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Wang R, Feng Y, Valm AM. A Framework of Multi-View Machine Learning for Biological Spectral Unmixing of Fluorophores with Overlapping Excitation and Emission Spectra. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.07.607102. [PMID: 39149334 PMCID: PMC11326303 DOI: 10.1101/2024.08.07.607102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
The accuracy in assigning fluorophore identity and abundance, termed spectral unmixing, in biological fluorescence microscopy images remains challenging due to the unavoidable and significant overlap in emission spectra among fluorophores. In conventional laser scanning confocal spectral microscopy, fluorophore information is acquired by recording emission spectra with a single combination of discrete excitation wavelengths. As a matter of fact, organic fluorophores have not only unique emission spectral signatures but also have unique and characteristic excitation spectra. In this paper, we propose a generalized multi-view machine learning approach, which makes use of both excitation and emission spectra to greatly improve the accuracy in differentiating multiple highly overlapping fluorophores in a single image. By recording emission spectra of the same field with multiple combinations of excitation wavelengths, we obtain data representing these different views of the underlying fluorophore distribution in the sample. We then propose a framework of multi-view machine learning methods, which allows us to flexibly incorporate noise information and abundance constraints, to extract the spectral signatures of fluorophores from their reference images and to efficiently recover their corresponding abundances in unknown mixed images. Numerical experiments on simulated image data demonstrate the method's efficacy in improving accuracy, allowing for the discrimination of 100 fluorophores with highly overlapping spectra. Furthermore, validation on images of mixtures of fluorescently labeled E. coli demonstrates the power of the proposed multi-view strategy in discriminating fluorophores with spectral overlap in real biological images.
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Affiliation(s)
- Ruogu Wang
- Department of Biology, University at Albany, SUNY, 1400 Washington Ave, 12222, NY, USA
- RNA Institute, University at Albany, SUNY, 1400 Washington Ave, 12222, NY, USA
| | - Yunlong Feng
- Department of Mathematics and Statistics, University at Albany, SUNY, 1400 Washington Ave, 12222, NY, USA
| | - Alex M. Valm
- Department of Biology, University at Albany, SUNY, 1400 Washington Ave, 12222, NY, USA
- RNA Institute, University at Albany, SUNY, 1400 Washington Ave, 12222, NY, USA
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2
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Huang X, Gao X, Fu L. BINGO: a blind unmixing algorithm for ultra-multiplexing fluorescence images. Bioinformatics 2024; 40:btae052. [PMID: 38291952 PMCID: PMC10873573 DOI: 10.1093/bioinformatics/btae052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2024] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 02/01/2024] Open
Abstract
MOTIVATION Spectral imaging is often used to observe different objects with multiple fluorescent labels to reveal the development of the biological event. As the number of observed objects increases, the spectral overlap between fluorophores becomes more serious, and obtaining a "pure" picture of each fluorophore becomes a major challenge. Here, we propose a blind spectral unmixing algorithm called BINGO (Blind unmixing via SVD-based Initialization Nmf with project Gradient descent and spare cOnstrain), which can extract all kinds of fluorophores more accurately from highly overlapping multichannel data, even if the spectra of the fluorophores are extremely similar or their fluorescence intensity varies greatly. RESULTS BINGO can isolate up to 10 fluorophores from spectral imaging data for a single excitation. nine-color living HeLa cells were visualized distinctly with BINGO. It provides an important algorithmic tool for multiplex imaging studies, especially in intravital imaging. BINGO shows great potential in multicolor imaging for biomedical sciences. AVAILABILITY AND IMPLEMENTATION The source code used for this paper is available with the test data at https://github.com/Xinyuan555/BINGO_unmixing.
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Affiliation(s)
- Xinyuan Huang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiujuan Gao
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Biomedical Engineering, Hainan University, Haikou 570228, China
- School of Physics and Optoelectronics Engineering, Hainan University, Haikou 570228, China
- Optics Valley Laboratory, Wuhan 430074, China
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3
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Parker M, Annamdevula NS, Pleshinger D, Ijaz Z, Jalkh J, Penn R, Deshpande D, Rich TC, Leavesley SJ. Comparing Performance of Spectral Image Analysis Approaches for Detection of Cellular Signals in Time-Lapse Hyperspectral Imaging Fluorescence Excitation-Scanning Microscopy. Bioengineering (Basel) 2023; 10:642. [PMID: 37370573 PMCID: PMC10295298 DOI: 10.3390/bioengineering10060642] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/13/2023] [Accepted: 05/16/2023] [Indexed: 06/29/2023] Open
Abstract
Hyperspectral imaging (HSI) technology has been applied in a range of fields for target detection and mixture analysis. While HSI was originally developed for remote sensing applications, modern uses include agriculture, historical document authentication, and medicine. HSI has also shown great utility in fluorescence microscopy. However, traditional fluorescence microscopy HSI systems have suffered from limited signal strength due to the need to filter or disperse the emitted light across many spectral bands. We have previously demonstrated that sampling the fluorescence excitation spectrum may provide an alternative approach with improved signal strength. Here, we report on the use of excitation-scanning HSI for dynamic cell signaling studies-in this case, the study of the second messenger Ca2+. Time-lapse excitation-scanning HSI data of Ca2+ signals in human airway smooth muscle cells (HASMCs) were acquired and analyzed using four spectral analysis algorithms: linear unmixing (LU), spectral angle mapper (SAM), constrained energy minimization (CEM), and matched filter (MF), and the performances were compared. Results indicate that LU and MF provided similar linear responses to increasing Ca2+ and could both be effectively used for excitation-scanning HSI. A theoretical sensitivity framework was used to enable the filtering of analyzed images to reject pixels with signals below a minimum detectable limit. The results indicated that subtle kinetic features might be revealed through pixel filtering. Overall, the results suggest that excitation-scanning HSI can be employed for kinetic measurements of cell signals or other dynamic cellular events and that the selection of an appropriate analysis algorithm and pixel filtering may aid in the extraction of quantitative signal traces. These approaches may be especially helpful for cases where the signal of interest is masked by strong cellular autofluorescence or other competing signals.
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Affiliation(s)
- Marina Parker
- Department of Chemical and Biomolecular Engineering, University of South Alabama, 150 Student Services Dr., Mobile, AL 36688, USA
- Department of Systems Engineering, University of South Alabama, 150 Student Services Dr., Mobile, AL 36688, USA
| | - Naga S. Annamdevula
- Department of Pharmacology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA; (N.S.A.)
- Center for Lung Biology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA
| | - Donald Pleshinger
- Department of Pharmacology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA; (N.S.A.)
- Center for Lung Biology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA
| | - Zara Ijaz
- College of Medicine, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA
| | - Josephine Jalkh
- College of Medicine, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA
| | - Raymond Penn
- College of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Deepak Deshpande
- College of Medicine, Thomas Jefferson University, Philadelphia, PA 19107, USA
| | - Thomas C. Rich
- Department of Pharmacology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA; (N.S.A.)
- Center for Lung Biology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA
| | - Silas J. Leavesley
- Department of Chemical and Biomolecular Engineering, University of South Alabama, 150 Student Services Dr., Mobile, AL 36688, USA
- Department of Systems Engineering, University of South Alabama, 150 Student Services Dr., Mobile, AL 36688, USA
- Department of Pharmacology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA; (N.S.A.)
- Center for Lung Biology, University of South Alabama, 5851 USA Drive N., Mobile, AL 36688, USA
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Wang R, Lemus AA, Henneberry CM, Ying Y, Feng Y, Valm AM. Unmixing Biological Fluorescence Image Data with Sparse and Low-Rank Poisson Regression. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.06.523044. [PMID: 36711559 PMCID: PMC9882077 DOI: 10.1101/2023.01.06.523044] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Multispectral biological fluorescence microscopy has enabled the identification of multiple targets in complex samples. The accuracy in the unmixing result degrades (1) as the number of fluorophores used in any experiment increases and (2) as the signal-to-noise ratio in the recorded images decreases. Further, the availability of prior knowledge regarding the expected spatial distributions of fluorophores in images of labeled cells provides an opportunity to improve the accuracy of fluorophore identification and abundance. We propose a regularized sparse and low-rank Poisson unmixing approach (SL-PRU) to deconvolve spectral images labeled with highly overlapping fluorophores which are recorded in low signal-to-noise regimes. Firstly, SL-PRU implements multi-penalty terms when pursuing sparseness and spatial correlation of the resulting abundances in small neighborhoods simultaneously. Secondly, SL-PRU makes use of Poisson regression for unmixing instead of least squares regression to better estimate photon abundance. Thirdly, we propose a method to tune the SL-PRU parameters involved in the unmixing procedure in the absence of knowledge of the ground truth abundance information in a recorded image. By validating on simulated and real-world images, we show that our proposed method leads to improved accuracy in unmixing fluorophores with highly overlapping spectra.
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Affiliation(s)
- Ruogu Wang
- Department of Mathematics and Statistics, University at Albany, SUNY, Albany, NY 12222, USA
| | - Alex A. Lemus
- Department of Biology, University at Albany, SUNY, Albany, NY 12222, USA,RNA Institute, University at Albany, SUNY, Albany, NY 12222, USA
| | - Colin M. Henneberry
- Department of Biology, University at Albany, SUNY, Albany, NY 12222, USA,RNA Institute, University at Albany, SUNY, Albany, NY 12222, USA
| | - Yiming Ying
- Department of Mathematics and Statistics, University at Albany, SUNY, Albany, NY 12222, USA
| | - Yunlong Feng
- Department of Mathematics and Statistics, University at Albany, SUNY, Albany, NY 12222, USA,To whom correspondence should be addressed
| | - Alex M. Valm
- Department of Biology, University at Albany, SUNY, Albany, NY 12222, USA,RNA Institute, University at Albany, SUNY, Albany, NY 12222, USA,To whom correspondence should be addressed
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Vorobjev IA, Kussanova A, Barteneva NS. Development of Spectral Imaging Cytometry. Methods Mol Biol 2023; 2635:3-22. [PMID: 37074654 DOI: 10.1007/978-1-0716-3020-4_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2023]
Abstract
Spectral flow cytometry is a new technology that enables measurements of fluorescent spectra and light scattering properties in diverse cellular populations with high precision. Modern instruments allow simultaneous determination of up to 40+ fluorescent dyes with heavily overlapping emission spectra, discrimination of autofluorescent signals in the stained specimens, and detailed analysis of diverse autofluorescence of different cells-from mammalian to chlorophyll-containing cells like cyanobacteria. In this paper, we review the history, compare modern conventional and spectral flow cytometers, and discuss several applications of spectral flow cytometry.
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Affiliation(s)
- Ivan A Vorobjev
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan.
- National Laboratory Astana, Nazarbayev University, Astana, Kazakhstan.
- A.N. Belozersky Insitute of Physico-Chemical Biology, Lomonosov Moscow State University, Moscow, Russian Federation.
- Biological Faculty, Lomonosov Moscow State University, Moscow, Russian Federation.
| | - Aigul Kussanova
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- Core Facilities, Nazarbayev University, Astana, Kazakhstan
| | - Natasha S Barteneva
- School of Sciences and Humanities, Nazarbayev University, Astana, Kazakhstan
- Brigham Women's Hospital, Harvard University, Boston, MA, USA
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6
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Eigenfeld M, Kerpes R, Whitehead I, Becker T. Autofluorescence prediction model for fluorescence unmixing and age determination. Biotechnol J 2022; 17:e2200091. [DOI: 10.1002/biot.202200091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 10/11/2022] [Accepted: 10/12/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Marco Eigenfeld
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
| | - Roland Kerpes
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
| | - Iain Whitehead
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
| | - Thomas Becker
- Technical University of Munich, School of Life Science Institute of Brewing and Beverage Technology Freising Germany
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Acuña-Rodriguez JP, Mena-Vega JP, Argüello-Miranda O. Live-cell fluorescence spectral imaging as a data science challenge. Biophys Rev 2022; 14:579-597. [PMID: 35528031 PMCID: PMC9043069 DOI: 10.1007/s12551-022-00941-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 03/09/2022] [Indexed: 12/13/2022] Open
Abstract
Live-cell fluorescence spectral imaging is an evolving modality of microscopy that uses specific properties of fluorophores, such as excitation or emission spectra, to detect multiple molecules and structures in intact cells. The main challenge of analyzing live-cell fluorescence spectral imaging data is the precise quantification of fluorescent molecules despite the weak signals and high noise found when imaging living cells under non-phototoxic conditions. Beyond the optimization of fluorophores and microscopy setups, quantifying multiple fluorophores requires algorithms that separate or unmix the contributions of the numerous fluorescent signals recorded at the single pixel level. This review aims to provide both the experimental scientist and the data analyst with a straightforward description of the evolution of spectral unmixing algorithms for fluorescence live-cell imaging. We show how the initial systems of linear equations used to determine the concentration of fluorophores in a pixel progressively evolved into matrix factorization, clustering, and deep learning approaches. We outline potential future trends on combining fluorescence spectral imaging with label-free detection methods, fluorescence lifetime imaging, and deep learning image analysis.
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Affiliation(s)
- Jessy Pamela Acuña-Rodriguez
- Center for Geophysical Research (CIGEFI), University of Costa Rica, San Pedro, San José Costa Rica
- School of Physics, University of Costa Rica, 2060 San Pedro, San José Costa Rica
| | - Jean Paul Mena-Vega
- School of Physics, University of Costa Rica, 2060 San Pedro, San José Costa Rica
| | - Orlando Argüello-Miranda
- Department of Plant and Microbial Biology, North Carolina State University, 112 DERIEUX PLACE, Raleigh, NC 27695-7612 USA
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8
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Reiche MA, Aaron JS, Boehm U, DeSantis MC, Hobson CM, Khuon S, Lee RM, Chew TL. When light meets biology - how the specimen affects quantitative microscopy. J Cell Sci 2022; 135:274812. [PMID: 35319069 DOI: 10.1242/jcs.259656] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Fluorescence microscopy images should not be treated as perfect representations of biology. Many factors within the biospecimen itself can drastically affect quantitative microscopy data. Whereas some sample-specific considerations, such as photobleaching and autofluorescence, are more commonly discussed, a holistic discussion of sample-related issues (which includes less-routine topics such as quenching, scattering and biological anisotropy) is required to appropriately guide life scientists through the subtleties inherent to bioimaging. Here, we consider how the interplay between light and a sample can cause common experimental pitfalls and unanticipated errors when drawing biological conclusions. Although some of these discrepancies can be minimized or controlled for, others require more pragmatic considerations when interpreting image data. Ultimately, the power lies in the hands of the experimenter. The goal of this Review is therefore to survey how biological samples can skew quantification and interpretation of microscopy data. Furthermore, we offer a perspective on how to manage many of these potential pitfalls.
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Affiliation(s)
- Michael A Reiche
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Jesse S Aaron
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Ulrike Boehm
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Michael C DeSantis
- Light Microscopy Facility, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147,USA
| | - Chad M Hobson
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Satya Khuon
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA.,Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Rachel M Lee
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA
| | - Teng-Leong Chew
- Advanced Imaging Center, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147, USA.,Light Microscopy Facility, Howard Hughes Medical Institute Janelia Research Campus, Ashburn, VA 20147,USA
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9
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Hobson CM, Aaron JS, Heddleston JM, Chew TL. Visualizing the Invisible: Advanced Optical Microscopy as a Tool to Measure Biomechanical Forces. Front Cell Dev Biol 2021; 9:706126. [PMID: 34552926 PMCID: PMC8450411 DOI: 10.3389/fcell.2021.706126] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 08/09/2021] [Indexed: 01/28/2023] Open
Abstract
The importance of mechanical force in biology is evident across diverse length scales, ranging from tissue morphogenesis during embryo development to mechanotransduction across single adhesion proteins at the cell surface. Consequently, many force measurement techniques rely on optical microscopy to measure forces being applied by cells on their environment, to visualize specimen deformations due to external forces, or even to directly apply a physical perturbation to the sample via photoablation or optogenetic tools. Recent developments in advanced microscopy offer improved approaches to enhance spatiotemporal resolution, imaging depth, and sample viability. These advances can be coupled with already existing force measurement methods to improve sensitivity, duration and speed, amongst other parameters. However, gaining access to advanced microscopy instrumentation and the expertise necessary to extract meaningful insights from these techniques is an unavoidable hurdle. In this Live Cell Imaging special issue Review, we survey common microscopy-based force measurement techniques and examine how they can be bolstered by emerging microscopy methods. We further explore challenges related to the accompanying data analysis in biomechanical studies and discuss the various resources available to tackle the global issue of technology dissemination, an important avenue for biologists to gain access to pre-commercial instruments that can be leveraged for biomechanical studies.
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Affiliation(s)
- Chad M. Hobson
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Jesse S. Aaron
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - John M. Heddleston
- Cleveland Clinic Florida Research and Innovation Center, Port St. Lucie, FL, United States
| | - Teng-Leong Chew
- Advanced Imaging Center, Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
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10
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Lee TB, Lee J, Jun JH. Three-Dimensional Approaches in Histopathological Tissue Clearing System. KOREAN JOURNAL OF CLINICAL LABORATORY SCIENCE 2020. [DOI: 10.15324/kjcls.2020.52.1.1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Affiliation(s)
- Tae Bok Lee
- Confocal Core Facility, Center for Medical Innovation, Seoul National University Hospital, Seoul, Korea
| | - Jaewang Lee
- Department of Biomedical Laboratory Science, College of Health Science, Eulji University, Seongnam, Korea
| | - Jin Hyun Jun
- Department of Biomedical Laboratory Science, College of Health Science, Eulji University, Seongnam, Korea
- Department of Senior Healthcare, BK21 Plus Program, Graduate School of Eulji University, Seongnam, Korea
- Eulji Medi-Bio Research Institute (EMBRI), Eulji University, Daejeon, Korea
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