1
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Sbrana F, Chellini F, Tani A, Parigi M, Garella R, Palmieri F, Zecchi-Orlandini S, Squecco R, Sassoli C. Label-free three-dimensional imaging and quantitative analysis of living fibroblasts and myofibroblasts by holotomographic microscopy. Microsc Res Tech 2024. [PMID: 38984377 DOI: 10.1002/jemt.24648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2024] [Revised: 06/18/2024] [Accepted: 06/24/2024] [Indexed: 07/11/2024]
Abstract
Holotomography (HT) is a cutting-edge fast live-cell quantitative label-free imaging technique. Based on the principle of quantitative phase imaging, it combines holography and tomography to record a three-dimensional map of the refractive index, used as intrinsic optical and quantitative imaging contrast parameter of biological samples, at a sub-micrometer spatial resolution. In this study HT has been employed for the first time to analyze the changes of fibroblasts differentiating towards myofibroblasts - recognized as the main cell player of fibrosis - when cultured in vitro with the pro-fibrotic factor, namely transforming growth factor-β1. In parallel, F-actin, vinculin, α-smooth muscle actin, phospho-myosin light chain 2, type-1 collagen, peroxisome proliferator-activated receptor-gamma coactivator-1α expression and mitochondria were evaluated by confocal laser scanning microscopy. Plasmamembrane passive properties and transient receptor potential canonical channels' currents were also recorded by whole-cell patch-clamp. The fluorescence images and electrophysiological results have been compared to the data obtained by HT and their congruence has been discussed. HT turned out to be a valid approach to morphologically distinguish fibroblasts from well differentiated myofibroblasts while obtaining objective measures concerning volume, surface area, projection area, surface index and dry mass (i.e., the mass of the non-aqueous content inside the cell including proteins and subcellular organelles) of the entire cell, nuclei and nucleoli with the major advantage to monitor outer and inner features in living cells in a non-invasive, rapid and label-free approach. HT might open up new research opportunities in the field of fibrotic diseases. RESEARCH HIGHLIGHTS: Holotomography (HT) is a label-free laser interferometric imaging technology exploiting the intrinsic optical property of cells namely refractive index (RI) to enable a direct imaging and analysis of whole cells or intracellular organelles. HT turned out a valid approach to distinguish morphological features of living unlabeled fibroblasts from differentiated myofibroblasts. HT provided quantitative information concerning volume, surface area, projection area, surface index and dry mass of the entire fibroblasts/myofibroblasts, nuclei and nucleoli.
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Affiliation(s)
| | - Flaminia Chellini
- Department of Experimental and Clinical Medicine, Section of Anatomy and Histology, Imaging Platform, University of Florence, Florence, Italy
| | - Alessia Tani
- Department of Experimental and Clinical Medicine, Section of Anatomy and Histology, Imaging Platform, University of Florence, Florence, Italy
| | - Martina Parigi
- Department of Experimental and Clinical Medicine, Section of Anatomy and Histology, Imaging Platform, University of Florence, Florence, Italy
| | - Rachele Garella
- Department of Experimental and Clinical Medicine, Section of Physiological Sciences, University of Florence, Florence, Italy
| | - Francesco Palmieri
- Department of Experimental and Clinical Medicine, Section of Physiological Sciences, University of Florence, Florence, Italy
| | - Sandra Zecchi-Orlandini
- Department of Experimental and Clinical Medicine, Section of Anatomy and Histology, Imaging Platform, University of Florence, Florence, Italy
| | - Roberta Squecco
- Department of Experimental and Clinical Medicine, Section of Physiological Sciences, University of Florence, Florence, Italy
| | - Chiara Sassoli
- Department of Experimental and Clinical Medicine, Section of Anatomy and Histology, Imaging Platform, University of Florence, Florence, Italy
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2
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Ciaparrone G, Pirone D, Fiore P, Xin L, Xiao W, Li X, Bardozzo F, Bianco V, Miccio L, Pan F, Memmolo P, Tagliaferri R, Ferraro P. Label-free cell classification in holographic flow cytometry through an unbiased learning strategy. LAB ON A CHIP 2024; 24:924-932. [PMID: 38264771 DOI: 10.1039/d3lc00385j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/25/2024]
Abstract
Nowadays, label-free imaging flow cytometry at the single-cell level is considered the stepforward lab-on-a-chip technology to address challenges in clinical diagnostics, biology, life sciences and healthcare. In this framework, digital holography in microscopy promises to be a powerful imaging modality thanks to its multi-refocusing and label-free quantitative phase imaging capabilities, along with the encoding of the highest information content within the imaged samples. Moreover, the recent achievements of new data analysis tools for cell classification based on deep/machine learning, combined with holographic imaging, are urging these systems toward the effective implementation of point of care devices. However, the generalization capabilities of learning-based models may be limited from biases caused by data obtained from other holographic imaging settings and/or different processing approaches. In this paper, we propose a combination of a Mask R-CNN to detect the cells, a convolutional auto-encoder, used to the image feature extraction and operating on unlabelled data, thus overcoming the bias due to data coming from different experimental settings, and a feedforward neural network for single cell classification, that operates on the above extracted features. We demonstrate the proposed approach in the challenging classification task related to the identification of drug-resistant endometrial cancer cells.
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Affiliation(s)
- Gioele Ciaparrone
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
| | - Daniele Pirone
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Pierpaolo Fiore
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
| | - Lu Xin
- Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China.
| | - Wen Xiao
- Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China.
| | - Xiaoping Li
- Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing 100044, China
| | - Francesco Bardozzo
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Vittorio Bianco
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Lisa Miccio
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Feng Pan
- Key Laboratory of Precision Opto-Mechatronics Technology of Ministry of Education, School of Instrumentation Science & Optoelectronics Engineering, Beihang University, 100191 Beijing, China.
| | - Pasquale Memmolo
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Roberto Tagliaferri
- Neurone Lab, Department of Management and Innovation Systems (DISA-MIS), University of Salerno, Fisciano, Italy.
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
| | - Pietro Ferraro
- CNR - Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Pozzuoli, Italy.
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3
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Burguete-Lopez A, Makarenko M, Bonifazi M, Menezes de Oliveira BN, Getman F, Tian Y, Mazzone V, Li N, Giammona A, Liberale C, Fratalocchi A. Real-time simultaneous refractive index and thickness mapping of sub-cellular biology at the diffraction limit. Commun Biol 2024; 7:154. [PMID: 38321111 PMCID: PMC10847501 DOI: 10.1038/s42003-024-05839-w] [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: 04/28/2023] [Accepted: 01/20/2024] [Indexed: 02/08/2024] Open
Abstract
Mapping the cellular refractive index (RI) is a central task for research involving the composition of microorganisms and the development of models providing automated medical screenings with accuracy beyond 95%. These models require significantly enhancing the state-of-the-art RI mapping capabilities to provide large amounts of accurate RI data at high throughput. Here, we present a machine-learning-based technique that obtains a biological specimen's real-time RI and thickness maps from a single image acquired with a conventional color camera. This technology leverages a suitably engineered nanostructured membrane that stretches a biological analyte over its surface and absorbs transmitted light, generating complex reflection spectra from each sample point. The technique does not need pre-existing sample knowledge. It achieves 10-4 RI sensitivity and sub-nanometer thickness resolution on diffraction-limited spatial areas. We illustrate practical application by performing sub-cellular segmentation of HCT-116 colorectal cancer cells, obtaining complete three-dimensional reconstruction of the cellular regions with a characteristic length of 30 μm. These results can facilitate the development of real-time label-free technologies for biomedical studies on microscopic multicellular dynamics.
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Affiliation(s)
- Arturo Burguete-Lopez
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Maksim Makarenko
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Marcella Bonifazi
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Physik-Institut, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Barbara Nicoly Menezes de Oliveira
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Fedor Getman
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yi Tian
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Valerio Mazzone
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Physik-Institut, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland
| | - Ning Li
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Alessandro Giammona
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), Segrate, Italy
| | - Carlo Liberale
- Biological and Environmental Science and Engineering Division (BESE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Andrea Fratalocchi
- PRIMALIGHT, Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia.
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4
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Sun J, Yang B, Koukourakis N, Guck J, Czarske JW. AI-driven projection tomography with multicore fibre-optic cell rotation. Nat Commun 2024; 15:147. [PMID: 38167247 PMCID: PMC10762230 DOI: 10.1038/s41467-023-44280-1] [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: 06/26/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024] Open
Abstract
Optical tomography has emerged as a non-invasive imaging method, providing three-dimensional insights into subcellular structures and thereby enabling a deeper understanding of cellular functions, interactions, and processes. Conventional optical tomography methods are constrained by a limited illumination scanning range, leading to anisotropic resolution and incomplete imaging of cellular structures. To overcome this problem, we employ a compact multi-core fibre-optic cell rotator system that facilitates precise optical manipulation of cells within a microfluidic chip, achieving full-angle projection tomography with isotropic resolution. Moreover, we demonstrate an AI-driven tomographic reconstruction workflow, which can be a paradigm shift from conventional computational methods, often demanding manual processing, to a fully autonomous process. The performance of the proposed cell rotation tomography approach is validated through the three-dimensional reconstruction of cell phantoms and HL60 human cancer cells. The versatility of this learning-based tomographic reconstruction workflow paves the way for its broad application across diverse tomographic imaging modalities, including but not limited to flow cytometry tomography and acoustic rotation tomography. Therefore, this AI-driven approach can propel advancements in cell biology, aiding in the inception of pioneering therapeutics, and augmenting early-stage cancer diagnostics.
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Affiliation(s)
- Jiawei Sun
- Shanghai Artificial Intelligence Laboratory, Longwen Road 129, Xuhui District, 200232, Shanghai, China.
- Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany.
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany.
| | - Bin Yang
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany
| | - Nektarios Koukourakis
- Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany
| | - Jochen Guck
- Max Planck Institute for the Science of Light & Max Planck-Zentrum für Physik und Medizin, 91058, Erlangen, Germany
| | - Juergen W Czarske
- Competence Center for Biomedical Computational Laser Systems (BIOLAS), TU Dresden, Helmholtzstrasse 18, 01069, Dresden, Germany.
- Laboratory of Measurement and Sensor System Technique (MST), TU Dresden, Dresden, Germany.
- Cluster of Excellence Physics of Life, TU Dresden, Dresden, Germany.
- Institute of Applied Physics, TU Dresden, Dresden, Germany.
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5
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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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6
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Wu T, Tan JHL, Sin W, Luah YH, Tan SY, Goh M, Birnbaum ME, Chen Q, Cheow LF. Cell Granularity Reflects Immune Cell Function and Enables Selection of Lymphocytes with Superior Attributes for Immunotherapy. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302175. [PMID: 37544893 PMCID: PMC10558660 DOI: 10.1002/advs.202302175] [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: 04/04/2023] [Revised: 07/20/2023] [Indexed: 08/08/2023]
Abstract
In keeping with the rule of "form follows function", morphological aspects of a cell can reflect its role. Here, it is shown that the cellular granularity of a lymphocyte, represented by its intrinsic side scatter (SSC), is a potent indicator of its cell state and function. The granularity of a lymphocyte increases from naïve to terminal effector state. High-throughput cell-sorting yields a SSChigh population that can mediate immediate effector functions, and a highly prolific SSClow population that can give rise to the replenishment of the memory pool. CAR-T cells derived from the younger SSClow population possess desirable attributes for immunotherapy, manifested by increased naïve-like cells and stem cell memory (TSCM )-like cells together with a balanced CD4/CD8 ratio, as well as enhanced target-killing in vitro and in vivo. Altogether, lymphocyte segregation based on biophysical properties is an effective approach for label-free selection of cells that share collective functions and can have important applications for cell-based immunotherapies.
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Affiliation(s)
- Tongjin Wu
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
| | - Joel Heng Loong Tan
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Wei‐Xiang Sin
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
| | - Yen Hoon Luah
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
| | - Sue Yee Tan
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Myra Goh
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Michael E. Birnbaum
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
- Department of Biological EngineeringMassachusetts Institute of TechnologyCambridgeMA02139USA
| | - Qingfeng Chen
- Institute of Molecular and Cell Biology (IMCB)Agency for ScienceTechnology and Research (A*STAR)Singapore138673Singapore
| | - Lih Feng Cheow
- Department of Biomedical EngineeringFaculty of EngineeringNational University of SingaporeSingapore117583Singapore
- Institute for Health Innovation and TechnologyNational University of SingaporeSingapore117599Singapore
- Critical Analytics for Manufacturing of Personalized MedicineSingapore‐MIT Alliance for Research and TechnologySingapore138602Singapore
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7
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Pirone D, Montella A, Sirico DG, Mugnano M, Villone MM, Bianco V, Miccio L, Porcelli AM, Kurelac I, Capasso M, Iolascon A, Maffettone PL, Memmolo P, Ferraro P. Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry. Sci Rep 2023; 13:6042. [PMID: 37055398 PMCID: PMC10101968 DOI: 10.1038/s41598-023-32110-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 03/21/2023] [Indexed: 04/15/2023] Open
Abstract
Image-based identification of circulating tumor cells in microfluidic cytometry condition is one of the most challenging perspectives in the Liquid Biopsy scenario. Here we show a machine learning-powered tomographic phase imaging flow cytometry system capable to provide high-throughput 3D phase-contrast tomograms of each single cell. In fact, we show that discrimination of tumor cells against white blood cells is potentially achievable with the aid of artificial intelligence in a label-free flow-cyto-tomography method. We propose a hierarchical machine learning decision-maker, working on a set of features calculated from the 3D tomograms of the cells' refractive index. We prove that 3D morphological features are adequately distinctive to identify tumor cells versus the white blood cell background in the first stage and, moreover, in recognizing the tumor type at the second decision step. Proof-of-concept experiments are shown, in which two different tumor cell lines, namely neuroblastoma cancer cells and ovarian cancer cells, are used against monocytes. The reported results allow claiming the identification of tumor cells with a success rate higher than 97% and with an accuracy over 97% in discriminating between the two cancer cell types, thus opening in a near future the route to a new Liquid Biopsy tool for detecting and classifying circulating tumor cells in blood by stain-free method.
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Affiliation(s)
- Daniele Pirone
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Annalaura Montella
- CEINGE Advanced Biotechnologies, Naples, Italy
- DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy
| | - Daniele G Sirico
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Martina Mugnano
- Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples "Federico II", Piazzale Tecchio 80, 80125, Naples, Italy
| | - Massimiliano M Villone
- Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples "Federico II", Piazzale Tecchio 80, 80125, Naples, Italy
| | - Vittorio Bianco
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Lisa Miccio
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy
| | - Anna Maria Porcelli
- Department of Pharmacy and Biotechnology (FABIT), University of Bologna, Bologna, Italy
- Interdepartmental Centre for Industrial Research 'Scienze Della Vita e Tecnologie per La Salute', University of Bologna, Bologna, Italy
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
| | - Ivana Kurelac
- Centre for Applied Biomedical Research (CRBA), University of Bologna, Bologna, Italy
- DIMEC, Department of Medical and Surgical Sciences, Centro di Studio e Ricerca Sulle Neoplasie (CSR) Ginecologiche, Alma Mater Studiorum-University of Bologna, 40138, Bologna, Italy
| | - Mario Capasso
- CEINGE Advanced Biotechnologies, Naples, Italy
- DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy
| | - Achille Iolascon
- CEINGE Advanced Biotechnologies, Naples, Italy
- DMMBM, Department of Molecular Medicine and Medical Biotechnology, University of Naples "Federico II", Naples, Italy
| | - Pier Luca Maffettone
- Department of Chemical, Materials and Production Engineering, DICMaPI, University of Naples "Federico II", Piazzale Tecchio 80, 80125, Naples, Italy
| | - Pasquale Memmolo
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy.
| | - Pietro Ferraro
- CNR-ISASI, Institute of Applied Sciences and Intelligent Systems "Eduardo Caianiello", Via Campi Flegrei 34, 80078, Pozzuoli, Naples, Italy.
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8
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Lu N, Tay HM, Petchakup C, He L, Gong L, Maw KK, Leong SY, Lok WW, Ong HB, Guo R, Li KHH, Hou HW. Label-free microfluidic cell sorting and detection for rapid blood analysis. LAB ON A CHIP 2023; 23:1226-1257. [PMID: 36655549 DOI: 10.1039/d2lc00904h] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.
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Affiliation(s)
- Nan Lu
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 65 Nanyang Drive, Block N3, 637460, Singapore
| | - Hui Min Tay
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Chayakorn Petchakup
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Linwei He
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Lingyan Gong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Kay Khine Maw
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Sheng Yuan Leong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Wan Wei Lok
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Hong Boon Ong
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
| | - Ruya Guo
- Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing, 100083, China
| | - King Ho Holden Li
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 65 Nanyang Drive, Block N3, 637460, Singapore
| | - Han Wei Hou
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, 50 Nanyang Avenue, Blk N3, Level 2, Room 86 (N3-02c-86), 639798, Singapore.
- HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 65 Nanyang Drive, Block N3, 637460, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, 11 Mandalay Road, Clinical Sciences Building, 308232, Singapore
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9
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Shin J, Kim G, Park J, Lee M, Park Y. Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization. Sci Rep 2023; 13:46. [PMID: 36593327 PMCID: PMC9806822 DOI: 10.1038/s41598-022-27158-y] [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: 09/13/2022] [Accepted: 12/27/2022] [Indexed: 01/03/2023] Open
Abstract
Three-dimensional (3D) quantitative phase imaging (QPI) enables long-term label-free tomographic imaging and quantitative analysis of live individual bacteria. However, the Brownian motion or motility of bacteria in a liquid medium produces motion artifacts during 3D measurements and hinders precise cell imaging and analysis. Meanwhile, existing cell immobilization methods produce noisy backgrounds and even alter cellular physiology. Here, we introduce a protocol that utilizes hydrogels for high-quality 3D QPI of live bacteria maintaining bacterial physiology. We demonstrate long-term high-resolution quantitative imaging and analysis of individual bacteria, including measuring the biophysical parameters of bacteria and responses to antibiotic treatments.
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Affiliation(s)
- Jeongwon Shin
- grid.37172.300000 0001 2292 0500Department of Biological Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea
| | - Geon Kim
- grid.37172.300000 0001 2292 0500Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea ,grid.37172.300000 0001 2292 0500KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea
| | - Jinho Park
- grid.37172.300000 0001 2292 0500Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea
| | - Moosung Lee
- grid.37172.300000 0001 2292 0500Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea ,grid.37172.300000 0001 2292 0500KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea
| | - YongKeun Park
- grid.37172.300000 0001 2292 0500Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea ,grid.37172.300000 0001 2292 0500KAIST Institute for Health Science and Technology, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141 South Korea ,Tomocube Inc., Daejeon, 34051 South Korea
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10
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Douglass PM, O'Connor T, Javidi B. Automated sickle cell disease identification in human red blood cells using a lensless single random phase encoding biosensor and convolutional neural networks. OPTICS EXPRESS 2022; 30:35965-35977. [PMID: 36258535 DOI: 10.1364/oe.469199] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 09/04/2022] [Indexed: 06/16/2023]
Abstract
We present a compact, field portable, lensless, single random phase encoding biosensor for automated classification between healthy and sickle cell disease human red blood cells. Microscope slides containing 3 µl wet mounts of whole blood samples from healthy and sickle cell disease afflicted human donors are input into a lensless single random phase encoding (SRPE) system for disease identification. A partially coherent laser source (laser diode) illuminates the cells under inspection wherein the object complex amplitude propagates to and is pseudorandomly encoded by a diffuser, then the intensity of the diffracted complex waveform is captured by a CMOS image sensor. The recorded opto-biological signatures are transformed using local binary pattern map generation during preprocessing then input into a pretrained convolutional neural network for classification between healthy and disease-states. We further provide analysis that compares the performance of several neural network architectures to optimize our classification strategy. Additionally, we assess the performance and computational savings of classifying on subsets of the opto-biological signatures with substantially reduced dimensionality, including one dimensional cropping of the recorded signatures. To the best of our knowledge, this is the first report of a lensless SRPE biosensor for human disease identification. As such, the presented approach and results can be significant for low-cost disease identification both in the field and for healthcare systems in developing countries which suffer from constrained resources.
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11
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Tang M, He H, Yu L. Real-time 3D imaging of ocean algae with crosstalk suppressed single-shot digital holographic microscopy. BIOMEDICAL OPTICS EXPRESS 2022; 13:4455-4467. [PMID: 36032587 PMCID: PMC9408253 DOI: 10.1364/boe.463678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/21/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
Digital holographic microscopy (DHM) has the potential to reconstruct the 3D shape of volumetric samples from a single-shot hologram in a label-free and noninvasive manner. However, the holographic reconstruction is significantly compromised by the out-of-focus image resulting from the crosstalk between refocused planes, leading to the low fidelity of the results. In this paper, we propose a crosstalk suppression algorithm-assisted 3D imaging method combined with a home built DHM system to achieve accurate 3D imaging of ocean algae using only a single hologram. As a key step in the algorithm, a hybrid edge detection strategy using gradient-based and deep learning-based methods is proposed to offer accurate boundary information for the downstream processing. With this information, the crosstalk of each refocused plane can be estimated with adjacent refocused planes. Empowered by this method, we demonstrated successful 3D imaging of six kinds of ocean algae that agree well with the ground truth; we further demonstrated that this method could achieve real-time 3D imaging of the quick swimming ocean algae in the water environment. To our knowledge, this is the first time single-shot DHM is reported in 3D imaging of ocean algae, paving the way for on-site monitoring of the ocean algae.
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Affiliation(s)
- Ming Tang
- School of Advanced Manufacturing, Nanchang University, Nanchang, Jiangxi 330031, China
| | - Hao He
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055, China
| | - Longkun Yu
- School of Advanced Manufacturing, Nanchang University, Nanchang, Jiangxi 330031, China
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12
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Kaza N, Ojaghi A, Robles FE. Virtual Staining, Segmentation, and Classification of Blood Smears for Label-Free Hematology Analysis. BME FRONTIERS 2022; 2022:9853606. [PMID: 37850166 PMCID: PMC10521747 DOI: 10.34133/2022/9853606] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Accepted: 05/05/2022] [Indexed: 10/19/2023] Open
Abstract
Objective and Impact Statement. We present a fully automated hematological analysis framework based on single-channel (single-wavelength), label-free deep-ultraviolet (UV) microscopy that serves as a fast, cost-effective alternative to conventional hematology analyzers. Introduction. Hematological analysis is essential for the diagnosis and monitoring of several diseases but requires complex systems operated by trained personnel, costly chemical reagents, and lengthy protocols. Label-free techniques eliminate the need for staining or additional preprocessing and can lead to faster analysis and a simpler workflow. In this work, we leverage the unique capabilities of deep-UV microscopy as a label-free, molecular imaging technique to develop a deep learning-based pipeline that enables virtual staining, segmentation, classification, and counting of white blood cells (WBCs) in single-channel images of peripheral blood smears. Methods. We train independent deep networks to virtually stain and segment grayscale images of smears. The segmented images are then used to train a classifier to yield a quantitative five-part WBC differential. Results. Our virtual staining scheme accurately recapitulates the appearance of cells under conventional Giemsa staining, the gold standard in hematology. The trained cellular and nuclear segmentation networks achieve high accuracy, and the classifier can achieve a quantitative five-part differential on unseen test data. Conclusion. This proposed automated hematology analysis framework could greatly simplify and improve current complete blood count and blood smear analysis and lead to the development of a simple, fast, and low-cost, point-of-care hematology analyzer.
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Affiliation(s)
- Nischita Kaza
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA
| | - Ashkan Ojaghi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
| | - Francisco E. Robles
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, Georgia, USA
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13
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Szittner Z, Péter B, Kurunczi S, Székács I, Horváth R. Functional blood cell analysis by label-free biosensors and single-cell technologies. Adv Colloid Interface Sci 2022; 308:102727. [DOI: 10.1016/j.cis.2022.102727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 05/25/2022] [Accepted: 06/27/2022] [Indexed: 11/01/2022]
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14
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Kim G, Ahn D, Kang M, Park J, Ryu D, Jo Y, Song J, Ryu JS, Choi G, Chung HJ, Kim K, Chung DR, Yoo IY, Huh HJ, Min HS, Lee NY, Park Y. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network. LIGHT, SCIENCE & APPLICATIONS 2022; 11:190. [PMID: 35739098 PMCID: PMC9226356 DOI: 10.1038/s41377-022-00881-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 06/03/2022] [Accepted: 06/09/2022] [Indexed: 05/14/2023]
Abstract
The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.
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Affiliation(s)
- Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - Daewoong Ahn
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Minhee Kang
- Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - Jinho Park
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - DongHun Ryu
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
| | - YoungJu Jo
- Department of Physics, Korea Advanced Institute of Science and Technology, 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
- Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA
| | - Jinyeop Song
- Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, Republic of Korea
- Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jea Sung Ryu
- Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Gunho Choi
- Tomocube Inc., Daejeon, 34109, Republic of Korea
| | - Hyun Jung Chung
- Graduate School of Nanoscience and Technology, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, 34141, Republic of Korea
| | - Kyuseok Kim
- Department of Emergency Medicine, Bundang CHA Hospital, Seongnam-si, Gyeonggi-Do, 13496, Korea
| | - Doo Ryeon Chung
- Division of Infectious Diseases, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | - In Young Yoo
- Department of Laboratory Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, 06591, Republic of Korea
| | - Hee Jae Huh
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea
| | | | - Nam Yong Lee
- Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, 06351, Republic of Korea.
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology, 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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15
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Pixel Super-Resolution Phase Retrieval for Lensless On-Chip Microscopy via Accelerated Wirtinger Flow. Cells 2022; 11:cells11131999. [PMID: 35805081 PMCID: PMC9265759 DOI: 10.3390/cells11131999] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 06/18/2022] [Accepted: 06/21/2022] [Indexed: 01/13/2023] Open
Abstract
Empowered by pixel super-resolution (PSR) and phase retrieval techniques, lensless on-chip microscopy opens up new possibilities for high-throughput biomedical imaging. However, the current PSR phase retrieval approaches are time consuming in terms of both the measurement and reconstruction procedures. In this work, we present a novel computational framework for PSR phase retrieval to address these concerns. Specifically, a sparsity-promoting regularizer is introduced to enhance the well posedness of the nonconvex problem under limited measurements, and Nesterov’s momentum is used to accelerate the iterations. The resulting algorithm, termed accelerated Wirtinger flow (AWF), achieves at least an order of magnitude faster rate of convergence and allows a twofold reduction in the measurement number while maintaining competitive reconstruction quality. Furthermore, we provide general guidance for step size selection based on theoretical analyses, facilitating simple implementation without the need for complicated parameter tuning. The proposed AWF algorithm is compatible with most of the existing lensless on-chip microscopes and could help achieve label-free rapid whole slide imaging of dynamic biological activities at subpixel resolution.
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16
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Digital Holographic Microscopy for Label-Free Detection of Leukocyte Alternations Associated with Perioperative Inflammation after Cardiac Surgery. Cells 2022; 11:cells11040755. [PMID: 35203403 PMCID: PMC8869820 DOI: 10.3390/cells11040755] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 02/16/2022] [Accepted: 02/19/2022] [Indexed: 02/05/2023] Open
Abstract
In a prospective observational pilot study on patients undergoing elective cardiac surgery with cardiopulmonary bypass, we evaluated label-free quantitative phase imaging (QPI) with digital holographic microscopy (DHM) to describe perioperative inflammation by changes in biophysical cell properties of lymphocytes and monocytes. Blood samples from 25 patients were investigated prior to cardiac surgery and postoperatively at day 1, 3 and 6. Biophysical and morphological cell parameters accessible with DHM, such as cell volume, refractive index, dry mass, and cell shape related form factor, were acquired and compared to common flow cytometric blood cell markers of inflammation and selected routine laboratory parameters. In all examined patients, cardiac surgery induced an acute inflammatory response as indicated by changes in routine laboratory parameters and flow cytometric cell markers. DHM results were associated with routine laboratory and flow cytometric data and correlated with complications in the postoperative course. In a subgroup analysis, patients were classified according to the inflammation related C-reactive protein (CRP) level, treatment with epinephrine and the occurrence of postoperative complications. Patients with regular courses, without epinephrine treatment and with low CRP values showed a postoperative lymphocyte volume increase. In contrast, the group of patients with increased CRP levels indicated an even further enlarged lymphocyte volume, while for the groups of epinephrine treated patients and patients with complicative courses, no postoperative lymphocyte volume changes were detected. In summary, the study demonstrates the capability of DHM to describe biophysical cell parameters of perioperative lymphocytes and monocytes changes in cardiac surgery patients. The pattern of correlations between biophysical DHM data and laboratory parameters, flow cytometric cell markers, and the postoperative course exemplify DHM as a promising diagnostic tool for a characterization of inflammatory processes and course of disease.
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17
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Label-free imaging and evaluation of characteristic properties of asthma-derived eosinophils using optical diffraction tomography. Biochem Biophys Res Commun 2022; 587:42-48. [PMID: 34864394 DOI: 10.1016/j.bbrc.2021.11.084] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2021] [Revised: 11/14/2021] [Accepted: 11/23/2021] [Indexed: 12/15/2022]
Abstract
Optical diffraction tomography (ODT), an emerging imaging technique that does not require fluorescent staining, can measure the three-dimensional distribution of the refractive index (RI) of organelles. In this study, we used ODT to characterize the pathological characteristics of human eosinophils derived from asthma patients presenting with eosinophilia. In addition to morphological information about organelles appearing in eosinophils, including the cytoplasm, nucleus, and vacuole, we succeeded in imaging specific granules and quantifying the RI values of the granules. Interestingly, ODT analysis showed that the RI (i.e., molecular density) of granules was significantly different between eosinophils from asthma patients and healthy individuals without eosinophilia, and that vacuoles were frequently found in the cells of asthma patients. Our results suggest that the physicochemical properties of eosinophils derived from patients with asthma can be quantitatively distinguished from those of healthy individuals. The method will provide insight into efficient evaluation of the characteristics of eosinophils at the organelle level for various diseases with eosinophilia.
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18
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Ugawa M, Kawamura Y, Toda K, Teranishi K, Morita H, Adachi H, Tamoto R, Nomaru H, Nakagawa K, Sugimoto K, Borisova E, An Y, Konishi Y, Tabata S, Morishita S, Imai M, Takaku T, Araki M, Komatsu N, Hayashi Y, Sato I, Horisaki R, Noji H, Ota S. In silico-labeled ghost cytometry. eLife 2021; 10:e67660. [PMID: 34930522 PMCID: PMC8691837 DOI: 10.7554/elife.67660] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Accepted: 10/26/2021] [Indexed: 11/13/2022] Open
Abstract
Characterization and isolation of a large population of cells are indispensable procedures in biological sciences. Flow cytometry is one of the standards that offers a method to characterize and isolate cells at high throughput. When performing flow cytometry, cells are molecularly stained with fluorescent labels to adopt biomolecular specificity which is essential for characterizing cells. However, molecular staining is costly and its chemical toxicity can cause side effects to the cells which becomes a critical issue when the cells are used downstream as medical products or for further analysis. Here, we introduce a high-throughput stain-free flow cytometry called in silico-labeled ghost cytometry which characterizes and sorts cells using machine-predicted labels. Instead of detecting molecular stains, we use machine learning to derive the molecular labels from compressive data obtained with diffractive and scattering imaging methods. By directly using the compressive 'imaging' data, our system can accurately assign the designated label to each cell in real time and perform sorting based on this judgment. With this method, we were able to distinguish different cell states, cell types derived from human induced pluripotent stem (iPS) cells, and subtypes of peripheral white blood cells using only stain-free modalities. Our method will find applications in cell manufacturing for regenerative medicine as well as in cell-based medical diagnostic assays in which fluorescence labeling of the cells is undesirable.
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Affiliation(s)
- Masashi Ugawa
- Thinkcyte IncTokyoJapan
- Center for Advanced Intelligence Project, RIKENTokyoJapan
- The University of TokyoTokyoJapan
| | | | | | | | | | | | | | | | | | | | | | - Yuri An
- BioResource Research Center, RIKENTsukubaJapan
| | | | | | | | | | | | | | | | | | - Issei Sato
- Thinkcyte IncTokyoJapan
- The University of TokyoTokyoJapan
| | - Ryoichi Horisaki
- Thinkcyte IncTokyoJapan
- The University of TokyoTokyoJapan
- PRESTO, Japan Science and Technology AgencyKawaguchiJapan
| | | | - Sadao Ota
- Thinkcyte IncTokyoJapan
- The University of TokyoTokyoJapan
- PRESTO, Japan Science and Technology AgencyKawaguchiJapan
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19
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Kim Y, Kim TK, Shin Y, Tak E, Song GW, Oh YM, Kim JK, Pack CG. Characterizing Organelles in Live Stem Cells Using Label-Free Optical Diffraction Tomography. Mol Cells 2021; 44:851-860. [PMID: 34819398 PMCID: PMC8627838 DOI: 10.14348/molcells.2021.0190] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 08/27/2021] [Accepted: 09/09/2021] [Indexed: 01/10/2023] Open
Abstract
Label-free optical diffraction tomography (ODT), an imaging technology that does not require fluorescent labeling or other pre-processing, can overcome the limitations of conventional cell imaging technologies, such as fluorescence and electron microscopy. In this study, we used ODT to characterize the cellular organelles of three different stem cells-namely, human liver derived stem cell, human umbilical cord matrix derived mesenchymal stem cell, and human induced pluripotent stem cell-based on their refractive index and volume of organelles. The physical property of each stem cell was compared with that of fibroblast. Based on our findings, the characteristic physical properties of specific stem cells can be quantitatively distinguished based on their refractive index and volume of cellular organelles. Altogether, the method employed herein could aid in the distinction of living stem cells from normal cells without the use of fluorescence or specific biomarkers.
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Affiliation(s)
- Youngkyu Kim
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
| | - Tae-Keun Kim
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
| | - Yeonhee Shin
- Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Eunyoung Tak
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Gi-Won Song
- Division of Hepatobiliary Surgery and Liver Transplantation, Department of Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Yeon-Mok Oh
- Department of Pulmonary and Critical Care Medicine, Clinical Research Center for Chronic Obstructive Airway Diseases, Asan Medical Center, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Jun Ki Kim
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
| | - Chan-Gi Pack
- Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Korea
- Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Korea
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20
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Pack CG. Application of quantitative cell imaging using label-free optical diffraction tomography. Biophys Physicobiol 2021; 18:244-253. [PMID: 34745809 PMCID: PMC8550874 DOI: 10.2142/biophysico.bppb-v18.027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/11/2021] [Indexed: 12/01/2022] Open
Abstract
The cell is three-dimensionally and dynamically organized into cellular compartments, including the endoplasmic reticulum, mitochondria, vesicles, and nucleus, which have high relative molecular density. The structure and functions of these compartments and organelles may be deduced from the diffusion and interaction of related biomolecules. Among these cellular components, various protein molecules can freely access the nucleolus or mitotic chromosome through Brownian diffusion, even though they have a densely packed structure. However, physicochemical properties of the nucleolus and chromosomes, such as molecular density and volume, are not yet fully understood under changing cellular conditions. Many studies have been conducted based on high-resolution imaging and analysis techniques using fluorescence. However, there are limitations in imaging only fluorescently labeled molecules, and cytotoxicity occurs during three-dimensional imaging. Alternatively, the recently developed label-free three-dimensional optical diffraction tomography (ODT) imaging technique can divide various organelles in cells into volumes and analyze them by refractive index, although specific molecules cannot be observed. A previous study established an analytical method that provides comprehensive insights into the physical properties of the nucleolus and mitotic chromosome by utilizing the advantages of ODT and fluorescence techniques, such as fluorescence correlation spectroscopy and confocal laser scanning microscopy. This review article summarizes a recent study and discusses the future aspects of the ODT for cellular compartments.
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Affiliation(s)
- Chan-Gi Pack
- Convergence Medicine Research Center (CREDIT), Asan Institute for Life Sciences, Asan Medical Center, Seoul 05505, Republic of Korea.,Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul 05505, Republic of Korea
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21
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Kim J, Go T, Lee SJ. Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning. JOURNAL OF HAZARDOUS MATERIALS 2021; 418:126351. [PMID: 34329034 DOI: 10.1016/j.jhazmat.2021.126351] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 05/21/2021] [Accepted: 06/04/2021] [Indexed: 06/13/2023]
Abstract
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome to handle for accurate and high throughput measurements. In addition, commercial particle counters have technical limitations in high-concentration measurement, and data fluctuations are induced during air sampling. In this study, a novel smartphone-based technique for monitoring airborne PM concentrations was developed using smartphone-based digital holographic microscopy (S-DHM) and deep learning network called Holo-SpeckleNet. Holographic speckle images of various PM concentrations were recorded by the S-DHM system. The recorded speckle images and the corresponding ground truth PM concentrations were used to train deep learning algorithms consisting of a deep autoencoder and regression layers. The performance of the proposed smartphone-based PM monitoring technique was validated through hyperparameter optimization. The developed S-DHM integrated with Holo-SpeckleNet can be smartly and effectively utilized for portable PM monitoring and safety alarm provision under perilous environmental conditions.
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Affiliation(s)
- Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Taesik Go
- Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
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22
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Chen X, Kandel ME, Popescu G. Spatial light interference microscopy: principle and applications to biomedicine. ADVANCES IN OPTICS AND PHOTONICS 2021; 13:353-425. [PMID: 35494404 PMCID: PMC9048520 DOI: 10.1364/aop.417837] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
In this paper, we review spatial light interference microscopy (SLIM), a common-path, phase-shifting interferometer, built onto a phase-contrast microscope, with white-light illumination. As one of the most sensitive quantitative phase imaging (QPI) methods, SLIM allows for speckle-free phase reconstruction with sub-nanometer path-length stability. We first review image formation in QPI, scattering, and full-field methods. Then, we outline SLIM imaging from theory and instrumentation to diffraction tomography. Zernike's phase-contrast microscopy, phase retrieval in SLIM, and halo removal algorithms are discussed. Next, we discuss the requirements for operation, with a focus on software developed in-house for SLIM that enables high-throughput acquisition, whole slide scanning, mosaic tile registration, and imaging with a color camera. We introduce two methods for solving the inverse problem using SLIM, white-light tomography, and Wolf phase tomography. Lastly, we review the applications of SLIM in basic science and clinical studies. SLIM can study cell dynamics, cell growth and proliferation, cell migration, mass transport, etc. In clinical settings, SLIM can assist with cancer studies, reproductive technology, blood testing, etc. Finally, we review an emerging trend, where SLIM imaging in conjunction with artificial intelligence brings computational specificity and, in turn, offers new solutions to outstanding challenges in cell biology and pathology.
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23
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Kräter M, Abuhattum S, Soteriou D, Jacobi A, Krüger T, Guck J, Herbig M. AIDeveloper: Deep Learning Image Classification in Life Science and Beyond. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:e2003743. [PMID: 34105281 PMCID: PMC8188199 DOI: 10.1002/advs.202003743] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Revised: 02/08/2021] [Indexed: 05/13/2023]
Abstract
Artificial intelligence (AI)-based image analysis has increased drastically in recent years. However, all applications use individual solutions, highly specialized for a particular task. Here, an easy-to-use, adaptable, and open source software, called AIDeveloper (AID) to train neural nets (NN) for image classification without the need for programming is presented. AID provides a variety of NN-architectures, allowing to apply trained models on new data, obtain performance metrics, and export final models to different formats. AID is benchmarked on large image datasets (CIFAR-10 and Fashion-MNIST). Furthermore, models are trained to distinguish areas of differentiated stem cells in images of cell culture. A conventional blood cell count and a blood count obtained using an NN are compared, trained on >1.2 million images, and demonstrated how AID can be used for label-free classification of B- and T-cells. All models are generated by non-programmers on generic computers, allowing for an interdisciplinary use.
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Affiliation(s)
- Martin Kräter
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Shada Abuhattum
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Despina Soteriou
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Angela Jacobi
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
- Department of Internal Medicine IUniversity Hospital Carl Gustav CarusTU DresdenDresden01307Germany
| | - Thomas Krüger
- Department of Internal Medicine IUniversity Hospital Carl Gustav CarusTU DresdenDresden01307Germany
- German Cancer Consortium (DKTK)Partner Site DresdenGerman Cancer Research Center (DKFZ)Heidelberg69120Germany
- Center for Regenerative Therapies (CRTD)TU DresdenDresden01307Germany
| | - Jochen Guck
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
| | - Maik Herbig
- Biotechnology CenterCenter for Molecular and Cellular BioengineeringTU DresdenDresden01307Germany
- Max Planck Institute for the Science of Light and Max‐Planck‐Zentrum für Physik und MedizinErlangen91058Germany
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24
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Cho SY, Gong X, Koman VB, Kuehne M, Moon SJ, Son M, Lew TTS, Gordiichuk P, Jin X, Sikes HD, Strano MS. Cellular lensing and near infrared fluorescent nanosensor arrays to enable chemical efflux cytometry. Nat Commun 2021; 12:3079. [PMID: 34035262 PMCID: PMC8149711 DOI: 10.1038/s41467-021-23416-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 04/20/2021] [Indexed: 02/07/2023] Open
Abstract
Nanosensors have proven to be powerful tools to monitor single cells, achieving spatiotemporal precision even at molecular level. However, there has not been way of extending this approach to statistically relevant numbers of living cells. Herein, we design and fabricate nanosensor array in microfluidics that addresses this limitation, creating a Nanosensor Chemical Cytometry (NCC). nIR fluorescent carbon nanotube array is integrated along microfluidic channel through which flowing cells is guided. We can utilize the flowing cell itself as highly informative Gaussian lenses projecting nIR profiles and extract rich information. This unique biophotonic waveguide allows for quantified cross-correlation of biomolecular information with various physical properties and creates label-free chemical cytometer for cellular heterogeneity measurement. As an example, the NCC can profile the immune heterogeneities of human monocyte populations at attomolar sensitivity in completely non-destructive and real-time manner with rate of ~600 cells/hr, highest range demonstrated to date for state-of-the-art chemical cytometry.
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Affiliation(s)
- Soo-Yeon Cho
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xun Gong
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Volodymyr B Koman
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthias Kuehne
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Sun Jin Moon
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Manki Son
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tedrick Thomas Salim Lew
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Pavlo Gordiichuk
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Xiaojia Jin
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hadley D Sikes
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Michael S Strano
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
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25
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Lee M, Kim K, Oh J, Park Y. Isotropically resolved label-free tomographic imaging based on tomographic moulds for optical trapping. LIGHT, SCIENCE & APPLICATIONS 2021; 10:102. [PMID: 33994544 PMCID: PMC8126562 DOI: 10.1038/s41377-021-00535-4] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 04/06/2021] [Accepted: 04/14/2021] [Indexed: 05/13/2023]
Abstract
A major challenge in three-dimensional (3D) microscopy is to obtain accurate spatial information while simultaneously keeping the microscopic samples in their native states. In conventional 3D microscopy, axial resolution is inferior to spatial resolution due to the inaccessibility to side scattering signals. In this study, we demonstrate the isotropic microtomography of free-floating samples by optically rotating a sample. Contrary to previous approaches using optical tweezers with multiple foci which are only applicable to simple shapes, we exploited 3D structured light traps that can stably rotate freestanding complex-shaped microscopic specimens, and side scattering information is measured at various sample orientations to achieve isotropic resolution. The proposed method yields an isotropic resolution of 230 nm and captures structural details of colloidal multimers and live red blood cells, which are inaccessible using conventional tomographic microscopy. We envision that the proposed approach can be deployed for solving diverse imaging problems that are beyond the examples shown here.
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Affiliation(s)
- Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea
| | - Kyoohyun Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea
- Max Planck Institute for the Science of Light & Max-Planck-Zentrum für Physik und Medizin, 91058, Erlangen, Germany
| | - 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
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, 34141, South Korea.
- KAIST Institute for Health Science and Technology, KAIST, Daejeon, 34141, South Korea.
- Tomocube Inc., Daejeon, 34109, South Korea.
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26
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Detection of intracellular monosodium urate crystals in gout synovial fluid using optical diffraction tomography. Sci Rep 2021; 11:10019. [PMID: 33976275 PMCID: PMC8113554 DOI: 10.1038/s41598-021-89337-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Accepted: 04/15/2021] [Indexed: 12/03/2022] Open
Abstract
Optical diffraction tomography (ODT) enables imaging of unlabeled intracellular components by measuring the three-dimensional (3D) refractive index (RI). We aimed to detect intracellular monosodium urate (MSU) crystals in synovial leukocytes derived from gout patients using ODT. The 3D RI values of the synthetic MSU crystals, measured by ODT, ranged between 1.383 and 1.440. After adding synthetic MSU crystals to a macrophage, RI tomograms were reconstructed using ODT, and the reconstructed RI tomograms discerned intracellular and extracellular MSU crystals. We observed unlabeled synthetic MSU crystal entry into the cytoplasm of a macrophage through time-lapse imaging. Furthermore, using gout patient-derived synovial leukocytes, we successfully obtained RI tomogram images of intracellular MSU crystals. The 3D RI identification of MSU crystals was verified with birefringence through polarization-sensitive ODT measurements. Together, our results provide evidence that this novel ODT can identify birefringent MSU crystals in synovial leukocytes of patients with gout.
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27
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Baczewska M, Eder K, Ketelhut S, Kemper B, Kujawińska M. Refractive Index Changes of Cells and Cellular Compartments Upon Paraformaldehyde Fixation Acquired by Tomographic Phase Microscopy. Cytometry A 2021; 99:388-398. [PMID: 32959478 PMCID: PMC8048569 DOI: 10.1002/cyto.a.24229] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 07/28/2020] [Accepted: 08/25/2020] [Indexed: 12/11/2022]
Abstract
Three-dimensional quantitative phase imaging is an emerging method, which provides the 3D distribution of the refractive index (RI) and the dry mass in live and fixed cells as well as in tissues. However, an insufficiently answered question is the influence of chemical cell fixation procedures on the results of RI reconstructions. Therefore, this work is devoted to systematic investigations on the RI in cellular organelles of live and fixed cells including nucleus, nucleolus, nucleoplasm, and cytoplasm. The research was carried out on four different cell lines using a common paraformaldehyde (PFA)-based fixation protocol. The selected cell types represent the diversity of mammalian cells and therefore the results presented provide a picture of fixation caused RI changes in a broader context. A commercial Tomocube HT-1S device was used for 3D RI acquisition. The changes in the RI values after the fixation process are detected in the reconstructed phase distributions and amount to the order of 10-3 . The RI values decrease and the observed RI changes are found to be different between various cell lines; however, all of them show the most significant loss in the nucleolus. In conclusion, our study demonstrates the evident need for standardized preparation procedures in phase tomographic measurements. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals LLC. on behalf of International Society for Advancement of Cytometry.
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Affiliation(s)
- Maria Baczewska
- Warsaw University of Technology, Institute of Micromechanics and Photonics, Sw. Boboli 8 St.Warsaw02‐525Poland
| | - Kai Eder
- Biomedical Technology Center of the Medical Faculty, University of Muenster Mendelstr 17MuensterD‐48149Germany
| | - Steffi Ketelhut
- Biomedical Technology Center of the Medical Faculty, University of Muenster Mendelstr 17MuensterD‐48149Germany
| | - Björn Kemper
- Biomedical Technology Center of the Medical Faculty, University of Muenster Mendelstr 17MuensterD‐48149Germany
| | - Małgorzata Kujawińska
- Warsaw University of Technology, Institute of Micromechanics and Photonics, Sw. Boboli 8 St.Warsaw02‐525Poland
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28
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Disease-Relevant Single Cell Photonic Signatures Identify S100β Stem Cells and their Myogenic Progeny in Vascular Lesions. Stem Cell Rev Rep 2021; 17:1713-1740. [PMID: 33730327 PMCID: PMC8446106 DOI: 10.1007/s12015-021-10125-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/20/2021] [Indexed: 10/31/2022]
Abstract
A hallmark of subclinical atherosclerosis is the accumulation of vascular smooth muscle cell (SMC)-like cells leading to intimal thickening and lesion formation. While medial SMCs contribute to vascular lesions, the involvement of resident vascular stem cells (vSCs) remains unclear. We evaluated single cell photonics as a discriminator of cell phenotype in vitro before the presence of vSC within vascular lesions was assessed ex vivo using supervised machine learning and further validated using lineage tracing analysis. Using a novel lab-on-a-Disk(Load) platform, label-free single cell photonic emissions from normal and injured vessels ex vivo were interrogated and compared to freshly isolated aortic SMCs, cultured Movas SMCs, macrophages, B-cells, S100β+ mVSc, bone marrow derived mesenchymal stem cells (MSC) and their respective myogenic progeny across five broadband light wavelengths (λ465 - λ670 ± 20 nm). We found that profiles were of sufficient coverage, specificity, and quality to clearly distinguish medial SMCs from different vascular beds (carotid vs aorta), discriminate normal carotid medial SMCs from lesional SMC-like cells ex vivo following flow restriction, and identify SMC differentiation of a series of multipotent stem cells following treatment with transforming growth factor beta 1 (TGF- β1), the Notch ligand Jagged1, and Sonic Hedgehog using multivariate analysis, in part, due to photonic emissions from enhanced collagen III and elastin expression. Supervised machine learning supported genetic lineage tracing analysis of S100β+ vSCs and identified the presence of S100β+vSC-derived myogenic progeny within vascular lesions. We conclude disease-relevant photonic signatures may have predictive value for vascular disease.
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29
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Go T, Kim J, Lee SJ. Three-dimensional volumetric monitoring of settling particulate matters on a leaf using digital in-line holographic microscopy. JOURNAL OF HAZARDOUS MATERIALS 2021; 404:124116. [PMID: 33049638 DOI: 10.1016/j.jhazmat.2020.124116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 09/17/2020] [Accepted: 09/24/2020] [Indexed: 06/11/2023]
Abstract
Plants are considered as a possible modality to reduce particulate matter (PM) particles from ambient air in an ecofriendly manner. A new precise monitoring technique that can explore interactions between individual PM particles and a leaf surface is necessary to understand the underlying mechanisms of PM removal of plant leaves. In this study, a digital in-line holographic microscopy (DIHM) was employed to experimentally investigate the settling motions of PM particles over the leaf surface. The in-plane positions and sizes of opaque PMs with irregular shapes were obtained from the projection images of numerically reconstructed holographic images. The depth positions of PMs were determined by using proper selection of an autofocusing criterion with automatic segmentation method. The edge of a hairy Perilla frutescens leaf was detected by adopting several digital imaging processing techniques. The DIHM technique was applied in this study to accurately detect 3D settling trajectories of PMs with velocity information of PMs in the midair and near leaf surface, simultaneously.
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Affiliation(s)
- Taesik Go
- Division of Biomedical Engineering, College of Engineering, Jeonbuk National University, 567 Baekje-daero, Deokjin-gu, Jeonju-si, Jeollabuk-do 54896, Republic of Korea
| | - Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang 37673, Republic of Korea.
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30
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Kim D, Lee S, Lee M, Oh J, Yang SA, Park Y. Holotomography: Refractive Index as an Intrinsic Imaging Contrast for 3-D Label-Free Live Cell Imaging. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 1310:211-238. [PMID: 33834439 DOI: 10.1007/978-981-33-6064-8_10] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight into how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.
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Affiliation(s)
- Doyeon Kim
- Department of Chemistry, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
| | - Sangyun Lee
- Department of Physics, KAIST, Daejeon, South Korea
| | - Moosung Lee
- Department of Physics, KAIST, Daejeon, South Korea
| | - Juntaek Oh
- Department of Physics, KAIST, Daejeon, South Korea
| | - Su-A Yang
- Department of Biological Sciences, KAIST, Daejeon, South Korea
| | - YongKeun Park
- Department of Physics, KAIST, Daejeon, South Korea. .,KAIST Institute Health Science and Technology, Daejeon, South Korea. .,Tomocube Inc., Daejeon, South Korea.
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31
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Lee M, Lee YH, Song J, Kim G, Jo Y, Min H, Kim CH, Park Y. Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells. eLife 2020; 9:49023. [PMID: 33331817 PMCID: PMC7817186 DOI: 10.7554/elife.49023] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Accepted: 12/16/2020] [Indexed: 12/14/2022] Open
Abstract
The immunological synapse (IS) is a cell-cell junction between a T cell and a professional antigen-presenting cell. Since the IS formation is a critical step for the initiation of an antigen-specific immune response, various live-cell imaging techniques, most of which rely on fluorescence microscopy, have been used to study the dynamics of IS. However, the inherent limitations associated with the fluorescence-based imaging, such as photo-bleaching and photo-toxicity, prevent the long-term assessment of dynamic changes of IS with high frequency. Here, we propose and experimentally validate a label-free, volumetric, and automated assessment method for IS dynamics using a combinational approach of optical diffraction tomography and deep learning-based segmentation. The proposed method enables an automatic and quantitative spatiotemporal analysis of IS kinetics of morphological and biochemical parameters associated with IS dynamics, providing a new option for immunological research.
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Affiliation(s)
- Moosung Lee
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Young-Ho Lee
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.,Curocell Inc, Daejeon, Republic of Korea
| | - Jinyeop Song
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - Geon Kim
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | - YoungJu Jo
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
| | | | - Chan Hyuk Kim
- Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.,KAIST Institute for Health Science and Technology, Daejeon, Republic of Korea
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32
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Cohen-Maslaton S, Barnea I, Taieb A, Shaked NT. Cell and nucleus refractive-index mapping by interferometric phase microscopy and rapid confocal fluorescence microscopy. JOURNAL OF BIOPHOTONICS 2020; 13:e202000117. [PMID: 32468735 DOI: 10.1002/jbio.202000117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Revised: 05/06/2020] [Accepted: 05/21/2020] [Indexed: 05/12/2023]
Abstract
We present a multimodal technique for measuring the integral refractive index and the thickness of biological cells and their organelles by integrating interferometric phase microscopy (IPM) and rapid confocal fluorescence microscopy. First, the actual thickness maps of the cellular compartments are reconstructed using the confocal fluorescent sections, and then the optical path difference (OPD) map of the same cell is reconstructed using IPM. Based on the co-registered data, the integral refractive index maps of the cell and its organelles are calculated. This technique enables rapidly measuring refractive index of live, dynamic cells, where IPM provides quantitative imaging capabilities and confocal fluorescence microscopy provides molecular specificity of the cell organelles. We acquire human colorectal adenocarcinoma cells and show that the integral refractive index values are similar for the whole cell, the cytoplasm and the nucleus on the population level, but significantly different on the single cell level.
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Affiliation(s)
- Shir Cohen-Maslaton
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Itay Barnea
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Almog Taieb
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Natan T Shaked
- Department of Biomedical Engineering, Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
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33
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Miccio L, Cimmino F, Kurelac I, Villone MM, Bianco V, Memmolo P, Merola F, Mugnano M, Capasso M, Iolascon A, Maffettone PL, Ferraro P. Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips. VIEW 2020. [DOI: 10.1002/viw.20200034] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022] Open
Affiliation(s)
- Lisa Miccio
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | | | - Ivana Kurelac
- Dipartimento di Scienze Mediche e Chirurgiche Università di Bologna Bologna Italy
- Centro di Ricerca Biomedica Applicata (CRBA) Università di Bologna Bologna Italy
| | - Massimiliano M. Villone
- Dipartimento di Ingegneria Chimica dei Materiali e della Produzione Industriale Università degli Studi di Napoli “Federico II” Napoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Vittorio Bianco
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Pasquale Memmolo
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Francesco Merola
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Martina Mugnano
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Mario Capasso
- CEINGE Biotecnologie Avanzate Naples Italy
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche Università degli Studi di Napoli Federico II Naples Italy
| | - Achille Iolascon
- CEINGE Biotecnologie Avanzate Naples Italy
- Dipartimento di Medicina Molecolare e Biotecnologie Mediche Università degli Studi di Napoli Federico II Naples Italy
| | - Pier Luca Maffettone
- Dipartimento di Ingegneria Chimica dei Materiali e della Produzione Industriale Università degli Studi di Napoli “Federico II” Napoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
| | - Pietro Ferraro
- CNR‐ISASI Institute of Applied Sciences and Intelligent Systems E. Caianiello Pozzuoli Italy
- NEAPoLIS, Numerical and Experimental Advanced Program on Liquids and Interface Systems Joint Research Center CNR ‐ Università degli Studi di Napoli “Federico II” Napoli Italy
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34
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Firdaus ER, Park JH, Lee SK, Park Y, Cha GH, Han ET. 3D morphological and biophysical changes in a single tachyzoite and its infected cells using three-dimensional quantitative phase imaging. JOURNAL OF BIOPHOTONICS 2020; 13:e202000055. [PMID: 32441392 DOI: 10.1002/jbio.202000055] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Revised: 05/18/2020] [Accepted: 05/19/2020] [Indexed: 06/11/2023]
Abstract
Toxoplasma gondii is an apicomplexan parasite that causes toxoplasmosis in the human body and commonly infects warm-blooded organisms. Pathophysiology of its diseases is still an interesting issue to be studied since T gondii can infect nearly all nucleated cells. Imaging techniques are crucial for studying its pathophysiology. In T gondii-infected cells structural and biochemical alterations occurred. To study that modification, we use digital holotomography to investigate the structure and biochemical alteration of single tachyzoite and its infected cells in a label-free and quantitative manner. Quantification analysis was done by measuring the refractive index distribution, which provides information about the concentration and dry mass of individual cells. This study showed that holotomography could be effectively used to identify the structural and biochemical alteration in tremendously different cells in supporting pathophysiological research in particular for T gondii-caused diseases.
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Affiliation(s)
- Egy Rahman Firdaus
- Department of Medical Environmental Biology and Tropical Medicine, School of Medicine, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea
| | - Ji-Hoon Park
- Department of Medical Environmental Biology and Tropical Medicine, School of Medicine, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea
| | - Seong-Kyun Lee
- Department of Medical Environmental Biology and Tropical Medicine, School of Medicine, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea
| | - YongKeun Park
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Guang-Ho Cha
- Department of Medical Science & Infection Biology, Chungnam National University, School of Medicine, Daejeon, Korea
| | - Eun-Taek Han
- Department of Medical Environmental Biology and Tropical Medicine, School of Medicine, Kangwon National University, Chuncheon, Gangwon-do, Republic of Korea
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35
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González-Bermúdez B, Kobayashi H, Navarrete Á, Nyblad C, González-Sánchez M, de la Fuente M, Fuentes G, Guinea GV, García C, Plaza GR. Single-cell biophysical study reveals deformability and internal ordering relationship in T cells. SOFT MATTER 2020; 16:5669-5678. [PMID: 32519732 DOI: 10.1039/d0sm00648c] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Deformability and internal ordering are key features related to cell function, particularly critical for cells that routinely undergo large deformations, like T cells during extravasation and migration. In the measurement of cell deformability, a considerable variability is typically obtained, masking the identification of possible interrelationships between deformability, internal ordering and cell function. We report the development of a single-cell methodology that combines measurements of living-cell deformability, using micropipette aspiration, and three-dimensional confocal analysis of the nucleus and cytoskeleton. We show that this single-cell approach can serve as a powerful tool to identify appropriate parameters that characterize deformability within a population of cells, not readably discernable in population-averaged data. By applying this single-cell methodology to mouse CD4+ T cells, our results demonstrate that the relative size of the nucleus, better than other geometrical or cytoskeletal features, effectively determines the overall deformability of the cells within the population.
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Affiliation(s)
- Blanca González-Bermúdez
- Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain. and Departamento de Ciencia de Materiales, ETSI de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain
| | - Hikaru Kobayashi
- Departamento de Genética, Fisiología y Microbiología, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, E-28040 Madrid, Spain
| | - Álvaro Navarrete
- Departamento de Ingeniería Mecánica, Universidad de Santiago de Chile, Chile
| | - César Nyblad
- Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain. and Departamento de Ciencia de Materiales, ETSI de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain
| | - Mónica González-Sánchez
- Departamento de Genética, Fisiología y Microbiología, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, E-28040 Madrid, Spain
| | - Mónica de la Fuente
- Departamento de Genética, Fisiología y Microbiología, Facultad de Ciencias Biológicas, Universidad Complutense de Madrid, E-28040 Madrid, Spain
| | - Gonzalo Fuentes
- Departamento de Ciencia de Materiales, ETSI de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain and Instituto de Sistemas Optoelectrónicos y Microtecnología, Universidad Politécnica de Madrid, E-28040 Madrid, Spain
| | - Gustavo V Guinea
- Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain. and Departamento de Ciencia de Materiales, ETSI de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain and Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain
| | - Claudio García
- Departamento de Ingeniería Mecánica, Universidad de Santiago de Chile, Chile
| | - Gustavo R Plaza
- Center for Biomedical Technology, Universidad Politécnica de Madrid, E-28223 Pozuelo de Alarcón, Spain. and Departamento de Ciencia de Materiales, ETSI de Caminos, Canales y Puertos, Universidad Politécnica de Madrid, E-28040 Madrid, Spain
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Abstract
Hematological analysis, via a complete blood count (CBC) and microscopy, is critical for screening, diagnosing, and monitoring blood conditions and diseases but requires complex equipment, multiple chemical reagents, laborious system calibration and procedures, and highly trained personnel for operation. Here we introduce a hematological assay based on label-free molecular imaging with deep-ultraviolet microscopy that can provide fast quantitative information of key hematological parameters to facilitate and improve hematological analysis. We demonstrate that this label-free approach yields 1) a quantitative five-part white blood cell differential, 2) quantitative red blood cell and hemoglobin characterization, 3) clear identification of platelets, and 4) detailed subcellular morphology. Analysis of tens of thousands of live cells is achieved in minutes without any sample preparation. Finally, we introduce a pseudocolorization scheme that accurately recapitulates the appearance of cells under conventional staining protocols for microscopic analysis of blood smears and bone marrow aspirates. Diagnostic efficacy is evaluated by a panel of hematologists performing a blind analysis of blood smears from healthy donors and thrombocytopenic and sickle cell disease patients. This work has significant implications toward simplifying and improving CBC and blood smear analysis, which is currently performed manually via bright-field microscopy, and toward the development of a low-cost, easy-to-use, and fast hematological analyzer as a point-of-care device and for low-resource settings.
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37
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Deep learning-based hologram generation using a white light source. Sci Rep 2020; 10:8977. [PMID: 32488035 PMCID: PMC7265409 DOI: 10.1038/s41598-020-65716-4] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2019] [Accepted: 05/04/2020] [Indexed: 01/10/2023] Open
Abstract
Digital holographic microscopy enables the recording of sample holograms which contain 3D volumetric information. However, additional optical elements, such as partially or fully coherent light source and a pinhole, are required to induce diffraction and interference. Here, we present a deep neural network based on generative adversarial network (GAN) to perform image transformation from a defocused bright-field (BF) image acquired from a general white light source to a holographic image. Training image pairs of 11,050 for image conversion were gathered by using a hybrid BF and hologram imaging technique. The performance of the trained network was evaluated by comparing generated and ground truth holograms of microspheres and erythrocytes distributed in 3D. Holograms generated from BF images through the trained GAN showed enhanced image contrast with 3-5 times increased signal-to-noise ratio compared to ground truth holograms and provided 3D positional information and light scattering patterns of the samples. The developed GAN-based method is a promising mean for dynamic analysis of microscale objects with providing detailed 3D positional information and monitoring biological samples precisely even though conventional BF microscopic setting is utilized.
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Wang ZJ, Walsh AJ, Skala MC, Gitter A. Classifying T cell activity in autofluorescence intensity images with convolutional neural networks. JOURNAL OF BIOPHOTONICS 2020; 13:e201960050. [PMID: 31661592 PMCID: PMC7065628 DOI: 10.1002/jbio.201960050] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 09/28/2019] [Accepted: 10/16/2019] [Indexed: 05/13/2023]
Abstract
The importance of T cells in immunotherapy has motivated developing technologies to improve therapeutic efficacy. One objective is assessing antigen-induced T cell activation because only functionally active T cells are capable of killing the desired targets. Autofluorescence imaging can distinguish T cell activity states in a non-destructive manner by detecting endogenous changes in metabolic co-enzymes such as NAD(P)H. However, recognizing robust activity patterns is computationally challenging in the absence of exogenous labels. We demonstrate machine learning methods that can accurately classify T cell activity across human donors from NAD(P)H intensity images. Using 8260 cropped single-cell images from six donors, we evaluate classifiers ranging from traditional models that use previously-extracted image features to convolutional neural networks (CNNs) pre-trained on general non-biological images. Adapting pre-trained CNNs for the T cell activity classification task provides substantially better performance than traditional models or a simple CNN trained with the autofluorescence images alone. Visualizing the images with dimension reduction provides intuition into why the CNNs achieve higher accuracy than other approaches. Our image processing and classifier training software is available at https://github.com/gitter-lab/t-cell-classification.
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Affiliation(s)
- Zijie J. Wang
- Department of Computer SciencesUniversity of Wisconsin‐MadisonMadisonWisconsin
- Morgridge Institute for ResearchMadisonWisconsin
| | | | - Melissa C. Skala
- Morgridge Institute for ResearchMadisonWisconsin
- Department of Biomedical EngineeringUniversity of Wisconsin‐MadisonMadisonWisconsin
| | - Anthony Gitter
- Department of Computer SciencesUniversity of Wisconsin‐MadisonMadisonWisconsin
- Morgridge Institute for ResearchMadisonWisconsin
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐MadisonMadisonWisconsin
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Park S, Ahn JW, Jo Y, Kang HY, Kim HJ, Cheon Y, Kim JW, Park Y, Lee S, Park K. Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs. ACS NANO 2020; 14:1856-1865. [PMID: 31909985 DOI: 10.1021/acsnano.9b07993] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods have limited application in living foam cells. Here, using optical diffraction tomography (ODT), we performed quantitative morphological and biophysical analysis of living foam cells in a label-free manner. We identified LDs in foam cells by verifying the specific refractive index using correlative imaging comprising ODT integrated with three-dimensional fluorescence imaging. Through time-lapse monitoring of three-dimensional dynamics of label-free living foam cells, we precisely and quantitatively evaluated the therapeutic effects of a nanodrug (mannose-polyethylene glycol-glycol chitosan-fluorescein isothiocyanate-lobeglitazone; MMR-Lobe) designed to affect the targeted delivery of lobeglitazone to foam cells based on high mannose receptor specificity. Furthermore, by exploiting machine-learning-based image analysis, we further demonstrated therapeutic evaluation at the single-cell level. These findings suggest that refractive index measurement is a promising tool to explore new drugs against LD-related metabolic diseases.
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Affiliation(s)
- Sangwoo Park
- Gwangju Center , Korea Basic Science Institute (KBSI) , Gwangju , 61186 , Korea
| | - Jae Won Ahn
- Department of Systems Biotechnology , Chung-Ang University , Anseong , Gyeonggi 17546 , Korea
| | - YoungJu Jo
- Department of Physics , Korea Advanced Institute of Science and Technology (KAIST) , Daejeon , 34141 , Korea
- KAIST Institute for Health Science and Technology, KAIST , Daejeon , 34141 , Korea
| | - Ha-Young Kang
- Gwangju Center , Korea Basic Science Institute (KBSI) , Gwangju , 61186 , Korea
| | - Hyun Jung Kim
- Cardiovascular Center , Korea University Guro Hospital , Seoul , 08308 , Korea
| | - Yeongmi Cheon
- Gwangju Center , Korea Basic Science Institute (KBSI) , Gwangju , 61186 , Korea
| | - Jin Won Kim
- Cardiovascular Center , Korea University Guro Hospital , Seoul , 08308 , Korea
| | - YongKeun Park
- Department of Physics , Korea Advanced Institute of Science and Technology (KAIST) , Daejeon , 34141 , Korea
- KAIST Institute for Health Science and Technology, KAIST , Daejeon , 34141 , Korea
- Tomocube Inc. , Daejeon , 34051 , Korea
| | - Seongsoo Lee
- Gwangju Center , Korea Basic Science Institute (KBSI) , Gwangju , 61186 , Korea
| | - Kyeongsoon Park
- Department of Systems Biotechnology , Chung-Ang University , Anseong , Gyeonggi 17546 , Korea
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40
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Paiva JS, Jorge PAS, Ribeiro RSR, Balmaña M, Campos D, Mereiter S, Jin C, Karlsson NG, Sampaio P, Reis CA, Cunha JPS. iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification. Sci Rep 2020; 10:3171. [PMID: 32081911 PMCID: PMC7035380 DOI: 10.1038/s41598-020-59661-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Accepted: 12/16/2019] [Indexed: 01/30/2023] Open
Abstract
With the advent of personalized medicine, there is a movement to develop "smaller" and "smarter" microdevices that are able to distinguish similar cancer subtypes. Tumor cells display major differences when compared to their natural counterparts, due to alterations in fundamental cellular processes such as glycosylation. Glycans are involved in tumor cell biology and they have been considered to be suitable cancer biomarkers. Thus, more selective cancer screening assays can be developed through the detection of specific altered glycans on the surface of circulating cancer cells. Currently, this is only possible through time-consuming assays. In this work, we propose the "intelligent" Lab on Fiber (iLoF) device, that has a high-resolution, and which is a fast and portable method for tumor single-cell type identification and isolation. We apply an Artificial Intelligence approach to the back-scattered signal arising from a trapped cell by a micro-lensed optical fiber. As a proof of concept, we show that iLoF is able to discriminate two human cancer cell models sharing the same genetic background but displaying a different surface glycosylation profile with an accuracy above 90% and a speed rate of 2.3 seconds. We envision the incorporation of the iLoF in an easy-to-operate microchip for cancer identification, which would allow further biological characterization of the captured circulating live cells.
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Affiliation(s)
- Joana S Paiva
- INESC TEC - INESC Technology and Science, Porto, Portugal
- Physics and Astronomy Department, Faculty of Sciences, University of Porto, Porto, Portugal
- Faculty of Engineering, University of Porto, Porto, Portugal
| | - Pedro A S Jorge
- INESC TEC - INESC Technology and Science, Porto, Portugal
- Physics and Astronomy Department, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Rita S R Ribeiro
- INESC TEC - INESC Technology and Science, Porto, Portugal
- Faculty of Engineering, University of Porto, Porto, Portugal
- 4DCell, Paris, France
| | - Meritxell Balmaña
- i3s - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- IPATIMUP - Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter Campus, 1030, Vienna, Austria
| | - Diana Campos
- i3s - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- IPATIMUP - Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
| | - Stefan Mereiter
- Faculty of Engineering, University of Porto, Porto, Portugal
- i3s - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- IPATIMUP - Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna BioCenter Campus, 1030, Vienna, Austria
| | - Chunsheng Jin
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Niclas G Karlsson
- Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Paula Sampaio
- i3s - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- IBMC - Instituto de Biologia Molecular e Celular, Universidade do Porto, Porto, Portugal
| | - Celso A Reis
- i3s - Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Porto, Portugal
- IPATIMUP - Institute of Molecular Pathology and Immunology, University of Porto, Porto, Portugal
- Instituto de Ciências Biomédicas Abel Salazar, University of Porto, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - João P S Cunha
- INESC TEC - INESC Technology and Science, Porto, Portugal.
- Faculty of Engineering, University of Porto, Porto, Portugal.
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Belashov AV, Zhikhoreva AA, Belyaeva TN, Kornilova ES, Salova AV, Semenova IV, Vasyutinskii OS. In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2020; 37:346-352. [PMID: 32118916 DOI: 10.1364/josaa.382135] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Accepted: 01/03/2020] [Indexed: 06/10/2023]
Abstract
Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in vitro. It is demonstrated that the developed algorithms operating with a set of optical, morphological, and physiological parameters of cells, obtained from their phase images, can be used for automatic distinction between live and necrotic cells. The developed classifier provides high accuracy of about 95.5% and allows for calculation of survival rates in the course of cell death.
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42
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Lam VK, Nguyen T, Bui V, Chung BM, Chang LC, Nehmetallah G, Raub CB. Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging. JOURNAL OF BIOMEDICAL OPTICS 2020; 25:1-17. [PMID: 32072775 PMCID: PMC7026523 DOI: 10.1117/1.jbo.25.2.026002] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2019] [Accepted: 01/30/2020] [Indexed: 05/07/2023]
Abstract
SIGNIFICANCE We introduce an application of machine learning trained on optical phase features of epithelial and mesenchymal cells to grade cancer cells' morphologies, relevant to evaluation of cancer phenotype in screening assays and clinical biopsies. AIM Our objective was to determine quantitative epithelial and mesenchymal qualities of breast cancer cells through an unbiased, generalizable, and linear score covering the range of observed morphologies. APPROACH Digital holographic microscopy was used to generate phase height maps of noncancerous epithelial (Gie-No3B11) and fibroblast (human gingival) cell lines, as well as MDA-MB-231 and MCF-7 breast cancer cell lines. Several machine learning algorithms were evaluated as binary classifiers of the noncancerous cells that graded the cancer cells by transfer learning. RESULTS Epithelial and mesenchymal cells were classified with 96% to 100% accuracy. Breast cancer cells had scores in between the noncancer scores, indicating both epithelial and mesenchymal morphological qualities. The MCF-7 cells skewed toward epithelial scores, while MDA-MB-231 cells skewed toward mesenchymal scores. Linear support vector machines (SVMs) produced the most distinct score distributions for each cell line. CONCLUSIONS The proposed epithelial-mesenchymal score, derived from linear SVM learning, is a sensitive and quantitative approach for detecting epithelial and mesenchymal characteristics of unknown cells based on well-characterized cell lines. We establish a framework for rapid and accurate morphological evaluation of single cells and subtle phenotypic shifts in imaged cell populations.
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Affiliation(s)
- Van K. Lam
- The Catholic University of America, Department of Biomedical Engineering, Washington, DC, United States
| | - Thanh Nguyen
- The Catholic University of America, Department of Electrical Engineering and Computer Science, Washington, DC, United States
| | - Vy Bui
- The Catholic University of America, Department of Electrical Engineering and Computer Science, Washington, DC, United States
| | - Byung Min Chung
- The Catholic University of America, Department of Biology, Washington, DC, United States
| | - Lin-Ching Chang
- The Catholic University of America, Department of Electrical Engineering and Computer Science, Washington, DC, United States
| | - George Nehmetallah
- The Catholic University of America, Department of Electrical Engineering and Computer Science, Washington, DC, United States
| | - Christopher B. Raub
- The Catholic University of America, Department of Biomedical Engineering, Washington, DC, United States
- Address all correspondence to Christopher B. Raub, E-mail:
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Rossi D, Dannhauser D, Telesco M, Netti PA, Causa F. CD4+ versus CD8+ T-lymphocyte identification in an integrated microfluidic chip using light scattering and machine learning. LAB ON A CHIP 2019; 19:3888-3898. [PMID: 31641710 DOI: 10.1039/c9lc00695h] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
T lymphocytes are a group of cells representing the main effectors of human adaptive immunity. Characterization of the most representative T-lymphocyte subclasses, CD4+ and CD8+, is challenging, but has a significant impact on clinical decisions. Up to now, T lymphocytes have been identified by quite complex cytometric assays, which are based on antibody labeling. However, a label-free approach based on pure biophysical evaluation at a single-cell level could enable the ability to distinguish between these subclasses. Here, we report a light-scattering approach, supported by accurate data mining, to evaluate cell biophysical properties on an integrated microfluidic chip. In order to perform single-cell optical analysis in viscoelastic fluids, such a chip is composed of mixing, alignment, readout and collection sections. In particular, we measured the cell dimensions, the refractive index of the cell nucleus, the refractive index of the cytosol, and the nucleus-to-cytosol ratio. Combining measurement of biophysical properties and machine learning allows us to both distinguish and count human CD4+ and CD8+ cells with an accuracy of 79%. An enhanced identification accuracy of 88% can be achieved by stimulating the cells with a selective anti-apoptotic protein, which results in increased biophysical differences between CD4+ and CD8+ cells. This approach has been successfully validated by analysis of samples that recapitulate physiological and pathological scenarios (CD4+/CD8+ ratios). The results are encouraging for the possible application of our approach in hematological clinical routines, as well as in diagnosis and follow-up of specific pathologies, such as human immunodeficiency virus (HIV) progression.
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Affiliation(s)
- Domenico Rossi
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - David Dannhauser
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Mariarosaria Telesco
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Paolo A Netti
- Center for Advanced Biomaterials for Healthcare@CRIB, Istituto Italiano di Tecnologia (IIT), Largo Barsanti e Matteucci 53, 80125 Naples, Italy and Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy.
| | - Filippo Causa
- Interdisciplinary Research Centre on Biomaterials (CRIB) and Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università degli Studi di Napoli "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy.
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44
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Rubin M, Stein O, Turko NA, Nygate Y, Roitshtain D, Karako L, Barnea I, Giryes R, Shaked NT. TOP-GAN: Stain-free cancer cell classification using deep learning with a small training set. Med Image Anal 2019; 57:176-185. [DOI: 10.1016/j.media.2019.06.014] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2018] [Revised: 05/18/2019] [Accepted: 06/25/2019] [Indexed: 01/01/2023]
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Soto JM, Mas A, Rodrigo JA, Alieva T, Domínguez-Bernal G. Label-free bioanalysis of Leishmania infantum using refractive index tomography with partially coherent illumination. JOURNAL OF BIOPHOTONICS 2019; 12:e201900030. [PMID: 31081235 DOI: 10.1002/jbio.201900030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 04/09/2019] [Accepted: 05/10/2019] [Indexed: 06/09/2023]
Abstract
In this work, we report the use of refractive index (RI) tomography for quantitative analysis of unstained DH82 cell line infected with Leishmania infantum. The cell RI is reconstructed by using a modality of optical diffraction tomography technique that employs partially coherent illumination, thus enabling inherent compatibility with conventional wide-field microscopes. The experimental results demonstrate that the cell dry mass concentration (DMC) obtained from the RI allows for reliable detection and quantitative characterization of the infection and its temporal evolution. The RI provides important insight for studying morphological changes, particularly membrane blebbing linked to an apoptosis (cell death) process induced by the disease. Moreover, the results evidence that infected DH82 cells exhibit a higher DMC than healthy samples. These findings open up promising perspectives for clinical diagnosis of Leishmania.
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Affiliation(s)
- Juan M Soto
- Department of Optics, Faculty of Physical Sciences, Complutense University of Madrid, Madrid, Spain
| | - Alicia Mas
- Department of Animal Health, Faculty of Veterinary Science, Complutense University of Madrid, Madrid, Spain
| | - José A Rodrigo
- Department of Optics, Faculty of Physical Sciences, Complutense University of Madrid, Madrid, Spain
| | - Tatiana Alieva
- Department of Optics, Faculty of Physical Sciences, Complutense University of Madrid, Madrid, Spain
| | - Gustavo Domínguez-Bernal
- Department of Animal Health, Faculty of Veterinary Science, Complutense University of Madrid, Madrid, Spain
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Tanhaemami M, Alizadeh E, Sanders CK, Marrone BL, Munsky B. Using flow cytometry and multistage machine learning to discover label-free signatures of algal lipid accumulation. Phys Biol 2019; 16:055001. [PMID: 31234155 PMCID: PMC6646084 DOI: 10.1088/1478-3975/ab2c60] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Most applications of flow cytometry or cell sorting rely on the conjugation of fluorescent dyes to specific biomarkers. However, labeled biomarkers are not always available, they can be costly, and they may disrupt natural cell behavior. Label-free quantification based upon machine learning approaches could help correct these issues, but label replacement strategies can be very difficult to discover when applied labels or other modifications in measurements inadvertently modify intrinsic cell properties. Here we demonstrate a new, but simple approach based upon feature selection and linear regression analyses to integrate statistical information collected from both labeled and unlabeled cell populations and to identify models for accurate label-free single-cell quantification. We verify the method’s accuracy to predict lipid content in algal cells (Picochlorum soloecismus) during a nitrogen starvation and lipid accumulation time course. Our general approach is expected to improve label-free single-cell analysis for other organisms or pathways, where biomarkers are inconvenient, expensive, or disruptive to downstream cellular processes.
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Affiliation(s)
- Mohammad Tanhaemami
- Department of Chemical and Biological Engineering, Colorado State University, Fort Collins, CO, United States of America
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Namgoong S, Kim NH. Meiotic spindle formation in mammalian oocytes: implications for human infertility. Biol Reprod 2019; 98:153-161. [PMID: 29342242 DOI: 10.1093/biolre/iox145] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2017] [Accepted: 12/27/2017] [Indexed: 12/12/2022] Open
Abstract
In the final stage of oogenesis, mammalian oocytes generate a meiotic spindle and undergo chromosome segregation to yield an egg that is ready for fertilization. Herein, we describe the recent advances in understanding the mechanisms controlling formation of the meiotic spindle in metaphase I (MI) and metaphase II (MII) in mammalian oocytes, and focus on the differences between mouse and human oocytes. Unlike mitotic cells, mammalian oocytes lack typical centrosomes that consist of two centrioles and the surrounding pericentriolar matrix proteins, which serve as microtubule-organizing centers (MTOCs) in most somatic cells. Instead, oocytes rely on different mechanisms for the formation of microtubules in MI spindles. Two different mechanisms have been described for MI spindle formation in mammalian oocytes. Chromosome-mediated microtubule formation, including RAN-mediated spindle formation and chromosomal passenger complex-mediated spindle elongation, controls the growth of microtubules from chromatin, while acentriolar MTOC-mediated microtubule formation contributes to spindle formation. Mouse oocytes utilize both chromatin- and MTOC-mediated pathways for microtubule formation. The existence of both pathways may provide a fail-safe mechanism to ensure high fidelity of chromosome segregation during meiosis. Unlike mouse oocytes, human oocytes considered unsuitable for clinical in vitro fertilization procedures, lack MTOCs; this may explain why meiosis in human oocytes is often error-prone. Understanding the mechanisms of MI/MII spindle formation, spindle assembly checkpoint, and chromosome segregation, in mammalian oocytes, will provide valuable insights into the molecular mechanisms of human infertility.
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Affiliation(s)
| | - Nam-Hyung Kim
- Department of Animal Science, Chungbuk National University, Cheong-Ju, Chungbuk, Republic of Korea
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LEE KYEOREH, SHIN SEUNGWOO, YAQOOB ZAHID, SO PETERTC, PARK YONGKEUN. Low-coherent optical diffraction tomography by angle-scanning illumination. JOURNAL OF BIOPHOTONICS 2019; 12:e201800289. [PMID: 30597743 PMCID: PMC6470054 DOI: 10.1002/jbio.201800289] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2018] [Revised: 11/27/2018] [Accepted: 12/28/2018] [Indexed: 05/20/2023]
Abstract
Temporally low-coherent optical diffraction tomography (ODT) is proposed and demonstrated based on angle-scanning Mach-Zehnder interferometry. Using a digital micromirror device based on diffractive tilting, the full-field interference of incoherent light is successfully maintained during every angle-scanning sequences. Further, current ODT reconstruction principles for temporally incoherent illuminations are thoroughly reviewed and developed. Several limitations of incoherent illumination are also discussed, such as the nondispersive assumption, optical sectioning capacity and illumination angle limitation. Using the proposed setup and reconstruction algorithms, low-coherent ODT imaging of plastic microspheres, human red blood cells and rat pheochromocytoma cells is experimentally demonstrated.
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Affiliation(s)
- KYEOREH LEE
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon 34141, Republic of Korea
| | - SEUNGWOO SHIN
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon 34141, Republic of Korea
| | - ZAHID YAQOOB
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
| | - PETER T. C. SO
- Laser Biomedical Research Center, G. R. Harrison Spectroscopy Laboratory, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts 02139, USA
- Department of Mechanical Engineering, MIT, Cambridge, Massachusetts 02139, USA
- Department of Biological Engineering, MIT, Cambridge, Massachusetts 02139, USA
| | - YONGKEUN PARK
- Department of Physics, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea
- KAIST Institute for Health Science and Technology, Daejeon 34141, Republic of Korea
- Tomocube Inc., Daejeon 34051, Republic of Korea
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Lam VK, Nguyen T, Phan T, Chung BM, Nehmetallah G, Raub CB. Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines. Cytometry A 2019; 95:757-768. [PMID: 31008570 DOI: 10.1002/cyto.a.23774] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/22/2019] [Accepted: 04/03/2019] [Indexed: 12/29/2022]
Abstract
Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non-cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry.
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Affiliation(s)
- Van K Lam
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC
| | - Thanh Nguyen
- Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC
| | - Thuc Phan
- Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC
| | - Byung-Min Chung
- Department of Biology, The Catholic University of America, Washington, DC
| | - George Nehmetallah
- Department of Electrical Engineering and Computer Science, The Catholic University of America, Washington, DC
| | - Christopher B Raub
- Department of Biomedical Engineering, The Catholic University of America, Washington, DC
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Karandikar SH, Zhang C, Meiyappan A, Barman I, Finck C, Srivastava PK, Pandey R. Reagent-Free and Rapid Assessment of T Cell Activation State Using Diffraction Phase Microscopy and Deep Learning. Anal Chem 2019; 91:3405-3411. [PMID: 30741527 PMCID: PMC6423970 DOI: 10.1021/acs.analchem.8b04895] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
CD8+ T cells constitute an essential compartment of the adaptive immune system. During immune responses, naı̈ve T cells become functional, as they are primed with their cognate determinants by the antigen presenting cells. Current methods of identifying activated CD8+ T cells are laborious, time-consuming and expensive due to the extensive list of required reagents. Here, we demonstrate an optical imaging approach featuring quantitative phase imaging to distinguish activated CD8+ T cells from naı̈ve CD8+ T cells in a rapid and reagent-free manner. We measured the dry mass of live cells and employed transport-based morphometry to better understand their differential morphological attributes. Our results reveal that, upon activation, the dry cell mass of T cells increases significantly in comparison to that of unstimulated cells. By employing deep learning formalism, we are able to accurately predict the population ratios of unknown mixed population based on the acquired quantitative phase images. We envision that, with further refinement, this label-free method of T cell phenotyping will lead to a rapid and cost-effective platform for assaying T cell responses to candidate antigens in the near future.
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Affiliation(s)
- Sukrut Hemant Karandikar
- Department of Immunology, University of Connecticut School of Medicine, Farmington, Connecticut 06030, United States
| | - Chi Zhang
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Akilan Meiyappan
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Ishan Barman
- Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, United States
- Department of Oncology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Christine Finck
- Department of Surgery, Connecticut Children’s Medical Center, Harford, Connecticut United States
- Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut 06032, United States
| | - Pramod Kumar Srivastava
- Department of Immunology, University of Connecticut School of Medicine, Farmington, Connecticut 06030, United States
- Carole and Ray Neag Comprehensive Cancer Center, University of Connecticut School of Medicine, Farmington, Connecticut 06030, United States
| | - Rishikesh Pandey
- Connecticut Children’s Innovation Center, University of Connecticut School of Medicine, Farmington, Connecticut 06032, United States
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