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Kim J, Lee SJ. Digital in-line holographic microscopy for label-free identification and tracking of biological cells. Mil Med Res 2024; 11:38. [PMID: 38867274 PMCID: PMC11170804 DOI: 10.1186/s40779-024-00541-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Accepted: 05/31/2024] [Indexed: 06/14/2024] Open
Abstract
Digital in-line holographic microscopy (DIHM) is a non-invasive, real-time, label-free technique that captures three-dimensional (3D) positional, orientational, and morphological information from digital holographic images of living biological cells. Unlike conventional microscopies, the DIHM technique enables precise measurements of dynamic behaviors exhibited by living cells within a 3D volume. This review outlines the fundamental principles and comprehensive digital image processing procedures employed in DIHM-based cell tracking methods. In addition, recent applications of DIHM technique for label-free identification and digital tracking of various motile biological cells, including human blood cells, spermatozoa, diseased cells, and unicellular microorganisms, are thoroughly examined. Leveraging artificial intelligence has significantly enhanced both the speed and accuracy of digital image processing for cell tracking and identification. The quantitative data on cell morphology and dynamics captured by DIHM can effectively elucidate the underlying mechanisms governing various microbial behaviors and contribute to the accumulation of diagnostic databases and the development of clinical treatments.
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Affiliation(s)
- Jihwan Kim
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, 37673, Republic of Korea
| | - Sang Joon Lee
- Department of Mechanical Engineering, Pohang University of Science and Technology, Pohang, Gyeongbuk, 37673, Republic of Korea.
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Yu L, Chen L, Liu Y, Zhu J, Wang F, Ma L, Yi K, Xiao H, Zhou F, Wang F, Bai L, Zhu Y, Xiao X, Yang Y. Magnetically Actuated Hydrogel Stamping-Assisted Cellular Mechanical Analyzer for Stored Blood Quality Detection. ACS Sens 2023; 8:1183-1191. [PMID: 36867892 DOI: 10.1021/acssensors.2c02507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
Abstract
Cellular mechanical property analysis reflecting the physiological and pathological states of cells plays a crucial role in assessing the quality of stored blood. However, its complex equipment needs, operation difficulty, and clogging issues hinder automated and rapid biomechanical testing. Here, we propose a promising biosensor assisted by magnetically actuated hydrogel stamping to fulfill it. The flexible magnetic actuator triggers the collective deformation of multiple cells in the light-cured hydrogel, and it allows for on-demand bioforce stimulation with the advantages of portability, cost-effectiveness, and simplicity of operation. The magnetically manipulated cell deformation processes are captured by the integrated miniaturized optical imaging system, and the cellular mechanical property parameters are extracted from the captured images for real-time analysis and intelligent sensing. In this work, 30 clinical blood samples with different storage durations (<14 days and >14 days) were tested. A deviation of 3.3% in the differentiation of blood storage durations by this system compared to physician annotation demonstrated its feasibility. This system should broaden the application of cellular mechanical assays in diverse clinical settings.
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Affiliation(s)
- Le Yu
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
- Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Longfei Chen
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
- Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Yantong Liu
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
- Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
| | - Jiaomeng Zhu
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Fang Wang
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Linlu Ma
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Kezhen Yi
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Hui Xiao
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Fuling Zhou
- Department of Hematology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Fubing Wang
- Department of Laboratory Medicine, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan 430071, China
| | - Long Bai
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Yimin Zhu
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xuan Xiao
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
| | - Yi Yang
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan 430072, China
- Shenzhen Research Institute, Wuhan University, Shenzhen 518000, China
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Kang YJ. Biomechanical Assessment of Red Blood Cells in Pulsatile Blood Flows. MICROMACHINES 2023; 14:317. [PMID: 36838017 PMCID: PMC9958583 DOI: 10.3390/mi14020317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Revised: 01/24/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
As rheological properties are substantially influenced by red blood cells (RBCs) and plasma, the separation of their individual contributions in blood is essential. The estimation of multiple rheological factors is a critical issue for effective early detection of diseases. In this study, three rheological properties (i.e., viscoelasticity, RBC aggregation, and blood junction pressure) are measured by analyzing the blood velocity and image intensity in a microfluidic device. Using a single syringe pump, the blood flow rate sets to a pulsatile flow pattern (Qb[t] = 1 + 0.5 sin(2πt/240) mL/h). Based on the discrete fluidic circuit model, the analytical formula of the time constant (λb) as viscoelasticity is derived and obtained at specific time intervals by analyzing the pulsatile blood velocity. To obtain RBC aggregation by reducing blood velocity substantially, an air compliance unit (ACU) is used to connect polyethylene tubing (i.d. = 250 µm, length = 150 mm) to the blood channel in parallel. The RBC aggregation index (AI) is obtained by analyzing the microscopic image intensity. The blood junction pressure (β) is obtained by integrating the blood velocity within the ACU. As a demonstration, the present method is then applied to detect either RBC-aggregated blood with different concentrations of dextran solution or hardened blood with thermally shocked RBCs. Thus, it can be concluded that the present method has the ability to consistently detect differences in diluent or RBCs in terms of three rheological properties.
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Affiliation(s)
- Yang Jun Kang
- Department of Mechanical Engineering, Chosun University, 309 Pilmun-daero, Dong-gu, Gwangju 61452, Republic of Korea
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Chen D, Wang Z, Chen K, Zeng Q, Wang L, Xu X, Liang J, Chen X. Classification of unlabeled cells using lensless digital holographic images and deep neural networks. Quant Imaging Med Surg 2021; 11:4137-4148. [PMID: 34476194 DOI: 10.21037/qims-21-16] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 05/08/2021] [Indexed: 11/06/2022]
Abstract
Background Image-based cell analytic methodologies offer a relatively simple and economical way to analyze and understand cell heterogeneities and developments. Owing to developments in high-resolution image sensors and high-performance computation processors, the emerging lensless digital holography technique enables a simple and cost-effective approach to obtain label-free cell images with a large field of view and microscopic spatial resolution. Methods The holograms of three types of cells, including MCF-10A, EC-109, and MDA-MB-231 cells, were recorded using a lensless digital holography system composed of a laser diode, a sample stage, an image sensor, and a laptop computer. The amplitude images were reconstructed using the angular spectrum method, and the sample to sensor distance was determined using the autofocusing criteria based on the sparsity of image edges and corner points. Four convolutional neural networks (CNNs) were used to classify the cell types based on the recovered holographic images. Results Classification of two cell types and three cell types achieved an accuracy of higher than 91% by all the networks used. The ResNet and the DenseNet models had similar classification accuracy of 95% or greater, outperforming the GoogLeNet and the CNN-5 models. Conclusions These experiments demonstrated that the CNNs were effective at classifying two or three types of tumor cells. The lensless holography combined with machine learning holds great promise in the application of stainless cell imaging and classification, such as in cancer diagnosis and cancer biology research, where distinguishing normal cells from cancer cells and recognizing different cancer cell types will be greatly beneficial.
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Affiliation(s)
- Duofang Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Zhaohui Wang
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Kai Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Qi Zeng
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Lin Wang
- School of Computer Science, Xi'an Polytechnic University, Xi'an, China
| | - Xinyi Xu
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Jimin Liang
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
| | - Xueli Chen
- Engineering Research Center of Molecular and Neuro Imaging, Ministry of Education, School of Life Science and Technology, Xidian University, Xi'an, China
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Amann S, Witzleben MV, Breuer S. 3D-printable portable open-source platform for low-cost lens-less holographic cellular imaging. Sci Rep 2019; 9:11260. [PMID: 31375772 PMCID: PMC6677730 DOI: 10.1038/s41598-019-47689-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Accepted: 07/22/2019] [Indexed: 02/06/2023] Open
Abstract
Digital holographic microscopy is an emerging, potentially low-cost alternative to conventional light microscopy for micro-object imaging on earth, underwater and in space. Immediate access to micron-scale objects however requires a well-balanced system design and sophisticated reconstruction algorithms, that are commercially available, however not accessible cost-efficiently. Here, we present an open-source implementation of a lens-less digital inline holographic microscope platform, based on off-the-shelf optical, electronic and mechanical components, costing less than $190. It employs a Blu-Ray semiconductor-laser-pickup or a light-emitting-diode, a pinhole, a 3D-printed housing consisting of 3 parts and a single-board portable computer and camera with an open-source implementation of the Fresnel-Kirchhoff routine. We demonstrate 1.55 μm spatial resolution by laser-pickup and 3.91 μm by the light-emitting-diode source. The housing and mechanical components are 3D printed. Both printer and reconstruction software source codes are open. The light-weight microscope allows to image label-free micro-spheres of 6.5 μm diameter, human red-blood-cells of about 8 μm diameter as well as fast-growing plant Nicotiana-tabacum-BY-2 suspension cells with 50 μm sizes. The imaging capability is validated by imaging-contrast quantification involving a standardized test target. The presented 3D-printable portable open-source platform represents a fully-open design, low-cost modular and versatile imaging-solution for use in high- and low-resource areas of the world.
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Affiliation(s)
- Stephan Amann
- Institute for Applied Physics, Technische Universität Darmstadt, Schlossgartenstraße 7, 64289, Darmstadt, Germany
| | - Max von Witzleben
- Institute for Applied Physics, Technische Universität Darmstadt, Schlossgartenstraße 7, 64289, Darmstadt, Germany
| | - Stefan Breuer
- Institute for Applied Physics, Technische Universität Darmstadt, Schlossgartenstraße 7, 64289, Darmstadt, Germany.
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