Honors & Awards
Bio-X Bowes Fellowship, Stanford University (2020)
Asan Foundation Biomedical Science Scholarship, Asan Foundation (2018)
KAIST Creativity and Challenge Award, KAIST (2018)
Samsung HumanTech Paper Award (Silver), Samsung Electronics (2017)
Talent Award of Korea, Republic of Korea Government (2015)
SPIE Optics and Photonics Education Scholarship, SPIE (2014)
KAIST Presidential Fellowship, KAIST (2013)
Education & Certifications
M.S., Stanford, Biology (2020)
B.S., KAIST, Physics and Mathematics (2018)
Karl Deisseroth, Doctoral Dissertation Advisor (AC)
Mechanogenetic role of actomyosin complex in branching morphogenesis of epithelial organs.
Development (Cambridge, England)
The actomyosin complex plays crucial roles in various life processes by balancing the forces generated by cellular components. In addition to its physical function, the actomyosin complex participates in mechanotransduction. However, the exact role of actomyosin contractility in force transmission and the related transcriptional changes during morphogenesis are not fully understood. Here, we report a mechanogenetic role of the actomyosin complex in branching morphogenesis using an organotypic culture system of mouse embryonic submandibular glands. We dissected the physical factors arranged by characteristic actin structures in developing epithelial buds and identified the spatial distribution of forces that is essential for buckling mechanism to promote the branching process. Moreover, the critical genes required for the distribution of epithelial progenitor cells were regulated by YAP/TAZ through a mechanotransduction process in epithelial organs. These findings are important for our understanding of the physical processes involved in the development of epithelial organs and provide a theoretical background for developing new approaches for organ regeneration.
View details for DOI 10.1242/dev.190785
View details for PubMedID 33658222
Calibration-free quantitative phase imaging using data-driven aberration modeling
2020; 28 (23): 34835–47
We present a data-driven approach to compensate for optical aberrations in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity and stability of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells and microbeads, benchmarking against the conventional method using background subtractions.
View details for DOI 10.1364/OE.412009
View details for Web of Science ID 000589869600084
View details for PubMedID 33182943
Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs.
Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods have limited application in living foam cells. Here, using optical diffraction tomography (ODT), we performed quantitative morphological and biophysical analysis of living foam cells in a label-free manner. We identified LDs in foam cells by verifying the specific refractive index using correlative imaging comprising ODT integrated with three-dimensional fluorescence imaging. Through time-lapse monitoring of three-dimensional dynamics of label-free living foam cells, we precisely and quantitatively evaluated the therapeutic effects of a nanodrug (mannose-polyethylene glycol-glycol chitosan-fluorescein isothiocyanate-lobeglitazone; MMR-Lobe) designed to affect the targeted delivery of lobeglitazone to foam cells based on high mannose receptor specificity. Furthermore, by exploiting machine-learning-based image analysis, we further demonstrated therapeutic evaluation at the single-cell level. These findings suggest that refractive index measurement is a promising tool to explore new drugs against LD-related metabolic diseases.
View details for DOI 10.1021/acsnano.9b07993
View details for PubMedID 31909985
Deep-learning based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells.
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.
View details for DOI 10.7554/eLife.49023
View details for PubMedID 33331817
Optogenetic activation of intracellular antibodies for direct modulation of endogenous proteins.
Intracellular antibodies have become powerful tools for imaging, modulating and neutralizing endogenous target proteins. Here, we describe an optogenetically activated intracellular antibody (optobody) consisting of split antibody fragments and blue-light inducible heterodimerization domains. We expanded this optobody platform by generating several optobodies from previously developed intracellular antibodies, and demonstrated that photoactivation of gelsolin and beta2-adrenergic receptor (beta2AR) optobodies suppressed endogenous gelsolin activity and beta2AR signaling, respectively.
View details for DOI 10.1038/s41592-019-0592-7
View details for PubMedID 31611691
- Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography OPTICS EXPRESS 2019; 27 (4): 4927–43
Intensiometric biosensors visualize the activity of multiple small GTPases in vivo
2019; 10: 211
Ras and Rho small GTPases are critical for numerous cellular processes including cell division, migration, and intercellular communication. Despite extensive efforts to visualize the spatiotemporal activity of these proteins, achieving the sensitivity and dynamic range necessary for in vivo application has been challenging. Here, we present highly sensitive intensiometric small GTPase biosensors visualizing the activity of multiple small GTPases in single cells in vivo. Red-shifted sensors combined with blue light-controllable optogenetic modules achieved simultaneous monitoring and manipulation of protein activities in a highly spatiotemporal manner. Our biosensors revealed spatial dynamics of Cdc42 and Ras activities upon structural plasticity of single dendritic spines, as well as a broad range of subcellular Ras activities in the brains of freely behaving mice. Thus, these intensiometric small GTPase sensors enable the spatiotemporal dissection of complex protein signaling networks in live animals.
View details for DOI 10.1038/s41467-018-08217-3
View details for Web of Science ID 000455595400034
View details for PubMedID 30643148
View details for PubMedCentralID PMC6331645
Deep learning-based optical field screening for robust optical diffraction tomography.
2019; 9 (1): 15239
In tomographic reconstruction, the image quality of the reconstructed images can be significantly degraded by defects in the measured two-dimensional (2D) raw image data. Despite the importance of screening defective 2D images for robust tomographic reconstruction, manual inspection and rule-based automation suffer from low-throughput and insufficient accuracy, respectively. Here, we present deep learning-enabled quality control for holographic data to produce robust and high-throughput optical diffraction tomography (ODT). The key idea is to distil the knowledge of an expert into a deep convolutional neural network. We built an extensive database of optical field images with clean/noisy annotations, and then trained a binary-classification network based upon the data. The trained network outperformed visual inspection by non-expert users and a widely used rule-based algorithm, with >90% test accuracy. Subsequently, we confirmed that the superior screening performance significantly improved the tomogram quality. To further confirm the trained model's performance and generalisability, we evaluated it on unseen biological cell data obtained with a setup that was not used to generate the training dataset. Lastly, we interpreted the trained model using various visualisation techniques that provided the saliency map underlying each model inference. We envision the proposed network would a powerful lightweight module in the tomographic reconstruction pipeline.
View details for DOI 10.1038/s41598-019-51363-x
View details for PubMedID 31645595
Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells
BIOSENSORS & BIOELECTRONICS
2019; 123: 69–76
We present a rapid and label-free method for hematologic screening for diseases and syndromes, utilizing quantitative phase imaging (QPI) and machine learning. We aim to establish an efficient blood examination framework that does not suffer from the drawbacks of conventional blood assays, which are incapable of profiling single cells or require labeling procedures. Our method involves the synergistic employment of QPI and machine learning. The high-dimensional refractive index information arising from the QPI-based profiling of single red blood cells is processed to screen for diseases and syndromes using machine learning, which can utilize high-dimensional data beyond the human level. Accurate screening for iron-deficiency anemia, reticulocytosis, hereditary spherocytosis, and diabetes mellitus is demonstrated (>98% accuracy) using the proposed method. Furthermore, we highlight the synergy between QPI and machine learning in the proposed method by analyzing the performance of the method.
View details for DOI 10.1016/j.bios.2018.09.068
View details for Web of Science ID 000450376600010
View details for PubMedID 30321758
- Deep-Learning-Based Label-Free Segmentation of Cell Nuclei in Time-Lapse Refractive Index Tomograms IEEE ACCESS 2019; 7: 83449–60
- Quantitative Phase Imaging and Artificial Intelligence: A Review IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS 2019; 25 (1)
Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning.
Journal of visualized experiments : JoVE
We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.
View details for PubMedID 30507910
Holographic deep learning for rapid optical screening of anthrax spores
2017; 3 (8): e1700606
Establishing early warning systems for anthrax attacks is crucial in biodefense. Despite numerous studies for decades, the limited sensitivity of conventional biochemical methods essentially requires preprocessing steps and thus has limitations to be used in realistic settings of biological warfare. We present an optical method for rapid and label-free screening of Bacillus anthracis spores through the synergistic application of holographic microscopy and deep learning. A deep convolutional neural network is designed to classify holographic images of unlabeled living cells. After training, the network outperforms previous techniques in all accuracy measures, achieving single-spore sensitivity and subgenus specificity. The unique "representation learning" capability of deep learning enables direct training from raw images instead of manually extracted features. The method automatically recognizes key biological traits encoded in the images and exploits them as fingerprints. This remarkable learning ability makes the proposed method readily applicable to classifying various single cells in addition to B. anthracis, as demonstrated for the diagnosis of Listeria monocytogenes, without any modification. We believe that our strategy will make holographic microscopy more accessible to medical doctors and biomedical scientists for easy, rapid, and accurate point-of-care diagnosis of pathogens.
View details for DOI 10.1126/sciadv.1700606
View details for Web of Science ID 000411589900007
View details for PubMedID 28798957
View details for PubMedCentralID PMC5544395
Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning
2017; 7: 6654
Identification of lymphocyte cell types are crucial for understanding their pathophysiological roles in human diseases. Current methods for discriminating lymphocyte cell types primarily rely on labelling techniques with magnetic beads or fluorescence agents, which take time and have costs for sample preparation and may also have a potential risk of altering cellular functions. Here, we present the identification of non-activated lymphocyte cell types at the single-cell level using refractive index (RI) tomography and machine learning. From the measurements of three-dimensional RI maps of individual lymphocytes, the morphological and biochemical properties of the cells are quantitatively retrieved. To construct cell type classification models, various statistical classification algorithms are compared, and the k-NN (k = 4) algorithm was selected. The algorithm combines multiple quantitative characteristics of the lymphocyte to construct the cell type classifiers. After optimizing the feature sets via cross-validation, the trained classifiers enable identification of three lymphocyte cell types (B, CD4+ T, and CD8+ T cells) with high sensitivity and specificity. The present method, which combines RI tomography and machine learning for the first time to our knowledge, could be a versatile tool for investigating the pathophysiological roles of lymphocytes in various diseases including cancers, autoimmune diseases, and virus infections.
View details for DOI 10.1038/s41598-017-06311-y
View details for Web of Science ID 000406366000012
View details for PubMedID 28751719
View details for PubMedCentralID PMC5532204
Collaborative effects of wavefront shaping and optical clearing agent in optical coherence tomography
JOURNAL OF BIOMEDICAL OPTICS
2016; 21 (12): 121510
We demonstrate that simultaneous application of optical clearing agents (OCAs) and complex wavefront shaping in optical coherence tomography (OCT) can provide significant enhancement of penetration depth and imaging quality. OCA reduces optical inhomogeneity of a highly scattering sample, and the wavefront shaping of illumination light controls multiple scattering, resulting in an enhancement of the penetration depth and signal-to-noise ratio. A tissue phantom study shows that concurrent applications of OCA and wavefront shaping successfully operate in OCT imaging. The penetration depth enhancement is further demonstrated for
View details for DOI 10.1117/1.JBO.21.12.121510
View details for Web of Science ID 000392914600012
View details for PubMedID 27792807
Label-free identification of individual bacteria using Fourier transform light scattering
2015; 23 (12): 15792–805
Rapid identification of bacterial species is crucial in medicine and food hygiene. In order to achieve rapid and label-free identification of bacterial species at the single bacterium level, we propose and experimentally demonstrate an optical method based on Fourier transform light scattering (FTLS) measurements and statistical classification. For individual rod-shaped bacteria belonging to four bacterial species (Listeria monocytogenes, Escherichia coli, Lactobacillus casei, and Bacillus subtilis), two-dimensional angle-resolved light scattering maps are precisely measured using FTLS technique. The scattering maps are then systematically analyzed, employing statistical classification in order to extract the unique fingerprint patterns for each species, so that a new unidentified bacterium can be identified by a single light scattering measurement. The single-bacterial and label-free nature of our method suggests wide applicability for rapid point-of-care bacterial diagnosis.
View details for DOI 10.1364/OE.23.015792
View details for Web of Science ID 000356902500066
View details for PubMedID 26193558
Angle-resolved light scattering of individual rod-shaped bacteria based on Fourier transform light scattering
2014; 4: 5090
Two-dimensional angle-resolved light scattering maps of individual rod-shaped bacteria are measured at the single-cell level. Using quantitative phase imaging and Fourier transform light scattering techniques, the light scattering patterns of individual bacteria in four rod-shaped species (Bacillus subtilis, Lactobacillus casei, Synechococcus elongatus, and Escherichia coli) are measured with unprecedented sensitivity in a broad angular range from -70° to 70°. The measured light scattering patterns are analyzed along the two principal axes of rod-shaped bacteria in order to systematically investigate the species-specific characteristics of anisotropic light scattering. In addition, the cellular dry mass of individual bacteria is calculated and used to demonstrate that the cell-to-cell variations in light scattering within bacterial species is related to the cellular dry mass and growth.
View details for DOI 10.1038/srep05090
View details for Web of Science ID 000336425400002
View details for PubMedID 24867385
View details for PubMedCentralID PMC4035574
Quantitative Phase Imaging Techniques for the Study of Cell Pathophysiology: From Principles to Applications
2013; 13 (4): 4170–91
A cellular-level study of the pathophysiology is crucial for understanding the mechanisms behind human diseases. Recent advances in quantitative phase imaging (QPI) techniques show promises for the cellular-level understanding of the pathophysiology of diseases. To provide important insight on how the QPI techniques potentially improve the study of cell pathophysiology, here we present the principles of QPI and highlight some of the recent applications of QPI ranging from cell homeostasis to infectious diseases and cancer.
View details for DOI 10.3390/s130404170
View details for Web of Science ID 000318036400013
View details for PubMedID 23539026
View details for PubMedCentralID PMC3673078