Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution

Enhancing precision for simultaneous 3D localization and 3D orientation with structured illumination

Determining the three-dimensional (3D) position and orientation of molecules from limited fluorescence photons is challenging. Existing modulated illumination localization microscopy partially transfers the photon burden to excitation intensity gradients, advancing precision to the molecular scale. However, most simultaneous localization and orientation methods rely on polarization splitting in the detection path and are sensitive to the signal-to-background ratio. This sensitivity is further exacerbated by sequential structured illumination (SSI), potentially compromising robustness. Here, we introduce V-SIMFLUX, which integrates SSI and the Vortex PSF without polarization-splitting detection. Our theoretical analysis shows that V-SIMFLUX improves the precision of all estimated parameters, notably achieving a typical 2.9-fold improvement in lateral localization and a 1.7-fold enhancement in azimuthal orientation compared to conventional wide-field illumination.

Monitoring Dynamic Conformations of a Single Fluorescent Molecule Inside a Protein Cavity

Fluorescence nanoscopy and single-molecule methods are entering the realm of structural biology, breaking new ground for dynamic structural measurements at room temperature and liquid environments. Here, single-molecule localization microscopy, polarization-dependent single-molecule excitation, and protein engineering are combined to determine the orientation of a fluorophore forming hydrogen bonds inside a protein cavity. The observed conformations are in good agreement with molecular dynamics simulations, enabling a new, more realistic interplay between experiments and simulations to identify stable conformations and the key interactions involved. Furthermore, jumps between conformations can be monitored with a precision of 3° and a time resolution of a few seconds, confirming the potential of this methodology for retrieving dynamic structural information of nanoscopic biological systems under physiologically compatible conditions.

Single-molecule orientation-localization microscopy: Applications and approaches

Single-molecule orientation-localization microscopy (SMOLM) builds upon super-resolved localization microscopy by imaging orientations and rotational dynamics of individual molecules in addition to their positions. This added dimensionality provides unparalleled insights into nanoscale biophysical and biochemical processes, including the organization of actin networks, movement of molecular motors, conformations of DNA strands, growth and remodeling of amyloid aggregates, and composition changes within lipid membranes. In this review, we discuss recent innovations in SMOLM and cover three key aspects: (1) biophysical insights enabled by labeling strategies that endow fluorescent probes to bind to targets with orientation specificity; (2) advanced imaging techniques that leverage the physics of light-matter interactions and estimation theory to encode orientation information with high fidelity into microscope images; and (3) computational methods that ensure accurate and precise data analysis and interpretation, even in the presence of severe shot noise. Additionally, we compare labeling approaches, imaging hardware, and publicly available analysis software to aid the community in choosing the best SMOLM implementation for their specific biophysical application. Finally, we highlight future directions for SMOLM, such as the development of probes with improved photostability and specificity, the design of “smart” adaptive hardware, and the use of advanced computational approaches to handle large, complex datasets. This review underscores the significant current and potential impact of SMOLM in deepening our understanding of molecular dynamics, paving the way for future breakthroughs in the fields of biophysics, biochemistry, and materials science.

Resolving the Nanoscale Structure of β-Sheet Peptide Self-Assemblies Using Single-Molecule Orientation-Localization Microscopy

Synthetic peptides that self-assemble into cross-β fibrils are versatile building blocks for engineered biomaterials due to their modularity and biocompatibility, but their structural and morphological similarities to amyloid species have been a long-standing concern for their translation. Further, their polymorphs are difficult to characterize by using spectroscopic and imaging techniques that rely on ensemble averaging to achieve high resolution. Here, we utilize Nile red (NR), an amyloidophilic fluorogenic probe, and single-molecule orientation-localization microscopy (SMOLM) to characterize fibrils formed by the designed amphipathic enantiomers KFE8L and KFE8D and the pathological amyloid-beta peptide Aβ42. Importantly, NR SMOLM reveals the helical (bilayer) ribbon structure of both KFE8 and Aβ42 and quantifies the precise tilt of the fibrils' inner and outer backbones in relevant buffer conditions without the need for covalent labeling or sequence mutations. SMOLM also distinguishes polymorphic branched and curved morphologies of KFE8, whose backbones exhibit much more heterogeneity than those of typical straight fibrils. Thus, SMOLM is a powerful tool to interrogate the structural differences and polymorphism between engineered and pathological cross-β-rich fibrils.

Resolving the nanoscale structure of β-sheet assemblies using single-molecule orientation-localization microscopy

Synthetic peptides that self-assemble into cross-β fibrils have remarkable utility as engineered biomaterials due to their modularity and biocompatibility, but their structural and morphological similarity to amyloid species has been a long-standing concern for their translation. Further, their polymorphs are difficult to characterize using spectroscopic and imaging techniques that rely on ensemble averaging to achieve high resolution. Here, we utilize single-molecule orientation-localization microscopy (SMOLM) to characterize fibrils formed by the designed amphipathic enantiomers, KFE8L and KFE8D, and the pathological amyloid-beta peptide Aβ42. SMOLM reveals that the orientations of Nile red, as it transiently binds to both KFE8 and Aβ42, are consistent with a helical (bilayer) ribbon structure and convey the precise tilt of the fibrils' inner and outer backbones. SMOLM also finds polymorphic branched and curved morphologies of KFE8 whose backbones exhibit much more heterogeneity than those of more typical straight fibrils. Thus, SMOLM is a powerful tool to interrogate the structural differences and polymorphism between engineered and pathological cross β-rich fibrils.

Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules

Point Spread Function (PSF) engineering is an effective method to increase the sensitivity of single-molecule fluorescence images to specific parameters. Classical phase mask optimization approaches have enabled the creation of new PSFs that can achieve, for example, localization precision of a few nanometers axially over a capture range of several microns with bright emitters. However, for complex high-dimensional optimization problems, classical approaches are difficult to implement and can be very time-consuming for computation. The advent of deep learning methods and their application to single-molecule imaging has provided a way to solve these problems. Here, we propose to combine PSF engineering and deep learning approaches to obtain both an optimized phase mask and a neural network structure to obtain the 3D position and 3D orientation of fixed fluorescent molecules. Our approach allows us to obtain an axial localization precision around 30 nanometers, as well as an orientation precision around 5 degrees for orientations and positions over a one micron depth range for a signal-to-noise ratio consistent with what is typical in single-molecule cellular imaging experiments.

Six-Dimensional Single-Molecule Imaging with Isotropic Resolution using a Multi-View Reflector Microscope

Imaging both the positions and orientations of single fluorophores, termed single-molecule orientation-localisation microscopy, is a powerful tool to study biochemical processes. However, the limited photon budget associated with single-molecule fluorescence makes high-dimensional imaging with isotropic, nanoscale spatial resolution a formidable challenge. Here, we realise a radially and azimuthally polarized multi-view reflector (raMVR) microscope for the imaging of the 3D positions and 3D orientations of single molecules, with precision of 10.9 nm and 2.0° over a 1.5 μm depth range. The raMVR microscope achieves 6D super-resolution imaging of Nile red (NR) molecules transiently bound to lipid-coated spheres, accurately resolving their spherical morphology despite refractive-index mismatch. By observing the rotational dynamics of NR, raMVR images also resolve the infiltration of lipid membranes by amyloid-beta oligomers without covalent labelling. Finally, we demonstrate 6D imaging of cell membranes, where the orientations of specific fluorophores reveal heterogeneity in membrane fluidity. With its nearly isotropic 3D spatial resolution and orientation measurement precision, we expect the raMVR microscope to enable 6D imaging of molecular dynamics within biological and chemical systems with exceptional detail.

Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution

Dipole-spread function (DSF) engineering reshapes the images of a microscope to maximize the sensitivity of measuring the 3D orientations of dipole-like emitters. However, severe Poisson shot noise, overlapping images, and simultaneously fitting high-dimensional information-both orientation and position-greatly complicates image analysis in single-molecule orientation-localization microscopy (SMOLM). Here, we report a deep-learning based estimator, termed Deep-SMOLM, that achieves superior 3D orientation and 2D position measurement precision within 3% of the theoretical limit (3.8° orientation, 0.32 sr wobble angle, and 8.5 nm lateral position using 1000 detected photons). Deep-SMOLM also demonstrates state-of-art estimation performance on overlapping images of emitters, e.g., a 0.95 Jaccard index for emitters separated by 139 nm, corresponding to a 43% image overlap. Deep-SMOLM accurately and precisely reconstructs 5D information of both simulated biological fibers and experimental amyloid fibrils from images containing highly overlapped DSFs at a speed ~10 times faster than iterative estimators.