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Zhou W, O’Neill CL, Ding T, Zhang O, Rudra JS, Lew MD. Resolving the Nanoscale Structure of β-Sheet Peptide Self-Assemblies Using Single-Molecule Orientation-Localization Microscopy. ACS NANO 2024; 18:8798-8810. [PMID: 38478911 PMCID: PMC11025465 DOI: 10.1021/acsnano.3c11771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
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.
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Affiliation(s)
- Weiyan Zhou
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Conor L. O’Neill
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Tianben Ding
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Oumeng Zhang
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Jai S. Rudra
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Matthew D. Lew
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
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Zhou W, O’Neill CL, Ding T, Zhang O, Rudra JS, Lew MD. Resolving the nanoscale structure of β-sheet assemblies using single-molecule orientation-localization microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.13.557571. [PMID: 37745382 PMCID: PMC10515885 DOI: 10.1101/2023.09.13.557571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
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.
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Affiliation(s)
- Weiyan Zhou
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Conor L. O’Neill
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Tianben Ding
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Oumeng Zhang
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Jai S. Rudra
- Department of Biomedical Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
| | - Matthew D. Lew
- Department of Electrical and Systems Engineering, McKelvey School of Engineering, Washington University in St. Louis, MO 63130, USA
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Jouchet P, Roy AR, Moerner W. Combining deep learning approaches and point spread function engineering for simultaneous 3D position and 3D orientation measurements of fluorescent single molecules. OPTICS COMMUNICATIONS 2023; 542:129589. [PMID: 37396964 PMCID: PMC10310311 DOI: 10.1016/j.optcom.2023.129589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/04/2023]
Abstract
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.
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Affiliation(s)
- Pierre Jouchet
- Department of Chemistry, Stanford University, 94305 Stanford CA, USA
| | - Anish R. Roy
- Department of Chemistry, Stanford University, 94305 Stanford CA, USA
| | - W.E. Moerner
- Department of Chemistry, Stanford University, 94305 Stanford CA, USA
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Zhang O, Guo Z, He Y, Wu T, Vahey MD, Lew MD. Six-Dimensional Single-Molecule Imaging with Isotropic Resolution using a Multi-View Reflector Microscope. NATURE PHOTONICS 2023; 17:179-186. [PMID: 36968242 PMCID: PMC10035538 DOI: 10.1038/s41566-022-01116-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/20/2022] [Indexed: 05/31/2023]
Abstract
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.
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Affiliation(s)
- Oumeng Zhang
- Department of Electrical and Systems Engineering
| | | | | | - Tingting Wu
- Department of Electrical and Systems Engineering
| | - Michael D. Vahey
- Department of Biomedical Engineering
- Center for Biomolecular Condensates
| | - Matthew D. Lew
- Department of Electrical and Systems Engineering
- Center for Biomolecular Condensates
- Institute of Materials Science and Engineering, Washington University in St. Louis, Missouri 63130, USA
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Wu T, Lu P, Rahman MA, Li X, Lew MD. Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution. OPTICS EXPRESS 2022; 30:36761-36773. [PMID: 36258598 PMCID: PMC9662599 DOI: 10.1364/oe.470146] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/08/2022] [Accepted: 09/08/2022] [Indexed: 06/01/2023]
Abstract
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.
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Affiliation(s)
- Tingting Wu
- Department of Electrical and Systems Engineering,
Washington University in St. Louis, Missouri 63130, USA
- Center for Science and Engineering of Living Systems,
Washington University in St. Louis, Missouri 63130, USA
| | - Peng Lu
- Department of Biomedical Engineering, Washington University in St. Louis, Missouri 63130, USA
- Department of Radiology, Washington University School of Medicine, Missouri 63110, USA
- These authors contributed equally to this work
| | - Md Ashequr Rahman
- Department of Biomedical Engineering, Washington University in St. Louis, Missouri 63130, USA
- Department of Radiology, Washington University School of Medicine, Missouri 63110, USA
- These authors contributed equally to this work
| | - Xiao Li
- Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, Michigan 48109, USA
- These authors contributed equally to this work
| | - Matthew D. Lew
- Department of Electrical and Systems Engineering,
Washington University in St. Louis, Missouri 63130, USA
- Center for Science and Engineering of Living Systems,
Washington University in St. Louis, Missouri 63130, USA
- Institute of Materials Science and Engineering,
Washington University in St. Louis, Missouri 63130, USA
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