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Li L, Bo W, Wang G, Juan X, Xue H, Zhang H. Progress and application of lung-on-a-chip for lung cancer. Front Bioeng Biotechnol 2024; 12:1378299. [PMID: 38854856 PMCID: PMC11157020 DOI: 10.3389/fbioe.2024.1378299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
Lung cancer is a malignant tumour with the highest incidence and mortality worldwide. Clinically effective therapy strategies are underutilized owing to the lack of efficient models for evaluating drug response. One of the main reasons for failure of anticancer drug therapy is development of drug resistance. Anticancer drugs face severe challenges such as poor biodistribution, restricted solubility, inadequate absorption, and drug accumulation. In recent years, "organ-on-a-chip" platforms, which can directly regulate the microenvironment of biomechanics, biochemistry and pathophysiology, have been developed rapidly and have shown great potential in clinical drug research. Lung-on-a-chip (LOC) is a new 3D model of bionic lungs with physiological functions created by micromachining technology on microfluidic chips. This approach may be able to partially replace animal and 2D cell culture models. To overcome drug resistance, LOC realizes personalized prediction of drug response by simulating the lung-related microenvironment in vitro, significantly enhancing therapeutic effectiveness, bioavailability, and pharmacokinetics while minimizing side effects. In this review, we present an overview of recent advances in the preparation of LOC and contrast it with earlier in vitro models. Finally, we describe recent advances in LOC. The combination of this technology with nanomedicine will provide an accurate and reliable treatment for preclinical evaluation.
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Affiliation(s)
- Lantao Li
- Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Wentao Bo
- Department of Hepatopancreatobiliary Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Guangyan Wang
- Department of General Internal Medicine, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Juan
- Department of Anesthesiology, West China Hospital, Sichuan University, Chengdu, China
| | - Haiyi Xue
- Department of Intensive Care Unit, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Hongwei Zhang
- Department of Anesthesiology, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital and Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
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2
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Jackson CE, Green NH, English WR, Claeyssens F. The use of microphysiological systems to model metastatic cancer. Biofabrication 2024; 16:032002. [PMID: 38579739 DOI: 10.1088/1758-5090/ad3b70] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/05/2024] [Indexed: 04/07/2024]
Abstract
Cancer is one of the leading causes of death in the 21st century, with metastasis of cancer attributing to 90% of cancer-related deaths. Therefore, to improve patient outcomes there is a need for better preclinical models to increase the success of translating oncological therapies into the clinic. Current traditional staticin vitromodels lack a perfusable network which is critical to overcome the diffusional mass transfer limit to provide a mechanism for the exchange of essential nutrients and waste removal, and increase their physiological relevance. Furthermore, these models typically lack cellular heterogeneity and key components of the immune system and tumour microenvironment. This review explores rapidly developing strategies utilising perfusable microphysiological systems (MPS) for investigating cancer cell metastasis. In this review we initially outline the mechanisms of cancer metastasis, highlighting key steps and identifying the current gaps in our understanding of the metastatic cascade, exploring MPS focused on investigating the individual steps of the metastatic cascade before detailing the latest MPS which can investigate multiple components of the cascade. This review then focuses on the factors which can affect the performance of an MPS designed for cancer applications with a final discussion summarising the challenges and future directions for the use of MPS for cancer models.
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Affiliation(s)
- Caitlin E Jackson
- Materials Science and Engineering, The Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, United Kingdom
- Insigneo Institute for In Silico Medicine, The Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, United Kingdom
| | - Nicola H Green
- Materials Science and Engineering, The Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, United Kingdom
- Insigneo Institute for In Silico Medicine, The Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, United Kingdom
| | - William R English
- Norwich Medical School, University of East Anglia, Norwich NR3 7TJ, United Kingdom
| | - Frederik Claeyssens
- Materials Science and Engineering, The Kroto Research Institute, University of Sheffield, Sheffield S3 7HQ, United Kingdom
- Insigneo Institute for In Silico Medicine, The Pam Liversidge Building, University of Sheffield, Sheffield S1 3JD, United Kingdom
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Ko J, Song J, Lee Y, Choi N, Kim HN. Understanding organotropism in cancer metastasis using microphysiological systems. LAB ON A CHIP 2024; 24:1542-1556. [PMID: 38192269 DOI: 10.1039/d3lc00889d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Abstract
Cancer metastasis, the leading cause of cancer-related deaths, remains a complex challenge in medical science. Stephen Paget's "seed and soil theory" introduced the concept of organotropism, suggesting that metastatic success depends on specific organ microenvironments. Understanding organotropism not only offers potential for curbing metastasis but also novel treatment strategies. Microphysiological systems (MPS), especially organ-on-a-chip models, have emerged as transformative tools in this quest. These systems, blending microfluidics, biology, and engineering, grant precise control over cell interactions within organ-specific microenvironments. MPS enable real-time monitoring, morphological analysis, and protein quantification, enhancing our comprehension of cancer dynamics, including tumor migration, vascularization, and pre-metastatic niches. In this review, we explore innovative applications of MPS in investigating cancer metastasis, particularly focusing on organotropism. This interdisciplinary approach converges the field of science, engineering, and medicine, thereby illuminating a path toward groundbreaking discoveries in cancer research.
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Affiliation(s)
- Jihoon Ko
- Department of BioNano Technology, Gachon University, Seongnam-si, Gyeonggi-do 13120, Republic of Korea.
| | - Jiyoung Song
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
| | - Yedam Lee
- Department of BioNano Technology, Gachon University, Seongnam-si, Gyeonggi-do 13120, Republic of Korea.
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea
- KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Korea
| | - Hong Nam Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea.
- Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology, Seoul 02792, Republic of Korea
- School of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul 03722, Republic of Korea
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Zhou J, Dong J, Hou H, Huang L, Li J. High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications. LAB ON A CHIP 2024; 24:1307-1326. [PMID: 38247405 DOI: 10.1039/d3lc01012k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
High-throughput microfluidic systems are widely used in biomedical fields for tasks like disease detection, drug testing, and material discovery. Despite the great advances in automation and throughput, the large amounts of data generated by the high-throughput microfluidic systems generally outpace the abilities of manual analysis. Recently, the convergence of microfluidic systems and artificial intelligence (AI) has been promising in solving the issue by significantly accelerating the process of data analysis as well as improving the capability of intelligent decision. This review offers a comprehensive introduction on AI methods and outlines the current advances of high-throughput microfluidic systems accelerated by AI, covering biomedical detection, drug screening, and automated system control and design. Furthermore, the challenges and opportunities in this field are critically discussed as well.
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Affiliation(s)
- Jianhua Zhou
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jianpei Dong
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Hongwei Hou
- Beijing Life Science Academy, Beijing 102209, China
| | - Lu Huang
- School of Biomedical Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
- Key Laboratory of Sensing Technology and Biomedical Instruments of Guangdong Province, School of Biomedical Engineering, Sun Yat-sen University, Shenzhen 518107, China
| | - Jinghong Li
- Department of Chemistry, Center for BioAnalytical Chemistry, Key Laboratory of Bioorganic Phosphorus Chemistry & Chemical Biology, Tsinghua University, Beijing 100084, China.
- New Cornerstone Science Laboratory, Shenzhen 518054, China
- Beijing Life Science Academy, Beijing 102209, China
- Center for BioAnalytical Chemistry, Hefei National Laboratory of Physical Science at Microscale, University of Science and Technology of China, Hefei 230026, China
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Yu Y, Zhou T, Cao L. Use and application of organ-on-a-chip platforms in cancer research. J Cell Commun Signal 2023:10.1007/s12079-023-00790-7. [PMID: 38032444 DOI: 10.1007/s12079-023-00790-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Tumors are a major cause of death worldwide, and much effort has been made to develop appropriate anti-tumor therapies. Existing in vitro and in vivo tumor models cannot reflect the critical features of cancer. The development of organ-on-a-chip models has enabled the integration of organoids, microfluidics, tissue engineering, biomaterials research, and microfabrication, offering conditions that mimic tumor physiology. Three-dimensional in vitro human tumor models that have been established as organ-on-a-chip models contain multiple cell types and a structure that is similar to the primary tumor. These models can be applied to various foci of oncology research. Moreover, the high-throughput features of microfluidic organ-on-a-chip models offer new opportunities for achieving large-scale drug screening and developing more personalized treatments. In this review of the literature, we explore the development of organ-on-a-chip technology and discuss its use as an innovative tool in basic and clinical applications and summarize its advancement of cancer research.
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Affiliation(s)
- Yifan Yu
- Department of Hepatobiliary and Transplant Surgery, Shengjing Hospital of China Medical University, Shenyang, Liaoning, China
| | - TingTing Zhou
- The College of Basic Medical Science, Health Sciences Institute, Key Laboratory of Cell Biology of Ministry of Public Health, Key Laboratory of Medical Cell Biology of Ministry of Education, Liaoning Province Collaborative Innovation Center of Aging Related Disease Diagnosis and Treatment and Prevention, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning, China
| | - Liu Cao
- The College of Basic Medical Science, Health Sciences Institute, Key Laboratory of Cell Biology of Ministry of Public Health, Key Laboratory of Medical Cell Biology of Ministry of Education, Liaoning Province Collaborative Innovation Center of Aging Related Disease Diagnosis and Treatment and Prevention, China Medical University, No. 77, Puhe Road, Shenyang North New Area, Shenyang, 110122, Liaoning, China.
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6
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Zhou S, Chen B, Fu ES, Yan H. Computer vision meets microfluidics: a label-free method for high-throughput cell analysis. MICROSYSTEMS & NANOENGINEERING 2023; 9:116. [PMID: 37744264 PMCID: PMC10511704 DOI: 10.1038/s41378-023-00562-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 03/21/2023] [Accepted: 04/10/2023] [Indexed: 09/26/2023]
Abstract
In this paper, we review the integration of microfluidic chips and computer vision, which has great potential to advance research in the life sciences and biology, particularly in the analysis of cell imaging data. Microfluidic chips enable the generation of large amounts of visual data at the single-cell level, while computer vision techniques can rapidly process and analyze these data to extract valuable information about cellular health and function. One of the key advantages of this integrative approach is that it allows for noninvasive and low-damage cellular characterization, which is important for studying delicate or fragile microbial cells. The use of microfluidic chips provides a highly controlled environment for cell growth and manipulation, minimizes experimental variability and improves the accuracy of data analysis. Computer vision can be used to recognize and analyze target species within heterogeneous microbial populations, which is important for understanding the physiological status of cells in complex biological systems. As hardware and artificial intelligence algorithms continue to improve, computer vision is expected to become an increasingly powerful tool for in situ cell analysis. The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.
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Affiliation(s)
- Shizheng Zhou
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
| | - Bingbing Chen
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
| | - Edgar S. Fu
- Graduate School of Computing and Information Science, University of Pittsburgh, Pittsburgh, PA 15260 USA
| | - Hong Yan
- State Key Laboratory of Marine Resource Utilization in South China Sea, Hainan University, Haikou, 570228 China
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7
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Deng S, Li C, Cao J, Cui Z, Du J, Fu Z, Yang H, Chen P. Organ-on-a-chip meets artificial intelligence in drug evaluation. Theranostics 2023; 13:4526-4558. [PMID: 37649608 PMCID: PMC10465229 DOI: 10.7150/thno.87266] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 08/02/2023] [Indexed: 09/01/2023] Open
Abstract
Drug evaluation has always been an important area of research in the pharmaceutical industry. However, animal welfare protection and other shortcomings of traditional drug development models pose obstacles and challenges to drug evaluation. Organ-on-a-chip (OoC) technology, which simulates human organs on a chip of the physiological environment and functionality, and with high fidelity reproduction organ-level of physiology or pathophysiology, exhibits great promise for innovating the drug development pipeline. Meanwhile, the advancement in artificial intelligence (AI) provides more improvements for the design and data processing of OoCs. Here, we review the current progress that has been made to generate OoC platforms, and how human single and multi-OoCs have been used in applications, including drug testing, disease modeling, and personalized medicine. Moreover, we discuss issues facing the field, such as large data processing and reproducibility, and point to the integration of OoCs and AI in data analysis and automation, which is of great benefit in future drug evaluation. Finally, we look forward to the opportunities and challenges faced by the coupling of OoCs and AI. In summary, advancements in OoCs development, and future combinations with AI, will eventually break the current state of drug evaluation.
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Affiliation(s)
- Shiwen Deng
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Caifeng Li
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Junxian Cao
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Zhao Cui
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jiang Du
- Yunnan Biovalley Pharmaceutical Co., Ltd, Kunming 650503, China
| | - Zheng Fu
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Hongjun Yang
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
| | - Peng Chen
- Beijing Key Laboratory of Traditional Chinese Medicine Basic Research on Prevention and Treatment for Major Diseases, Experimental Research Center, China Academy of Chinese Medical Sciences, Beijing 100700, China
- Yunnan Biovalley Pharmaceutical Co., Ltd, Kunming 650503, China
- Robot Intelligent Laboratory of Traditional Chinese Medicine, Experimental Research Center, China Academy of Chinese Medical Sciences & MEGAROBO, Beijing 100700, China
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8
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Guo J, Hu J, Zheng Y, Zhao S, Ma J. Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer. Br J Cancer 2023; 128:2141-2149. [PMID: 36871044 PMCID: PMC10241896 DOI: 10.1038/s41416-023-02215-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/08/2023] [Accepted: 02/21/2023] [Indexed: 03/06/2023] Open
Abstract
Triple-negative breast cancer (TNBC) accounts for 15-20% of all invasive breast cancer subtypes. Owing to its clinical characteristics, such as the lack of effective therapeutic targets, high invasiveness, and high recurrence rate, TNBC is difficult to treat and has a poor prognosis. Currently, with the accumulation of large amounts of medical data and the development of computing technology, artificial intelligence (AI), particularly machine learning, has been applied to various aspects of TNBC research, including early screening, diagnosis, identification of molecular subtypes, personalised treatment, and prediction of prognosis and treatment response. In this review, we discussed the general principles of artificial intelligence, summarised its main applications in the diagnosis and treatment of TNBC, and provided new ideas and theoretical basis for the clinical diagnosis and treatment of TNBC.
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Affiliation(s)
- Jiamin Guo
- Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China
| | - Junjie Hu
- Machine Intelligence Laboratory, College of Computer Science, Sichuan University, 610065, Chengdu, Sichuan Province, P. R. China
| | - Yichen Zheng
- Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China
| | - Shuang Zhao
- Department of Radiology, West China Hospital of Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China.
| | - Ji Ma
- Department of Medical Oncology, West China Hospital, Sichuan University, 610041, Chengdu, Sichuan Province, P. R. China.
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9
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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10
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Westerhof TM, Yang BA, Merill NM, Yates JA, Altemus M, Russell L, Miller AJ, Bao L, Wu Z, Ulintz PJ, Aguilar CA, Morikawa A, Castro MG, Merajver SD, Oliver CR. Blood-brain barrier remodeling in an organ-on-a-chip device shows Dkk1 to be a regulator of early metastasis. ADVANCED NANOBIOMED RESEARCH 2023; 3:2200036. [PMID: 37234365 PMCID: PMC10208594 DOI: 10.1002/anbr.202200036] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2024] Open
Abstract
Brain metastases are the most lethal progression event, in part because the biological processes underpinning brain metastases are poorly understood. There is a paucity of realistic models of metastasis, as current in vivo murine models are slow to manifest metastasis. We set out to delineate metabolic and secretory modulators of brain metastases by utilizing two models consisting of in vitro microfluidic devices: 1) a blood brain niche (BBN) chip that recapitulates the blood-brain-barrier and niche; and 2) a migration chip that assesses cell migration. We report secretory cues provided by the brain niche that attract metastatic cancer cells to colonize the brain niche region. Astrocytic Dkk-1 is increased in response to brain-seeking breast cancer cells and stimulates cancer cell migration. Brain-metastatic cancer cells under Dkk-1 stimulation increase gene expression of FGF-13 and PLCB1. Further, extracellular Dkk-1 modulates cancer cell migration upon entering the brain niche.
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Affiliation(s)
- Trisha M Westerhof
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Benjamin A Yang
- School of Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nathan M Merill
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Joel A Yates
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Megan Altemus
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Liam Russell
- School of Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Anna J Miller
- School of Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Liwei Bao
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Zhifen Wu
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Peter J Ulintz
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Carlos A Aguilar
- School of Engineering, Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | - Aki Morikawa
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Maria G Castro
- Michigan Medicine, Department of Neurosurgery, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Medicine, Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Sofia D Merajver
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Christopher R Oliver
- Michigan Medicine, Department of Internal Medicine, Division of Hematology/Oncology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
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Biosensor integrated brain-on-a-chip platforms: Progress and prospects in clinical translation. Biosens Bioelectron 2023; 225:115100. [PMID: 36709589 DOI: 10.1016/j.bios.2023.115100] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Revised: 01/07/2023] [Accepted: 01/22/2023] [Indexed: 01/26/2023]
Abstract
Because of the brain's complexity, developing effective treatments for neurological disorders is a formidable challenge. Research efforts to this end are advancing as in vitro systems have reached the point that they can imitate critical components of the brain's structure and function. Brain-on-a-chip (BoC) was first used for microfluidics-based systems with small synthetic tissues but has expanded recently to include in vitro simulation of the central nervous system (CNS). Defining the system's qualifying parameters may improve the BoC for the next generation of in vitro platforms. These parameters show how well a given platform solves the problems unique to in vitro CNS modeling (like recreating the brain's microenvironment and including essential parts like the blood-brain barrier (BBB)) and how much more value it offers than traditional cell culture systems. This review provides an overview of the practical concerns of creating and deploying BoC systems and elaborates on how these technologies might be used. Not only how advanced biosensing technologies could be integrated with BoC system but also how novel approaches will automate assays and improve point-of-care (PoC) diagnostics and accurate quantitative analyses are discussed. Key challenges providing opportunities for clinical translation of BoC in neurodegenerative disorders are also addressed.
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Hasanzadeh A, Hamblin MR, Kiani J, Noori H, Hardie JM, Karimi M, Shafiee H. Could artificial intelligence revolutionize the development of nanovectors for gene therapy and mRNA vaccines? NANO TODAY 2022; 47:101665. [PMID: 37034382 PMCID: PMC10081506 DOI: 10.1016/j.nantod.2022.101665] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Gene therapy enables the introduction of nucleic acids like DNA and RNA into host cells, and is expected to revolutionize the treatment of a wide range of diseases. This growth has been further accelerated by the discovery of CRISPR/Cas technology, which allows accurate genomic editing in a broad range of cells and organisms in vitro and in vivo. Despite many advances in gene delivery and the development of various viral and non-viral gene delivery vectors, the lack of highly efficient non-viral systems with low cellular toxicity remains a challenge. The application of cutting-edge technologies such as artificial intelligence (AI) has great potential to find new paradigms to solve this issue. Herein, we review AI and its major subfields including machine learning (ML), neural networks (NNs), expert systems, deep learning (DL), computer vision and robotics. We discuss the potential of AI-based models and algorithms in the design of targeted gene delivery vehicles capable of crossing extracellular and intracellular barriers by viral mimicry strategies. We finally discuss the role of AI in improving the function of CRISPR/Cas systems, developing novel nanobots, and mRNA vaccine carriers.
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Affiliation(s)
- Akbar Hasanzadeh
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Michael R Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
- Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran
| | - Jafar Kiani
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Molecular Medicine, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Hamid Noori
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
| | - Joseph M. Hardie
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
| | - Mahdi Karimi
- Cellular and Molecular Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Department of Medical Nanotechnology, Faculty of Advanced Technologies in Medicine, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Oncopathology Research Center, Iran University of Medical Sciences, Tehran 1449614535, Iran
- Research Center for Science and Technology in Medicine, Tehran University of Medical Sciences, Tehran 141556559, Iran
- Applied Biotechnology Research Centre, Tehran Medical Science, Islamic Azad University, Tehran 1584743311, Iran
| | - Hadi Shafiee
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02139 USA
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13
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He Y, Zhang Y, Chong W, Pei Y, Zhang R, Liu Z, Yu J, Peng X, Fang F. Association of Underweight and Weight Loss With Poor Prognosis and Poor Therapy Effectiveness in Brain Metastases: A Retrospective Study. Front Nutr 2022; 9:851629. [PMID: 35845778 PMCID: PMC9286517 DOI: 10.3389/fnut.2022.851629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Accepted: 05/26/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundThe prognostic role of body mass index (BMI) in patients with brain metastases is controversial. We aim to investigate the impact of BMI on prognosis and anti-cancer therapy effectiveness in brain metastases.MethodsPatients diagnosed with brain metastases between Oct 2010 and July 2019 were followed for mortality through April 2021. The prognostic role of BMI on overall survival was assessed by a restricted cubic spline (RCS) using a flexible model to visualize the relationship between the BMI values and hazard ratios of all-cause mortality, followed by a cox regression model. The disparity of survival outcomes in patients receiving anti-cancer therapies or those did not was evaluated according to the classification of BMI.ResultsA total of 2,466 patients were included in the analysis, including 241 in the underweight (BMI < 18.5 kg/m2) group, 1,503 in the normal weight group (BMI 18.5–23.9 kg/m2), and 722 in the overweight (BMI ≥ 24 kg/m2) group. Relative to the normal weight group, underweight patients were associated with poor prognosis (adjusted HR 1.25, 95% CI 1.07–1.46, p = 0.005). However, those in the overweight group showed similar overall survival when compared to the normal-weight group. Patients with weight loss were associated with a higher risk of mortality compared with patients without significant weight loss. In underweight patients, there was an insignificant difference in survival outcomes whether they received anti-cancer therapies or not.ConclusionUnderweight and significant weight loss were associated with poor prognosis in brain metastases. Meanwhile, anti-cancer therapies did not significantly improve overall survival in patients with underweight. These findings suggest that improving nutrition to maintain body weight is critical for patients with brain metastases.
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Affiliation(s)
- Yan He
- West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhang
- Evidence-Based Medicine Center, Affiliated Hospital of Chengdu University, Chengdu, China
| | - Weelic Chong
- Department of Medical Oncology, Thomas Jefferson University, Philadelphia, PA, United States
| | - Yiyan Pei
- West China Hospital, Sichuan University, Chengdu, China
| | - Renjie Zhang
- West China Hospital, Sichuan University, Chengdu, China
| | - Zheran Liu
- West China Hospital, Sichuan University, Chengdu, China
| | - Jiayi Yu
- West China Hospital, Sichuan University, Chengdu, China
| | - Xingchen Peng
- West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Xingchen Peng,
| | - Fang Fang
- West China Hospital, Sichuan University, Chengdu, China
- Fang Fang,
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14
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Koyilot MC, Natarajan P, Hunt CR, Sivarajkumar S, Roy R, Joglekar S, Pandita S, Tong CW, Marakkar S, Subramanian L, Yadav SS, Cherian AV, Pandita TK, Shameer K, Yadav KK. Breakthroughs and Applications of Organ-on-a-Chip Technology. Cells 2022; 11:cells11111828. [PMID: 35681523 PMCID: PMC9180073 DOI: 10.3390/cells11111828] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/28/2022] [Accepted: 05/30/2022] [Indexed: 12/10/2022] Open
Abstract
Organ-on-a-chip (OOAC) is an emerging technology based on microfluid platforms and in vitro cell culture that has a promising future in the healthcare industry. The numerous advantages of OOAC over conventional systems make it highly popular. The chip is an innovative combination of novel technologies, including lab-on-a-chip, microfluidics, biomaterials, and tissue engineering. This paper begins by analyzing the need for the development of OOAC followed by a brief introduction to the technology. Later sections discuss and review the various types of OOACs and the fabrication materials used. The implementation of artificial intelligence in the system makes it more advanced, thereby helping to provide a more accurate diagnosis as well as convenient data management. We introduce selected OOAC projects, including applications to organ/disease modelling, pharmacology, personalized medicine, and dentistry. Finally, we point out certain challenges that need to be surmounted in order to further develop and upgrade the current systems.
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Affiliation(s)
- Mufeeda C. Koyilot
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | - Priyadarshini Natarajan
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | - Clayton R. Hunt
- Houston Methodist Research Institute, Houston, TX 77030, USA;
| | - Sonish Sivarajkumar
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | - Romy Roy
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | - Shreeram Joglekar
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | - Shruti Pandita
- Mays Cancer Center, University of Texas Health Sciences Center at San Antonio, San Antonio, TX 78229, USA;
| | - Carl W. Tong
- School of Engineering Medicine, Texas A&M University, Houston, TX 77030, USA;
| | - Shamsudheen Marakkar
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | | | - Shalini S. Yadav
- Department of Immunology, UT MD Anderson Cancer Center, Houston, TX 77030, USA;
| | - Anoop V. Cherian
- Molecular Robotics, Cochin 682033, India; (M.C.K.); (P.N.); (S.S.); (R.R.); (S.J.); (S.M.); (A.V.C.)
| | - Tej K. Pandita
- Houston Methodist Research Institute, Houston, TX 77030, USA;
- Center for Genomic and Precision Medicine, Institute of Biosciences and Technology, Department of Translational Medical Sciences, Texas A&M University, Houston, TX 77030, USA
- Correspondence: (T.K.P.); (K.S.); (K.K.Y.)
| | - Khader Shameer
- School of Public Health, Faculty of Medicine, Imperial College London, South Kensington, London SW7 2AZ, UK
- Correspondence: (T.K.P.); (K.S.); (K.K.Y.)
| | - Kamlesh K. Yadav
- School of Engineering Medicine, Texas A&M University, Houston, TX 77030, USA;
- Center for Genomic and Precision Medicine, Institute of Biosciences and Technology, Department of Translational Medical Sciences, Texas A&M University, Houston, TX 77030, USA
- Correspondence: (T.K.P.); (K.S.); (K.K.Y.)
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15
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Imparato G, Urciuolo F, Netti PA. Organ on Chip Technology to Model Cancer Growth and Metastasis. Bioengineering (Basel) 2022; 9:28. [PMID: 35049737 PMCID: PMC8772984 DOI: 10.3390/bioengineering9010028] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Revised: 01/05/2022] [Accepted: 01/10/2022] [Indexed: 12/18/2022] Open
Abstract
Organ on chip (OOC) has emerged as a major technological breakthrough and distinct model system revolutionizing biomedical research and drug discovery by recapitulating the crucial structural and functional complexity of human organs in vitro. OOC are rapidly emerging as powerful tools for oncology research. Indeed, Cancer on chip (COC) can ideally reproduce certain key aspects of the tumor microenvironment (TME), such as biochemical gradients and niche factors, dynamic cell-cell and cell-matrix interactions, and complex tissue structures composed of tumor and stromal cells. Here, we review the state of the art in COC models with a focus on the microphysiological systems that host multicellular 3D tissue engineering models and can help elucidate the complex biology of TME and cancer growth and progression. Finally, some examples of microengineered tumor models integrated with multi-organ microdevices to study disease progression in different tissues will be presented.
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Affiliation(s)
- Giorgia Imparato
- Center for Advanced Biomaterials for HealthCare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy; (F.U.); (P.A.N.)
| | - Francesco Urciuolo
- Center for Advanced Biomaterials for HealthCare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy; (F.U.); (P.A.N.)
- Department of Chemical, Materials and Industrial Production (DICMAPI), Interdisciplinary Research Centre on Biomaterials (CRIB), University of Naples Federico II, P.leTecchio 80, 80125 Naples, Italy
| | - Paolo Antonio Netti
- Center for Advanced Biomaterials for HealthCare@CRIB, Istituto Italiano di Tecnologia, Largo Barsanti e Matteucci 53, 80125 Naples, Italy; (F.U.); (P.A.N.)
- Department of Chemical, Materials and Industrial Production (DICMAPI), Interdisciplinary Research Centre on Biomaterials (CRIB), University of Naples Federico II, P.leTecchio 80, 80125 Naples, Italy
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16
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Liu L, Bi M, Wang Y, Liu J, Jiang X, Xu Z, Zhang X. Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis. NANOSCALE 2021; 13:19352-19366. [PMID: 34812823 DOI: 10.1039/d1nr06195j] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
Artificial intelligence (AI) is an emerging technology with great potential, and its robust calculation and analysis capabilities are unmatched by traditional calculation tools. With the promotion of deep learning and open-source platforms, the threshold of AI has also become lower. Combining artificial intelligence with traditional fields to create new fields of high research and application value has become a trend. AI has been involved in many disciplines, such as medicine, materials, energy, and economics. The development of AI requires the support of many kinds of data, and microfluidic systems can often mine object data on a large scale to support AI. Due to the excellent synergy between the two technologies, excellent research results have emerged in many fields. In this review, we briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis. Finally, we discuss the development trend of the combination of the two technologies.
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Affiliation(s)
- Linbo Liu
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Mingcheng Bi
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Yunhua Wang
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
| | - Junfeng Liu
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Xiwen Jiang
- College of Biological Science and Engineering, Fuzhou university, Fuzhou 350108, P.R. China
| | - Zhongbin Xu
- Institute of Process Equipment, College of Energy Engineering, Zhejiang University, Hangzhou 310027, P.R. China
| | - Xingcai Zhang
- John A. Paulson School of Engineering and Applied Science, Harvard University, Cambridge, MA 02138, USA
- School of Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
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17
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Elbez R, Folz J, McLean A, Roca H, Labuz JM, Pienta KJ, Takayama S, Kopelman R. Cell-morphodynamic phenotype classification with application to cancer metastasis using cell magnetorotation and machine-learning. PLoS One 2021; 16:e0259462. [PMID: 34788313 PMCID: PMC8598033 DOI: 10.1371/journal.pone.0259462] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 10/19/2021] [Indexed: 01/09/2023] Open
Abstract
We define cell morphodynamics as the cell's time dependent morphology. It could be called the cell's shape shifting ability. To measure it we use a biomarker free, dynamic histology method, which is based on multiplexed Cell Magneto-Rotation and Machine Learning. We note that standard studies looking at cells immobilized on microscope slides cannot reveal their shape shifting, no more than pinned butterfly collections can reveal their flight patterns. Using cell magnetorotation, with the aid of cell embedded magnetic nanoparticles, our method allows each cell to move freely in 3 dimensions, with a rapid following of cell deformations in all 3-dimensions, so as to identify and classify a cell by its dynamic morphology. Using object recognition and machine learning algorithms, we continuously measure the real-time shape dynamics of each cell, where from we successfully resolve the inherent broad heterogeneity of the morphological phenotypes found in a given cancer cell population. In three illustrative experiments we have achieved clustering, differentiation, and identification of cells from (A) two distinct cell lines, (B) cells having gone through the epithelial-to-mesenchymal transition, and (C) cells differing only by their motility. This microfluidic method may enable a fast screening and identification of invasive cells, e.g., metastatic cancer cells, even in the absence of biomarkers, thus providing a rapid diagnostics and assessment protocol for effective personalized cancer therapy.
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Affiliation(s)
- Remy Elbez
- Applied Physics Program, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Jeff Folz
- Biophysics Program, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Alan McLean
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Hernan Roca
- Department of Urology, University of Michigan School of Medicine, Ann Arbor, Michigan, United States of America
| | - Joseph M. Labuz
- Department of Biomedical Engineering, University of Michigan, Ann Arbor, Michigan, United States of America
| | - Kenneth J. Pienta
- Department of Urology, The James Buchanan Brady Urological Institute, Johns Hopkins Hospital, Baltimore, Maryland, United States of America
| | - Shuichi Takayama
- Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Raoul Kopelman
- Applied Physics Program, University of Michigan, Ann Arbor, Michigan, United States of America
- Biophysics Program, University of Michigan, Ann Arbor, Michigan, United States of America
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan, United States of America
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18
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Yoon JK, Kim J, Shah Z, Awasthi A, Mahajan A, Kim Y. Advanced Human BBB-on-a-Chip: A New Platform for Alzheimer's Disease Studies. Adv Healthc Mater 2021; 10:e2002285. [PMID: 34075728 PMCID: PMC8349886 DOI: 10.1002/adhm.202002285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2020] [Revised: 02/17/2021] [Indexed: 12/14/2022]
Abstract
The blood-brain barrier (BBB) is a unique vascular structure that serves as a molecular transport gateway for the maintenance of brain homeostasis. Chronic disruption or breakdown of the BBB reportedly leads to neurodegenerative diseases. Nonetheless, research on human BBB pathophysiology and drug development remains highly dependent on studies using inherently different animals. Moreover, more studies have shown that animal models are not appropriate in modeling Alzheimer's disease (AD), underlining the importance of in vitro models of the human BBB with physiological relevance. In this review, recent advances in human BBB-on-a-chip technologies are highlighted and their potential for pathogenesis studies and drug prescreening for AD treatment are discussed.
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Affiliation(s)
- Jeong-Kee Yoon
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Jaehoon Kim
- Mepsgen Co. Ltd., Seoul, 05836, Republic of Korea
| | - Zachary Shah
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Ashi Awasthi
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Advay Mahajan
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - YongTae Kim
- George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Mepsgen Co. Ltd., Seoul, 05836, Republic of Korea
- Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Parker H. Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, GA 30332, USA
- Institute for Electronics and Nanotechnology, Georgia Institute of Technology, Atlanta, GA 30332, USA
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19
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Del Piccolo N, Shirure VS, Bi Y, Goedegebuure SP, Gholami S, Hughes CC, Fields RC, George SC. Tumor-on-chip modeling of organ-specific cancer and metastasis. Adv Drug Deliv Rev 2021; 175:113798. [PMID: 34015419 DOI: 10.1016/j.addr.2021.05.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2021] [Revised: 05/04/2021] [Accepted: 05/11/2021] [Indexed: 02/08/2023]
Abstract
Every year, cancer claims millions of lives around the globe. Unfortunately, model systems that accurately mimic human oncology - a requirement for the development of more effective therapies for these patients - remain elusive. Tumor development is an organ-specific process that involves modification of existing tissue features, recruitment of other cell types, and eventual metastasis to distant organs. Recently, tissue engineered microfluidic devices have emerged as a powerful in vitro tool to model human physiology and pathology with organ-specificity. These organ-on-chip platforms consist of cells cultured in 3D hydrogels and offer precise control over geometry, biological components, and physiochemical properties. Here, we review progress towards organ-specific microfluidic models of the primary and metastatic tumor microenvironments. Despite the field's infancy, these tumor-on-chip models have enabled discoveries about cancer immunobiology and response to therapy. Future work should focus on the development of autologous or multi-organ systems and inclusion of the immune system.
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20
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Graney PL, Tavakol DN, Chramiec A, Ronaldson-Bouchard K, Vunjak-Novakovic G. Engineered models of tumor metastasis with immune cell contributions. iScience 2021; 24:102179. [PMID: 33718831 PMCID: PMC7921600 DOI: 10.1016/j.isci.2021.102179] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Most cancer deaths are due to tumor metastasis rather than the primary tumor. Metastasis is a highly complex and dynamic process that requires orchestration of signaling between the tumor, its local environment, distant tissue sites, and immune system. Animal models of cancer metastasis provide the necessary systemic environment but lack control over factors that regulate cancer progression and often do not recapitulate the properties of human cancers. Bioengineered "organs-on-a-chip" that incorporate the primary tumor, metastatic tissue targets, and microfluidic perfusion are now emerging as quantitative human models of tumor metastasis. The ability of these systems to model tumor metastasis in individualized, patient-specific settings makes them uniquely suitable for studies of cancer biology and developmental testing of new treatments. In this review, we focus on human multi-organ platforms that incorporate circulating and tissue-resident immune cells in studies of tumor metastasis.
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21
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Construction of cancer-on-a-chip for drug screening. Drug Discov Today 2021; 26:1875-1890. [PMID: 33731317 DOI: 10.1016/j.drudis.2021.03.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 10/16/2020] [Accepted: 03/09/2021] [Indexed: 12/13/2022]
Abstract
Cancer-on-a-chip has effectively contributed to the development of drug screening, holding great promise for more convenient and reliable drug development as well as personalized drug administration.
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22
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Sigdel I, Gupta N, Faizee F, Khare VM, Tiwari AK, Tang Y. Biomimetic Microfluidic Platforms for the Assessment of Breast Cancer Metastasis. Front Bioeng Biotechnol 2021; 9:633671. [PMID: 33777909 PMCID: PMC7992012 DOI: 10.3389/fbioe.2021.633671] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/05/2021] [Indexed: 12/27/2022] Open
Abstract
Of around half a million women dying of breast cancer each year, more than 90% die due to metastasis. Models necessary to understand the metastatic process, particularly breast cancer cell extravasation and colonization, are currently limited and urgently needed to develop therapeutic interventions necessary to prevent breast cancer metastasis. Microfluidic approaches aim to reconstitute functional units of organs that cannot be modeled easily in traditional cell culture or animal studies by reproducing vascular networks and parenchyma on a chip in a three-dimensional, physiologically relevant in vitro system. In recent years, microfluidics models utilizing innovative biomaterials and micro-engineering technologies have shown great potential in our effort of mechanistic understanding of the breast cancer metastasis cascade by providing 3D constructs that can mimic in vivo cellular microenvironment and the ability to visualize and monitor cellular interactions in real-time. In this review, we will provide readers with a detailed discussion on the application of the most up-to-date, state-of-the-art microfluidics-based breast cancer models, with a special focus on their application in the engineering approaches to recapitulate the metastasis process, including invasion, intravasation, extravasation, breast cancer metastasis organotropism, and metastasis niche formation.
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Affiliation(s)
- Indira Sigdel
- Biofluidics Laboratory, Department of Bioengineering, College of Engineering, University of Toledo, Toledo, OH, United States
| | - Niraj Gupta
- Biofluidics Laboratory, Department of Bioengineering, College of Engineering, University of Toledo, Toledo, OH, United States
| | - Fairuz Faizee
- Biofluidics Laboratory, Department of Bioengineering, College of Engineering, University of Toledo, Toledo, OH, United States
| | - Vishwa M Khare
- Eurofins Lancaster Laboratories, Philadelphia, PA, United States
| | - Amit K Tiwari
- Department of Pharmacology and Experimental Therapeutics, College of Pharmacy & Pharmaceutical Sciences, University of Toledo, Toledo, OH, United States
| | - Yuan Tang
- Biofluidics Laboratory, Department of Bioengineering, College of Engineering, University of Toledo, Toledo, OH, United States
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23
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Abstract
Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.
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Affiliation(s)
- Feiyun Cui
- Department of Chemical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609, United States
| | - Yun Yue
- Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - Yi Zhang
- Department of Biomedical Engineering, University of Connecticut, Storrs, Connecticut 06269, United States
| | - Ziming Zhang
- Department of Electrical & Computer Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts 01609, United States
| | - H. Susan Zhou
- Department of Chemical Engineering, Worcester Polytechnic Institute, 100 Institute Road, Worcester, Massachusetts 01609, United States
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24
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Low LA, Mummery C, Berridge BR, Austin CP, Tagle DA. Organs-on-chips: into the next decade. Nat Rev Drug Discov 2020; 20:345-361. [PMID: 32913334 DOI: 10.1038/s41573-020-0079-3] [Citation(s) in RCA: 372] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/08/2020] [Indexed: 02/06/2023]
Abstract
Organs-on-chips (OoCs), also known as microphysiological systems or 'tissue chips' (the terms are synonymous), have attracted substantial interest in recent years owing to their potential to be informative at multiple stages of the drug discovery and development process. These innovative devices could provide insights into normal human organ function and disease pathophysiology, as well as more accurately predict the safety and efficacy of investigational drugs in humans. Therefore, they are likely to become useful additions to traditional preclinical cell culture methods and in vivo animal studies in the near term, and in some cases replacements for them in the longer term. In the past decade, the OoC field has seen dramatic advances in the sophistication of biology and engineering, in the demonstration of physiological relevance and in the range of applications. These advances have also revealed new challenges and opportunities, and expertise from multiple biomedical and engineering fields will be needed to fully realize the promise of OoCs for fundamental and translational applications. This Review provides a snapshot of this fast-evolving technology, discusses current applications and caveats for their implementation, and offers suggestions for directions in the next decade.
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Affiliation(s)
- Lucie A Low
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA.
| | - Christine Mummery
- Leiden University Medical Center, Leiden, Netherlands.,University of Twente, Enschede, Netherlands
| | - Brian R Berridge
- National Institute for Environmental Health Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Christopher P Austin
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA
| | - Danilo A Tagle
- National Center for Advancing Translational Sciences, National Institutes of Health, Bethesda, MD, USA.
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Isozaki A, Harmon J, Zhou Y, Li S, Nakagawa Y, Hayashi M, Mikami H, Lei C, Goda K. AI on a chip. LAB ON A CHIP 2020; 20:3074-3090. [PMID: 32644061 DOI: 10.1039/d0lc00521e] [Citation(s) in RCA: 60] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.
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Affiliation(s)
- Akihiro Isozaki
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Kanagawa Institute of Industrial Science and Technology, Kanagawa 213-0012, Japan
| | - Jeffrey Harmon
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Yuqi Zhou
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Shuai Li
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and The Cambridge Centre for Data-Driven Discovery, Cambridge University, Cambridge CB3 0WA, UK
| | - Yuta Nakagawa
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Mika Hayashi
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Hideharu Mikami
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan.
| | - Cheng Lei
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China
| | - Keisuke Goda
- Department of Chemistry, University of Tokyo, Tokyo 113-0033, Japan. and Institute of Technological Sciences, Wuhan University, Hubei 430072, China and Department of Bioengineering, University of California, Los Angeles, California 90095, USA
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Oliver CR, Westerhof TM, Castro MG, Merajver SD. Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography. J Vis Exp 2020. [PMID: 32865534 DOI: 10.3791/61654] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023] Open
Abstract
Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depending on the cancer type. To reduce the brain metastatic tumor burden, gaps in basic and translational knowledge need to be addressed. Major challenges include a paucity of reproducible preclinical models and associated tools. Three-dimensional models of brain metastasis can yield the relevant molecular and phenotypic data used to address these needs when combined with dedicated analysis tools. Moreover, compared to murine models, organ-on-a-chip models of patient tumor cells traversing the blood brain barrier into the brain microenvironment generate results rapidly and are more interpretable with quantitative methods, thus amenable to high throughput testing. Here we describe and demonstrate the use of a novel 3D microfluidic blood brain niche (µmBBN) platform where multiple elements of the niche can be cultured for an extended period (several days), fluorescently imaged by confocal microscopy, and the images reconstructed using an innovative confocal tomography technique; all aimed to understand the development of micro-metastasis and changes to the tumor micro-environment (TME) in a repeatable and quantitative manner. We demonstrate how to fabricate, seed, image, and analyze the cancer cells and TME cellular and humoral components, using this platform. Moreover, we show how artificial intelligence (AI) is used to identify the intrinsic phenotypic differences of cancer cells that are capable of transit through a model µmBBN and to assign them an objective index of brain metastatic potential. The data sets generated by this method can be used to answer basic and translational questions about metastasis, the efficacy of therapeutic strategies, and the role of the TME in both.
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Affiliation(s)
- C Ryan Oliver
- Department of Internal Medicine, University of Michigan Ann Arbor; Rogel Cancer Center, University of Michigan Ann Arbor
| | - Trisha M Westerhof
- Department of Internal Medicine, University of Michigan Ann Arbor; Rogel Cancer Center, University of Michigan Ann Arbor
| | - Maria G Castro
- Rogel Cancer Center, University of Michigan Ann Arbor; Department of Neurosurgery, University of Michigan Ann Arbor; Department of Cell and Developmental Biology, University of Michigan Ann Arbor
| | - Sofia D Merajver
- Department of Internal Medicine, University of Michigan Ann Arbor; Rogel Cancer Center, University of Michigan Ann Arbor;
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Wang Y, Wu D, Wu G, Wu J, Lu S, Lo J, He Y, Zhao C, Zhao X, Zhang H, Wang S. Metastasis-on-a-chip mimicking the progression of kidney cancer in the liver for predicting treatment efficacy. Theranostics 2020; 10:300-311. [PMID: 31903121 PMCID: PMC6929630 DOI: 10.7150/thno.38736] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Accepted: 08/29/2019] [Indexed: 12/21/2022] Open
Abstract
Metastasis is one of the most important factors that lead to poor prognosis in cancer patients, and effective suppression of the growth of primary cancer cells in a metastatic site is paramount in averting cancer progression. However, there is a lack of biomimetic three-dimensional (3D) in vitro models that can closely mimic the continuous growth of metastatic cancer cells in an organ-specific extracellular microenvironment (ECM) for assessing effective therapeutic strategies. Methods: In this metastatic tumor progression model, kidney cancer cells (Caki-1) and hepatocytes (i.e., HepLL cells) were co-cultured at an increasing ratio from 1:9 to 9:1 in a decellularized liver matrix (DLM)/gelatin methacryloyl (GelMA)-based biomimetic liver microtissue in a microfluidic device. Results:Via this model, we successfully demonstrated a linear anti-cancer relationship between the concentration of anti-cancer drug 5-Fluorouracil (5-FU) and the percentage of Caki-1 cells in the co-culture system (R2 = 0.89). Furthermore, the Poly(lactide-co-glycolide) (PLGA)-poly(ethylene glycol) (PEG)-based delivery system showed superior efficacy to free 5-FU in killing Caki-1 cells. Conclusions: In this study, we present a novel 3D metastasis-on-a-chip model mimicking the progression of kidney cancer cells metastasized to the liver for predicting treatment efficacy. Taken together, our study proved that the tumor progression model based on metastasis-on-a-chip with organ-specific ECM would provide a valuable tool for rapidly assessing treatment regimens and developing new chemotherapeutic agents.
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Affiliation(s)
- Yimin Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Di Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Guohua Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Jianguo Wu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Siming Lu
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - James Lo
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
- Department of Bioengineering, University of California Berkeley, Berkeley, CA, 94720, United States of America
| | - Yong He
- State Key Laboratory of Fluid Power and Mechatronic Systems, Key Laboratory of 3D Printing Process and Equipment of Zhejiang Province College of Mechanical Engineering, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
| | - Chao Zhao
- Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute and Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0AH, United Kingdom
| | - Xin Zhao
- Department of Biomedical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
| | - Hongbo Zhang
- Department of Pharmaceutical Science, Åbo Akademic University, FI-20520, Turku, Finland
| | - ShuQi Wang
- State Key Laboratory for Diagnosis and Treatment of Infectious Diseases, National Clinical Research Center for Infectious Diseases, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310003, China
- Institute for Translational Medicine, Zhejiang University, Hangzhou, Zhejiang Province, 310029, China
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Wang X, Liu Z, Fan F, Hou Y, Yang H, Meng X, Zhang Y, Ren F. Microfluidic chip and its application in autophagy detection. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.05.043] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
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