1
|
Leng Y, Li X, Zheng F, Liu H, Wang C, Wang X, Liao Y, Liu J, Meng K, Yu J, Zhang J, Wang B, Tan Y, Liu M, Jia X, Li D, Li Y, Gu Z, Fan Y. Advances in In Vitro Models of Neuromuscular Junction: Focusing on Organ-on-a-Chip, Organoids, and Biohybrid Robotics. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2023; 35:e2211059. [PMID: 36934404 DOI: 10.1002/adma.202211059] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Revised: 02/18/2023] [Indexed: 06/18/2023]
Abstract
The neuromuscular junction (NMJ) is a peripheral synaptic connection between presynaptic motor neurons and postsynaptic skeletal muscle fibers that enables muscle contraction and voluntary motor movement. Many traumatic, neurodegenerative, and neuroimmunological diseases are classically believed to mainly affect either the neuronal or the muscle side of the NMJ, and treatment options are lacking. Recent advances in novel techniques have helped develop in vitro physiological and pathophysiological models of the NMJ as well as enable precise control and evaluation of its functions. This paper reviews the recent developments in in vitro NMJ models with 2D or 3D cultures, from organ-on-a-chip and organoids to biohybrid robotics. Related derivative techniques are introduced for functional analysis of the NMJ, such as the patch-clamp technique, microelectrode arrays, calcium imaging, and stimulus methods, particularly optogenetic-mediated light stimulation, microelectrode-mediated electrical stimulation, and biochemical stimulation. Finally, the applications of the in vitro NMJ models as disease models or for drug screening related to suitable neuromuscular diseases are summarized and their future development trends and challenges are discussed.
Collapse
Affiliation(s)
- Yubing Leng
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Xiaorui Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Fuyin Zheng
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Hui Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Chunyan Wang
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Xudong Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Yulong Liao
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Jiangyue Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Kaiqi Meng
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Jiaheng Yu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Jingyi Zhang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Binyu Wang
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Yingjun Tan
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China
| | - Meili Liu
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Xiaoling Jia
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Deyu Li
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| | - Yinghui Li
- State Key Laboratory of Space Medicine Fundamentals and Application, China Astronaut Research and Training Center, Beijing, 100094, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, 210096, China
| | - Yubo Fan
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, and with the School of Engineering Medicine, Beihang University, Beijing, 100083, China
| |
Collapse
|
3
|
Fusco F, Perottoni S, Giordano C, Riva A, Iannone LF, De Caro C, Russo E, Albani D, Striano P. The microbiota‐gut‐brain axis and epilepsy from a multidisciplinary perspective: clinical evidence and technological solutions for improvement of
in vitro
preclinical models. Bioeng Transl Med 2022; 7:e10296. [PMID: 35600638 PMCID: PMC9115712 DOI: 10.1002/btm2.10296] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 01/10/2022] [Accepted: 01/15/2022] [Indexed: 11/09/2022] Open
Affiliation(s)
- Federica Fusco
- Dipartimento di Chimica, materiali e ingegneria chimica "Giulio Natta" Politecnico di Milano Milan Italy
| | - Simone Perottoni
- Dipartimento di Chimica, materiali e ingegneria chimica "Giulio Natta" Politecnico di Milano Milan Italy
| | - Carmen Giordano
- Dipartimento di Chimica, materiali e ingegneria chimica "Giulio Natta" Politecnico di Milano Milan Italy
| | - Antonella Riva
- Paediatric Neurology and Muscular Disease Unit, IRCCS Istituto Giannina Gaslini Genova Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health Università degli Studi di Genova Genova Italy
| | | | - Carmen De Caro
- Science of Health Department Magna Graecia University Catanzaro Italy
| | - Emilio Russo
- Science of Health Department Magna Graecia University Catanzaro Italy
| | - Diego Albani
- Istituto di Ricerche Farmacologiche Mario Negri IRCCS Milan Italy
| | - Pasquale Striano
- Paediatric Neurology and Muscular Disease Unit, IRCCS Istituto Giannina Gaslini Genova Italy
- Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health Università degli Studi di Genova Genova Italy
| |
Collapse
|
4
|
Xu W, Song Y, Chen S, Xue C, Hu G, Qi W, Ma W, Lin X, Chen J. An ALE Meta-Analysis of Specific Functional MRI Studies on Subcortical Vascular Cognitive Impairment. Front Neurol 2021; 12:649233. [PMID: 34630270 PMCID: PMC8492914 DOI: 10.3389/fneur.2021.649233] [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: 01/06/2021] [Accepted: 07/28/2021] [Indexed: 11/23/2022] Open
Abstract
Background: Subcortical vascular cognitive impairment (sVCI), caused by cerebral small vessel disease, accounts for the majority of vascular cognitive impairment, and is characterized by an insidious onset and impaired memory and executive function. If not recognized early, it inevitably develops into vascular dementia. Several quantitative studies have reported the consistent results of brain regions in sVCI patients that can be used to predict dementia conversion. The purpose of the study was to explore the exact abnormalities within the brain in sVCI patients by combining the coordinates reported in previous studies. Methods: The PubMed, Embase, and Web of Science databases were thoroughly searched to obtain neuroimaging articles on the amplitude of low-frequency fluctuation, regional homogeneity, and functional connectivity in sVCI patients. According to the activation likelihood estimation (ALE) algorithm, a meta-analysis based on coordinate and functional connectivity modeling was conducted. Results: The quantitative meta-analysis included 20 functional imaging studies on sVCI patients. Alterations in specific brain regions were mainly concentrated in the frontal lobes including the middle frontal gyrus, superior frontal gyrus, medial frontal gyrus, and precentral gyrus; parietal lobes including the precuneus, angular gyrus, postcentral gyrus, and inferior parietal lobule; occipital lobes including the lingual gyrus and cuneus; temporal lobes including the fusiform gyrus and middle temporal gyrus; and the limbic system including the cingulate gyrus. These specific brain regions belonged to important networks known as the default mode network, the executive control network, and the visual network. Conclusion: The present study determined specific abnormal brain regions in sVCI patients, and these brain regions with specific changes were found to belong to important brain functional networks. The findings objectively present the exact abnormalities within the brain, which help further understand the pathogenesis of sVCI and identify them as potential imaging biomarkers. The results may also provide a basis for new approaches to treatment.
Collapse
Affiliation(s)
- Wenwen Xu
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Yu Song
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Shanshan Chen
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Chen Xue
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Guanjie Hu
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China
| | - Wenzhang Qi
- Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Wenying Ma
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Xingjian Lin
- Department of Neurology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| | - Jiu Chen
- Institute of Brain Functional Imaging, Nanjing Medical University, Nanjing, China.,Institute of Neuropsychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, China
| |
Collapse
|
5
|
Park MU, Bae Y, Lee KS, Song JH, Lee SM, Yoo KH. Collective dynamics of neuronal activities in various modular networks. LAB ON A CHIP 2021; 21:951-961. [PMID: 33475100 DOI: 10.1039/d0lc01106a] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Modularity is a key feature of structural and functional brain networks. However, the association between the structure and function of modular brain networks has not been revealed. We constructed three types of modular cortical networks in vitro and investigated their neuronal activities. The modular networks comprising 4, 3, or 2 modules were constructed using polydimethylsiloxane (PDMS) microstructures fabricated directly on a multi-electrode array (MEA) without transfer. The 4-module network had the strongest modular connectivity, followed by the 3-module and 2-module networks. To investigate how neuronal activities were affected by the modular network structure, spontaneous neuronal activities were recorded on different days in vitro and analyzed based on spike amplitudes, network bursts, and the propagation properties of individual spikes. Different characteristics were observed depending on the network topology and modular connectivity. Moreover, when an electrode was stimulated by biphasic voltage pulses, bursts were elicited for the 4-module network, whereas spikes were elicited for the 3-module and 2-module networks. Direct fabrication of the PDMS microstructures on the MEA without transfer allows microscale construction of modular networks and high-density functional recording; therefore, the technique utilizing the PDMS microstructures can be applied to the systematic study of the dynamics of modular neuronal networks in vitro.
Collapse
Affiliation(s)
- Myung Uk Park
- Department of Physics, Yonsei University, Seoul 03722, Republic of Korea.
| | | | | | | | | | | |
Collapse
|
6
|
Ferrari E, Palma C, Vesentini S, Occhetta P, Rasponi M. Integrating Biosensors in Organs-on-Chip Devices: A Perspective on Current Strategies to Monitor Microphysiological Systems. BIOSENSORS 2020; 10:E110. [PMID: 32872228 PMCID: PMC7558092 DOI: 10.3390/bios10090110] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/25/2020] [Accepted: 08/26/2020] [Indexed: 01/20/2023]
Abstract
Organs-on-chip (OoC), often referred to as microphysiological systems (MPS), are advanced in vitro tools able to replicate essential functions of human organs. Owing to their unprecedented ability to recapitulate key features of the native cellular environments, they represent promising tools for tissue engineering and drug screening applications. The achievement of proper functionalities within OoC is crucial; to this purpose, several parameters (e.g., chemical, physical) need to be assessed. Currently, most approaches rely on off-chip analysis and imaging techniques. However, the urgent demand for continuous, noninvasive, and real-time monitoring of tissue constructs requires the direct integration of biosensors. In this review, we focus on recent strategies to miniaturize and embed biosensing systems into organs-on-chip platforms. Biosensors for monitoring biological models with metabolic activities, models with tissue barrier functions, as well as models with electromechanical properties will be described and critically evaluated. In addition, multisensor integration within multiorgan platforms will be further reviewed and discussed.
Collapse
Affiliation(s)
| | | | | | | | - Marco Rasponi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy; (E.F.); (C.P.); (S.V.); (P.O.)
| |
Collapse
|