Single-molecule Localization Microscopy Images Research Articles

Temporal-Focusing Multiphoton Excitation Single-Molecule Localization Microscopy Using Spontaneously Blinking Fluorophores.

Single-molecule localization microscopy (SMLM) based on temporal-focusing multiphoton excitation (TFMPE) and single-wavelength excitation is used to visualize the three-dimensional (3D) distribution of spontaneously blinking fluorophore-labeled subcellular structures in a thick specimen with a nanoscale-level spatial resolution. To eliminate the photobleaching effect of unlocalized molecules in out-of-focus regions for improving the utilization rate of the photon budget in 3D SMLM imaging, SMLM with single-wavelength TFMPE achieves wide-field and axially confined two-photon excitation (TPE) of spontaneously blinking fluorophores. TPE spectral measurement of blinking fluorophores is then conducted through TFMPE imaging at a tunable excitation wavelength, yielding the optimal TPE wavelength for increasing the number of detected photons from a single blinking event during SMLM. Subsequently, the TPE fluorescence of blinking fluorophores is recorded to obtain a two-dimensional TFMPE-SMLM image of the microtubules in cancer cells with a localization precision of 18 ± 6 nm and an overall imaging resolution of approximately 51 nm, which is estimated based on the contribution of Nyquist resolution and localization precision. Combined with astigmatic imaging, the system is capable of 3D TFMPE-SMLM imaging of brain tissue section of a 5XFAD transgenic mouse with the pathological features of Alzheimer's disease, revealing the distribution of neurotoxic amyloid-beta peptide deposits.

Photoswitching Reagent for Super-Resolution Fluorescence Microscopy.

Single-molecule localization microscopy (SMLM) has revolutionized optical microscopy by exceeding the diffraction limit and revealing previously unattainable nanoscale details of cellular structures and molecular dynamics. This super-resolution imaging capability relies on fluorophore photoswitching, which is crucial for optimizing the imaging conditions and accurately determining the fluorophore positions. To understand the general on and off photoswitching mechanisms of single dye molecules, various photoswitching reagents were evaluated. Systematic measurement of the single-molecule-level fluorescence on and off rates (kon and koff) in the presence of various photoswitching reagents and theoretical calculation of the structure of the photoswitching reagent-fluorophore pair indicated that the switch-off mechanism is mainly determined by the nucleophilicity of the photoswitching reagent, and the switch-on mechanism is a two-photon-induced dissociation process, which is related to the power of the illuminating laser and bond dissociation energy of this pair. This study contributes to a broader understanding of the molecular photoswitching mechanism in SMLM imaging and provides a basis for designing improved photoswitching reagents with potential applications extending to materials science and chemistry.

Large-FOV 3D localization microscopy by spatially variant point spread function generation.

Accurate characterization of the microscopic point spread function (PSF) is crucial for achieving high-performance localization microscopy (LM). Traditionally, LM assumes a spatially invariant PSF to simplify the modeling of the imaging system. However, for large fields of view (FOV) imaging, it becomes important to account for the spatially variant nature of the PSF. Here, we propose an accurate and fast principal components analysis-based field-dependent 3D PSF generator (PPG3D) and localizer for LM. Through simulations and experimental three-dimensional (3D) single-molecule localization microscopy (SMLM), we demonstrate the effectiveness of PPG3D, enabling super-resolution imaging of mitochondria and microtubules with high fidelity over a large FOV. A comparison of PPG3D with a shift-variant PSF generator for 3D LM reveals a threefold improvement in accuracy. Moreover, PPG3D is approximately 100 times faster than existing PSF generators, when used in image plane-based interpolation mode. Given its user-friendliness, we believe that PPG3D holds great potential for widespread application in SMLM and other imaging modalities.

Near-Infrared Perylenecarboximide Fluorophores for Live-Cell Super-Resolution Imaging.

Organic near-infrared (NIR) photoblinking fluorophores are highly desirable for live-cell super-resolution imaging based on single-molecule localization microscopy (SMLM). Herein we introduce a novel small chromophore, PMIP, through the fusion of perylenecarboximide with 2,2-dimetheylpyrimidine. PMIP exhibits an emission maximum at 732 nm with a high fluorescence quantum yield of 60% in the wavelength range of 700-1000 nm and excellent photoblinking without any additives. With resorcinol-functionalized PMIP (PMIP-OH), NIR SMLM imaging of lysosomes is demonstrated for the first time in living mammalian cells under physiological conditions. Moreover, metabolically labeled nascent DNA is site-specifically detected using azido-functionalized PMIP (PMIP-N3) via click chemistry, thereby enabling the super-resolution imaging of nascent DNA in phosphate-buffered saline with a 9-fold improvement in spatial resolution. These results indicate the potential of PMIP-based NIR blinking fluorophores for biological applications of SMLM.

Quantifying nanoscopic alterations associated with mitochondrial dysfunction using three-dimensional single-molecule localization microscopy.

Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.

Local Water Content in Polymer Gels Measured with Super-Resolved Fluorescence Lifetime Imaging.

Water molecules play an important role in the structure, function, and dynamics of (bio-) materials. A direct access to the number of water molecules in nanoscopic volumes can thus give new molecular insights into materials and allow for fine-tuning their properties in sophisticated applications. The determination of the local water content has become possible by the finding that H2 O quenches the fluorescence of red-emitting dyes. Since deuterated water, D2 O, does not induce significant fluorescence quenching, fluorescence lifetime measurements performed in different H2 O/D2 O-ratios yield the local water concentration. We combined this effect with the recently developed fluorescence lifetime single molecule localization microscopy imaging (FL-SMLM) in order to nanoscopically determine the local water content in microgels, i.e. soft hydrogel particles consisting of a cross-linked polymer swollen in water. The change in water content of thermo-responsive microgels when changing from their swollen state at room temperature to a collapsed state at elevated temperature could be analyzed. A clear decrease in water content was found that was, to our surprise, rather uniform throughout the entire microgel volume. Only a slightly higher water content around the dye was found in the periphery with respect to the center of the swollen microgels.

Lokaler Wassergehalt in Polymergelen bestimmt über superauflösende Fluoreszenzlebensdauer‐Bildgebung

AbstractWassermoleküle spielen eine wichtige Rolle für die Struktur, Funktion und Dynamik von (Bio‐) Materialien. Ein direkter Zugang zur Anzahl der Wassermoleküle in nanoskopischen Volumina kann daher neue molekulare Einblicke in Materialien geben und eine Feinabstimmung ihrer Eigenschaften in anspruchsvollen Anwendungen ermöglichen. Die Bestimmung des lokalen Wassergehalts wurde durch die Entdeckung möglich, dass H2O die Fluoreszenz von rot‐emittierenden Farbstoffen löscht. Da deuteriertes Wasser, D2O, keine signifikante Fluoreszenzlöschung hervorruft, lässt sich über Fluoreszenzlebensdauermessungen bei unterschiedlichen H2O/D2O‐Verhältnissen die lokale Wasserkonzentration bestimmen. Wir kombinierten diesen Effekt mit der vor Kurzem entwickelten Fluoreszenzlebensdauer‐Einzelmolekül‐Lokalisierungsmikroskopie (FL‐SMLM), um die lokale Wasserkonzentration in Mikrogelen, d. h. weichen Hydrogelpartikeln, die aus einem in Wasser gequollenen vernetzten Polymer bestehen, nanoskopisch zu bestimmen. Die Änderung des Wassergehalts von thermoresponsiven Mikrogelen beim Übergang von ihrem gequollenen Zustand bei Raumtemperatur zu einem kollabierten Zustand bei erhöhter Temperatur konnte analysiert werden. Es wurde eine deutliche Abnahme des Wassergehalts festgestellt, die zu unserer Überraschung über das gesamte Mikrogelvolumen recht gleichmäßig war. In der Peripherie der gequollenen Mikrogele war die Wasserkonzentration um den Farbstoff herum nur geringfügig größer als in deren Zentrum.

Intrinsic Burst-Blinking Nanographenes for Super-Resolution Bioimaging.

Single-molecule localization microscopy (SMLM) is a powerful technique to achieve super-resolution imaging beyond the diffraction limit. Although various types of blinking fluorophores are currently considered for SMLM, intrinsic blinking fluorophores remain rare at the single-molecule level. Here, we report the synthesis of nanographene-based intrinsic burst-blinking fluorophores for highly versatile SMLM. We image amyloid fibrils in air and in various pH solutions without any additive and lysosome dynamics in live mammalian cells under physiological conditions. In addition, the single-molecule labeling of nascent proteins in primary sensory neurons was achieved with azide-functionalized nanographenes via click chemistry. SMLM imaging reveals higher local translation at axonal branching with unprecedented detail, while the size of translation foci remained similar throughout the entire network. These various results demonstrate the potential of nanographene-based fluorophores to drastically expand the applicability of super-resolution imaging.

Edge roughness analysis in nanoscale for single-molecule localization microscopy images

Abstract The recent advances in super-resolution fluorescence microscopy, including single-molecule localization microscopy (SMLM), has enabled the study of previously inaccessible details, such as the organization of proteins within cellular compartments and even nanostructures in nonbiological nanomaterials, such as the polymers and semiconductors. With such developments, the need for the development of various computational nanostructure analysis methods for SMLM images is also increasing; however, this has been limited to protein cluster analysis. In this study, we developed an edge structure analysis method for pointillistic SMLM images based on the line edge roughness and power spectral density analyses. By investigating the effect of point properties in SMLM images, such as the size, density, and localization precision on the roughness measurement, we successfully demonstrated this analysis method for experimental SMLM images of actual samples, including the semiconductor line patterns, cytoskeletal elements, and cell membranes. This systematic investigation of the effect of each localization rendering parameter on edge roughness measurement provides a range for the optimal rendering parameters that preserve the relevant nanoscale structure of interest. These new methods are expected to expand our understanding of the targets by providing valuable insights into edge nanoscale structures that have not been previously obtained quantitatively.

Deep learning based denoising of super-resolution images of protein aggregates

Neurodegenerative diseases (NDs) are closely associated with the amyloid aggregation of proteins like Amyloid-, -synuclein, and tau. Understanding the pathogenesis of NDs requires studying the structures and morphological features of these aggregates, which are typically below 100 nm in size. Traditional fluorescence microscopy is limited by the diffraction limit of light (~250 nm). Single-molecule localization microscopy (SMLM) offers a resolution down to ~20 nm, enabling the visualization of these aggregates. However, issues such as non-specific binding (NSB) of fluorophores and background noise degrade the quality of SMLM images. This study presents a U-net-based convolutional neural network (CNN) to denoise SMLM images of protein aggregates. The training dataset includes noise-free super-resolution images of aggregates and their noisy counterparts with non-specific binding signals. Various imaging conditions are simulated to mimic real-world scenarios. The U-net's output is evaluated against ground-truth images for denoising performance. Post-processing techniques further enhance denoised images. The fine-tuned U-net model achieves a validation loss of 0.0042 and low prediction errors of 0.32% and 3.71% in the area and number of aggregates, respectively. This research offers a powerful tool for denoising SMLM images, facilitating accurate characterization of protein aggregate structures and morphological features.

Motion screening of fiducial marker for improved localization precision and resolution in SMLM.

Single-molecule localization microscopy (SMLM) provides unmatched high resolution but relies on accurate drift correction due to the long acquisition time for each field of view. A popular drift correction is implemented via referencing to fiducial markers that are assumed to be firmly immobilized and remain stationary relative to the imaged sample. However, there is so far lack of efficient approaches for evaluating other motions except sample drifting of immobilized markers and for addressing their potential impacts on images. Here, we developed a new approach for quantitatively assessing the motions of fiducial markers relative to the sample via mean squared displacement (MSD) analysis. Our findings revealed that over 90% of immobilized fluorescent beads in the SMLM imaging buffer exhibited higher MSDs compared to stationary beads in dry samples and displayed varying degrees of wobbling relative to the imaged field. By excluding extremely high-MSD beads in each field from drift correction, we optimized drift correction and experimentally measured localization precision. In SMLM experiments of cellular microtubules, we also found that including only relatively low-MSD beads for drift correction significantly improved the image resolution and quality. Our study presents a simple and effective approach to assess the potential relative motions of fiducial markers and emphasizes the importance of pre-screening fiducial markers for improved image quality and resolution in SMLM imaging.

Investigating Uncertainties in Single-Molecule Localization Microscopy Using Experimentally Informed Monte Carlo Simulation.

Single-molecule localization microscopy (SMLM) enables the visualization of cellular nanostructures in vitro with sub-20 nm resolution. While substructures can generally be imaged with SMLM, the structural understanding of the images remains elusive. To better understand the link between SMLM images and the underlying structure, we developed a Monte Carlo (MC) simulation based on experimental imaging parameters and geometric information to generate synthetic SMLM images. We chose the nuclear pore complex (NPC), a nanosized channel on the nuclear membrane which gates nucleo-cytoplasmic transport of biomolecules, as a test geometry for testing our MC model. Using the MC model to simulate SMLM images, we first optimized our clustering algorithm to separate >106 molecular localizations of fluorescently labeled NPC proteins into hundreds of individual NPCs in each cell. We then illustrated using our MC model to generate cellular substructures with different angles of labeling to inform our structural understanding through the SMLM images obtained.

Nanoscale imaging of tumor cell exosomes by expansion single molecule localization microscopy (ExSMLM)

Tumor cell exosomes play a very important role in the process of tumor cell proliferation and metastasis. However, due to the nanoscale size and high heterogeneity of exosomes, in-depth understanding of their appearance and biological characteristics is still lacking. Expansion microscopy (ExM) is a method that embeds biological samples in a swellable gel to physically magnify the samples to improve the imaging resolution. Before the emergence of ExM, scientists had invented several super-resolution imaging techniques that could break the diffraction limit. Among them, single molecule localization microscopy (SMLM) usually has the best spatial resolution (20–50 nm). However, considering the small size of exosomes (30–150 nm), the resolution of SMLM is still not high enough for detailed imaging of exosomes. Hence, we propose a tumor cell exosomes imaging method that combines ExM and SMLM (i.e. Expansion SMLM, denoted as ExSMLM), which can realize the expansion and super-resolution imaging of tumor cell exosomes. In this technique, immunofluorescence was first performed to fluorescently label the protein markers on the exosomes, then the exosomes were polymerized into a swellable polyelectrolyte gel. The electrolytic nature of the gel made the fluorescently labeled exosomes undergo isotropic linear physical expansion. The expansion factor obtained in the experiment was about 4.6. Finally, SMLM imaging of the expanded exosomes was performed. Owing to the improved resolution of ExSMLM, nanoscale substructures of closely packed proteins were observed on single exosomes, which has never been achieved before. With such a high resolution, ExSMLM would have a great potential in detailed investigation of exosomes and exosome-related biological processes.

Live-Cell SOFI Correlation with SMLM and AFM Imaging.

Standard optical imaging is diffraction-limited and lacks the resolving power to visualize many of the organelles and proteins found within the cell. The advent of super-resolution techniques overcame this barrier, enabling observation of subcellular structures down to tens of nanometers in size; however these techniques require or are typically applied to fixed samples. This raises the question of how well a fixed-cell image represents the system prior to fixation. Here we present the addition of live-cell Super-Resolution Optical Fluctuation Imaging (SOFI) to a previously reported correlative process using Single Molecule Localization Microscopy (SMLM) and Atomic Force Microscopy (AFM). SOFI was used with fluorescent proteins and low laser power to observe cellular ultrastructure in live COS-7 cells. SOFI-SMLM-AFM of microtubules showed minimal changes to the microtubule network in the 20 min between live-cell SOFI and fixation. Microtubule diameters were also analyzed through all microscopies; SOFI found diameters of 249 ± 68 nm and SMLM was 71 ± 33 nm. AFM height measurements found microtubules to protrude 26 ± 13 nm above the surrounding cellular material. The correlation of SMLM and AFM was extended to two-color SMLM to image both microtubules and actin. Two target SOFI was performed with various fluorescent protein combinations. rsGreen1-rsKAME, rsGreen1-Dronpa, and ffDronpaF-rsKAME fluorescent protein combinations were determined to be suitable for two target SOFI imaging. This correlative application of super-resolution live-cell and fixed-cell imaging revealed minimal artifacts created for the imaged target structures through the sample preparation procedure and emphasizes the power of correlative microscopy.

Inverse modelling of point spread function for single-molecule localization microscopy.

Single-molecule localization microscopy (SMLM) is one major type of super-resolution microscopy. SMLM is based on localizing single-molecule emission patterns, i.e. the point spread function (PSF) at a precision much smaller than the diffraction limit. PSF is the impulse response of an imaging system, it contains information of the image formation, systematic aberrations and imperfections. PSF modelling is essential in SMLM to achieve both high accuracy and precision. Here I present a universal PSF learning algorithm to reconstruct the PSF models from fluorescence bead data. It can extract the PSF models from single and multi-channel systems, learn the field dependent aberration and model the depth dependent aberrations. The learned PSF model can be in forms of 3D matrices, Zernike coefficients or image-based pupil functions according to user selections. The learning framework can be also extended to in-situ PSF learning, where the PSF models are directly extracted from single-molecule blinking data from SMLM imaging. I demonstrate in-situ PSF learning from single-channel systems with various aberrations.

High-throughput multi-color reliable SMLM nanoscopy.

Beyond all super-resolution microscopy methods, single molecule localization microscopy (SMLM) offers best 3D spatial resolution, up to several nanometers. Despite its super-resolutive property, SMLM has still a low throughput. In this context, SMLM offers a mono-color limited field of view (FOV). SMLM suffers indeed from fluorophore photoswitching characteristics, which depends on the wavelength, and significant chromatic aberration at the nanoscale. Multicolor SMLM imaging is classically achieved by sequential acquisitions at different excitation wavelengths, but lengthy acquisition times, drifts between the different color recordings, and cross excitation by different lasers may hamper quantitative reconstruction. In this talk, we will present two strategies to increase the FOV up to 200 × 200 µm² while improving the number of colors up to 3: Adaptable Scanning for Tunable Excitation Regions (ASTER) and spectral-demixing respectively. The association of ASTER and spectral demixing leads to increase the throughput by a factor 50 compared to standard nanoscopy approach, in the same acquisition time. We here present a reliable characterization of those methods for homogeneity, localisation precision, crosstalk, labelling density influence. By using complementary samples, we could obtain a rigorous estimation of the performances in various settings: DNA origamis, gold standard samples for SMLM, up to real biological applications as hippocampal neurons or extracellular vesicles characterization.

Machine learning framework to segment sarcomeric structures in SMLM data

Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network’s internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data.

Recent development of computational cluster analysis methods for single-molecule localization microscopy images

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Application of Lacunarity for Quantification of Single Molecule Localization Microscopy Images.

The quantitative analysis of datasets achieved by single molecule localization microscopy is vital for studying the structure of subcellular organizations. Cluster analysis has emerged as a multi-faceted tool in the structural analysis of localization datasets. However, the results it produces greatly depend on the set parameters, and the process can be computationally intensive. Here we present a new approach for structural analysis using lacunarity. Unlike cluster analysis, lacunarity can be calculated quickly while providing definitive information about the structure of the localizations. Using simulated data, we demonstrate how lacunarity results can be interpreted. We use these interpretations to compare our lacunarity analysis with our previous cluster analysis-based results in the field of DNA repair, showing the new algorithm’s efficiency.

Fluorescent d-Amino Acids for Super-resolution Microscopy of the Bacterial Cell Wall.

Fluorescent d-amino acids (FDAAs) have previously been developed to enable in situ highlighting of locations of bacterial cell wall growth. Most bacterial cells lie at the edge of the diffraction limit of visible light; thus, resolving the precise details of peptidoglycan (PG) biosynthesis requires super-resolution microscopy after probe incorporation. Single molecule localization microscopy (SMLM) has stringent requirements on the fluorophore photophysical properties and therefore has remained challenging in this context. Here, we report the synthesis and characterization of new FDAAs compatible with one-step labeling and SMLM imaging. We demonstrate the incorporation of our probes and their utility for visualizing PG at the nanoscale in Gram-negative, Gram-positive, and mycobacteria species. This improved FDAA toolkit will endow researchers with a nanoscale perspective on the spatial distribution of PG biosynthesis for a broad range of bacterial species.