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Wang Y, Zhou S, Quan Y, Liu Y, Zhou B, Chen X, Ma Z, Zhou Y. Label-free spatiotemporal decoding of single-cell fate via acoustic driven 3D tomography. Mater Today Bio 2024; 28:101201. [PMID: 39221213 PMCID: PMC11364901 DOI: 10.1016/j.mtbio.2024.101201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2024] [Revised: 08/08/2024] [Accepted: 08/11/2024] [Indexed: 09/04/2024] Open
Abstract
Label-free three-dimensional imaging plays a crucial role in unraveling the complexities of cellular functions and interactions in biomedical research. Conventional single-cell optical tomography techniques offer affordability and the convenience of bypassing laborious cell labelling protocols. However, these methods are encumbered by restricted illumination scanning ranges on abaxial plane, resulting in the loss of intricate cellular imaging details. The ability to fully control cellular rotation across all angles has emerged as an optimal solution for capturing comprehensive structural details of cells. Here, we introduce a label-free, cost-effective, and readily fabricated contactless acoustic-induced vibration system, specifically designed to enable multi-degree-of-freedom rotation of cells, ultimately attaining stable in-situ rotation. Furthermore, by integrating this system with advanced deep learning technologies, we perform 3D reconstruction and morphological analysis on diverse cell types, thus validating groups of high-precision cell identification. Notably, long-term observation of cells reveals distinct features associated with drug-induced apoptosis in both cancerous and normal cells populations. This methodology, based on deep learning-enabled cell 3D reconstruction, charts a novel trajectory for groups of real-time cellular visualization, offering promising advancements in the realms of drug screening and post-single-cell analysis, thereby addressing potential clinical requisites.
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Affiliation(s)
- Yuxin Wang
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Shizheng Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Yue Quan
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Yu Liu
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Bingpu Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Xiuping Chen
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
| | - Zhichao Ma
- Institute of Medical Robotics, School of Biomedical Engineering, Shanghai Jiao Tong University, No.800 Dongchuan Road, Shanghai, 200240, China
| | - Yinning Zhou
- Joint Key Laboratory of the Ministry of Education, Institute of Applied Physics and Materials Engineering, University of Macau, Avenida da Universidade, Taipa, Macau, 999078, China
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2
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Fanous MJ, Casteleiro Costa P, Işıl Ç, Huang L, Ozcan A. Neural network-based processing and reconstruction of compromised biophotonic image data. LIGHT, SCIENCE & APPLICATIONS 2024; 13:231. [PMID: 39237561 PMCID: PMC11377739 DOI: 10.1038/s41377-024-01544-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 09/07/2024]
Abstract
In recent years, the integration of deep learning techniques with biophotonic setups has opened new horizons in bioimaging. A compelling trend in this field involves deliberately compromising certain measurement metrics to engineer better bioimaging tools in terms of e.g., cost, speed, and form-factor, followed by compensating for the resulting defects through the utilization of deep learning models trained on a large amount of ideal, superior or alternative data. This strategic approach has found increasing popularity due to its potential to enhance various aspects of biophotonic imaging. One of the primary motivations for employing this strategy is the pursuit of higher temporal resolution or increased imaging speed, critical for capturing fine dynamic biological processes. Additionally, this approach offers the prospect of simplifying hardware requirements and complexities, thereby making advanced imaging standards more accessible in terms of cost and/or size. This article provides an in-depth review of the diverse measurement aspects that researchers intentionally impair in their biophotonic setups, including the point spread function (PSF), signal-to-noise ratio (SNR), sampling density, and pixel resolution. By deliberately compromising these metrics, researchers aim to not only recuperate them through the application of deep learning networks, but also bolster in return other crucial parameters, such as the field of view (FOV), depth of field (DOF), and space-bandwidth product (SBP). Throughout this article, we discuss various biophotonic methods that have successfully employed this strategic approach. These techniques span a wide range of applications and showcase the versatility and effectiveness of deep learning in the context of compromised biophotonic data. Finally, by offering our perspectives on the exciting future possibilities of this rapidly evolving concept, we hope to motivate our readers from various disciplines to explore novel ways of balancing hardware compromises with compensation via artificial intelligence (AI).
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Affiliation(s)
- Michael John Fanous
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
| | - Paloma Casteleiro Costa
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
| | - Çağatay Işıl
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, CA, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Luzhe Huang
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA
- Bioengineering Department, University of California, Los Angeles, CA, USA
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, University of California, Los Angeles, CA, USA.
- Bioengineering Department, University of California, Los Angeles, CA, USA.
- California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, USA.
- Department of Surgery, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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3
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Pan G, Xun M, Zhou X, Sun Y, Dong Y, Wu D. Harnessing the capabilities of VCSELs: unlocking the potential for advanced integrated photonic devices and systems. LIGHT, SCIENCE & APPLICATIONS 2024; 13:229. [PMID: 39227573 PMCID: PMC11372081 DOI: 10.1038/s41377-024-01561-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 07/03/2024] [Accepted: 07/31/2024] [Indexed: 09/05/2024]
Abstract
Vertical cavity surface emitting lasers (VCSELs) have emerged as a versatile and promising platform for developing advanced integrated photonic devices and systems due to their low power consumption, high modulation bandwidth, small footprint, excellent scalability, and compatibility with monolithic integration. By combining these unique capabilities of VCSELs with the functionalities offered by micro/nano optical structures (e.g. metasurfaces), it enables various versatile energy-efficient integrated photonic devices and systems with compact size, enhanced performance, and improved reliability and functionality. This review provides a comprehensive overview of the state-of-the-art versatile integrated photonic devices/systems based on VCSELs, including photonic neural networks, vortex beam emitters, holographic devices, beam deflectors, atomic sensors, and biosensors. By leveraging the capabilities of VCSELs, these integrated photonic devices/systems open up new opportunities in various fields, including artificial intelligence, large-capacity optical communication, imaging, biosensing, and so on. Through this comprehensive review, we aim to provide a detailed understanding of the pivotal role played by VCSELs in integrated photonics and highlight their significance in advancing the field towards efficient, compact, and versatile photonic solutions.
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Affiliation(s)
- Guanzhong Pan
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China
| | - Meng Xun
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China.
| | - Xiaoli Zhou
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China
| | - Yun Sun
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China
| | - Yibo Dong
- Institute of Photonic Chips, University of Shanghai for Science and Technology, Shanghai, China.
| | - Dexin Wu
- Institute of Microelectronics, Chinese Academy of Sciences, Beijing, China
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4
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Chi J, Xue Y, Zhou Y, Han T, Ning B, Cheng L, Xie H, Wang H, Wang W, Meng Q, Fan K, Gong F, Fan J, Jiang N, Liu Z, Pan K, Sun H, Zhang J, Zheng Q, Wang J, Su M, Song Y. Perovskite Probe-Based Machine Learning Imaging Model for Rapid Pathologic Diagnosis of Cancers. ACS NANO 2024; 18:24295-24305. [PMID: 39164203 DOI: 10.1021/acsnano.4c06351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/22/2024]
Abstract
Accurately distinguishing tumor cells from normal cells is a key issue in tumor diagnosis, evaluation, and treatment. Fluorescence-based immunohistochemistry as the standard method faces the inherent challenges of the heterogeneity of tumor cells and the lack of big data analysis of probing images. Here, we have demonstrated a machine learning-driven imaging method for rapid pathological diagnosis of five types of cancers (breast, colon, liver, lung, and stomach) using a perovskite nanocrystal probe. After conducting the bioanalysis of survivin expression in five different cancers, high-efficiency perovskite nanocrystal probes modified with the survivin antibody can recognize the cancer tissue section at the single cell level. The tumor to normal (T/N) ratio is 10.3-fold higher than that of a conventional fluorescent probe, which can successfully differentiate between tumors and adjacent normal tissues within 10 min. The features of the fluorescence intensity and pathological texture morphology have been extracted and analyzed from 1000 fluorescence images by machine learning. The final integrated decision model makes the area under the receiver operating characteristic curve (area under the curve) value of machine learning classification of breast, colon, liver, lung, and stomach above 90% while predicting the tumor organ of 92% of positive patients. This method demonstrates a high T/N ratio probe in the precise diagnosis of multiple cancers, which will be good for improving the accuracy of surgical resection and reducing cancer mortality.
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Affiliation(s)
- Jimei Chi
- Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yonggan Xue
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Yinying Zhou
- School of Software, Northwestern Polytechnical University, Xi'an, 710129, China
| | - Teng Han
- Institute of Software, Chinese Academy of Sciences, Beijing, 100191, China
| | - Bobin Ning
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Lijun Cheng
- Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Hongfei Xie
- Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Huadong Wang
- Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Wenchen Wang
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Qingyu Meng
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Kaijie Fan
- Department of Thoracic Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Fangming Gong
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Junzhen Fan
- Department of Pathology, the Third Medical Center, Chinese PLA General Hospital, Beijing, 100089, China
| | - Nan Jiang
- Faculty of Hepatopancreatobiliary Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Zheng Liu
- Senior Department of Orthopedics, the Fourth Medical Center of PLA General Hospital, Beijing, 100048, China
| | - Ke Pan
- Institute of Hepato-Pancreato-Biliary Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Hongyu Sun
- Department of Gastroenterology, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Jiajin Zhang
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Qian Zheng
- Department of Thoracic Surgery, the First Medical Center, Chinese PLA General Hospital, Beijing, 100853, China
| | - Jiandong Wang
- Department of General Surgery, the First Medical Centre, Chinese PLA General Hospital, Beijing, 100853, China
| | - Meng Su
- Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
| | - Yanlin Song
- Key Laboratory of Green Printing, CAS Research/Education Center for Excellence in Molecular Sciences, Institute of Chemistry, Chinese Academy of Sciences (ICCAS), Beijing Engineering Research Center of Nanomaterials for Green Printing Technology, Beijing National Laboratory for Molecular Sciences (BNLMS), Beijing 100190, P. R. China
- University of Chinese Academy of Sciences, Beijing 100049, P. R. China
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5
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Rosen J, Alford S, Allan B, Anand V, Arnon S, Arockiaraj FG, Art J, Bai B, Balasubramaniam GM, Birnbaum T, Bisht NS, Blinder D, Cao L, Chen Q, Chen Z, Dubey V, Egiazarian K, Ercan M, Forbes A, Gopakumar G, Gao Y, Gigan S, Gocłowski P, Gopinath S, Greenbaum A, Horisaki R, Ierodiaconou D, Juodkazis S, Karmakar T, Katkovnik V, Khonina SN, Kner P, Kravets V, Kumar R, Lai Y, Li C, Li J, Li S, Li Y, Liang J, Manavalan G, Mandal AC, Manisha M, Mann C, Marzejon MJ, Moodley C, Morikawa J, Muniraj I, Narbutis D, Ng SH, Nothlawala F, Oh J, Ozcan A, Park Y, Porfirev AP, Potcoava M, Prabhakar S, Pu J, Rai MR, Rogalski M, Ryu M, Choudhary S, Salla GR, Schelkens P, Şener SF, Shevkunov I, Shimobaba T, Singh RK, Singh RP, Stern A, Sun J, Zhou S, Zuo C, Zurawski Z, Tahara T, Tiwari V, Trusiak M, Vinu RV, Volotovskiy SG, Yılmaz H, De Aguiar HB, Ahluwalia BS, Ahmad A. Roadmap on computational methods in optical imaging and holography [invited]. APPLIED PHYSICS. B, LASERS AND OPTICS 2024; 130:166. [PMID: 39220178 PMCID: PMC11362238 DOI: 10.1007/s00340-024-08280-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 07/10/2024] [Indexed: 09/04/2024]
Abstract
Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography. Supplementary Information The online version contains supplementary material available at 10.1007/s00340-024-08280-3.
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Affiliation(s)
- Joseph Rosen
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
| | - Simon Alford
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Blake Allan
- Faculty of Science Engineering and Built Environment, Deakin University, Princes Highway, Warrnambool, VIC 3280 Australia
| | - Vijayakumar Anand
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
- Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Computing and Engineering Technologies, Optical Sciences Center, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122 Australia
| | - Shlomi Arnon
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Francis Gracy Arockiaraj
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
| | - Jonathan Art
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Bijie Bai
- Electrical and Computer Engineering Department, Bioengineering Department, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA USA
| | - Ganesh M. Balasubramaniam
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Tobias Birnbaum
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel VUB), Pleinlaan 2, 1050 Brussel, Belgium
- Swave BV, Gaston Geenslaan 2, 3001 Leuven, Belgium
| | - Nandan S. Bisht
- Applied Optics and Spectroscopy Laboratory, Department of Physics, Soban Singh Jeena University Campus Almora, Almora, Uttarakhand 263601 India
| | - David Blinder
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel VUB), Pleinlaan 2, 1050 Brussel, Belgium
- IMEC, Kapeldreef 75, 3001 Leuven, Belgium
- Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba Japan
| | - Liangcai Cao
- Department of Precision Instruments, Tsinghua University, Beijing, 100084 China
| | - Qian Chen
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
| | - Ziyang Chen
- Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science and Engineering, Huaqiao University, Xiamen, 361021 Fujian China
| | - Vishesh Dubey
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Karen Egiazarian
- Computational Imaging Group, Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland
| | - Mert Ercan
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM), Bilkent University, 06800 Ankara, Turkey
- Department of Physics, Bilkent University, 06800 Ankara, Turkey
| | - Andrew Forbes
- School of Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - G. Gopakumar
- Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Vallikavu, Kerala India
| | - Yunhui Gao
- Department of Precision Instruments, Tsinghua University, Beijing, 100084 China
| | - Sylvain Gigan
- Laboratoire Kastler Brossel, Centre National de la Recherche Scientifique (CNRS) UMR 8552, Sorbonne Universite ´, Ecole Normale Supe ´rieure-Paris Sciences et Lettres (PSL) Research University, Collège de France, 24 rue Lhomond, 75005 Paris, France
| | - Paweł Gocłowski
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | | | - Alon Greenbaum
- Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695 USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695 USA
- Bioinformatics Research Center, North Carolina State University, Raleigh, NC 27695 USA
| | - Ryoichi Horisaki
- Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656 Japan
| | - Daniel Ierodiaconou
- Faculty of Science Engineering and Built Environment, Deakin University, Princes Highway, Warrnambool, VIC 3280 Australia
| | - Saulius Juodkazis
- Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Computing and Engineering Technologies, Optical Sciences Center, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122 Australia
- World Research Hub Initiative (WRHI), Tokyo Institute of Technology, 2-12-1, Ookayama, Tokyo, 152-8550 Japan
| | - Tanushree Karmakar
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Vladimir Katkovnik
- Computational Imaging Group, Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland
| | - Svetlana N. Khonina
- IPSI RAS-Branch of the FSRC “Crystallography and Photonics” RAS, 443001 Samara, Russia
- Samara National Research University, 443086 Samara, Russia
| | - Peter Kner
- School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602 USA
| | - Vladislav Kravets
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Ravi Kumar
- Department of Physics, SRM University – AP, Amaravati, Andhra Pradesh 522502 India
| | - Yingming Lai
- Laboratory of Applied Computational Imaging, Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Université du Québec, Varennes, QC J3X1Pd7 Canada
| | - Chen Li
- Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695 USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695 USA
| | - Jiaji Li
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Shaoheng Li
- School of Electrical and Computer Engineering, University of Georgia, Athens, GA 30602 USA
| | - Yuzhu Li
- Electrical and Computer Engineering Department, Bioengineering Department, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA USA
| | - Jinyang Liang
- Laboratory of Applied Computational Imaging, Centre Énergie Matériaux Télécommunications, Institut National de la Recherche Scientifique, Université du Québec, Varennes, QC J3X1Pd7 Canada
| | - Gokul Manavalan
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Aditya Chandra Mandal
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Manisha Manisha
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Christopher Mann
- Department of Applied Physics and Materials Science, Northern Arizona University, Flagstaff, AZ 86011 USA
- Center for Materials Interfaces in Research and Development, Northern Arizona University, Flagstaff, AZ 86011 USA
| | - Marcin J. Marzejon
- Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland
| | - Chané Moodley
- School of Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - Junko Morikawa
- World Research Hub Initiative (WRHI), Tokyo Institute of Technology, 2-12-1, Ookayama, Tokyo, 152-8550 Japan
| | - Inbarasan Muniraj
- LiFE Lab, Department of Electronics and Communication Engineering, Alliance School of Applied Engineering, Alliance University, Bangalore, Karnataka 562106 India
| | - Donatas Narbutis
- Institute of Theoretical Physics and Astronomy, Faculty of Physics, Vilnius University, Sauletekio 9, 10222 Vilnius, Lithuania
| | - Soon Hock Ng
- Optical Sciences Center and ARC Training Centre in Surface Engineering for Advanced Materials (SEAM), School of Science, Computing and Engineering Technologies, Optical Sciences Center, Swinburne University of Technology, Hawthorn, Melbourne, VIC 3122 Australia
| | - Fazilah Nothlawala
- School of Physics, University of the Witwatersrand, Johannesburg, South Africa
| | - Jeonghun Oh
- 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
| | - Aydogan Ozcan
- Electrical and Computer Engineering Department, Bioengineering Department, California NanoSystems Institute, University of California, Los Angeles (UCLA), Los Angeles, CA USA
| | - 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, 34051 South Korea
| | - Alexey P. Porfirev
- IPSI RAS-Branch of the FSRC “Crystallography and Photonics” RAS, 443001 Samara, Russia
| | - Mariana Potcoava
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Shashi Prabhakar
- Quantum Science and Technology Laboratory, Physical Research Laboratory, Navrangpura, Ahmedabad, 380009 India
| | - Jixiong Pu
- Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science and Engineering, Huaqiao University, Xiamen, 361021 Fujian China
| | - Mani Ratnam Rai
- Department of Biomedical Engineering, North Carolina State University and University of North Carolina at Chapel Hill, Raleigh, NC 27695 USA
- Comparative Medicine Institute, North Carolina State University, Raleigh, NC 27695 USA
| | - Mikołaj Rogalski
- Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland
| | - Meguya Ryu
- Research Institute for Material and Chemical Measurement, National Metrology Institute of Japan (AIST), 1-1-1 Umezono, Tsukuba, 305-8563 Japan
| | - Sakshi Choudhary
- Department Chemical Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Shiva, Israel
| | - Gangi Reddy Salla
- Department of Physics, SRM University – AP, Amaravati, Andhra Pradesh 522502 India
| | - Peter Schelkens
- Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel VUB), Pleinlaan 2, 1050 Brussel, Belgium
- IMEC, Kapeldreef 75, 3001 Leuven, Belgium
| | - Sarp Feykun Şener
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM), Bilkent University, 06800 Ankara, Turkey
- Department of Physics, Bilkent University, 06800 Ankara, Turkey
| | - Igor Shevkunov
- Computational Imaging Group, Faculty of Information Technology and Communication Sciences, Tampere University, 33100 Tampere, Finland
| | - Tomoyoshi Shimobaba
- Graduate School of Engineering, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba, Chiba Japan
| | - Rakesh K. Singh
- Laboratory of Information Photonics and Optical Metrology, Department of Physics, Indian Institute of Technology (Banaras Hindu University), Varanasi, Uttar Pradesh 221005 India
| | - Ravindra P. Singh
- Quantum Science and Technology Laboratory, Physical Research Laboratory, Navrangpura, Ahmedabad, 380009 India
| | - Adrian Stern
- School of Electrical and Computer Engineering, Ben-Gurion University of the Negev, 8410501 Beer-Sheva, Israel
| | - Jiasong Sun
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Shun Zhou
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Chao Zuo
- Jiangsu Key Laboratory of Spectral Imaging and Intelligent Sense, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Laboratory (SCILab), School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, 210094 Jiangsu China
- Smart Computational Imaging Research Institute (SCIRI), Nanjing, 210019 Jiangsu China
| | - Zack Zurawski
- Department of Anatomy and Cell Biology, University of Illinois at Chicago, 808 South Wood Street, Chicago, IL 60612 USA
| | - Tatsuki Tahara
- Applied Electromagnetic Research Center, Radio Research Institute, National Institute of Information and Communications Technology (NICT), 4-2-1 Nukuikitamachi, Koganei, Tokyo 184-8795 Japan
| | - Vipin Tiwari
- Institute of Physics, University of Tartu, W. Ostwaldi 1, 50411 Tartu, Estonia
| | - Maciej Trusiak
- Institute of Micromechanics and Photonics, Warsaw University of Technology, 8 Sw. A. Boboli St., 02-525 Warsaw, Poland
| | - R. V. Vinu
- Fujian Provincial Key Laboratory of Light Propagation and Transformation, College of Information Science and Engineering, Huaqiao University, Xiamen, 361021 Fujian China
| | - Sergey G. Volotovskiy
- IPSI RAS-Branch of the FSRC “Crystallography and Photonics” RAS, 443001 Samara, Russia
| | - Hasan Yılmaz
- Institute of Materials Science and Nanotechnology, National Nanotechnology Research Center (UNAM), Bilkent University, 06800 Ankara, Turkey
| | - Hilton Barbosa De Aguiar
- Laboratoire Kastler Brossel, Centre National de la Recherche Scientifique (CNRS) UMR 8552, Sorbonne Universite ´, Ecole Normale Supe ´rieure-Paris Sciences et Lettres (PSL) Research University, Collège de France, 24 rue Lhomond, 75005 Paris, France
| | - Balpreet S. Ahluwalia
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
| | - Azeem Ahmad
- Department of Physics and Technology, UiT The Arctic University of Norway, 9037 Tromsø, Norway
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6
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Elmalam N, Ben Nedava L, Zaritsky A. In silico labeling in cell biology: Potential and limitations. Curr Opin Cell Biol 2024; 89:102378. [PMID: 38838549 DOI: 10.1016/j.ceb.2024.102378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 05/16/2024] [Accepted: 05/16/2024] [Indexed: 06/07/2024]
Abstract
In silico labeling is the computational cross-modality image translation where the output modality is a subcellular marker that is not specifically encoded in the input image, for example, in silico localization of organelles from transmitted light images. In principle, in silico labeling has the potential to facilitate rapid live imaging of multiple organelles with reduced photobleaching and phototoxicity, a technology enabling a major leap toward understanding the cell as an integrated complex system. However, five years have passed since feasibility was attained, without any demonstration of using in silico labeling to uncover new biological insight. In here, we discuss the current state of in silico labeling, the limitations preventing it from becoming a practical tool, and how we can overcome these limitations to reach its full potential.
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Affiliation(s)
- Nitsan Elmalam
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Lion Ben Nedava
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel
| | - Assaf Zaritsky
- Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
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7
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Zhou J, Tian F, Hu J, Shi ZL, Godinez VG, Tsai DP, Liu Z. Eagle-Eye Inspired Meta-Device for Phase Imaging. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2024; 36:e2402751. [PMID: 38816897 DOI: 10.1002/adma.202402751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 05/14/2024] [Indexed: 06/01/2024]
Abstract
The dual-focus vision observed in eagles' eyes is an intriguing phenomenon captivates scientists since a long time. Inspired by this natural occurrence, the authors' research introduces a novel bifocal meta-device incorporating a polarized camera capable of simultaneously capturing images for two different polarizations with slightly different focal distances. This innovative approach facilitates the concurrent acquisition of underfocused and overfocused images in a single snapshot, enabling the effective extraction of quantitative phase information from the object using the transport of intensity equation. Experimental demonstrations showcase the application of quantitative phase imaging to artificial objects and human embryonic kidney cells, particularly emphasizing the meta-device's relevance in dynamic scenarios such as laser-induced ablation in human embryonic kidney cells. Moreover, it provides a solution for the quantification during the dynamic process at the cellular level. Notably, the proposed eagle-eye inspired meta-device for phase imaging (EIMPI), due to its simplicity and compact nature, holds promise for significant applications in fields such as endoscopy and headsets, where a lightweight and compact setup is essential.
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Affiliation(s)
- Junxiao Zhou
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong, 999077, China
| | - Fanglin Tian
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Jie Hu
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Zhixia Linda Shi
- Institute of Engineering in Medicine, University of California, San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Veronica Gomez Godinez
- Institute of Engineering in Medicine, University of California, San Diego, La Jolla, San Diego, CA, 92093, USA
| | - Din Ping Tsai
- Department of Electrical Engineering, City University of Hong Kong, Kowloon, Hong Kong, 999077, China
| | - Zhaowei Liu
- Department of Electrical and Computer Engineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA
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8
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Zhang M, You Y, Zhang H, Zhang J, Yang F, Wang X, Lin C, Wang B, Chen L, Wang Z, Dai Z. Rapid Glutathione Analysis with SERS Microneedles for Deep Glioblastoma Tissue Differentiation. Anal Chem 2024; 96:10200-10209. [PMID: 38867357 DOI: 10.1021/acs.analchem.4c00483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024]
Abstract
Rapid tissue differentiation at the molecular level is a prerequisite for precise surgical resection, which is of special value for the treatment of malignant tumors, such as glioblastoma (GBM). Herein, a SERS-active microneedle is prepared by modifying glutathione (GSH)-responsive molecules, 5,5'-dithiobis(2-nitrobenzoic acid) (DTNB), on the surface of Au@Ag substrates for the distinction of different GBM tissues. Since the Raman signals on the surface of the DTNB@Au@Ag microneedle can be collected by both portable and benchtop Raman spectrometers, the distribution of GSH in different tissues at centimeter scale can be displayed through Raman spectroscopy and Raman imaging, and the entire analysis process can be accomplished within 12 min. Accordingly, in vivo brain tissues of orthotopic GBM xenograft mice and ex vivo tissues of GBM patients are accurately differentiated with the microneedle, and the results are well consistent with tissue staining and postoperative pathological reports. In addition, the outline of tumor, peritumoral, and normal tissues can be indicated by the DTNB@Au@Ag microneedle for at least 56 days. Considering that the tumor tissues are quickly discriminated at the molecular level without the restriction of depth, the DTNB@Au@Ag microneedle is promising to be a powerful intraoperative diagnostic tool for surgery navigation.
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Affiliation(s)
- Min Zhang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Yongping You
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Hang Zhang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Junxia Zhang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Furong Yang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Xiefeng Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Chao Lin
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Binbin Wang
- Department of Neurosurgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, P. R. China
| | - Li Chen
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Zhaoyin Wang
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
| | - Zhihui Dai
- Jiangsu Collaborative Innovation Center of Biomedical Functional Materials and Jiangsu Key Laboratory of Biofunctional Materials, School of Chemistry and Materials Science, Nanjing Normal University, Nanjing 210023, P. R. China
- School of Chemistry and Molecular Engineering, Nanjing Tech University, Nanjing 211816, P. R. China
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9
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Ivanov IE, Hirata-Miyasaki E, Chandler T, Cheloor-Kovilakam R, Liu Z, Pradeep S, Liu C, Bhave M, Khadka S, Arias C, Leonetti MD, Huang B, Mehta SB. Mantis: high-throughput 4D imaging and analysis of the molecular and physical architecture of cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.19.572435. [PMID: 38187521 PMCID: PMC10769231 DOI: 10.1101/2023.12.19.572435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
High-throughput dynamic imaging of cells and organelles is essential for understanding complex cellular responses. We report Mantis, a high-throughput 4D microscope that integrates two complementary, gentle, live-cell imaging technologies: remote-refocus label-free microscopy and oblique light-sheet fluorescence microscopy. Additionally, we report shrimPy, an open-source software for high-throughput imaging, deconvolution, and single-cell phenotyping of 4D data. Using Mantis and shrimPy, we achieved high-content correlative imaging of molecular dynamics and the physical architecture of 20 cell lines every 15 minutes over 7.5 hours. This platform also facilitated detailed measurements of the impacts of viral infection on the architecture of host cells and host proteins. The Mantis platform can enable high-throughput profiling of intracellular dynamics, long-term imaging and analysis of cellular responses to perturbations, and live-cell optical screens to dissect gene regulatory networks.
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Affiliation(s)
- Ivan E. Ivanov
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | | | - Talon Chandler
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Rasmi Cheloor-Kovilakam
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, United States
| | - Ziwen Liu
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Soorya Pradeep
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Chad Liu
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Madhura Bhave
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Sudip Khadka
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | - Carolina Arias
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
| | | | - Bo Huang
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
- Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, United States
| | - Shalin B. Mehta
- Chan Zuckerberg Biohub San Francisco, San Francisco, United States
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10
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Jo H, Lee EY, Cho HS, Rayhan MA, Cho A, Chae CS, You HJ. THP-1 Monocytic Cells Are Polarized to More Antitumorigenic Macrophages by Serial Treatment with Phorbol-12-Myristate-13-Acetate and PD98059. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1009. [PMID: 38929626 PMCID: PMC11205341 DOI: 10.3390/medicina60061009] [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] [Received: 04/24/2024] [Revised: 05/27/2024] [Accepted: 06/17/2024] [Indexed: 06/28/2024]
Abstract
Background and Objectives: As modulators of the tumor microenvironment, macrophages have been extensively studied for their potential in developing anticancer strategies, particularly in regulating macrophage polarization towards an antitumorigenic (M1) phenotype rather than a protumorigenic (M2) one in various experimental models. Here, we evaluated the effect of PD98059, a mitogen-activated protein kinase kinase MAPKK MEK1-linked pathway inhibitor, on the differentiation and polarization of THP-1 monocytes in response to phorbol-12-myristate-13-acetate (PMA) under various culture conditions for tumor microenvironmental application. Materials and Methods: Differentiation and polarization of THP-1 were analyzed by flow cytometry and RT-PCR. Polarized THP-1 subsets with different treatment were compared by motility, phagocytosis, and so on. Results: Clearly, PMA induced THP-1 differentiation occurs in adherent culture conditions more than nonadherent culture conditions by increasing CD11b expression up to 90%, which was not affected by PD98059 when cells were exposed to PMA first (post-PD) but inhibited when PD98059 was treated prior to PMA treatment (pre-PD). CD11bhigh THP-1 cells treated with PMA and PMA-post-PD were categorized into M0 (HLA-DRlow and CD206low), M1 (HLA-DRhigh and CD206low), and M2 (HLA-DRlow and CD206high), resulting in an increased population of M1 macrophages. The transcription levels of markers of macrophage differentiation and polarization confirmed the increased M1 polarization of THP-1 cells with post-PD treatment rather than with PMA-only treatment. The motility and cytotoxicity of THP-1 cells with post-PD treatment were higher than THP-1 cells with PMA, suggesting that post-PD treatment enhanced the anti-tumorigenicity of THP-1 cells. Confocal microscopy and flow cytometry showed the effect of post-PD treatment on phagocytosis by THP-1 cells. Conclusions: We have developed an experimental model of macrophage polarization with THP-1 cells which will be useful for further studies related to the tumor microenvironment.
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Affiliation(s)
- Hantae Jo
- Cancer Microenvironment Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Republic of Korea; (H.J.); (E.-Y.L.); (H.S.C.); (A.C.); (C.-S.C.)
| | - Eun-Young Lee
- Cancer Microenvironment Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Republic of Korea; (H.J.); (E.-Y.L.); (H.S.C.); (A.C.); (C.-S.C.)
| | - Hyun Sang Cho
- Cancer Microenvironment Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Republic of Korea; (H.J.); (E.-Y.L.); (H.S.C.); (A.C.); (C.-S.C.)
| | - Md Abu Rayhan
- Department of Cancer Biomedical Science, National Cancer Center-Graduate School of Cancer Science and Policy, National Cancer Center, Goyang 10408, Republic of Korea;
| | - Ahyoung Cho
- Cancer Microenvironment Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Republic of Korea; (H.J.); (E.-Y.L.); (H.S.C.); (A.C.); (C.-S.C.)
| | - Chang-Suk Chae
- Cancer Microenvironment Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Republic of Korea; (H.J.); (E.-Y.L.); (H.S.C.); (A.C.); (C.-S.C.)
- Department of Cancer Biomedical Science, National Cancer Center-Graduate School of Cancer Science and Policy, National Cancer Center, Goyang 10408, Republic of Korea;
| | - Hye Jin You
- Cancer Microenvironment Branch, Division of Cancer Biology, Research Institute, National Cancer Center, Goyang 10408, Republic of Korea; (H.J.); (E.-Y.L.); (H.S.C.); (A.C.); (C.-S.C.)
- Department of Cancer Biomedical Science, National Cancer Center-Graduate School of Cancer Science and Policy, National Cancer Center, Goyang 10408, Republic of Korea;
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11
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Saunders N, Monel B, Cayet N, Archetti L, Moreno H, Jeanne A, Marguier A, Buchrieser J, Wai T, Schwartz O, Fréchin M. Dynamic label-free analysis of SARS-CoV-2 infection reveals virus-induced subcellular remodeling. Nat Commun 2024; 15:4996. [PMID: 38862527 PMCID: PMC11166935 DOI: 10.1038/s41467-024-49260-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2023] [Accepted: 05/30/2024] [Indexed: 06/13/2024] Open
Abstract
Assessing the impact of SARS-CoV-2 on organelle dynamics allows a better understanding of the mechanisms of viral replication. We combine label-free holotomographic microscopy with Artificial Intelligence to visualize and quantify the subcellular changes triggered by SARS-CoV-2 infection. We study the dynamics of shape, position and dry mass of nucleoli, nuclei, lipid droplets and mitochondria within hundreds of single cells from early infection to syncytia formation and death. SARS-CoV-2 infection enlarges nucleoli, perturbs lipid droplets, changes mitochondrial shape and dry mass, and separates lipid droplets from mitochondria. We then used Bayesian network modeling on organelle dry mass states to define organelle cross-regulation networks and report modifications of organelle cross-regulation that are triggered by infection and syncytia formation. Our work highlights the subcellular remodeling induced by SARS-CoV-2 infection and provides an Artificial Intelligence-enhanced, label-free methodology to study in real-time the dynamics of cell populations and their content.
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Affiliation(s)
- Nell Saunders
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France
| | - Blandine Monel
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France
| | - Nadège Cayet
- Institut Pasteur, Université Paris Cité, Ultrastructural Bioimaging Unit, 75015, Paris, France
| | - Lorenzo Archetti
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Hugo Moreno
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Alexandre Jeanne
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Agathe Marguier
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland
| | - Julian Buchrieser
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France
| | - Timothy Wai
- Mitochondrial Biology Group, Institut Pasteur, Université Paris Cité, CNRS, UMR 3691, Paris, France
| | - Olivier Schwartz
- Virus & Immunity Unit, Institut Pasteur, Université Paris Cité, CNRS, UMR 3569, Paris, France.
- Vaccine Research Institute, Creteil, France.
| | - Mathieu Fréchin
- Deep Quantitative Biology Department, Nanolive SA, Tolochenaz, Switzerland.
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12
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Zhang J, Sarollahi M, Luckhart S, Harrison MJ, Vasdekis AE. Quantitative phase imaging by gradient retardance optical microscopy. Sci Rep 2024; 14:9754. [PMID: 38679622 PMCID: PMC11056386 DOI: 10.1038/s41598-024-60057-y] [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: 12/29/2023] [Accepted: 04/18/2024] [Indexed: 05/01/2024] Open
Abstract
Quantitative phase imaging (QPI) has become a vital tool in bioimaging, offering precise measurements of wavefront distortion and, thus, of key cellular metabolism metrics, such as dry mass and density. However, only a few QPI applications have been demonstrated in optically thick specimens, where scattering increases background and reduces contrast. Building upon the concept of structured illumination interferometry, we introduce Gradient Retardance Optical Microscopy (GROM) for QPI of both thin and thick samples. GROM transforms any standard Differential Interference Contrast (DIC) microscope into a QPI platform by incorporating a liquid crystal retarder into the illumination path, enabling independent phase-shifting of the DIC microscope's sheared beams. GROM greatly simplifies related configurations, reduces costs, and eradicates energy losses in parallel imaging modalities, such as fluorescence. We successfully tested GROM on a diverse range of specimens, from microbes and red blood cells to optically thick (~ 300 μm) plant roots without fixation or clearing.
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Affiliation(s)
- Jinming Zhang
- Department of Physics, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
| | - Mirsaeid Sarollahi
- Department of Physics, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
| | - Shirley Luckhart
- Department of Entomology, Plant Pathology and Nematology, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
- Department of Biological Sciences, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA
| | | | - Andreas E Vasdekis
- Department of Physics, University of Idaho, 875 Perimeter Drive, Moscow, ID, 83844, USA.
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13
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Winiarska E, Chaszczewska-Markowska M, Ghete D, Jutel M, Zemelka-Wiacek M. Nanoplastics Penetrate Human Bronchial Smooth Muscle and Small Airway Epithelial Cells and Affect Mitochondrial Metabolism. Int J Mol Sci 2024; 25:4724. [PMID: 38731941 PMCID: PMC11083782 DOI: 10.3390/ijms25094724] [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: 03/26/2024] [Revised: 04/22/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024] Open
Abstract
Micro- and nanoplastic particles, including common forms like polyethylene and polystyrene, have been identified as relevant pollutants, potentially causing health problems in living organisms. The mechanisms at the cellular level largely remain to be elucidated. This study aims to visualize nanoplastics in bronchial smooth muscle (BSMC) and small airway epithelial cells (SAEC), and to assess the impact on mitochondrial metabolism. Healthy and asthmatic human BSMC and SAEC in vitro cultures were stimulated with polystyrene nanoplastics (PS-NPs) of 25 or 50 nm size, for 1 or 24 h. Live cell, label-free imaging by holotomography microscopy and mitochondrial respiration and glycolysis assessment were performed. Furthermore, 25 and 50 nm NPs were shown to penetrate SAEC, along with healthy and diseased BSMC, and they impaired bioenergetics and induce mitochondrial dysfunction compared to cells not treated with NPs, including changes in oxygen consumption rate and extracellular acidification rate. NPs pose a serious threat to human health by penetrating airway tissues and cells, and affecting both oxidative and glycolytic metabolism.
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Affiliation(s)
- Ewa Winiarska
- Department of Clinical Immunology, Wroclaw Medical University, 51-616 Wroclaw, Poland; (E.W.)
| | - Monika Chaszczewska-Markowska
- Laboratory of Clinical Immunogenetics and Pharmacogenetics, Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, Poland
| | - Daniel Ghete
- Bioscience Technology Facility, Department of Biology, University of York, York YO10 5DD, UK
| | - Marek Jutel
- Department of Clinical Immunology, Wroclaw Medical University, 51-616 Wroclaw, Poland; (E.W.)
- ALL-MED Medical Research Institute, 53-201 Wroclaw, Poland
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14
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Wang L, Li M, Hwang TH. The 3D Revolution in Cancer Discovery. Cancer Discov 2024; 14:625-629. [PMID: 38571426 DOI: 10.1158/2159-8290.cd-23-1499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
SUMMARY The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.
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Affiliation(s)
- Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tae Hyun Hwang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida
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15
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Kim H, Oh S, Lee S, Lee KS, Park Y. Recent advances in label-free imaging and quantification techniques for the study of lipid droplets in cells. Curr Opin Cell Biol 2024; 87:102342. [PMID: 38428224 DOI: 10.1016/j.ceb.2024.102342] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 01/20/2024] [Accepted: 02/01/2024] [Indexed: 03/03/2024]
Abstract
Lipid droplets (LDs), once considered mere storage depots for lipids, have gained recognition for their intricate roles in cellular processes, including metabolism, membrane trafficking, and disease states like obesity and cancer. This review explores label-free imaging techniques' applications in LD research. We discuss holotomography and vibrational spectroscopic microscopy, emphasizing their potential for studying LDs without molecular labels, and we highlight the growing integration of artificial intelligence. Clinical applications in disease diagnosis and therapy are also considered.
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Affiliation(s)
- Hyeonwoo Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
| | - Seungeun Oh
- Department of Physics, Department of Cellular Molecular Medicine, University of California, San Diego, CA 2093, USA
| | - Seongsoo Lee
- Gwangju Center, Korea Basic Science Institute (KBSI), Gwangju 61751, Republic of Korea; Department of Systems Biotechnology, Chung-Ang University Anseong-si, Gyeonggi-do 17546, Republic of Korea
| | - Kwang Suk Lee
- Department of Urology, Yonsei University College of Medicine, Gangnam Severance Hospital, Seoul 06229, Republic of Korea
| | - YongKeun Park
- Department of Physics, KAIST, Daejeon 34141, Republic of Korea; KAIST Institute for Health Science and Technology, KAIST, Daejeon 34141, Republic of Korea; Tomocube Inc., Daejeon 34109, Republic of Korea.
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Wang K, Song L, Wang C, Ren Z, Zhao G, Dou J, Di J, Barbastathis G, Zhou R, Zhao J, Lam EY. On the use of deep learning for phase recovery. LIGHT, SCIENCE & APPLICATIONS 2024; 13:4. [PMID: 38161203 PMCID: PMC10758000 DOI: 10.1038/s41377-023-01340-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/13/2023] [Accepted: 11/16/2023] [Indexed: 01/03/2024]
Abstract
Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and provide an outlook on how to better use DL to improve the reliability and efficiency of PR. Furthermore, we present a live-updating resource ( https://github.com/kqwang/phase-recovery ) for readers to learn more about PR.
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Affiliation(s)
- Kaiqiang Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Li Song
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Chutian Wang
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China
| | - Zhenbo Ren
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China
| | - Guangyuan Zhao
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jiazhen Dou
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - Jianglei Di
- School of Information Engineering, Guangdong University of Technology, Guangzhou, China
| | - George Barbastathis
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Renjie Zhou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jianlin Zhao
- School of Physical Science and Technology, Northwestern Polytechnical University, Xi'an, China.
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong SAR, China.
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