and centrioles.