Korea Advanced Institute of Science and Technology · Social Sciences
Professor Moosung Lee's research lab specializes in advancing quantitative, label-free, and volumetric imaging technologies for biological and biomedical applications. The lab focuses on developing innovative optical microscopy techniques—such as holographic microscopy, optical sectioning, and inverse scattering methods—to enable high-resolution, three-dimensional visualization of living cells and tissues without the need for fluorescent labels or genetic tags. Key research directions include quantitative phase imaging, structural characterization of brain tissues in disease models (e.g., Alzheimer’s), and the integration of machine learning with correlative imaging to enhance 3D organelle analysis. The lab emphasizes physical principles-driven imaging solutions that combine optics, wave physics, and computational methods for dynamic and accurate biological imaging.
Figures are computed from collected data and may differ slightly.
We present a wide-field quantitative label-free imaging of mouse brain tissue slices with sub-micrometre resolution, employing holographic microscopy and an automated scanning platform. From the measured light field images, scattering coefficients and anisotropies are quantitatively retrieved by using the modified the scattering-phase theorem, which enables access to structural information about brain tissues. As a proof of principle, we demonstrate that these scattering parameters enable us to
The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The algorithm,
A critical requirement for studying cell mechanics is three-dimensional assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), which simultaneously quantifies the volumetric morphology and traction force of cells using a high-speed illumination
A groundbreaking work in 1970 by Arthur Ashkin paved the way for developing various optical trapping techniques. Optical tweezers have become an established method for the manipulation of biological objects, due to their noninvasiveness and precise controllability. Recent innovations are accelerating and now enable single-cell manipulation through holographic light structuring. In this review, we provide an overview of recent advances in optical tweezer techniques for studies at the individual c
Optically levitated dielectric nanoparticles have become valuable tools for precision sensing and quantum optomechanical experiments. To predict the dynamic properties of a particle trapped in an optical tweezer with high fidelity, a tool is needed to compute the particle's response to the given optical field accurately. Here, we utilise a numerical solution of the three-dimensional trapping light to accurately simulate optical tweezers and predict key optomechanical parameters. By controlling t
Abstract The precise, quantitative evaluation of intracellular organelles in three-dimensional (3D) imaging data poses a significant challenge due to the inherent constraints of traditional microscopy techniques, the requirements of the use of exogenous labeling agents, and existing computational methods. To counter these challenges, we present a hybrid machine-learning framework exploiting correlative imaging of 3D quantitative phase imaging with 3D fluorescence imaging of labeled cells. The al
ABSTRACT A critical requirement for studying cell mechanics is three-dimensional (3D) assessment of cellular shapes and forces with high spatiotemporal resolution. Traction force microscopy (TFM) with fluorescence imaging enables the measurement of cellular forces, but it is limited by photobleaching and a slow 3D acquisition speed. Here, we present refractive-index traction force microscopy (RI-TFM), a high-speed volumetric technique that simultaneously quantifies the 3D morphology and traction
We present a quantitative label-free imaging of mouse whole brain tissue\nslices with sub-micrometre resolution, employing holographic microscopy and an\nautomated scanning platform. From the measured light field images, scattering\ncoefficients and anisotropies are quantitatively retrieved, which enables\naccess to structural information about brain tissues. As a proof of principle,\nwe demonstrate that these scattering parameters enable us to quantitatively\naddress structural alteration in th
We employ quantitative phase microscopy technique to investigate structural alterations in brains due to Alzheimer’s disease. Quantifying optical scattering parameters, we show that Alzheimer’s disease is associated with the morphological inhomogeneity of brains.
We demonstrate that quantitative phase imaging (QPI) can detect amyloid plaques in the brain of Alzheimer's disease (AD). Comparing QPIs and fluorescence images from wild-type and AD mice brains, we suggest that digital microscopic holography can be utilized for diagnosing AD.
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