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Zhou C, Tian Y, Li G, Ye Y, Gao L, Li J, Liu Z, Su H, Lu Y, Li M, Zhou Z, Wei X, Qin L, Tao TH, Sun L. Through-polymer, via technology-enabled, flexible, lightweight, and integrated devices for implantable neural probes. MICROSYSTEMS & NANOENGINEERING 2024; 10:54. [PMID: 38654844 PMCID: PMC11035623 DOI: 10.1038/s41378-024-00691-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/29/2024] [Accepted: 03/11/2024] [Indexed: 04/26/2024]
Abstract
In implantable electrophysiological recording systems, the headstage typically comprises neural probes that interface with brain tissue and integrated circuit chips for signal processing. While advancements in MEMS and CMOS technology have significantly improved these components, their interconnection still relies on conventional printed circuit boards and sophisticated adapters. This conventional approach adds considerable weight and volume to the package, especially for high channel count systems. To address this issue, we developed a through-polymer via (TPV) method inspired by the through-silicon via (TSV) technique in advanced three-dimensional packaging. This innovation enables the vertical integration of flexible probes, amplifier chips, and PCBs, realizing a flexible, lightweight, and integrated device (FLID). The total weight of the FLIDis only 25% that of its conventional counterparts relying on adapters, which significantly increased the activity levels of animals wearing the FLIDs to nearly match the levels of control animals without implants. Furthermore, by incorporating a platinum-iridium alloy as the top layer material for electrical contact, the FLID realizes exceptional electrical performance, enabling in vivo measurements of both local field potentials and individual neuron action potentials. These findings showcase the potential of FLIDs in scaling up implantable neural recording systems and mark a significant advancement in the field of neurotechnology.
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Grants
- This work was partially supported by the National Key R & D Program of China (Grant Nos. 2021ZD0201600, 2022YFF0706504, 2022ZD0209300, 2019YFA0905200, 2021YFC2501500, 2021YFF1200700, 2022ZD0212300), National Natural Science Foundation of China (Grant No. 61974154), Key Research Program of Frontier Sciences, CAS (Grant No. ZDBS-LY-JSC024), Shanghai Pilot Program for Basic Research-Chinese Academy of Science, Shanghai Branch (Grant No. JCYJ-SHFY-2022-01 and JCYJ-SHFY-2022-0xx), Shanghai Municipal Science and Technology Major Project (Grant No. 2021SHZDZX), CAS Pioneer Hundred Talents Program, Shanghai Pujiang Program (Grant Nos. 21PJ1415100, 19PJ1410900), the Science and Technology Commission Foundation of Shanghai (Nos. 21JM0010200 and 21142200300), Shanghai Rising-Star Program (Grant No. 22QA1410900), Shanghai Sailing Program (No. 22YF1454700), the Innovative Research Team of High-level Local Universities in Shanghai, the Jiangxi Province 03 Special Project and 5G Project (Grant No. 20212ABC03W07), Fund for Central Government in Guidance of Local Science and Technology Development (Grant No. 20201ZDE04013), Special Fund for Science and Technology Innovation Strategy of Guangdong Province (Grant Nos. 2021B0909060002, 2021B0909050004).
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Affiliation(s)
- Cunkai Zhou
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ye Tian
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
| | - Gen Li
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
| | - Yifei Ye
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Lusha Gao
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Jiazhi Li
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Ziwei Liu
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Haoyang Su
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yunxiao Lu
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Meng Li
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Zhitao Zhou
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Xiaoling Wei
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
| | - Lunming Qin
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
| | - Tiger H. Tao
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing, China
- School of Physical Science and Technology, ShanghaiTech University, Shanghai, China
- Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Neuroxess Co., Ltd. (Jiangxi), Nanchang, Jiangxi China
- Guangdong Institute of Intelligence Science and Technology, Hengqin, Zhuhai, Guangdong China
- Tianqiao and Chrissy Chen Institute for Translational Research, Shanghai, China
| | - Liuyang Sun
- College of Electronics and Information Engineering, Shanghai University of Electric Power, Shanghai, China
- 2020 X-Lab, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
- School of Graduate Study, University of Chinese Academy of Sciences, Beijing, China
- State Key Laboratory of Transducer Technology, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai, China
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Guo Z, Ai N, Ge W, Xu Q. Design of an automated robotic microinjection system for batch injection of zebrafish embryos and larvae. MICROSYSTEMS & NANOENGINEERING 2024; 10:20. [PMID: 38292776 PMCID: PMC10825123 DOI: 10.1038/s41378-023-00645-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 10/28/2023] [Accepted: 11/22/2023] [Indexed: 02/01/2024]
Abstract
This paper presents the design of a vision-based automated robotic microinjection system for batch injection of both zebrafish embryos and larvae. A novel visual recognition algorithm based on an automatic threshold and excessive dilatation is introduced to accurately identify the center of zebrafish embryos and larval yolks. A corresponding software system is developed using the producer-consumer model as the framework structure, and a friendly user interface is designed to allow operators to choose from a range of desired functions according to their different needs. In addition, a novel microstructural agarose device is designed and fabricated to simultaneously immobilize mixed batches of embryos and larvae. Moreover, a prototype microinjection system is fabricated by integrating hardware devices with visual algorithms. An experimental study is conducted to verify the performance of the robotic microinjection system. The results show that the reported system can accurately identify zebrafish embryos and larvae and efficiently complete batch microinjection tasks of the mixtures with an injection success rate of 92.05% in 13.88 s per sample. Compared with manual and existing microinjection systems, the proposed system demonstrates the merits of versatility, excellent efficiency, high success rate, high survival rate, and sufficient stability.
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Affiliation(s)
- Zhongyi Guo
- Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Nana Ai
- Department of Biomedical Sciences and Centre of Reproduction, Development and Aging (CRDA), Faculty of Health Sciences, University of Macau, Macau, China
| | - Wei Ge
- Department of Biomedical Sciences and Centre of Reproduction, Development and Aging (CRDA), Faculty of Health Sciences, University of Macau, Macau, China
| | - Qingsong Xu
- Department of Electromechanical Engineering, Faculty of Science and Technology, University of Macau, Macau, China
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Lv S, He E, Luo J, Liu Y, Liang W, Xu S, Zhang K, Yang Y, Wang M, Song Y, Wu Y, Cai X. Using Human-Induced Pluripotent Stem Cell Derived Neurons on Microelectrode Arrays to Model Neurological Disease: A Review. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301828. [PMID: 37863819 PMCID: PMC10667858 DOI: 10.1002/advs.202301828] [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: 03/22/2023] [Revised: 09/04/2023] [Indexed: 10/22/2023]
Abstract
In situ physiological signals of in vitro neural disease models are essential for studying pathogenesis and drug screening. Currently, an increasing number of in vitro neural disease models are established using human-induced pluripotent stem cell (hiPSC) derived neurons (hiPSC-DNs) to overcome interspecific gene expression differences. Microelectrode arrays (MEAs) can be readily interfaced with two-dimensional (2D), and more recently, three-dimensional (3D) neural stem cell-derived in vitro models of the human brain to monitor their physiological activity in real time. Therefore, MEAs are emerging and useful tools to model neurological disorders and disease in vitro using human iPSCs. This is enabling a real-time window into neuronal signaling at the network scale from patient derived. This paper provides a comprehensive review of MEA's role in analyzing neural disease models established by hiPSC-DNs. It covers the significance of MEA fabrication, surface structure and modification schemes for hiPSC-DNs culturing and signal detection. Additionally, this review discusses advances in the development and use of MEA technology to study in vitro neural disease models, including epilepsy, autism spectrum developmental disorder (ASD), and others established using hiPSC-DNs. The paper also highlights the application of MEAs combined with hiPSC-DNs in detecting in vitro neurotoxic substances. Finally, the future development and outlook of multifunctional and integrated devices for in vitro medical diagnostics and treatment are discussed.
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Affiliation(s)
- Shiya Lv
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Enhui He
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
- The State Key Lab of Brain‐Machine IntelligenceZhejiang UniversityHangzhou321100China
| | - Jinping Luo
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yaoyao Liu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Wei Liang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Shihong Xu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Kui Zhang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yan Yang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Mixia Wang
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yilin Song
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Yirong Wu
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
| | - Xinxia Cai
- State Key Laboratory of Transducer TechnologyAerospace Information Research InstituteChinese Academy of SciencesBeijing100190China
- University of Chinese Academy of SciencesBeijing100049China
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