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Yoo J, Kim J, Lee J, Kim HH. Red blood cell trapping using single-beam acoustic tweezers in the Rayleigh regime. iScience 2023; 26:108178. [PMID: 37915606 PMCID: PMC10616376 DOI: 10.1016/j.isci.2023.108178] [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: 04/04/2023] [Revised: 08/02/2023] [Accepted: 10/09/2023] [Indexed: 11/03/2023] Open
Abstract
Acoustic tweezers (ATs) are a promising technology that can trap and manipulate microparticles or cells with the focused ultrasound beam without physical contact. Unlike optical tweezers, ATs may be used for in vivo studies because they can manipulate cells through tissues. However, in previous non-invasive microparticle trapping studies, ATs could only trap spherical particles, such as beads. Here, we present a theoretical analysis of how the acoustic beam traps red blood cells (RBCs) with experimental demonstration. The proposed modeling shows that the trapping of a non-spherical, biconcave-shaped RBC could be successfully done by single-beam acoustic tweezers (SBATs). We demonstrate this by trapping RBCs using SBATs in the Rayleigh regime, where the cell size is smaller than the wavelength of the beam. Suggested SBAT is a promising tool for cell transportation and sorting.
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Affiliation(s)
- Jinhee Yoo
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang-si, Gyeongbuk 37673, Republic of Korea
| | - Jinhyuk Kim
- Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Jungwoo Lee
- Department of Electronic Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Hyung Ham Kim
- School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang-si, Gyeongbuk 37673, Republic of Korea
- Department of Convergence IT Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang-si, Gyeongbuk 37673, Republic of Korea
- Department of Electrical Engineering, Pohang University of Science and Technology (POSTECH), 77 Cheongam-ro, Nam-gu, Pohang-si, Gyeongbuk 37673, Republic of Korea
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Zeng Y, Hao J, Zhang J, Jiang L, Youn S, Lu G, Yan D, Kang H, Sun Y, Shung KK, Shen K, Zhou Q. Manipulation and Mechanical Deformation of Leukemia Cells by High-Frequency Ultrasound Single Beam. IEEE TRANSACTIONS ON ULTRASONICS, FERROELECTRICS, AND FREQUENCY CONTROL 2022; 69:1889-1897. [PMID: 35468061 PMCID: PMC9753557 DOI: 10.1109/tuffc.2022.3170074] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Ultrasound single-beam acoustic tweezer system has attracted increasing attention in the field of biomechanics. Cell biomechanics play a pivotal role in leukemia cell functions. To better understand and compare the cell mechanics of the leukemia cells, herein, we fabricated an acoustic tweezer system in-house connected with a 50-MHz high-frequency cylinder ultrasound transducer. Selected leukemia cells (Jurkat, K562, and MV-411 cells) were cultured, trapped, and manipulated by high-frequency ultrasound single beam, which was transmitted from the ultrasound transducer without contacting any cells. The relative deformability of each leukemia cell was measured, characterized, and compared, and the leukemia cell (Jurkat cell) gaining the highest deformability was highlighted. Our results demonstrate that the high-frequency ultrasound single beam can be utilized to manipulate and characterize leukemia cells, which can be applied to study potential mechanisms in the immune system and cell biomechanics in other cell types.
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Youn S, Lee K, Son J, Yang IH, Hwang JY. Fully-automatic deep learning-based analysis for determination of the invasiveness of breast cancer cells in an acoustic trap. BIOMEDICAL OPTICS EXPRESS 2020; 11:2976-2995. [PMID: 32637236 PMCID: PMC7316006 DOI: 10.1364/boe.390558] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 04/28/2020] [Accepted: 04/28/2020] [Indexed: 05/03/2023]
Abstract
A single-beam acoustic trapping technique has been shown to be very useful for determining the invasiveness of suspended breast cancer cells in an acoustic trap with a manual calcium analysis method. However, for the rapid translation of the technology into the clinic, the development of an efficient/accurate analytical method is needed. We, therefore, develop a fully-automatic deep learning-based calcium image analysis algorithm for determining the invasiveness of suspended breast cancer cells using a single-beam acoustic trapping system. The algorithm allows to segment cells, find trapped cells, and quantify their calcium changes over time. For better segmentation of calcium fluorescent cells even with vague boundaries, a novel deep learning architecture with multi-scale/multi-channel convolution operations (MM-Net) is devised and constructed by a target inversion training method. The MM-Net outperforms other deep learning models in the cell segmentation. Also, a detection/quantification algorithm is developed and implemented to automatically determine the invasiveness of a trapped cell. For the evaluation of the algorithm, it is applied to quantify the invasiveness of breast cancer cells. The results show that the algorithm offers similar performance to the manual calcium analysis method for determining the invasiveness of cancer cells, suggesting that it may serve as a novel tool to automatically determine the invasiveness of cancer cells with high-efficiency.
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Affiliation(s)
- Sangyeon Youn
- Daegu Gyeongbuk Institute of Science and Technology,Department of Information and Communication Engineering, 333 Techno Jungang-daero, Hyeonpung-myun, Dalseong-gun, Daegu, 42988, South Korea
- S. Youn and K. Lee are equally contributed to this study
| | - Kyungsu Lee
- Daegu Gyeongbuk Institute of Science and Technology,Department of Information and Communication Engineering, 333 Techno Jungang-daero, Hyeonpung-myun, Dalseong-gun, Daegu, 42988, South Korea
- S. Youn and K. Lee are equally contributed to this study
| | - Jeehoon Son
- Daegu Gyeongbuk Institute of Science and Technology,Department of Information and Communication Engineering, 333 Techno Jungang-daero, Hyeonpung-myun, Dalseong-gun, Daegu, 42988, South Korea
| | - In-Hwan Yang
- Kyonggi University, Department of Chemical Engineering, 154-42, Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do, 16227, South Korea
| | - Jae Youn Hwang
- Daegu Gyeongbuk Institute of Science and Technology,Department of Information and Communication Engineering, 333 Techno Jungang-daero, Hyeonpung-myun, Dalseong-gun, Daegu, 42988, South Korea
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Lim HG, Lee OJ, Shung KK, Kim JT, Kim HH. Classification of Breast Cancer Cells Using the Integration of High-Frequency Single-Beam Acoustic Tweezers and Convolutional Neural Networks. Cancers (Basel) 2020; 12:cancers12051212. [PMID: 32408544 PMCID: PMC7281163 DOI: 10.3390/cancers12051212] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/06/2020] [Accepted: 05/09/2020] [Indexed: 12/20/2022] Open
Abstract
Single-beam acoustic tweezers (SBAT) is a widely used trapping technique to manipulate microscopic particles or cells. Recently, the characterization of a single cancer cell using high-frequency (>30 MHz) SBAT has been reported to determine its invasiveness and metastatic potential. Investigation of cell elasticity and invasiveness is based on the deformability of cells under SBAT’s radiation forces, and in general, more physically deformed cells exhibit higher levels of invasiveness and therefore higher metastatic potential. However, previous imaging analysis to determine substantial differences in cell deformation, where the SBAT is turned ON or OFF, relies on the subjective observation that may vary and requires follow-up evaluations from experts. In this study, we propose an automatic and reliable cancer cell classification method based on SBAT and a convolutional neural network (CNN), which provides objective and accurate quantitative measurement results. We used a custom-designed 50 MHz SBAT transducer to obtain a series of images of deformed human breast cancer cells. CNN-based classification methods with data augmentation applied to collected images determined and validated the metastatic potential of cancer cells. As a result, with the selected optimizers, precision, and recall of the model were found to be greater than 0.95, which highly validates the classification performance of our integrated method. CNN-guided cancer cell deformation analysis using SBAT may be a promising alternative to current histological image analysis, and this pretrained model will significantly reduce the evaluation time for a larger population of cells.
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Affiliation(s)
- Hae Gyun Lim
- Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea; (H.G.L.); (O.-J.L.)
| | - O-Joun Lee
- Future IT Innovation Laboratory, Pohang University of Science and Technology, Pohang 37673, Korea; (H.G.L.); (O.-J.L.)
| | - K. Kirk Shung
- Department of Biomedical Engineering, University of Southern California, Los Angeles, CA 90089, USA;
| | - Jin-Taek Kim
- Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Correspondence: (J.-T.K.); (H.H.K.); Tel.: +82-54-279-8853 (J.-T.K.); +82-54-279-8864 (H.H.K.)
| | - Hyung Ham Kim
- Department of Creative IT Engineering, Pohang University of Science and Technology, Pohang 37673, Korea
- Correspondence: (J.-T.K.); (H.H.K.); Tel.: +82-54-279-8853 (J.-T.K.); +82-54-279-8864 (H.H.K.)
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