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Chung Y, Hugonnet H, Hong SM, Park Y. Fourier space aberration correction for high resolution refractive index imaging using incoherent light. OPTICS EXPRESS 2024; 32:18790-18799. [PMID: 38859028 DOI: 10.1364/oe.518479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 04/23/2024] [Indexed: 06/12/2024]
Abstract
An aberration correction method is introduced for 3D phase deconvolution microscopy. Our technique capitalizes on multiple illumination patterns to iteratively extract Fourier space aberrations, utilizing the overlapping information inherent in these patterns. By refining the point spread function based on the retrieved aberration data, we significantly improve the precision of refractive index deconvolution. We validate the effectiveness of our method on both synthetic and biological three-dimensional samples, achieving notable enhancements in resolution and measurement accuracy. The method's reliability in aberration retrieval is further confirmed through controlled experiments with intentionally induced spherical aberrations, underscoring its potential for wide-ranging applications in microscopy and biomedicine.
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Verrier N, Debailleul M, Haeberlé O. Recent Advances and Current Trends in Transmission Tomographic Diffraction Microscopy. SENSORS (BASEL, SWITZERLAND) 2024; 24:1594. [PMID: 38475130 DOI: 10.3390/s24051594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Revised: 02/21/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
Optical microscopy techniques are among the most used methods in biomedical sample characterization. In their more advanced realization, optical microscopes demonstrate resolution down to the nanometric scale. These methods rely on the use of fluorescent sample labeling in order to break the diffraction limit. However, fluorescent molecules' phototoxicity or photobleaching is not always compatible with the investigated samples. To overcome this limitation, quantitative phase imaging techniques have been proposed. Among these, holographic imaging has demonstrated its ability to image living microscopic samples without staining. However, for a 3D assessment of samples, tomographic acquisitions are needed. Tomographic Diffraction Microscopy (TDM) combines holographic acquisitions with tomographic reconstructions. Relying on a 3D synthetic aperture process, TDM allows for 3D quantitative measurements of the complex refractive index of the investigated sample. Since its initial proposition by Emil Wolf in 1969, the concept of TDM has found a lot of applications and has become one of the hot topics in biomedical imaging. This review focuses on recent achievements in TDM development. Current trends and perspectives of the technique are also discussed.
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Affiliation(s)
- Nicolas Verrier
- Institut Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS UR UHA 7499), Université de Haute-Alsace, IUT Mulhouse, 61 rue Albert Camus, 68093 Mulhouse, France
| | - Matthieu Debailleul
- Institut Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS UR UHA 7499), Université de Haute-Alsace, IUT Mulhouse, 61 rue Albert Camus, 68093 Mulhouse, France
| | - Olivier Haeberlé
- Institut Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS UR UHA 7499), Université de Haute-Alsace, IUT Mulhouse, 61 rue Albert Camus, 68093 Mulhouse, France
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3
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Lee M, Jeong H, Lee C, Lee MJ, Delmo BR, Heo WD, Shin JH, Park Y. High-resolution assessment of multidimensional cellular mechanics using label-free refractive-index traction force microscopy. Commun Biol 2024; 7:115. [PMID: 38245624 PMCID: PMC10799850 DOI: 10.1038/s42003-024-05788-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 01/03/2024] [Indexed: 01/22/2024] Open
Abstract
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 scheme with 0.5-Hz temporal resolution. Without labelling, our method enables quantitative analyses of dry-mass distributions and shear (in-plane) and normal (out-of-plane) tractions of single cells on the extracellular matrix. When combined with a constrained total variation-based deconvolution algorithm, it provides 0.55-Pa shear and 1.59-Pa normal traction sensitivity for a 1-kPa hydrogel substrate. We demonstrate its utility by assessing the effects of compromised intracellular stress and capturing the rapid dynamics of cellular junction formation in the spatiotemporal changes in non-planar traction components.
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Affiliation(s)
- Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea
- Institute for Functional Matter and Quantum Technologies, Universität Stuttgart, 70569, Stuttgart, Germany
| | - Hyuntae Jeong
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Chaeyeon Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Benedict Reve Delmo
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
| | - Won Do Heo
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
- KAIST Institute for the BioCentury (KIB), KAIST, Jaejeo, Daejeon, 34141, South Korea.
| | - Jennifer H Shin
- Department of Mechanical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea.
- Tomocube Inc., Daejeon, 34109, South Korea.
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4
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Astratov VN, Sahel YB, Eldar YC, Huang L, Ozcan A, Zheludev N, Zhao J, Burns Z, Liu Z, Narimanov E, Goswami N, Popescu G, Pfitzner E, Kukura P, Hsiao YT, Hsieh CL, Abbey B, Diaspro A, LeGratiet A, Bianchini P, Shaked NT, Simon B, Verrier N, Debailleul M, Haeberlé O, Wang S, Liu M, Bai Y, Cheng JX, Kariman BS, Fujita K, Sinvani M, Zalevsky Z, Li X, Huang GJ, Chu SW, Tzang O, Hershkovitz D, Cheshnovsky O, Huttunen MJ, Stanciu SG, Smolyaninova VN, Smolyaninov II, Leonhardt U, Sahebdivan S, Wang Z, Luk’yanchuk B, Wu L, Maslov AV, Jin B, Simovski CR, Perrin S, Montgomery P, Lecler S. Roadmap on Label-Free Super-Resolution Imaging. LASER & PHOTONICS REVIEWS 2023; 17:2200029. [PMID: 38883699 PMCID: PMC11178318 DOI: 10.1002/lpor.202200029] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Indexed: 06/18/2024]
Abstract
Label-free super-resolution (LFSR) imaging relies on light-scattering processes in nanoscale objects without a need for fluorescent (FL) staining required in super-resolved FL microscopy. The objectives of this Roadmap are to present a comprehensive vision of the developments, the state-of-the-art in this field, and to discuss the resolution boundaries and hurdles which need to be overcome to break the classical diffraction limit of the LFSR imaging. The scope of this Roadmap spans from the advanced interference detection techniques, where the diffraction-limited lateral resolution is combined with unsurpassed axial and temporal resolution, to techniques with true lateral super-resolution capability which are based on understanding resolution as an information science problem, on using novel structured illumination, near-field scanning, and nonlinear optics approaches, and on designing superlenses based on nanoplasmonics, metamaterials, transformation optics, and microsphere-assisted approaches. To this end, this Roadmap brings under the same umbrella researchers from the physics and biomedical optics communities in which such studies have often been developing separately. The ultimate intent of this paper is to create a vision for the current and future developments of LFSR imaging based on its physical mechanisms and to create a great opening for the series of articles in this field.
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Affiliation(s)
- Vasily N. Astratov
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223-0001, USA
| | - Yair Ben Sahel
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Yonina C. Eldar
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA
- Bioengineering Department, University of California, Los Angeles, California 90095, USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, California 90095, USA
- Bioengineering Department, University of California, Los Angeles, California 90095, USA
- California Nano Systems Institute (CNSI), University of California, Los Angeles, California 90095, USA
- David Geffen School of Medicine, University of California, Los Angeles, California 90095, USA
| | - Nikolay Zheludev
- Optoelectronics Research Centre, University of Southampton, Southampton, SO17 1BJ, UK
- Centre for Disruptive Photonic Technologies, The Photonics Institute, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371, Singapore
| | - Junxiang Zhao
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Zachary Burns
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Zhaowei Liu
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
- Material Science and Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, California 92093, USA
| | - Evgenii Narimanov
- School of Electrical Engineering, and Birck Nanotechnology Center, Purdue University, West Lafayette, Indiana 47907, USA
| | - Neha Goswami
- Quantitative Light Imaging Laboratory, Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - Gabriel Popescu
- Quantitative Light Imaging Laboratory, Beckman Institute of Advanced Science and Technology, University of Illinois at Urbana-Champaign, Illinois 61801, USA
| | - Emanuel Pfitzner
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, United Kingdom
| | - Philipp Kukura
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, United Kingdom
| | - Yi-Teng Hsiao
- Institute of Atomic and Molecular Sciences (IAMS), Academia Sinica 1, Roosevelt Rd. Sec. 4, Taipei 10617 Taiwan
| | - Chia-Lung Hsieh
- Institute of Atomic and Molecular Sciences (IAMS), Academia Sinica 1, Roosevelt Rd. Sec. 4, Taipei 10617 Taiwan
| | - Brian Abbey
- Australian Research Council Centre of Excellence for Advanced Molecular Imaging, La Trobe University, Melbourne, Victoria, Australia
- Department of Chemistry and Physics, La Trobe Institute for Molecular Science (LIMS), La Trobe University, Melbourne, Victoria, Australia
| | - Alberto Diaspro
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- DIFILAB, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy
| | - Aymeric LeGratiet
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- Université de Rennes, CNRS, Institut FOTON - UMR 6082, F-22305 Lannion, France
| | - Paolo Bianchini
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- DIFILAB, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy
| | - Natan T. Shaked
- Tel Aviv University, Faculty of Engineering, Department of Biomedical Engineering, Tel Aviv 6997801, Israel
| | - Bertrand Simon
- LP2N, Institut d’Optique Graduate School, CNRS UMR 5298, Université de Bordeaux, Talence France
| | - Nicolas Verrier
- IRIMAS UR UHA 7499, Université de Haute-Alsace, Mulhouse, France
| | | | - Olivier Haeberlé
- IRIMAS UR UHA 7499, Université de Haute-Alsace, Mulhouse, France
| | - Sheng Wang
- School of Physics and Technology, Wuhan University, China
- Wuhan Institute of Quantum Technology, China
| | - Mengkun Liu
- Department of Physics and Astronomy, Stony Brook University, USA
- National Synchrotron Light Source II, Brookhaven National Laboratory, USA
| | - Yeran Bai
- Boston University Photonics Center, Boston, MA 02215, USA
| | - Ji-Xin Cheng
- Boston University Photonics Center, Boston, MA 02215, USA
| | - Behjat S. Kariman
- Optical Nanoscopy and NIC@IIT, CHT, Istituto Italiano di Tecnologia, Via Enrico Melen 83B, 16152 Genoa, Italy
- DIFILAB, Department of Physics, University of Genoa, Via Dodecaneso 33, 16146 Genoa, Italy
| | - Katsumasa Fujita
- Department of Applied Physics and the Advanced Photonics and Biosensing Open Innovation Laboratory (AIST); and the Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Osaka, Japan
| | - Moshe Sinvani
- Faculty of Engineering and the Nano-Technology Center, Bar-Ilan University, Ramat Gan, 52900 Israel
| | - Zeev Zalevsky
- Faculty of Engineering and the Nano-Technology Center, Bar-Ilan University, Ramat Gan, 52900 Israel
| | - Xiangping Li
- Guangdong Provincial Key Laboratory of Optical Fiber Sensing and Communications, Institute of Photonics Technology, Jinan University, Guangzhou 510632, China
| | - Guan-Jie Huang
- Department of Physics and Molecular Imaging Center, National Taiwan University, Taipei 10617, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Shi-Wei Chu
- Department of Physics and Molecular Imaging Center, National Taiwan University, Taipei 10617, Taiwan
- Brain Research Center, National Tsing Hua University, Hsinchu 30013, Taiwan
| | - Omer Tzang
- School of Chemistry, The Sackler faculty of Exact Sciences, and the Center for Light matter Interactions, and the Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 69978, Israel
| | - Dror Hershkovitz
- School of Chemistry, The Sackler faculty of Exact Sciences, and the Center for Light matter Interactions, and the Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 69978, Israel
| | - Ori Cheshnovsky
- School of Chemistry, The Sackler faculty of Exact Sciences, and the Center for Light matter Interactions, and the Tel Aviv University Center for Nanoscience and Nanotechnology, Tel Aviv 69978, Israel
| | - Mikko J. Huttunen
- Laboratory of Photonics, Physics Unit, Tampere University, FI-33014, Tampere, Finland
| | - Stefan G. Stanciu
- Center for Microscopy – Microanalysis and Information Processing, Politehnica University of Bucharest, 313 Splaiul Independentei, 060042, Bucharest, Romania
| | - Vera N. Smolyaninova
- Department of Physics Astronomy and Geosciences, Towson University, 8000 York Rd., Towson, MD 21252, USA
| | - Igor I. Smolyaninov
- Department of Electrical and Computer Engineering, University of Maryland, College Park, MD 20742, USA
| | - Ulf Leonhardt
- Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Sahar Sahebdivan
- EMTensor GmbH, TechGate, Donau-City-Strasse 1, 1220 Wien, Austria
| | - Zengbo Wang
- School of Computer Science and Electronic Engineering, Bangor University, Bangor, LL57 1UT, United Kingdom
| | - Boris Luk’yanchuk
- Faculty of Physics, Lomonosov Moscow State University, Moscow 119991, Russia
| | - Limin Wu
- Department of Materials Science and State Key Laboratory of Molecular Engineering of Polymers, Fudan University, Shanghai 200433, China
| | - Alexey V. Maslov
- Department of Radiophysics, University of Nizhny Novgorod, Nizhny Novgorod, 603022, Russia
| | - Boya Jin
- Department of Physics and Optical Science, University of North Carolina at Charlotte, Charlotte, North Carolina 28223-0001, USA
| | - Constantin R. Simovski
- Department of Electronics and Nano-Engineering, Aalto University, FI-00076, Espoo, Finland
- Faculty of Physics and Engineering, ITMO University, 199034, St-Petersburg, Russia
| | - Stephane Perrin
- ICube Research Institute, University of Strasbourg - CNRS - INSA de Strasbourg, 300 Bd. Sébastien Brant, 67412 Illkirch, France
| | - Paul Montgomery
- ICube Research Institute, University of Strasbourg - CNRS - INSA de Strasbourg, 300 Bd. Sébastien Brant, 67412 Illkirch, France
| | - Sylvain Lecler
- ICube Research Institute, University of Strasbourg - CNRS - INSA de Strasbourg, 300 Bd. Sébastien Brant, 67412 Illkirch, France
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5
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Park J, Bai B, Ryu D, Liu T, Lee C, Luo Y, Lee MJ, Huang L, Shin J, Zhang Y, Ryu D, Li Y, Kim G, Min HS, Ozcan A, Park Y. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat Methods 2023; 20:1645-1660. [PMID: 37872244 DOI: 10.1038/s41592-023-02041-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023]
Abstract
Quantitative phase imaging, integrated with artificial intelligence, allows for the rapid and label-free investigation of the physiology and pathology of biological systems. This review presents the principles of various two-dimensional and three-dimensional label-free phase imaging techniques that exploit refractive index as an intrinsic optical imaging contrast. In particular, we discuss artificial intelligence-based analysis methodologies for biomedical studies including image enhancement, segmentation of cellular or subcellular structures, classification of types of biological samples and image translation to furnish subcellular and histochemical information from label-free phase images. We also discuss the advantages and challenges of artificial intelligence-enabled quantitative phase imaging analyses, summarize recent notable applications in the life sciences, and cover the potential of this field for basic and industrial research in the life sciences.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Bijie Bai
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tairan Liu
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Chungha Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | - Yi Luo
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jeongwon Shin
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Yijie Zhang
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Yuzhu Li
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea
| | | | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, Los Angeles, CA, USA.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, Republic of Korea.
- Tomocube, Daejeon, Republic of Korea.
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6
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Ryu D, Bak T, Ahn D, Kang H, Oh S, Min HS, Lee S, Lee J. Deep learning-based label-free hematology analysis framework using optical diffraction tomography. Heliyon 2023; 9:e18297. [PMID: 37576294 PMCID: PMC10412892 DOI: 10.1016/j.heliyon.2023.e18297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 08/15/2023] Open
Abstract
Hematology analysis, a common clinical test for screening various diseases, has conventionally required a chemical staining process that is time-consuming and labor-intensive. To reduce the costs of chemical staining, label-free imaging can be utilized in hematology analysis. In this work, we exploit optical diffraction tomography and the fully convolutional one-stage object detector or FCOS, a deep learning architecture for object detection, to develop a label-free hematology analysis framework. Detected cells are classified into four groups: red blood cell, abnormal red blood cell, platelet, and white blood cell. In the results, the trained object detection model showed superior detection performance for blood cells in refractive index tomograms (0.977 mAP) and also showed high accuracy in the four-class classification of blood cells (0.9708 weighted F1 score, 0.9712 total accuracy). For further verification, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) were compared with values obtained from reference hematology equipment, with our results showing reasonable correlation in both MCV (0.905) and MCH (0.889). This study provides a successful demonstration of the proposed framework in detecting and classifying blood cells using optical diffraction tomography for label-free hematology analysis.
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Affiliation(s)
- Dongmin Ryu
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Taeyoung Bak
- Department of Computer Science and Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hayoung Kang
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Sanggeun Oh
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | | | - Sumin Lee
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Jimin Lee
- Department of Nuclear Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
- Graduate School of Artificial Intelligence (AIGS), Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
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7
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Park D, Lee D, Kim Y, Park Y, Lee YJ, Lee JE, Yeo MK, Kang MW, Chong Y, Han SJ, Choi J, Park JE, Koh Y, Lee J, Park Y, Kim R, Lee JS, Choi J, Lee SH, Ku B, Kang DH, Chung C. Cryobiopsy: A Breakthrough Strategy for Clinical Utilization of Lung Cancer Organoids. Cells 2023; 12:1854. [PMID: 37508518 PMCID: PMC10377875 DOI: 10.3390/cells12141854] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Revised: 07/05/2023] [Accepted: 07/07/2023] [Indexed: 07/30/2023] Open
Abstract
One major challenge associated with lung cancer organoids (LCOs) is their predominant derivation from surgical specimens of patients with early-stage lung cancer. However, patients with advanced lung cancer, who are in need of chemotherapy, often cannot undergo surgery. Therefore, there is an urgent need to successfully generate LCOs from biopsy specimens. Conventional lung biopsy techniques, such as transthoracic needle biopsy and forceps biopsy, only yield small amounts of lung tissue, resulting in a low success rate for culturing LCOs from biopsy samples. Furthermore, potential complications, like bleeding and pneumothorax, make it difficult to obtain sufficient tissue. Another critical issue is the overgrowth of normal lung cells in later passages of LCO culture, and the optimal culture conditions for LCOs are yet to be determined. To address these limitations, we attempted to create LCOs from cryobiopsy specimens obtained from patients with lung cancer (n = 113). Overall, the initial success rate of establishing LCOs from cryobiopsy samples was 40.7% (n = 46). Transbronchial cryobiopsy enables the retrieval of significantly larger amounts of lung tissue than bronchoscopic forceps biopsy. Additionally, cryobiopsy can be employed for peripheral lesions, and it is aided via radial endobronchial ultrasonography. This study significantly improved the success rate of LCO culture and demonstrated that the LCOs retained characteristics that resembled the primary tumors. Single-cell RNA sequencing confirmed high cancer cell purity in early passages of LCOs derived from patients with advanced lung cancer. Furthermore, the three-dimensional structure and intracellular components of LCOs were characterized using three-dimensional holotomography. Finally, drug screening was performed using a specialized micropillar culture system with cryobiopsy-derived LCOs. LCOs derived from cryobiopsy specimens offer a promising solution to the critical limitations of conventional LCOs. Cryobiopsy can be applied to patients with lung cancer at all stages, including those with peripheral lesions, and can provide sufficient cells for LCO generation. Therefore, we anticipate that cryobiopsy will serve as a breakthrough strategy for the clinical application of LCOs in all stages of lung cancer.
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Affiliation(s)
- Dongil Park
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Dahye Lee
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Yoonjoo Kim
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Yeonhee Park
- Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Daejeon St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul 34943, Republic of Korea
| | - Yeon-Jae Lee
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jeong Eun Lee
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Min-Kyung Yeo
- Department of Pathology, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Min-Woong Kang
- Thoracic and Cardiovascular Surgery, School of Medicine, Chungnam National University, Munhwa-ro 282, Jung-Gu, Daejeon 35015, Republic of Korea
| | - Yooyoung Chong
- Thoracic and Cardiovascular Surgery, School of Medicine, Chungnam National University, Munhwa-ro 282, Jung-Gu, Daejeon 35015, Republic of Korea
| | - Sung Joon Han
- Thoracic and Cardiovascular Surgery, School of Medicine, Chungnam National University, Munhwa-ro 282, Jung-Gu, Daejeon 35015, Republic of Korea
| | - Jinwook Choi
- School of Life Science, Gwangju Institute of Science and Technology, Gwangju 61005, Republic of Korea
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Yongjun Koh
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | | | - YongKeun Park
- Tomocube Inc., Daejeon 34141, Republic of Korea
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Ryul Kim
- GENOME INSIGHT Inc., Daejeon 34051, Republic of Korea
| | - Jeong Seok Lee
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- GENOME INSIGHT Inc., Daejeon 34051, Republic of Korea
| | - Jimin Choi
- Central R&D Center, Medical & Bio Decision Co., Ltd., Suwon 16229, Republic of Korea
| | - Sang-Hyun Lee
- Central R&D Center, Medical & Bio Decision Co., Ltd., Suwon 16229, Republic of Korea
| | - Bosung Ku
- Central R&D Center, Medical & Bio Decision Co., Ltd., Suwon 16229, Republic of Korea
| | - Da Hyun Kang
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Chaeuk Chung
- Division of Pulmonology and Critical Care Medicine, Department of Internal Medicine, College of Medicine, Chungnam National University, Daejeon 34134, Republic of Korea
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8
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Lee C, Hugonnet H, Park J, Lee MJ, Park W, Park Y. Single-shot refractive index slice imaging using spectrally multiplexed optical transfer function reshaping. OPTICS EXPRESS 2023; 31:13806-13816. [PMID: 37157259 DOI: 10.1364/oe.485559] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The refractive index (RI) of cells and tissues is crucial in pathophysiology as a noninvasive and quantitative imaging contrast. Although its measurements have been demonstrated using three-dimensional quantitative phase imaging methods, these methods often require bulky interferometric setups or multiple measurements, which limits the measurement sensitivity and speed. Here, we present a single-shot RI imaging method that visualizes the RI of the in-focus region of a sample. By exploiting spectral multiplexing and optical transfer function engineering, three color-coded intensity images of a sample with three optimized illuminations were simultaneously obtained in a single-shot measurement. The measured intensity images were then deconvoluted to obtain the RI image of the in-focus slice of the sample. As a proof of concept, a setup was built using Fresnel lenses and a liquid-crystal display. For validation purposes, we measured microspheres of known RI and cross-validated the results with simulated results. Various static and highly dynamic biological cells were imaged to demonstrate that the proposed method can conduct single-shot RI slice imaging of biological samples with subcellular resolution.
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9
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Park J, Shin SJ, Shin J, Lee AJ, Lee M, Lee MJ, Kim G, Heo JE, Suk lee K, Park Y. Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:1071-1081. [PMID: 36950245 PMCID: PMC10026583 DOI: 10.1364/boe.484092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 01/16/2023] [Accepted: 01/16/2023] [Indexed: 06/18/2023]
Abstract
Clear cell renal cell carcinoma (ccRCC) is a common histopathological subtype of renal cancer and is notorious for its poor prognosis. Its accurate diagnosis by histopathology, which relies on manual microscopic inspection of stained slides, is challenging. Here, we present a correlative approach to utilize stained images and refractive index (RI) tomography and demonstrate quantitative assessments of the structural heterogeneities of ccRCC slides obtained from human patients. Machine-learning-assisted segmentation of nuclei and cytoplasm enabled the quantification at the subcellular level. Compared to benign regions, malignant regions exhibited a considerable increase in structural heterogeneities. The results demonstrate that RI tomography provides quantitative information in synergy with stained images on the structural heterogeneities in ccRCC.
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Affiliation(s)
- Juyeon Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Contributed equally
| | - Su-Jin Shin
- Department of Pathology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
- Contributed equally
| | - Jeongwon Shin
- Department of Biological Sciences, KAIST, Daejeon, 34141, Republic of Korea
- Contributed equally
| | - Ariel J. Lee
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Mahn Jae Lee
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
| | - Ji Eun Heo
- Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - Kwang Suk lee
- Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea
- Tomocube Inc., Daejeon 34051, Republic of Korea
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10
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Hugonnet H, Shin S, Park Y. Regularization of dielectric tensor tomography. OPTICS EXPRESS 2023; 31:3774-3783. [PMID: 36785362 DOI: 10.1364/oe.478260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 01/02/2023] [Indexed: 06/18/2023]
Abstract
Dielectric tensor tomography reconstructs the three-dimensional dielectric tensors of microscopic objects and provides information about the crystalline structure orientations and principal refractive indices. Because dielectric tensor tomography is based on transmission measurement, it suffers from the missing cone problem, which causes poor axial resolution, underestimation of the refractive index, and halo artifacts. In this study, we study the application of total variation and positive semi-definiteness regularization to three-dimensional tensor distributions. In particular, we demonstrate the reduction of artifacts when applied to dielectric tensor tomography.
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11
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Hugonnet H, Lee MJ, Park YK. Quantitative phase and refractive index imaging of 3D objects via optical transfer function reshaping. OPTICS EXPRESS 2022; 30:13802-13809. [PMID: 35472985 DOI: 10.1364/oe.454533] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 03/19/2022] [Indexed: 06/14/2023]
Abstract
Deconvolution phase microscopy enables high-contrast visualization of transparent samples through reconstructions of their transmitted phases or refractive indexes. Herein, we propose a method to extend 2D deconvolution phase microscopy to thick 3D samples. The refractive index distribution of a sample can be obtained at a specific axial plane by measuring only four intensity images obtained under optimized illumination patterns. Also, the optical phase delay of a sample can be measured using different illumination patterns.
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12
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Cao R, Kellman M, Ren D, Eckert R, Waller L. Self-calibrated 3D differential phase contrast microscopy with optimized illumination. BIOMEDICAL OPTICS EXPRESS 2022; 13:1671-1684. [PMID: 35414990 PMCID: PMC8973190 DOI: 10.1364/boe.450838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 02/08/2022] [Accepted: 02/21/2022] [Indexed: 05/30/2023]
Abstract
3D phase imaging recovers an object's volumetric refractive index from intensity and/or holographic measurements. Partially coherent methods, such as illumination-based differential phase contrast (DPC), are particularly simple to implement in a commercial brightfield microscope. 3D DPC acquires images at multiple focus positions and with different illumination source patterns in order to reconstruct 3D refractive index. Here, we present a practical extension of the 3D DPC method that does not require a precise motion stage for scanning the focus and uses optimized illumination patterns for improved performance. The user scans the focus by hand, using the microscope's focus knob, and the algorithm self-calibrates the axial position to solve for the 3D refractive index of the sample through a computational inverse problem. We further show that the illumination patterns can be optimized by an end-to-end learning procedure. Combining these two, we demonstrate improved 3D DPC with a commercial microscope whose only hardware modification is LED array illumination.
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Affiliation(s)
- Ruiming Cao
- Department of Bioengineering, University of California, Berkeley, CA 94720, USA
| | - Michael Kellman
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - David Ren
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - Regina Eckert
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
| | - Laura Waller
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720, USA
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