1
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Liu S, Zou W, Sha H, Feng X, Chen B, Zhang J, Han S, Li X, Zhang Y. Deep learning-enhanced snapshot hyperspectral confocal microscopy imaging system. OPTICS EXPRESS 2024; 32:13918-13931. [PMID: 38859350 DOI: 10.1364/oe.519045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Accepted: 03/17/2024] [Indexed: 06/12/2024]
Abstract
Laser-scanning confocal hyperspectral microscopy is a powerful technique to identify the different sample constituents and their spatial distribution in three-dimensional (3D). However, it suffers from low imaging speed because of the mechanical scanning methods. To overcome this challenge, we propose a snapshot hyperspectral confocal microscopy imaging system (SHCMS). It combined coded illumination microscopy based on a digital micromirror device (DMD) with a snapshot hyperspectral confocal neural network (SHCNet) to realize single-shot confocal hyperspectral imaging. With SHCMS, high-contrast 160-bands confocal hyperspectral images of potato tuber autofluorescence can be collected by only single-shot, which is almost 5 times improvement in the number of spectral channels than previously reported methods. Moreover, our approach can efficiently record hyperspectral volumetric imaging due to the optical sectioning capability. This fast high-resolution hyperspectral imaging method may pave the way for real-time highly multiplexed biological imaging.
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2
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Yang D, Wang W, Yuan Z, Liang Y. Information-Rich Multi-Functional OCT for Adult Zebrafish Intra- and Extracranial Imaging. Bioengineering (Basel) 2023; 10:856. [PMID: 37508883 PMCID: PMC10375992 DOI: 10.3390/bioengineering10070856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2023] [Revised: 07/03/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023] Open
Abstract
The zebrafish serves as a valuable animal model for both intra- and extracranial research, particularly in relation to the brain and skull. To effectively investigate the development and regeneration of adult zebrafish, a versatile in vivo imaging technique capable of showing both intra- and extracranial conditions is essential. In this paper, we utilized a high-resolution multi-functional optical coherence tomography (OCT) to obtain rich intra- and extracranial imaging outcomes of adult zebrafish, encompassing pigmentation distribution, tissue-specific information, cranial vascular imaging, and the monitoring of traumatic brain injury (TBI). Notably, it is the first that the channels through the zebrafish cranial suture, which may have a crucial function in maintaining the patency of the cranial sutures, have been observed. Rich imaging results demonstrated that a high-resolution multi-functional OCT system can provide a wealth of novel and interpretable biological information for intra- and extracranial studies of adult zebrafish.
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Affiliation(s)
- Di Yang
- Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, Tianjin 300350, China
| | - Weike Wang
- Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, Tianjin 300350, China
| | - Zhuoqun Yuan
- Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, Tianjin 300350, China
| | - Yanmei Liang
- Tianjin Key Laboratory of Micro-Scale Optical Information Science and Technology, Institute of Modern Optics, Nankai University, Tianjin 300350, China
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3
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Wang P, Kitano M, Keomanee-Dizon K, Truong TV, Fraser SE, Cutrale F. A single-shot hyperspectral phasor camera for fast, multi-color fluorescence microscopy. CELL REPORTS METHODS 2023; 3:100441. [PMID: 37159674 PMCID: PMC10162951 DOI: 10.1016/j.crmeth.2023.100441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 11/16/2022] [Accepted: 03/09/2023] [Indexed: 05/11/2023]
Abstract
Hyperspectral fluorescence imaging improves multiplexed observations of biological samples by utilizing multiple color channels across the spectral range to compensate for spectral overlap between labels. Typically, spectral resolution comes at a cost of decreased detection efficiency, which both hampers imaging speed and increases photo-toxicity to the samples. Here, we present a high-speed, high-efficiency snapshot spectral acquisition method, based on optical compression of the fluorescence spectra via Fourier transform, that overcomes the challenges of discrete spectral sampling: single-shot hyperspectral phasor camera (SHy-Cam). SHy-Cam captures fluorescence spatial and spectral information in a single exposure with a standard scientific CMOS camera, with photon efficiency of over 80%, easily and with acquisition rates exceeding 30 datasets per second, making it a powerful tool for multi-color in vivo imaging. Its simple design, using readily available optical components, and its easy integration provide a low-cost solution for multi-color fluorescence imaging with increased efficiency and speed.
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Affiliation(s)
- Pu Wang
- Translational Imaging Center, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Biomedical Engineering, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
| | - Masahiro Kitano
- Translational Imaging Center, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Molecular and Computational Biology, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
| | - Kevin Keomanee-Dizon
- Translational Imaging Center, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Joseph Henry Laboratories of Physics, Princeton University, Princeton, NJ 08544, USA
| | - Thai V. Truong
- Translational Imaging Center, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Molecular and Computational Biology, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
| | - Scott E. Fraser
- Translational Imaging Center, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Biomedical Engineering, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Molecular and Computational Biology, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
| | - Francesco Cutrale
- Translational Imaging Center, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
- Biomedical Engineering, University of Southern California, 1002 West Childs Way, Los Angeles, CA 90089, USA
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4
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Liu L, Miteva T, Delnevo G, Mirri S, Walter P, de Viguerie L, Pouyet E. Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:2419. [PMID: 36904623 PMCID: PMC10006919 DOI: 10.3390/s23052419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 02/17/2023] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method.
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Affiliation(s)
- Lingxi Liu
- Department of Computer Science and Engineering—Interdepartmental Centre for Industrial ICT Research (CIRI ICT), University of Bologna, 40126 Bologna, Italy
| | - Tsveta Miteva
- Laboratoire de Chimie Physique—Matière et Rayonnement (LCPMR), UMR 7614, CNRS, Sorbonne Université, 75005 Paris, France
| | - Giovanni Delnevo
- Department of Computer Science and Engineering—Interdepartmental Centre for Industrial ICT Research (CIRI ICT), University of Bologna, 40126 Bologna, Italy
| | - Silvia Mirri
- Department of Computer Science and Engineering—Interdepartmental Centre for Industrial ICT Research (CIRI ICT), University of Bologna, 40126 Bologna, Italy
| | - Philippe Walter
- Laboratoire d’Archéologie Moléculaire et Structurale (LAMS), CNRS, Sorbonne Université, 75005 Paris, France
| | - Laurence de Viguerie
- Laboratoire d’Archéologie Moléculaire et Structurale (LAMS), CNRS, Sorbonne Université, 75005 Paris, France
| | - Emeline Pouyet
- Laboratoire d’Archéologie Moléculaire et Structurale (LAMS), CNRS, Sorbonne Université, 75005 Paris, France
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5
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SPLIT-PIN software enabling confocal and super-resolution imaging with a virtually closed pinhole. Sci Rep 2023; 13:2741. [PMID: 36792719 PMCID: PMC9931717 DOI: 10.1038/s41598-023-29951-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 02/13/2023] [Indexed: 02/17/2023] Open
Abstract
In point-scanning microscopy, optical sectioning is achieved using a small aperture placed in front of the detector, i.e. the detection pinhole, which rejects the out-of-focus background. The maximum level of optical sectioning is theoretically obtained for the minimum size of the pinhole aperture, but this is normally prevented by the dramatic reduction of the detected signal when the pinhole is closed, leading to a compromise between axial resolution and signal-to-noise ratio. We have recently demonstrated that, instead of closing the pinhole, one can reach a similar level of optical sectioning by tuning the pinhole size in a confocal microscope and by analyzing the resulting image series. The method, consisting in the application of the separation of photons by lifetime tuning (SPLIT) algorithm to series of images acquired with tunable pinhole size, is called SPLIT-pinhole (SPLIT-PIN). Here, we share and describe a SPLIT-PIN software for the processing of series of images acquired at tunable pinhole size, which generates images with reduced out-of-focus background. The software can be used on series of at least two images acquired on available commercial microscopes equipped with a tunable pinhole, including confocal and stimulated emission depletion (STED) microscopes. We demonstrate applicability on different types of imaging modalities: (1) confocal imaging of DNA in a non-adherent cell line; (2) removal of out-of-focus background in super-resolved STED microscopy; (3) imaging of live intestinal organoids stained with a membrane dye.
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6
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D'Amico M, Di Franco E, Cerutti E, Barresi V, Condorelli D, Diaspro A, Lanzanò L. A phasor-based approach to improve optical sectioning in any confocal microscope with a tunable pinhole. Microsc Res Tech 2022; 85:3207-3216. [PMID: 35686877 PMCID: PMC9542401 DOI: 10.1002/jemt.24178] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/26/2022] [Accepted: 05/29/2022] [Indexed: 01/20/2023]
Abstract
Confocal fluorescence microscopy is a well‐established imaging technique capable of generating thin optical sections of biological specimens. Optical sectioning in confocal microscopy is mainly determined by the size of the pinhole, a small aperture placed in front of a point detector. In principle, imaging with a closed pinhole provides the highest degree of optical sectioning. In practice, the dramatic reduction of signal‐to‐noise ratio (SNR) at smaller pinhole sizes makes challenging the use of pinhole sizes significantly smaller than 1 Airy Unit (AU). Here, we introduce a simple method to “virtually” perform confocal imaging at smaller pinhole sizes without the dramatic reduction of SNR. The method is based on the sequential acquisition of multiple confocal images acquired at different pinhole aperture sizes and image processing based on a phasor analysis. The implementation is conceptually similar to separation of photons by lifetime tuning (SPLIT), a technique that exploits the phasor analysis to achieve super‐resolution, and for this reason we call this method SPLIT‐pinhole (SPLIT‐PIN). We show with simulated data that the SPLIT‐PIN image can provide improved optical sectioning (i.e., virtually smaller pinhole size) but better SNR with respect to an image obtained with closed pinhole. For instance, two images acquired at 2 and 1 AU can be combined to obtain a SPLIT‐PIN image with a virtual pinhole size of 0.2 AU but with better SNR. As an example of application to biological imaging, we show that SPLIT‐PIN improves confocal imaging of the apical membrane in an in vitro model of the intestinal epithelium.
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Affiliation(s)
- Morgana D'Amico
- Department of Physics and Astronomy "Ettore Majorana", University of Catania, Catania, Italy
| | - Elisabetta Di Franco
- Department of Physics and Astronomy "Ettore Majorana", University of Catania, Catania, Italy
| | - Elena Cerutti
- Department of Physics and Astronomy "Ettore Majorana", University of Catania, Catania, Italy.,Nanoscopy, CHT Erzelli, Istituto Italiano di Tecnologia, Genoa, Italy
| | - Vincenza Barresi
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, Catania, Italy
| | - Daniele Condorelli
- Department of Biomedical and Biotechnological Sciences, Section of Medical Biochemistry, University of Catania, Catania, Italy
| | - Alberto Diaspro
- Nanoscopy, CHT Erzelli, Istituto Italiano di Tecnologia, Genoa, Italy.,DIFILAB, Department of Physics, University of Genoa, Genoa, Italy
| | - Luca Lanzanò
- Department of Physics and Astronomy "Ettore Majorana", University of Catania, Catania, Italy.,Nanoscopy, CHT Erzelli, Istituto Italiano di Tecnologia, Genoa, Italy
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7
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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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Vu T, Vallmitjana A, Gu J, La K, Xu Q, Flores J, Zimak J, Shiu J, Hosohama L, Wu J, Douglas C, Waterman ML, Ganesan A, Hedde PN, Gratton E, Zhao W. Spatial transcriptomics using combinatorial fluorescence spectral and lifetime encoding, imaging and analysis. Nat Commun 2022; 13:169. [PMID: 35013281 PMCID: PMC8748653 DOI: 10.1038/s41467-021-27798-0] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 12/15/2021] [Indexed: 12/14/2022] Open
Abstract
Multiplexed mRNA profiling in the spatial context provides new information enabling basic research and clinical applications. Unfortunately, existing spatial transcriptomics methods are limited due to either low multiplexing or complexity. Here, we introduce a spatialomics technology, termed Multi Omic Single-scan Assay with Integrated Combinatorial Analysis (MOSAICA), that integrates in situ labeling of mRNA and protein markers in cells or tissues with combinatorial fluorescence spectral and lifetime encoded probes, spectral and time-resolved fluorescence imaging, and machine learning-based decoding. We demonstrate MOSAICA's multiplexing scalability in detecting 10-plex targets in fixed colorectal cancer cells using combinatorial labeling of five fluorophores with facile error-detection and removal of autofluorescence. MOSAICA's analysis is strongly correlated with sequencing data (Pearson's r = 0.96) and was further benchmarked using RNAscopeTM and LGC StellarisTM. We further apply MOSAICA for multiplexed analysis of clinical melanoma Formalin-Fixed Paraffin-Embedded (FFPE) tissues. We finally demonstrate simultaneous co-detection of protein and mRNA in cancer cells.
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Affiliation(s)
- Tam Vu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Alexander Vallmitjana
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA
| | - Joshua Gu
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
| | - Kieu La
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
| | - Qi Xu
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jesus Flores
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA
- CIRM Stem Cell Research Biotechnology Training Program at California State University, Long Beach, Long Beach, CA, 90840, USA
| | - Jan Zimak
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jessica Shiu
- Department of Dermatology, University of California, Irvine, Irvine, CA, 92697, USA
| | - Linzi Hosohama
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, 92697, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Christopher Douglas
- Department of Pathology & Laboratory Medicine, University of California, Irvine, Irvine, CA, 92617, USA
| | - Marian L Waterman
- Department of Microbiology and Molecular Genetics, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Anand Ganesan
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Dermatology, University of California, Irvine, Irvine, CA, 92697, USA
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA
| | - Per Niklas Hedde
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, Irvine, CA, 92697, USA
| | - Enrico Gratton
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA.
- Laboratory for Fluorescence Dynamics, University of California, Irvine, Irvine, CA, 92697, USA.
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, Irvine, CA, 92697, USA.
| | - Weian Zhao
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, 92697, USA.
- Sue and Bill Gross Stem Cell Research Center, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Biological Chemistry, University of California, Irvine, Irvine, CA, 92697, USA.
- Department of Pharmaceutical Sciences, University of California, Irvine, Irvine, CA, 92697, USA.
- Chao Family Comprehensive Cancer Center, University of California, Irvine, Irvine, CA, 92697, USA.
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9
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Alafeef M, Moitra P, Dighe K, Pan D. Hyperspectral Mapping for the Detection of SARS-CoV-2 Using Nanomolecular Probes with Yoctomole Sensitivity. ACS NANO 2021; 15:13742-13758. [PMID: 34279093 PMCID: PMC8315249 DOI: 10.1021/acsnano.1c05226] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 07/14/2021] [Indexed: 05/02/2023]
Abstract
Efficient monitoring of SARS-CoV-2 outbreak requires the use of a sensitive and rapid diagnostic test. Although SARS-CoV-2 RNA can be detected by RT-qPCR, the molecular-level quantification of the viral load is still challenging, time-consuming, and labor-intensive. Here, we report an ultrasensitive hyperspectral sensor (HyperSENSE) based on hafnium nanoparticles (HfNPs) for specific detection of COVID-19 causative virus, SARS-CoV-2. Density functional theoretical calculations reveal that HfNPs exhibit higher changes in their absorption wavelength and light scattering when bound to their target SARS-CoV-2 RNA sequence relative to the gold nanoparticles. The assay has a turnaround time of a few seconds and has a limit of detection in the yoctomolar range, which is 1 000 000-fold times higher than the currently available COVID-19 tests. We demonstrated in ∼100 COVID-19 clinical samples that the assay is highly sensitive and has a specificity of 100%. We also show that HyperSENSE can rapidly detect other viruses such as influenza A H1N1. The outstanding sensitivity indicates the potential of the current biosensor in detecting the prevailing presymptomatic and asymptomatic COVID-19 cases. Thus, integrating hyperspectral imaging with nanomaterials establishes a diagnostic platform for ultrasensitive detection of COVID-19 that can potentially be applied to any emerging infectious pathogen.
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Affiliation(s)
- Maha Alafeef
- Bioengineering Department, The University
of Illinois at Urbana−Champaign, Urbana, Illinois 61801,
United States
- Departments of Diagnostic Radiology and Nuclear
Medicine and Pediatrics, Center for Blood Oxygen Transport and Hemostasis,
University of Maryland Baltimore School of Medicine, Health
Sciences Research Facility III, 670 W. Baltimore Street, Baltimore, Maryland 21201,
United States
- Biomedical Engineering Department, Jordan
University of Science and Technology, Irbid 22110,
Jordan
- Department of Chemical, Biochemical and Environmental
Engineering, University of Maryland Baltimore County,
Interdisciplinary Health Sciences Facility, 1000 Hilltop Circle, Baltimore, Maryland
21250, United States
| | - Parikshit Moitra
- Departments of Diagnostic Radiology and Nuclear
Medicine and Pediatrics, Center for Blood Oxygen Transport and Hemostasis,
University of Maryland Baltimore School of Medicine, Health
Sciences Research Facility III, 670 W. Baltimore Street, Baltimore, Maryland 21201,
United States
| | - Ketan Dighe
- Departments of Diagnostic Radiology and Nuclear
Medicine and Pediatrics, Center for Blood Oxygen Transport and Hemostasis,
University of Maryland Baltimore School of Medicine, Health
Sciences Research Facility III, 670 W. Baltimore Street, Baltimore, Maryland 21201,
United States
- Department of Chemical, Biochemical and Environmental
Engineering, University of Maryland Baltimore County,
Interdisciplinary Health Sciences Facility, 1000 Hilltop Circle, Baltimore, Maryland
21250, United States
| | - Dipanjan Pan
- Bioengineering Department, The University
of Illinois at Urbana−Champaign, Urbana, Illinois 61801,
United States
- Departments of Diagnostic Radiology and Nuclear
Medicine and Pediatrics, Center for Blood Oxygen Transport and Hemostasis,
University of Maryland Baltimore School of Medicine, Health
Sciences Research Facility III, 670 W. Baltimore Street, Baltimore, Maryland 21201,
United States
- Department of Chemical, Biochemical and Environmental
Engineering, University of Maryland Baltimore County,
Interdisciplinary Health Sciences Facility, 1000 Hilltop Circle, Baltimore, Maryland
21250, United States
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10
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Saed B, Munaweera R, Anderson J, O'Neill WD, Hu YS. Rapid statistical discrimination of fluorescence images of T cell receptors on immobilizing surfaces with different coating conditions. Sci Rep 2021; 11:15488. [PMID: 34326382 PMCID: PMC8322097 DOI: 10.1038/s41598-021-94730-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Accepted: 07/15/2021] [Indexed: 11/24/2022] Open
Abstract
The spatial organization of T cell receptors (TCRs) correlates with membrane-associated signal amplification, dispersion, and regulation during T cell activation. Despite its potential clinical importance, quantitative analysis of the spatial arrangement of TCRs from standard fluorescence images remains difficult. Here, we report Statistical Classification Analyses of Membrane Protein Images or SCAMPI as a technique capable of analyzing the spatial arrangement of TCRs on the plasma membrane of T cells. We leveraged medical image analysis techniques that utilize pixel-based values. We transformed grayscale pixel values from fluorescence images of TCRs into estimated model parameters of partial differential equations. The estimated model parameters enabled an accurate classification using linear discrimination techniques, including Fisher Linear Discriminant (FLD) and Logistic Regression (LR). In a proof-of-principle study, we modeled and discriminated images of fluorescently tagged TCRs from Jurkat T cells on uncoated cover glass surfaces (Null) or coated cover glass surfaces with either positively charged poly-L-lysine (PLL) or TCR cross-linking anti-CD3 antibodies (OKT3). Using 80 training images and 20 test images per class, our statistical technique achieved 85% discrimination accuracy for both OKT3 versus PLL and OKT3 versus Null conditions. The run time of image data download, model construction, and image discrimination was 21.89 s on a laptop computer, comprised of 20.43 s for image data download, 1.30 s on the FLD-SCAMPI analysis, and 0.16 s on the LR-SCAMPI analysis. SCAMPI represents an alternative approach to morphology-based qualifications for discriminating complex patterns of membrane proteins conditioned on a small sample size and fast runtime. The technique paves pathways to characterize various physiological and pathological conditions using the spatial organization of TCRs from patient T cells.
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Affiliation(s)
- Badeia Saed
- Department of Chemistry, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Rangika Munaweera
- Department of Chemistry, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - Jesse Anderson
- Department of Chemical Engineering, University of Illinois at Chicago, Chicago, IL, 60607, USA
| | - William D O'Neill
- Department of Bioengineering, Colleges of Engineering and Medicine, University of Illinois at Chicago, Chicago, IL, 60607, USA.
| | - Ying S Hu
- Department of Chemistry, College of Liberal Arts and Sciences, University of Illinois at Chicago, Chicago, IL, 60607, USA.
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11
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Hedde PN, Cinco R, Malacrida L, Kamaid A, Gratton E. Phasor-based hyperspectral snapshot microscopy allows fast imaging of live, three-dimensional tissues for biomedical applications. Commun Biol 2021; 4:721. [PMID: 34117344 PMCID: PMC8195998 DOI: 10.1038/s42003-021-02266-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/26/2021] [Indexed: 01/31/2023] Open
Abstract
Hyperspectral imaging is highly sought after in many fields including mineralogy and geology, environment and agriculture, astronomy and, importantly, biomedical imaging and biological fluorescence. We developed ultrafast phasor-based hyperspectral snapshot microscopy based on sine/cosine interference filters for biomedical imaging not feasible with conventional hyperspectral detection methods. Current approaches rely on slow spatial or spectral scanning limiting their application in living biological tissues, while faster snapshot methods such as image mapping spectrometry and multispectral interferometry are limited in spatial and/or spectral resolution, are computationally demanding, and imaging devices are very expensive to manufacture. Leveraging light sheet microscopy, phasor-based hyperspectral snapshot microscopy improved imaging speed 10-100 fold which, combined with minimal light exposure and high detection efficiency, enabled hyperspectral metabolic imaging of live, three-dimensional mouse tissues not feasible with other methods. As a fit-free method that does not require any a priori information often unavailable in complex and evolving biological systems, the rule of linear combinations of the phasor could spectrally resolve subtle differences between cell types in the developing zebrafish retina and spectrally separate and track multiple organelles in 3D cultured cells over time. The sine/cosine snapshot method is adaptable to any microscope or imaging device thus making hyperspectral imaging and fit-free analysis based on linear combinations broadly available to researchers and the public.
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Affiliation(s)
- Per Niklas Hedde
- Laboratory for Fluorescence Dynamics, University of California, Irvine, CA, USA.
- Department of Pharmaceutical Sciences, University of California, Irvine, CA, USA.
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, CA, USA.
| | - Rachel Cinco
- Laboratory for Fluorescence Dynamics, University of California, Irvine, CA, USA
| | - Leonel Malacrida
- Departamento de Fisiopatología, Hospital de Clínicas, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
- Advanced Bioimaging Unit, Institut Pasteur of Montevideo and Universidad de la República, Montevideo, Uruguay
| | - Andrés Kamaid
- Advanced Bioimaging Unit, Institut Pasteur of Montevideo and Universidad de la República, Montevideo, Uruguay
| | - Enrico Gratton
- Laboratory for Fluorescence Dynamics, University of California, Irvine, CA, USA.
- Beckman Laser Institute & Medical Clinic, University of California, Irvine, CA, USA.
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Vallmitjana A, Torrado B, Gratton E. Phasor-based image segmentation: machine learning clustering techniques. BIOMEDICAL OPTICS EXPRESS 2021; 12:3410-3422. [PMID: 34221668 PMCID: PMC8221971 DOI: 10.1364/boe.422766] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 04/20/2021] [Accepted: 04/22/2021] [Indexed: 05/30/2023]
Abstract
The phasor approach is a well-established method for data visualization and image analysis in spectral and lifetime fluorescence microscopy. Nevertheless, it is typically applied in a user-dependent manner by manually selecting regions of interest on the phasor space to find distinct regions in the fluorescence images. In this paper we present our work on using machine learning clustering techniques to establish an unsupervised and automatic method that can be used for identifying populations of fluorescent species in spectral and lifetime imaging. We demonstrate our method using both synthetic data, created by sampling photon arrival times and plotting the distributions on the phasor plot, and real live cells samples, by staining cellular organelles with a selection of commercial probes.
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Affiliation(s)
- Alex Vallmitjana
- Laboratory for Fluorescence Dynamics, Biomedical Engineering, University of California, Irvine, CA 92697, USA
| | - Belén Torrado
- Laboratory for Fluorescence Dynamics, Biomedical Engineering, University of California, Irvine, CA 92697, USA
| | - Enrico Gratton
- Laboratory for Fluorescence Dynamics, Biomedical Engineering, University of California, Irvine, CA 92697, USA
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