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Bittman-Soto XS, Thomas ES, Ganshert ME, Mendez-Santacruz LL, Harrell JC. The Transformative Role of 3D Culture Models in Triple-Negative Breast Cancer Research. Cancers (Basel) 2024; 16:1859. [PMID: 38791938 PMCID: PMC11119918 DOI: 10.3390/cancers16101859] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2024] [Revised: 05/03/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Advancements in cell culturing techniques have allowed the development of three-dimensional (3D) cell culture models sourced directly from patients' tissues and tumors, faithfully replicating the native tissue environment. These models provide a more clinically relevant platform for studying disease progression and treatment responses compared to traditional two-dimensional (2D) models. Patient-derived organoids (PDOs) and patient-derived xenograft organoids (PDXOs) emerge as innovative 3D cancer models capable of accurately mimicking the tumor's unique features, enhancing our understanding of tumor complexities, and predicting clinical outcomes. Triple-negative breast cancer (TNBC) poses significant clinical challenges due to its aggressive nature, propensity for early metastasis, and limited treatment options. TNBC PDOs and PDXOs have significantly contributed to the comprehension of TNBC, providing novel insights into its underlying mechanism and identifying potential therapeutic targets. This review explores the transformative role of various 3D cancer models in elucidating TNBC pathogenesis and guiding novel therapeutic strategies. It also provides an overview of diverse 3D cell culture models, derived from cell lines and tumors, highlighting their advantages and culturing challenges. Finally, it delves into live-cell imaging techniques, endpoint assays, and alternative cell culture media and methodologies, such as scaffold-free and scaffold-based systems, essential for advancing 3D cancer model research and development.
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Affiliation(s)
- Xavier S. Bittman-Soto
- Department of Pathology, Virginia Commonwealth University, Richmond, VA 23284, USA; (E.S.T.)
- Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA
- Division of Cancer Biology, University of Puerto Rico Comprehensive Cancer Center, San Juan, PR 00921, USA
| | - Evelyn S. Thomas
- Department of Pathology, Virginia Commonwealth University, Richmond, VA 23284, USA; (E.S.T.)
| | | | | | - J. Chuck Harrell
- Department of Pathology, Virginia Commonwealth University, Richmond, VA 23284, USA; (E.S.T.)
- Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA 23284, USA
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Cong J, Wu J, Fang Y, Wang J, Kong X, Wang L, Duan Z. Application of organoid technology in the human health risk assessment of microplastics: A review of progresses and challenges. ENVIRONMENT INTERNATIONAL 2024; 188:108744. [PMID: 38761429 DOI: 10.1016/j.envint.2024.108744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/16/2024] [Accepted: 05/10/2024] [Indexed: 05/20/2024]
Abstract
Microplastic (MP) pollution has become a global environmental issue, and increasing concern has been raised about its impact on human health. Current studies on the toxic effects and mechanisms of MPs have mostly been conducted in animal models or in vitro cell cultures, which have limitations regarding inter-species differences or stimulation of cellular functions. Organoid technology derived from human pluripotent or adult stem cells has broader prospects for predicting the potential health risks of MPs to humans. Herein, we reviewed the current application advancements and opportunities for different organoids, including brain, retinal, intestinal, liver, and lung organoids, to assess the human health risks of MPs. Organoid techniques accurately simulate the complex processes of MPs and reflect phenotypes related to diseases caused by MPs such as liver fibrosis, neurodegeneration, impaired intestinal barrier and cardiac hypertrophy. Future perspectives were also proposed for technological innovation in human risk assessment of MPs using organoids, including extending the lifespan of organoids to assess the chronic toxicity of MPs, and reconstructing multi-organ interactions to explore their potential in studying the microbiome-gut-brainaxis effect of MPs.
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Affiliation(s)
- Jiaoyue Cong
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Jin Wu
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Yanjun Fang
- Tianjin Institute of Environment and Operational Medicine, Tianjin 300050, China
| | - Jing Wang
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Xiaoyan Kong
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China
| | - Lei Wang
- College of Environmental Science and Engineering, Nankai University, Tianjin 300071, China
| | - Zhenghua Duan
- School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China.
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Han X, Cai C, Deng W, Shi Y, Li L, Wang C, Zhang J, Rong M, Liu J, Fang B, He H, Liu X, Deng C, He X, Cao X. Landscape of human organoids: Ideal model in clinics and research. Innovation (N Y) 2024; 5:100620. [PMID: 38706954 PMCID: PMC11066475 DOI: 10.1016/j.xinn.2024.100620] [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: 10/31/2023] [Accepted: 03/29/2024] [Indexed: 05/07/2024] Open
Abstract
In the last decade, organoid research has entered a golden era, signifying a pivotal shift in the biomedical landscape. The year 2023 marked a milestone with the publication of thousands of papers in this arena, reflecting exponential growth. However, amid this burgeoning expansion, a comprehensive and accurate overview of the field has been conspicuously absent. Our review is intended to bridge this gap, providing a panoramic view of the rapidly evolving organoid landscape. We meticulously analyze the organoid field from eight distinctive vantage points, harnessing our rich experience in academic research, industrial application, and clinical practice. We present a deep exploration of the advances in organoid technology, underpinned by our long-standing involvement in this arena. Our narrative traverses the historical genesis of organoids and their transformative impact across various biomedical sectors, including oncology, toxicology, and drug development. We delve into the synergy between organoids and avant-garde technologies such as synthetic biology and single-cell omics and discuss their pivotal role in tailoring personalized medicine, enhancing high-throughput drug screening, and constructing physiologically pertinent disease models. Our comprehensive analysis and reflective discourse provide a deep dive into the existing landscape and emerging trends in organoid technology. We spotlight technological innovations, methodological evolution, and the broadening spectrum of applications, emphasizing the revolutionary influence of organoids in personalized medicine, oncology, drug discovery, and other fields. Looking ahead, we cautiously anticipate future developments in the field of organoid research, especially its potential implications for personalized patient care, new avenues of drug discovery, and clinical research. We trust that our comprehensive review will be an asset for researchers, clinicians, and patients with keen interest in personalized medical strategies. We offer a broad view of the present and prospective capabilities of organoid technology, encompassing a wide range of current and future applications. In summary, in this review we attempt a comprehensive exploration of the organoid field. We offer reflections, summaries, and projections that might be useful for current researchers and clinicians, and we hope to contribute to shaping the evolving trajectory of this dynamic and rapidly advancing field.
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Affiliation(s)
- Xinxin Han
- Organ Regeneration X Lab, Lisheng East China Institute of Biotechnology, Peking University, Jiangsu 226200, China
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Chunhui Cai
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Wei Deng
- LongHua Hospital, Shanghai University of Traditional Chinese Medicine, 725 Wanping South Road, Xuhui District, Shanghai 200032, China
- Department of Oncology, Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200125, China
| | - Yanghua Shi
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Lanyang Li
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Chen Wang
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Jian Zhang
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Mingjie Rong
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Jiping Liu
- Shanghai Lisheng Biotech, Shanghai 200092, China
| | - Bangjiang Fang
- LongHua Hospital, Shanghai University of Traditional Chinese Medicine, 725 Wanping South Road, Xuhui District, Shanghai 200032, China
| | - Hua He
- Department of Neurosurgery, Third Affiliated Hospital, Naval Medical University, Shanghai 200438, China
| | - Xiling Liu
- Shanghai Key Laboratory of Forensic Medicine, Shanghai Forensic Service Platform, Academy of Forensic Science, Ministry of Justice, Shanghai 200063, China
| | - Chuxia Deng
- Cancer Center, Faculty of Health Sciences, University of Macau, Taipa, Macau SAR, China
- Ministry of Education Frontiers Science Center for Precision Oncology, University of Macau, Taipa, Macau SAR 999078, China
| | - Xiao He
- CAS Key Lab for Biomedical Effects of Nanomaterials and Nanosafety, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China
| | - Xin Cao
- Zhongshan Hospital Institute of Clinical Science, Fudan University Shanghai Medical College, Shanghai 200032, China
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Shi H, Kowalczewski A, Vu D, Liu X, Salekin A, Yang H, Ma Z. Organoid intelligence: Integration of organoid technology and artificial intelligence in the new era of in vitro models. MEDICINE IN NOVEL TECHNOLOGY AND DEVICES 2024; 21:100276. [PMID: 38646471 PMCID: PMC11027187 DOI: 10.1016/j.medntd.2023.100276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024] Open
Abstract
Organoid Intelligence ushers in a new era by seamlessly integrating cutting-edge organoid technology with the power of artificial intelligence. Organoids, three-dimensional miniature organ-like structures cultivated from stem cells, offer an unparalleled opportunity to simulate complex human organ systems in vitro. Through the convergence of organoid technology and AI, researchers gain the means to accelerate discoveries and insights across various disciplines. Artificial intelligence algorithms enable the comprehensive analysis of intricate organoid behaviors, intricate cellular interactions, and dynamic responses to stimuli. This synergy empowers the development of predictive models, precise disease simulations, and personalized medicine approaches, revolutionizing our understanding of human development, disease mechanisms, and therapeutic interventions. Organoid Intelligence holds the promise of reshaping how we perceive in vitro modeling, propelling us toward a future where these advanced systems play a pivotal role in biomedical research and drug development.
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Affiliation(s)
- Huaiyu Shi
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
- BioInspired Institute for Material and Living Systems, Syracuse University, Syracuse, NY, USA
| | - Andrew Kowalczewski
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
- BioInspired Institute for Material and Living Systems, Syracuse University, Syracuse, NY, USA
| | - Danny Vu
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
| | - Xiyuan Liu
- Department of Mechanical & Aerospace Engineering, Syracuse University, Syracuse, NY, USA
| | - Asif Salekin
- Department of Electrical Engineering & Computer Science, Syracuse University, Syracuse, NY, USA
| | - Huaxiao Yang
- Department of Biomedical Engineering, University of North Texas, Denton, TX, USA
| | - Zhen Ma
- Department of Biomedical & Chemical Engineering, Syracuse University, Syracuse, NY, USA
- BioInspired Institute for Material and Living Systems, Syracuse University, Syracuse, NY, USA
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Leng B, Jiang H, Wang B, Wang J, Luo G. Deep-Orga: An improved deep learning-based lightweight model for intestinal organoid detection. Comput Biol Med 2024; 169:107847. [PMID: 38141452 DOI: 10.1016/j.compbiomed.2023.107847] [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: 08/29/2023] [Revised: 12/03/2023] [Accepted: 12/11/2023] [Indexed: 12/25/2023]
Abstract
PROBLEM Organoids are 3D cultures that are commonly used for biological and medical research in vitro due to their functional and structural similarity to source organs. The development of organoids can be assessed by morphological tests. However, manual analysis of organoid morphology requires intensive labor from professionals and is prone to observer discrepancies. AIM Computer-assisted methods alleviate the pressure of manual labor, especially with the development of deep learning, the performance of morphological detection has been further improved. The aim of this paper is to automate the assessment of organoid morphology using deep learning techniques to reduce the labor pressure of professionals. METHODS Based on the lightweight model YOLOX, a lightweight intestinal organoid detection model named Deep-Orga is proposed. First, the performance of the Deep-Orga model is compared with other classical models on the intestinal organoids dataset. Then, ablation experiments are used to validate the improvement of the model detection performance by the improved module. Finally, Deep-Orga is compared with other methods. RESULTS Deep-Orga achieves optimal organoid detection with a partial increase in computational effort. Using Deep-Orga to replace the manual analysis process provides a new automated method for organoid morphology evaluation. CONCLUSION Deep-Orga proposed in this paper is able to accurately assess organoid development, effectively relieving the labor pressure of professionals and avoiding the subjectivity of assessment. This paper demonstrates the potential application of deep learning in the field of organoid morphology analysis.
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Affiliation(s)
- Bing Leng
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, People's Republic of China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, People's Republic of China
| | - Hao Jiang
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, People's Republic of China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, People's Republic of China
| | - Bidou Wang
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, People's Republic of China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, People's Republic of China
| | - Jinxian Wang
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, People's Republic of China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, People's Republic of China.
| | - Gangyin Luo
- School of Biomedical Engineering (Suzhou), Division of Life Science and Medicine, University of Science and Technology of China, Hefei, 230026, People's Republic of China; Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, People's Republic of China.
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Wadkin LE, Makarenko I, Parker NG, Shukurov A, Figueiredo FC, Lako M. Human Stem Cells for Ophthalmology: Recent Advances in Diagnostic Image Analysis and Computational Modelling. CURRENT STEM CELL REPORTS 2023; 9:57-66. [PMID: 38145008 PMCID: PMC10739444 DOI: 10.1007/s40778-023-00229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/07/2023] [Indexed: 12/26/2023]
Abstract
Purpose of Review To explore the advances and future research directions in image analysis and computational modelling of human stem cells (hSCs) for ophthalmological applications. Recent Findings hSCs hold great potential in ocular regenerative medicine due to their application in cell-based therapies and in disease modelling and drug discovery using state-of-the-art 2D and 3D organoid models. However, a deeper characterisation of their complex, multi-scale properties is required to optimise their translation to clinical practice. Image analysis combined with computational modelling is a powerful tool to explore mechanisms of hSC behaviour and aid clinical diagnosis and therapy. Summary Many computational models draw on a variety of techniques, often blending continuum and discrete approaches, and have been used to describe cell differentiation and self-organisation. Machine learning tools are having a significant impact in model development and improving image classification processes for clinical diagnosis and treatment and will be the focus of much future research.
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Affiliation(s)
- L. E. Wadkin
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - I. Makarenko
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - N. G. Parker
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - A. Shukurov
- School of Mathematics, Statistics and Physics, Newcastle University, Newcastle upon Tyne, UK
| | - F. C. Figueiredo
- Department of Ophthalmology, Royal Victoria Infirmary, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, UK
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
| | - M. Lako
- Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne, UK
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Kutle I, Polten R, Hachenberg J, Klapdor R, Morgan M, Schambach A. Tumor Organoid and Spheroid Models for Cervical Cancer. Cancers (Basel) 2023; 15:cancers15092518. [PMID: 37173984 PMCID: PMC10177622 DOI: 10.3390/cancers15092518] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 04/25/2023] [Accepted: 04/26/2023] [Indexed: 05/15/2023] Open
Abstract
Cervical cancer is one of the most common malignant diseases in women worldwide. Despite the global introduction of a preventive vaccine against the leading cause of cervical cancer, human papillomavirus (HPV) infection, the incidence of this malignant disease is still very high, especially in economically challenged areas. New advances in cancer therapy, especially the rapid development and application of different immunotherapy strategies, have shown promising pre-clinical and clinical results. However, mortality from advanced stages of cervical cancer remains a significant concern. Precise and thorough evaluation of potential novel anti-cancer therapies in pre-clinical phases is indispensable for efficient development of new, more successful treatment options for cancer patients. Recently, 3D tumor models have become the gold standard in pre-clinical cancer research due to their capacity to better mimic the architecture and microenvironment of tumor tissue as compared to standard two-dimensional (2D) cell cultures. This review will focus on the application of spheroids and patient-derived organoids (PDOs) as tumor models to develop novel therapies against cervical cancer, with an emphasis on the immunotherapies that specifically target cancer cells and modulate the tumor microenvironment (TME).
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Affiliation(s)
- Ivana Kutle
- Institute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany
| | - Robert Polten
- Institute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany
| | - Jens Hachenberg
- Institute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany
- Department of Obstetrics and Gynecology, Hannover Medical School, 30625 Hannover, Germany
| | - Rüdiger Klapdor
- Institute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany
- Department of Obstetrics and Gynecology, Hannover Medical School, 30625 Hannover, Germany
| | - Michael Morgan
- Institute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany
| | - Axel Schambach
- Institute of Experimental Hematology, Hannover Medical School, 30625 Hannover, Germany
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
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