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Marvin Tan XH, Wang Y, Zhu X, Mendes FN, Chung PS, Chow YT, Man T, Lan H, Lin YJ, Zhang X, Zhang X, Nguyen T, Ardehali R, Teitell MA, Deb A, Chiou PY. Massive field-of-view sub-cellular traction force videography enabled by Single-Pixel Optical Tracers (SPOT). Biosens Bioelectron 2024; 258:116318. [PMID: 38701538 DOI: 10.1016/j.bios.2024.116318] [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: 02/07/2024] [Revised: 04/15/2024] [Accepted: 04/17/2024] [Indexed: 05/05/2024]
Abstract
We report a massive field-of-view and high-speed videography platform for measuring the sub-cellular traction forces of more than 10,000 biological cells over 13 mm2 at 83 frames per second. Our Single-Pixel Optical Tracers (SPOT) tool uses 2-dimensional diffraction gratings embedded into a soft substrate to convert cells' mechanical traction force into optical colors detectable by a video camera. The platform measures the sub-cellular traction forces of diverse cell types, including tightly connected tissue sheets and near isolated cells. We used this platform to explore the mechanical wave propagation in a tightly connected sheet of Neonatal Rat Ventricular Myocytes (NRVMs) and discovered that the activation time of some tissue regions are heterogeneous from the overall spiral wave behavior of the cardiac wave.
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Affiliation(s)
- Xing Haw Marvin Tan
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Electronics and Photonics, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, 138632, Singapore
| | - Yijie Wang
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Xiongfeng Zhu
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Felipe Nanni Mendes
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Pei-Shan Chung
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Yu Ting Chow
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Tianxing Man
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Hsin Lan
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Yen-Ju Lin
- Department of Electrical and Computer Engineering, University of California at Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Xiang Zhang
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Xiaohe Zhang
- Department of Mathematics, University of California Los Angeles, 520 Portola Plaza, Los Angeles, CA, 90095, United States
| | - Thang Nguyen
- Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States
| | - Reza Ardehali
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Michael A Teitell
- Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Arjun Deb
- Division of Cardiology, Department of Medicine, David Geffen School of Medicine, University of California Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, United States
| | - Pei-Yu Chiou
- Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States; Department of Bioengineering, University of California Los Angeles, Westwood Plaza, Los Angeles, CA, 90095, United States.
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Aubry G, Lee HJ, Lu H. Advances in Microfluidics: Technical Innovations and Applications in Diagnostics and Therapeutics. Anal Chem 2023; 95:444-467. [PMID: 36625114 DOI: 10.1021/acs.analchem.2c04562] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Guillaume Aubry
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hyun Jee Lee
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Hang Lu
- School of Chemical & Biomolecular Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States.,Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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