Our Research | Lew Lab at Washington University in St. Louis

Since the earliest invention of telescopes, microscopes, and eyeglasses, imaging systems have been designed to help humans visualize the world around us – big and small, near and far. These imaging systems collect the light reflected or emitted from an object and focus it onto our eyes or a camera. The design of these systems dictates that their images only contain two-dimensional (2D) information about an object and that their 2D images are blurred if the object is out of focus. We build imaging systems with new capabilities that surpass these shortcomings.

Super-resolution is a key feature of many of our imaging systems–the ability to overcome the resolution limit of wave physics, called the diffraction limit, in order to visualize the nanoscale world. Single-molecule imaging is also a central theme of our research–enabling our technology to see individual molecules as they drive biological and chemical dynamics at the nanoscale.

• Background on super-resolution imaging
• Our software - GitHub repository
• Data supporting our publications - OSF

Biomolecular condensates formed by intrisically disordered proteins are viscoelastic materials--network fluids defined by an inhomogenous distribution of molecules. Using single-fluorogen tracking and super-resolution imaging of different disordered-protein based condensates, my find that they are organized into slow-moving nanoscale hubs and fast-moving dispersed molecules. We mapped internal chemical environments and the organization of interfaces of condensates using fluorogens, molecules that are sensitive to their chemical and viscoelastic environments. We show the nanoscale clusters within condensates are more hydrophobic than regions outside the clusters, and regions within condensates that lie outside clusters are more hydrophobic than the dilute phases outside the condensates.

Read:

• A closer look at biomolecular ‘Silly Putty’ - The Source
• T. Wu*, M. R. King*, Y. Qiu, M. Farag, R. V. Pappu, and M. D. Lew. “Single-fluorogen imaging reveals distinct environmental and structural features of biomolecular condensates,” Nat. Phys. (2025). [The Source - Washington University, EurekAlert!, Article, Data]

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How do the nanoscale architectures of amyloid-beta fibrils affect their growth and decay?

We applied time-lapse SMOLM to measure the orientations and rotational “wobble” of Nile blue as they bound transiently to amyloid fibrils. These data revealed the local architecture of the amyloid fibrils with nanoscale resolution, and we correlated this with their growth and decay over 5-20 min. We discovered that stable fibrils tended to be well-ordered with Nile blue orientations that were well aligned with little wobble. We also observed that increasing order in the organization of fibrils was correlated with growth. SMOLM was also able to detect fibril remodeling that was invisible to standard single-molecule localization microscopy.

Read:

• WashU Researchers Shine Light on Amyloid Architecture - The Source
• B. Sun, T. Ding, W. Zhou, T. S. Porter, and M. D. Lew, “Single-Molecule Orientation Imaging Reveals the Nano-Architecture of Amyloid Fibrils Undergoing Growth and Decay,” Nano Lett. 24, 7276 (2024). [The Source - Washington University, EurekAlert!, Open Scholarship, Article, Data]

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How can we discern engineered biomaterials from pathological amyloid species using orientation microscopy?

We used Nile red, a fluorogenic probe that “lights up” near amyloid-like structures, to characterize fibrils formed by designed amphipathic peptides and amyloid-beta. SMOLM revealed the helical bilayer structure of both types of fibrils and quantified the precise tilt of the fibrils' inner and outer backbones. SMOLM also distinguished the structural characteristics of branched and curved fibril networks from those of typical straight fibrils.

Read:

• Imaging Technique Shows New Details of Peptide Structures - The Source
• W. Zhou, C. L. O’Neill, T. Ding, O. Zhang, J. S. Rudra, and M. D. Lew, “Resolving the Nanoscale Structure of β-Sheet Peptide Self-Assemblies Using Single-Molecule Orientation–Localization Microscopy,” ACS Nano 18, 8798 (2024). [Journal cover, The Source - Washington University, EurekAlert!, Open Scholarship, Article, Data]

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How can we measure the translational and rotational dynamics of single molecules simultaneously with nanoscale precision?

Measuring radially and azimuthally polarized light enables high detection and estimation performance in single-molecule orientation-localization microscopy (SMOLM). This microscope reveals two interesting complementary phenomena: Nile red simultaneously exhibits large orientational motions but be motionless in the lateral direction within cholesterol-poor model lipid membranes. Nile red also shows dramatic jump diffusion while also being rotationally fixed in cholesterol-rich membranes.

Read:

• Lew lab sheds new light on cell membranes - The Source
• O. Zhang, W. Zhou, J. Lu, T. Wu, and M. D. Lew, “Resolving the three-dimensional rotational and translational dynamics of single molecules using radially and azimuthally polarized fluorescence,” Nano Lett. 22, 1024 (2022). [Open Scholarship, Article, Data, Summary PDF, Video abstract]

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Read more sensing research

The orientations and rotations of fluorescent molecules have been used to study the movements of molecular motors along microtubules, the stretching and bending of DNA, the dynamics and composition of lipid membranes, and the spatial and temporal organization of various soft materials. However, current technologies have trouble distinguishing and resolving these dynamics, e.g., neighboring molecules that are fixed in space vs. one molecule that is translationally fixed but rotating over time. We are designing optimal imaging systems and point spread functions (PSFs) for measuring molecular positions and orientations. Such technologies leverage 1) how molecules are excited by polarized illumination, 2) the polarization of fluorescence light emitted by the molecules, and 3) the radiation pattern of the fluorescence emitted by the molecules.

Explore how we design new imaging capabilities:

• What is the best-possible performance achievable by any imaging system?
• How can we design new dipole- and point-spread functions for 6D (3D position & 3D orientation) imaging?
  • Multi-view reflector microscope
  • pixOL dipole-spread function
  • Polarized vortex dipole-spread function
  • Tri-spot dipole-spread function
  • Crescent point-spread function

Our theoretical study proves that it is impossible to distinguish two nearby fluorescent molecules from a single rotating molecule using polarization microscopy. This barrier cannot be overcome by modulating the polarization of the illuminating light or modulating the polarization or phase of the detected fluorescence. If the imaging object is known to be a pair of dipoles, existing techniques perform poorly when measuring their angular separation. However, we show that modulating both the excitation polarization and the microscope's dipole-spread function enables robust discrimination between dipole pairs and single molecules.

Read:

• In molecular imaging, details matter - McKelvey Engineering News
• Y. Chen, Y. Qiu, and M. D. Lew, “Resolving the Orientations of and Angular Separation Between a Pair of Dipole Emitters,” Phys. Rev. Lett. 134, 093805 (2025). [McKelvey Engineering News, arXiv, Article]

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How can new optical architectures boost imaging performance?

• Extremely little light is collected from individual blinking molecules.
• Measuring a molecule's 3D position and 3D orientation simultaneously with isotropic, nanoscale resolution is a formidable challenge.

The multi-view reflector (MVR) architecture:

• Measures the 3D positions and 3D orientations of single molecules with 10.9 nm and 2.0° precisions, respectively, over a 1.5 µm depth range.
• Accurately resolves spherical lipid bilayers despite refractive-index mismatch.
• Resolves the infiltration of lipid membranes by amyloid-beta oligomers by measuring the rotational dynamics of Nile red fluorophores.
• Detects heterogeneities in the fluidity of cellular membranes via 6D imaging of merocyanine 540 fluorophores.

Read:

• Telescope-inspired microscope sees molecules in 6D - The Source
• O. Zhang, Z. Guo, Y. He, T. Wu, M. D. Vahey, and M. D. Lew, “Six-dimensional single-molecule imaging with isotropic resolution using a multi-view reflector microscope,” Nat. Photon. 17, 179 (2023). [The Source - Washington University, Open Scholarship, Article, Data, Video abstract]

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Challenges in 6D single-molecule orientation-localization microscopy:

• Spatial and angular information are mixed within the image of a fluorescent molecule, called its dipole-spread function (DSF).
• How can we optimize a phase mask to optimally encode 6D molecular information: 3D position (x, y, and z)and 3D orientation (polar angle θ, azimuthal angle ϕ, and rotational “wobble” solid angle Ω)?

We demonstrate the pixOL microscope:

• Uses a pixel-wise optimization algorithm to engineer the Green's tensor of a microscope--the dipole extension of point-spread function engineering
• Achieves optimal precision to simultaneously measure the 3D orientation and 3D location of a single molecule, i.e., 4.1° orientation, 0.44 sr wobble angle, 23.2 nm lateral localization, and 19.5 nm axial localization precisions over a 700 nm depth range using 2500 detected photons
• First demonstration of nanoscale super-resolved imaging with accurate molecular 3D position and 3D orientation determination over an entire extended object

Read:

• Lew lab sheds new light on cell membranes - The Source
• T. Wu, J. Lu, and M. D. Lew, “Dipole-spread-function engineering for simultaneously measuring the 3D orientations and 3D positions of fluorescent molecules,” Optica 9, 505 (2022). [The Source - Washington University, Article, Data, Video abstract]

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Read more design research

Severe shot noise, overlapping images of blinking molecules, and simultaneously fitting high-dimensional information—both orientation and position—greatly complicates image analysis in single-molecule orientation-localization microscopy (SMOLM).

How can we design an SMOLM image analysis algorithm that is precise, computationally efficient, and robust against these effects?

Deep-SMOLM is a deep-learning based estimator that:

• Achieves superior 3D orientation and 2D position measurement precision above and beyond traditional iterative approaches, within 3% of the theoretical limit
• Demonstrates state-of-the-art estimation performance when molecule images overlap, e.g., achieving 95% accuracy for emitters separated by 139 nm, corresponding to a 43% image overlap
• Accurately and precisely reconstructs 5D images of both simulated biological fibers and experimental amyloid fibrils at a speed ˜10 times faster than iterative estimators

Read:

• Machine learning generates pictures of proteins in 5D - McKelvey Engineering News
• T. Wu, P. Lu, M. A. Rahman, X. Li, and M. D. Lew, “Deep-SMOLM: deep learning resolves the 3D orientations and 2D positions of overlapping single molecules with optimal nanoscale resolution,” Opt. Express 30, 36761 (2022). [McKelvey Engineering News, Article, Code, Data]

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Any image analysis algorithm (both physics-based methods and data-driven deep learning techniques) uses an internal model of the imaging system to estimate the parameters of interest (e.g., the position of a single molecule). How can we detect if the model is incorrect for some or all of the observed data without knowing the ground truth?

WIF is a technique that tests the stability of a localization under a controlled computational perturbation and measures the consistency of any computational imaging model to explain the observed raw microscope images. We demonstrate WIF's ability to detect localization errors stemming from overlapping images, dipole emission patterns, and optical aberrations. WIF also quantifies heterogeneities in the observed single-molecule images that arise from different molecular orientations.

Read:

• New computational method validates images without ‘ground truth’ - The Source
• H. Mazidi, T. Ding, A. Nehorai, and M. D. Lew, “Quantifying accuracy and heterogeneity in single-molecule super-resolution microscopy,” Nat. Commun. 11, 6353 (2020). [The Source - Washington University, EurekAlert!, Article, Data, Summary PDF]

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How can we estimate the likelihood that a fluorescent molecule is emitting light from each pixel within an object domain? And how can we estimate the likelihood that a molecule within a pixel belongs to an unknown target structure?

RoSE works for arbitrary 3D point spread functions (PSFs) and biological structures. It calculates the likelihood that each image pixel contains a molecule, and not background light, by leveraging spatial sparsity. Further, by analyzing blinking statistics, it can also calculate the confidence of each pixel in truly representing the target structure. RoSE thereby minimizes false localizations that cause bias and artifacts in super-resolution microscopy.

Read:

1. H. Mazidi, J. Lu, A. Nehorai, and M. D. Lew, “Minimizing Structural Bias in Single-Molecule Super-Resolution Microscopy,” Sci. Rep. 8, 13133 (2018). [Article, Data, Summary PDF]
2. H. Mazidi, E. S. King, O. Zhang, A. Nehorai, and M. D. Lew, “Dense Super-Resolution Imaging of Molecular Orientation via Joint Sparse Basis Deconvolution and Spatial Pooling,” 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 325 (2019). [arXiv.org, Article]

Check out our repositories on GitHub. Contact us if you need any assistance.

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