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Veith I, Nurmik M, Mencattini A, Damei I, Lansche C, Brosseau S, Gropplero G, Corgnac S, Filippi J, Poté N, Guenzi E, Chassac A, Mordant P, Tosello J, Sedlik C, Piaggio E, Girard N, Camonis J, Shirvani H, Mami-Chouaib F, Mechta-Grigoriou F, Descroix S, Martinelli E, Zalcman G, Parrini MC. Assessing personalized responses to anti-PD-1 treatment using patient-derived lung tumor-on-chip. Cell Rep Med 2024; 5:101549. [PMID: 38703767 PMCID: PMC11148770 DOI: 10.1016/j.xcrm.2024.101549] [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: 10/28/2023] [Revised: 02/29/2024] [Accepted: 04/10/2024] [Indexed: 05/06/2024]
Abstract
There is a compelling need for approaches to predict the efficacy of immunotherapy drugs. Tumor-on-chip technology exploits microfluidics to generate 3D cell co-cultures embedded in hydrogels that recapitulate simplified tumor ecosystems. Here, we present the development and validation of lung tumor-on-chip platforms to quickly and precisely measure ex vivo the effects of immune checkpoint inhibitors on T cell-mediated cancer cell death by exploiting the power of live imaging and advanced image analysis algorithms. The integration of autologous immunosuppressive FAP+ cancer-associated fibroblasts impaired the response to anti-PD-1, indicating that tumors-on-chips are capable of recapitulating stroma-dependent mechanisms of immunotherapy resistance. For a small cohort of non-small cell lung cancer patients, we generated personalized tumors-on-chips with their autologous primary cells isolated from fresh tumor samples, and we measured the responses to anti-PD-1 treatment. These results support the power of tumor-on-chip technology in immuno-oncology research and open a path to future clinical validations.
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Affiliation(s)
- Irina Veith
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France; Institut Roche, 30 Cours de l'Île Seguin, 92100 Boulogne-Billancourt, France
| | - Martin Nurmik
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Isabelle Damei
- INSERM UMR 1186, Integrative Tumor Immunology and Immunotherapy, Gustave Roussy, Fac. de Médecine - Université Paris-Sud, Université Paris-Saclay, 94805 Villejuif, France
| | - Christine Lansche
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France
| | - Solenn Brosseau
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France; Université Paris Cité, Thoracic Oncology Department and CIC INSERM 1425, Hôpital Bichat-Claude Bernard, 75018 Paris, France
| | - Giacomo Gropplero
- Institut Curie, CNRS UMR168, Laboratoire Physico Chimie Curie, Institut Pierre-Gilles de Gennes, PSL Research University, 75005 Paris, France
| | - Stéphanie Corgnac
- INSERM UMR 1186, Integrative Tumor Immunology and Immunotherapy, Gustave Roussy, Fac. de Médecine - Université Paris-Sud, Université Paris-Saclay, 94805 Villejuif, France
| | - Joanna Filippi
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Nicolas Poté
- Université Paris Cité, INSERM UMR1152, Hôpital Bichat-Claude Bernard, 75018 Paris, France; Department of Pathology, Hôpital Bichat-Claude Bernard, 75018 Paris, France
| | - Edouard Guenzi
- Université Paris Cité, INSERM UMR1152, Hôpital Bichat-Claude Bernard, 75018 Paris, France; Department of Pathology, Hôpital Bichat-Claude Bernard, 75018 Paris, France
| | - Anaïs Chassac
- Department of Pathology, Hôpital Bichat-Claude Bernard, 75018 Paris, France
| | - Pierre Mordant
- Université Paris Cité, Thoracic Surgery Department, Hôpital Bichat-Claude Bernard, 75018 Paris, France
| | - Jimena Tosello
- INSERM U932, PSL Research University, Institut Curie Research Center, Paris, France; Department of Translational Research, PSL Research University, Institut Curie Research Center, Paris, France
| | - Christine Sedlik
- INSERM U932, PSL Research University, Institut Curie Research Center, Paris, France; Department of Translational Research, PSL Research University, Institut Curie Research Center, Paris, France
| | - Eliane Piaggio
- INSERM U932, PSL Research University, Institut Curie Research Center, Paris, France; Department of Translational Research, PSL Research University, Institut Curie Research Center, Paris, France
| | - Nicolas Girard
- INSERM U932, PSL Research University, Institut Curie Research Center, Paris, France; Institut Curie, Institut du Thorax Curie Montsouris, Paris, France; Paris Saclay University, UVSQ, Versailles, France
| | - Jacques Camonis
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France
| | - Hamasseh Shirvani
- Institut Roche, 30 Cours de l'Île Seguin, 92100 Boulogne-Billancourt, France
| | - Fathia Mami-Chouaib
- INSERM UMR 1186, Integrative Tumor Immunology and Immunotherapy, Gustave Roussy, Fac. de Médecine - Université Paris-Sud, Université Paris-Saclay, 94805 Villejuif, France
| | - Fatima Mechta-Grigoriou
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France
| | - Stéphanie Descroix
- Institut Curie, CNRS UMR168, Laboratoire Physico Chimie Curie, Institut Pierre-Gilles de Gennes, PSL Research University, 75005 Paris, France
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Gérard Zalcman
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France; Université Paris Cité, Thoracic Oncology Department and CIC INSERM 1425, Hôpital Bichat-Claude Bernard, 75018 Paris, France.
| | - Maria Carla Parrini
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, 26 rue d'Ulm, 75005 Paris, France.
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Yu T, Yang Q, Peng B, Gu Z, Zhu D. Vascularized organoid-on-a-chip: design, imaging, and analysis. Angiogenesis 2024; 27:147-172. [PMID: 38409567 DOI: 10.1007/s10456-024-09905-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/11/2024] [Indexed: 02/28/2024]
Abstract
Vascularized organoid-on-a-chip (VOoC) models achieve substance exchange in deep layers of organoids and provide a more physiologically relevant system in vitro. Common designs for VOoC primarily involve two categories: self-assembly of endothelial cells (ECs) to form microvessels and pre-patterned vessel lumens, both of which include the hydrogel region for EC growth and allow for controlled fluid perfusion on the chip. Characterizing the vasculature of VOoC often relies on high-resolution microscopic imaging. However, the high scattering of turbid tissues can limit optical imaging depth. To overcome this limitation, tissue optical clearing (TOC) techniques have emerged, allowing for 3D visualization of VOoC in conjunction with optical imaging techniques. The acquisition of large-scale imaging data, coupled with high-resolution imaging in whole-mount preparations, necessitates the development of highly efficient analysis methods. In this review, we provide an overview of the chip designs and culturing strategies employed for VOoC, as well as the applicable optical imaging and TOC methods. Furthermore, we summarize the vascular analysis techniques employed in VOoC, including deep learning. Finally, we discuss the existing challenges in VOoC and vascular analysis methods and provide an outlook for future development.
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Affiliation(s)
- Tingting Yu
- Britton Chance Center for Biomedical Photonics - MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Wuhan National Laboratory for Optoelectronics - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Qihang Yang
- Britton Chance Center for Biomedical Photonics - MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
- Wuhan National Laboratory for Optoelectronics - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China
| | - Bo Peng
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an, Shanxi, 710072, China
| | - Zhongze Gu
- State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
- Institute of Biomaterials and Medical Devices, Southeast University, Suzhou, Jiangsu, 215163, China
| | - Dan Zhu
- Britton Chance Center for Biomedical Photonics - MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
- Wuhan National Laboratory for Optoelectronics - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
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Seo B, Lee D, Jeon H, Ha J, Suh S. MotGen: a closed-loop bacterial motility control framework using generative adversarial networks. Bioinformatics 2024; 40:btae170. [PMID: 38552318 PMCID: PMC11031359 DOI: 10.1093/bioinformatics/btae170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 03/02/2024] [Accepted: 03/27/2024] [Indexed: 04/21/2024] Open
Abstract
MOTIVATION Many organisms' survival and behavior hinge on their responses to environmental signals. While research on bacteria-directed therapeutic agents has increased, systematic exploration of real-time modulation of bacterial motility remains limited. Current studies often focus on permanent motility changes through genetic alterations, restricting the ability to modulate bacterial motility dynamically on a large scale. To address this gap, we propose a novel real-time control framework for systematically modulating bacterial motility dynamics. RESULTS We introduce MotGen, a deep learning approach leveraging Generative Adversarial Networks to analyze swimming performance statistics of motile bacteria based on live cell imaging data. By tracking objects and optimizing cell trajectory mapping under environmentally altered conditions, we trained MotGen on a comprehensive statistical dataset derived from real image data. Our experimental results demonstrate MotGen's ability to capture motility dynamics from real bacterial populations with low mean absolute error in both simulated and real datasets. MotGen allows us to approach optimal swimming conditions for desired motility statistics in real-time. MotGen's potential extends to practical biomedical applications, including immune response prediction, by providing imputation of bacterial motility patterns based on external environmental conditions. Our short-term, in-situ interventions for controlling motility behavior offer a promising foundation for the development of bacteria-based biomedical applications. AVAILABILITY AND IMPLEMENTATION MotGen is presented as a combination of Matlab image analysis code and a machine learning workflow in Python. Codes are available at https://github.com/bgmseo/MotGen, for cell tracking and implementation of trained models to generate bacterial motility statistics.
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Affiliation(s)
- BoGeum Seo
- Department of Mechanical Engineering, Seoul National University, 08826 Seoul, Republic of Korea
| | - DoHee Lee
- Center for Healthcare Robotics, Korea Institute of Science & Technology, 02792 Seoul, Republic of Korea
| | - Heungjin Jeon
- Infection Control Convergence Research Center, Chungnam National University, 34134 Daejeon, Republic of Korea
| | - Junhyoung Ha
- Center for Healthcare Robotics, Korea Institute of Science & Technology, 02792 Seoul, Republic of Korea
| | - SeungBeum Suh
- Center for Healthcare Robotics, Korea Institute of Science & Technology, 02792 Seoul, Republic of Korea
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4
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Kim S, Lee J, Ko J, Park S, Lee SR, Kim Y, Lee T, Choi S, Kim J, Kim W, Chung Y, Kwon OH, Jeon NL. Angio-Net: deep learning-based label-free detection and morphometric analysis of in vitro angiogenesis. LAB ON A CHIP 2024; 24:751-763. [PMID: 38193617 DOI: 10.1039/d3lc00935a] [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
Despite significant advancements in three-dimensional (3D) cell culture technology and the acquisition of extensive data, there is an ongoing need for more effective and dependable data analysis methods. These concerns arise from the continued reliance on manual quantification techniques. In this study, we introduce a microphysiological system (MPS) that seamlessly integrates 3D cell culture to acquire large-scale imaging data and employs deep learning-based virtual staining for quantitative angiogenesis analysis. We utilize a standardized microfluidic device to obtain comprehensive angiogenesis data. Introducing Angio-Net, a novel solution that replaces conventional immunocytochemistry, we convert brightfield images into label-free virtual fluorescence images through the fusion of SegNet and cGAN. Moreover, we develop a tool capable of extracting morphological blood vessel features and automating their measurement, facilitating precise quantitative analysis. This integrated system proves to be invaluable for evaluating drug efficacy, including the assessment of anticancer drugs on targets such as the tumor microenvironment. Additionally, its unique ability to enable live cell imaging without the need for cell fixation promises to broaden the horizons of pharmaceutical and biological research. Our study pioneers a powerful approach to high-throughput angiogenesis analysis, marking a significant advancement in MPS.
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Affiliation(s)
- Suryong Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jungseub Lee
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jihoon Ko
- Department of BioNano Technology, Gachon University, Gyeonggi, 13120, Republic of Korea
| | - Seonghyuk Park
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Seung-Ryeol Lee
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Youngtaek Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Taeseung Lee
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunbeen Choi
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Jiho Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Wonbae Kim
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Yoojin Chung
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, 17035, Republic of Korea
| | - Oh-Heum Kwon
- Department of IT convergence and Applications Engineering, Pukyong National University, Busan, 48513, Republic of Korea
| | - Noo Li Jeon
- Department of Mechanical Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Institute of Advanced Machines and Design, Seoul National University, Seoul, 08826, Republic of Korea
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5
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Alieva M, Wezenaar AKL, Wehrens EJ, Rios AC. Bridging live-cell imaging and next-generation cancer treatment. Nat Rev Cancer 2023; 23:731-745. [PMID: 37704740 DOI: 10.1038/s41568-023-00610-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/25/2023] [Indexed: 09/15/2023]
Abstract
By providing spatial, molecular and morphological data over time, live-cell imaging can provide a deeper understanding of the cellular and signalling events that determine cancer response to treatment. Understanding this dynamic response has the potential to enhance clinical outcome by identifying biomarkers or actionable targets to improve therapeutic efficacy. Here, we review recent applications of live-cell imaging for uncovering both tumour heterogeneity in treatment response and the mode of action of cancer-targeting drugs. Given the increasing uses of T cell therapies, we discuss the unique opportunity of time-lapse imaging for capturing the interactivity and motility of immunotherapies. Although traditionally limited in the number of molecular features captured, novel developments in multidimensional imaging and multi-omics data integration offer strategies to connect single-cell dynamics to molecular phenotypes. We review the effect of these recent technological advances on our understanding of the cellular dynamics of tumour targeting and discuss their implication for next-generation precision medicine.
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Affiliation(s)
- Maria Alieva
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Instituto de Investigaciones Biomedicas Sols-Morreale (IIBM), CSIC-UAM, Madrid, Spain
| | - Amber K L Wezenaar
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands
- Oncode Institute, Utrecht, The Netherlands
| | - Ellen J Wehrens
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
| | - Anne C Rios
- Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands.
- Oncode Institute, Utrecht, The Netherlands.
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Bouquerel C, Dubrova A, Hofer I, Phan DTT, Bernheim M, Ladaigue S, Cavaniol C, Maddalo D, Cabel L, Mechta-Grigoriou F, Wilhelm C, Zalcman G, Parrini MC, Descroix S. Bridging the gap between tumor-on-chip and clinics: a systematic review of 15 years of studies. LAB ON A CHIP 2023; 23:3906-3935. [PMID: 37592893 DOI: 10.1039/d3lc00531c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Over the past 15 years, the field of oncology research has witnessed significant progress in the development of new cell culture models, such as tumor-on-chip (ToC) systems. In this comprehensive overview, we present a multidisciplinary perspective by bringing together physicists, biologists, clinicians, and experts from pharmaceutical companies to highlight the current state of ToC research, its unique features, and the challenges it faces. To offer readers a clear and quantitative understanding of the ToC field, we conducted an extensive systematic analysis of more than 300 publications related to ToC from 2005 to 2022. ToC offer key advantages over other in vitro models by enabling precise control over various parameters. These parameters include the properties of the extracellular matrix, mechanical forces exerted on cells, the physico-chemical environment, cell composition, and the architecture of the tumor microenvironment. Such fine control allows ToC to closely replicate the complex microenvironment and interactions within tumors, facilitating the study of cancer progression and therapeutic responses in a highly representative manner. Importantly, by incorporating patient-derived cells or tumor xenografts, ToC models have demonstrated promising results in terms of clinical validation. We also examined the potential of ToC for pharmaceutical industries in which ToC adoption is expected to occur gradually. Looking ahead, given the high failure rate of clinical trials and the increasing emphasis on the 3Rs principles (replacement, reduction, refinement of animal experimentation), ToC models hold immense potential for cancer research. In the next decade, data generated from ToC models could potentially be employed for discovering new therapeutic targets, contributing to regulatory purposes, refining preclinical drug testing and reducing reliance on animal models.
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Affiliation(s)
- Charlotte Bouquerel
- Macromolécules et Microsystèmes en Biologie et Médecine, UMR 168, Institut Curie, Institut Pierre Gilles de Gennes, 6 rue Jean Calvin, 75005, Paris, France
- Stress and Cancer Laboratory, Inserm, U830, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
- Fluigent, 67 avenue de Fontainebleau, 94270, Le Kremlin-Bicêtre, France
| | - Anastasiia Dubrova
- Macromolécules et Microsystèmes en Biologie et Médecine, UMR 168, Institut Curie, Institut Pierre Gilles de Gennes, 6 rue Jean Calvin, 75005, Paris, France
| | - Isabella Hofer
- Stress and Cancer Laboratory, Inserm, U830, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Duc T T Phan
- Biomedicine Design, Pfizer Inc., San Diego, CA, USA
| | - Moencopi Bernheim
- Macromolécules et Microsystèmes en Biologie et Médecine, UMR 168, Institut Curie, Institut Pierre Gilles de Gennes, 6 rue Jean Calvin, 75005, Paris, France
| | - Ségolène Ladaigue
- Stress and Cancer Laboratory, Inserm, U830, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Charles Cavaniol
- Macromolécules et Microsystèmes en Biologie et Médecine, UMR 168, Institut Curie, Institut Pierre Gilles de Gennes, 6 rue Jean Calvin, 75005, Paris, France
| | - Danilo Maddalo
- Department of Translational Oncology, Genentech, Inc., South San Francisco, CA 94080, USA
| | - Luc Cabel
- Institut Curie, Department of Medical Oncology, 26 rue d'Ulm, 75005, Paris, France
| | - Fatima Mechta-Grigoriou
- Stress and Cancer Laboratory, Inserm, U830, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Claire Wilhelm
- Macromolécules et Microsystèmes en Biologie et Médecine, UMR 168, Institut Curie, Institut Pierre Gilles de Gennes, 6 rue Jean Calvin, 75005, Paris, France
| | - Gérard Zalcman
- Stress and Cancer Laboratory, Inserm, U830, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
- Université Paris Cité, Thoracic Oncology Department, INSERM CIC1425, Bichat Hospital, Cancer Institute AP-HP. Nord, Paris, France.
| | - Maria Carla Parrini
- Stress and Cancer Laboratory, Inserm, U830, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Stéphanie Descroix
- Macromolécules et Microsystèmes en Biologie et Médecine, UMR 168, Institut Curie, Institut Pierre Gilles de Gennes, 6 rue Jean Calvin, 75005, Paris, France
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7
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Carpentier Solorio Y, Lemaître F, Jabbour B, Tastet O, Arbour N, Bou Assi E. Classification of T lymphocyte motility behaviors using a machine learning approach. PLoS Comput Biol 2023; 19:e1011449. [PMID: 37695797 PMCID: PMC10513376 DOI: 10.1371/journal.pcbi.1011449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 09/21/2023] [Accepted: 08/21/2023] [Indexed: 09/13/2023] Open
Abstract
T lymphocytes migrate into organs and interact with local cells to perform their functions. How human T lymphocytes communicate with organ-specific cells and participate in pathobiological processes remains unresolved. Brain infiltration of T lymphocytes is associated with multiple neurological disorders. Thus, to characterize the behavior of human T lymphocytes reaching the human brain, we performed time-lapse microscopy on human CD8+ T lymphocytes co-cultured with either primary human astrocytes or neurons. Using traditional manual and visual assessment of microscopy data, we identified distinct CD8+ T lymphocyte motility behaviors. However, such characterization is time and labor-intensive. In this work, we trained and validated a machine-learning model for the automated classification of behaviors of CD8+ T lymphocytes interacting with astrocytes and neurons. A balanced random forest was trained for the binary classification of established classes of cell behaviors (synapse vs. kinapse) as well as visually identified behaviors (scanning, dancing, and poking). Feature selection was performed during 3-fold cross-validation using the minimum redundancy maximum relevance algorithm. Results show promising performances when tested on a held-out dataset of CD8+ T lymphocytes interacting with astrocytes with a new experimenter and a held-out independent dataset of CD8+ T lymphocytes interacting with neurons. When tested on the independent CD8+ T cell-neuron dataset, the final model achieved a binary classification accuracy of 0.82 and a 3-class accuracy of 0.79. This novel automated classification approach could significantly reduce the time required to label cell motility behaviors while facilitating the identification of interactions of T lymphocytes with multiple cell types.
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Affiliation(s)
- Yves Carpentier Solorio
- Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
| | - Florent Lemaître
- Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
| | - Bassam Jabbour
- Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
| | - Olivier Tastet
- Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
| | - Nathalie Arbour
- Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
| | - Elie Bou Assi
- Centre de Recherche du CHUM (CRCHUM), Montréal, Québec, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec, Canada
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8
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Kim J, Kim J, Jin Y, Cho SW. In situbiosensing technologies for an organ-on-a-chip. Biofabrication 2023; 15:042002. [PMID: 37587753 DOI: 10.1088/1758-5090/aceaae] [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: 12/19/2022] [Accepted: 07/25/2023] [Indexed: 08/18/2023]
Abstract
Thein vitrosimulation of organs resolves the accuracy, ethical, and cost challenges accompanyingin vivoexperiments. Organoids and organs-on-chips have been developed to model thein vitro, real-time biological and physiological features of organs. Numerous studies have deployed these systems to assess thein vitro, real-time responses of an organ to external stimuli. Particularly, organs-on-chips can be most efficiently employed in pharmaceutical drug development to predict the responses of organs before approving such drugs. Furthermore, multi-organ-on-a-chip systems facilitate the close representations of thein vivoenvironment. In this review, we discuss the biosensing technology that facilitates thein situ, real-time measurements of organ responses as readouts on organ-on-a-chip systems, including multi-organ models. Notably, a human-on-a-chip system integrated with automated multi-sensing will be established by further advancing the development of chips, as well as their assessment techniques.
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Affiliation(s)
- Jinyoung Kim
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Junghoon Kim
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Yoonhee Jin
- Department of Physiology, Yonsei University College of Medicine, Seoul 03722, Republic of Korea
| | - Seung-Woo Cho
- Department of Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
- Institute for Basic Science (IBS), Center for Nanomedicine, Seoul 03722, Republic of Korea
- Graduate Program of Nano Biomedical Engineering (NanoBME), Advanced Science Institute, Yonsei University, Seoul 03722, Republic of Korea
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9
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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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10
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Zeng X, Ma Q, Li XK, You LT, Li J, Fu X, You FM, Ren YF. Patient-derived organoids of lung cancer based on organoids-on-a-chip: enhancing clinical and translational applications. Front Bioeng Biotechnol 2023; 11:1205157. [PMID: 37304140 PMCID: PMC10250649 DOI: 10.3389/fbioe.2023.1205157] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/16/2023] [Indexed: 06/13/2023] Open
Abstract
Lung cancer is one of the most common malignant tumors worldwide, with high morbidity and mortality due to significant individual characteristics and genetic heterogeneity. Personalized treatment is necessary to improve the overall survival rate of the patients. In recent years, the development of patient-derived organoids (PDOs) enables lung cancer diseases to be simulated in the real world, and closely reflects the pathophysiological characteristics of natural tumor occurrence and metastasis, highlighting their great potential in biomedical applications, translational medicine, and personalized treatment. However, the inherent defects of traditional organoids, such as poor stability, the tumor microenvironment with simple components and low throughput, limit their further clinical transformation and applications. In this review, we summarized the developments and applications of lung cancer PDOs and discussed the limitations of traditional PDOs in clinical transformation. Herein, we looked into the future and proposed that organoids-on-a-chip based on microfluidic technology are advantageous for personalized drug screening. In addition, combined with recent advances in lung cancer research, we explored the translational value and future development direction of organoids-on-a-chip in the precision treatment of lung cancer.
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Affiliation(s)
- Xiao Zeng
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Qiong Ma
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xue-Ke Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
- Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Li-Ting You
- Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan, China
| | - Jia Li
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Xi Fu
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Feng-Ming You
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
- Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
| | - Yi-Feng Ren
- Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
- Cancer Institute, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
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11
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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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12
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Sabaté Del Río J, Ro J, Yoon H, Park TE, Cho YK. Integrated technologies for continuous monitoring of organs-on-chips: Current challenges and potential solutions. Biosens Bioelectron 2023; 224:115057. [PMID: 36640548 DOI: 10.1016/j.bios.2022.115057] [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: 06/30/2022] [Revised: 12/29/2022] [Accepted: 12/30/2022] [Indexed: 01/03/2023]
Abstract
Organs-on-chips (OoCs) are biomimetic in vitro systems based on microfluidic cell cultures that recapitulate the in vivo physicochemical microenvironments and the physiologies and key functional units of specific human organs. These systems are versatile and can be customized to investigate organ-specific physiology, pathology, or pharmacology. They are more physiologically relevant than traditional two-dimensional cultures, can potentially replace the animal models or reduce the use of these models, and represent a unique opportunity for the development of personalized medicine when combined with human induced pluripotent stem cells. Continuous monitoring of important quality parameters of OoCs via a label-free, non-destructive, reliable, high-throughput, and multiplex method is critical for assessing the conditions of these systems and generating relevant analytical data; moreover, elaboration of quality predictive models is required for clinical trials of OoCs. Presently, these analytical data are obtained by manual or automatic sampling and analyzed using single-point, off-chip traditional methods. In this review, we describe recent efforts to integrate biosensing technologies into OoCs for monitoring the physiologies, functions, and physicochemical microenvironments of OoCs. Furthermore, we present potential alternative solutions to current challenges and future directions for the application of artificial intelligence in the development of OoCs and cyber-physical systems. These "smart" OoCs can learn and make autonomous decisions for process optimization, self-regulation, and data analysis.
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Affiliation(s)
- Jonathan Sabaté Del Río
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea
| | - Jooyoung Ro
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea; Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Heejeong Yoon
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea
| | - Tae-Eun Park
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.
| | - Yoon-Kyoung Cho
- Center for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea; Department of Biomedical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, 44919, Republic of Korea.
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13
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Dai M, Xiao G, Shao M, Zhang YS. The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review. BIOSENSORS 2023; 13:389. [PMID: 36979601 PMCID: PMC10046732 DOI: 10.3390/bios13030389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/17/2022] [Revised: 02/22/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.
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Affiliation(s)
- Manna Dai
- College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China
- Computing and Intelligence Department, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore
| | - Gao Xiao
- College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350108, China
- Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China
| | - Ming Shao
- Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA
| | - Yu Shrike Zhang
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA 02139, USA
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14
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Siu DMD, Lee KCM, Chung BMF, Wong JSJ, Zheng G, Tsia KK. Optofluidic imaging meets deep learning: from merging to emerging. LAB ON A CHIP 2023; 23:1011-1033. [PMID: 36601812 DOI: 10.1039/d2lc00813k] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.
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Affiliation(s)
- Dickson M D Siu
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Kelvin C M Lee
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
| | - Bob M F Chung
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Justin S J Wong
- Conzeb Limited, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Guoan Zheng
- Department of Biomedical Engineering, University of Connecticut, Storrs, CT, USA
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong
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15
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Qi R, Zou Q. Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-Cell Level. RESEARCH (WASHINGTON, D.C.) 2023; 6:0050. [PMID: 36930772 PMCID: PMC10013796 DOI: 10.34133/research.0050] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Accepted: 12/27/2022] [Indexed: 01/12/2023]
Abstract
Cancer treatments always face challenging problems, particularly drug resistance due to tumor cell heterogeneity. The existing datasets include the relationship between gene expression and drug sensitivities; however, the majority are based on tissue-level studies. Study drugs at the single-cell level are perspective to overcome minimal residual disease caused by subclonal resistant cancer cells retained after initial curative therapy. Fortunately, machine learning techniques can help us understand how different types of cells respond to different cancer drugs from the perspective of single-cell gene expression. Good modeling using single-cell data and drug response information will not only improve machine learning for cell-drug outcome prediction but also facilitate the discovery of drugs for specific cancer subgroups and specific cancer treatments. In this paper, we review machine learning and deep learning approaches in drug research. By analyzing the application of these methods on cancer cell lines and single-cell data and comparing the technical gap between single-cell sequencing data analysis and single-cell drug sensitivity analysis, we hope to explore the trends and potential of drug research at the single-cell data level and provide more inspiration for drug research at the single-cell level. We anticipate that this review will stimulate the innovative use of machine learning methods to address new challenges in precision medicine more broadly.
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Affiliation(s)
- Ren Qi
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.,School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Quan Zou
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China.,Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
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16
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Fu X, Bates PA. Application of deep learning methods: From molecular modelling to patient classification. Exp Cell Res 2022; 418:113278. [PMID: 35810775 DOI: 10.1016/j.yexcr.2022.113278] [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: 01/10/2022] [Revised: 06/16/2022] [Accepted: 07/05/2022] [Indexed: 11/28/2022]
Abstract
We are now well into the information driven age with complex, heterogeneous, datasets in the biological sciences continuing to grow at a rapid pace. Moreover, distilling of such datasets, to find new governing principles, are underway. Leading the surge are new and exciting algorithmic developments in computer simulation and machine learning, most notably for the latter, those centred on deep learning. However, practical applications of cell centric computations within the biological sciences, even when carefully benchmarked against existing experimental datasets, remain challenging. Here we discuss the application of deep learning methodologies to support our understanding of cell functionality and as an aid to patient classification. Whilst comprehensive end-to-end deep learning approaches that utilise knowledge of the cell and its molecular components to aid human disease classification are yet to be implemented, important for opening the door to more effective molecular and cell-based therapies, we illustrate that many deep learning applications have been developed to tackle components of such an ambitious pipeline. We end our discussion on what the future may hold, especially how an integrated framework of computer simulations and deep learning, in conjunction with wet-bench experimentation, could enable to reveal the governing principles underlying cell functionalities within the tissue environments cells operate.
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Affiliation(s)
- Xiao Fu
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Rd, London, NW1 1AT, UK.
| | - Paul A Bates
- Biomolecular Modelling Laboratory, The Francis Crick Institute, 1 Midland Rd, London, NW1 1AT, UK.
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17
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Massafra R, Comes MC, Bove S, Didonna V, Gatta G, Giotta F, Fanizzi A, La Forgia D, Latorre A, Pastena MI, Pomarico D, Rinaldi L, Tamborra P, Zito A, Lorusso V, Paradiso AV. Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy. J Pers Med 2022; 12:jpm12060953. [PMID: 35743737 PMCID: PMC9225219 DOI: 10.3390/jpm12060953] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 05/24/2022] [Accepted: 06/07/2022] [Indexed: 02/04/2023] Open
Abstract
To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.
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Affiliation(s)
- Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Samantha Bove
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Gianluca Gatta
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
- Correspondence: (A.F.); (D.L.F.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy
- Correspondence: (A.F.); (D.L.F.)
| | - Agnese Latorre
- Dipartimento di Medicina di Precisione Università della Campania “Luigi Vanvitelli”, 80131 Naples, Italy; (G.G.); (A.L.)
| | - Maria Irene Pastena
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Domenico Pomarico
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Lucia Rinaldi
- Struttura Semplice Dipartimentale di Oncologia Per la Presa in Carico Globale del Paziente, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (R.M.); (M.C.C.); (S.B.); (V.D.); (D.P.); (P.T.)
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.I.P.); (A.Z.)
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (V.L.)
| | - Angelo Virgilio Paradiso
- Oncologia Sperimentale e Biobanca, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
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18
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Computer vision-aided bioprinting for bone research. Bone Res 2022; 10:21. [PMID: 35217642 PMCID: PMC8881598 DOI: 10.1038/s41413-022-00192-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 12/10/2021] [Accepted: 12/21/2021] [Indexed: 02/08/2023] Open
Abstract
Bioprinting is an emerging additive manufacturing technology that has enormous potential in bone implantation and repair. The insufficient accuracy of the shape of bioprinted parts is a primary clinical barrier that prevents widespread utilization of bioprinting, especially for bone design with high-resolution requirements. During the last five years, the use of computer vision for process control has been widely practiced in the manufacturing field. Computer vision can improve the performance of bioprinting for bone research with respect to various aspects, including accuracy, resolution, and cell survival rate. Hence, computer vision plays a substantial role in addressing the current defect problem in bioprinting for bone research. In this review, recent advances in the application of computer vision in bioprinting for bone research are summarized and categorized into three groups based on different defect types: bone scaffold process control, deep learning, and cell viability models. The collection of printing parameters, data processing, and feedback of bioprinting information, which ultimately improves printing capabilities, are further discussed. We envision that computer vision may offer opportunities to accelerate bioprinting development and provide a new perception for bone research.
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19
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Li J, Chen J, Bai H, Wang H, Hao S, Ding Y, Peng B, Zhang J, Li L, Huang W. An Overview of Organs-on-Chips Based on Deep Learning. RESEARCH (WASHINGTON, D.C.) 2022; 2022:9869518. [PMID: 35136860 PMCID: PMC8795883 DOI: 10.34133/2022/9869518] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 12/08/2021] [Indexed: 12/15/2022]
Abstract
Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of "big data" and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.
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Affiliation(s)
- Jintao Li
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jie Chen
- Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education, School of Electronics and Information Engineering, Anhui University, Hefei 230601, China
- 38th Research Institute of China Electronics Technology Group Corporation, Hefei 230088, China
| | - Hua Bai
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Haiwei Wang
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shiping Hao
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Yang Ding
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Bo Peng
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
| | - Jing Zhang
- College of Biomedical Engineering, Sichuan University, Chengdu 610065, China
| | - Lin Li
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech), Nanjing 211800, China
| | - Wei Huang
- Frontiers Science Center for Flexible Electronics, Xi'an Institute of Flexible Electronics (IFE) and Xi'an Institute of Biomedical Materials & Engineering, Northwestern Polytechnical University, Xi'an 710072, China
- Key Laboratory of Flexible Electronics (KLOFE) and Institute of Advanced Materials (IAM) Nanjing Tech University (NanjingTech), Nanjing 211800, China
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20
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DaOrazio M, Reale R, De Ninno A, Brighetti MA, Mencattini A, Businaro L, Martinelli E, Bisegna P, Travaglini A, Caselli F. Electro-optical classification of pollen grains via microfluidics and machine learning. IEEE Trans Biomed Eng 2021; 69:921-931. [PMID: 34478361 DOI: 10.1109/tbme.2021.3109384] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In aerobiological monitoring and agriculture there is a pressing need for accurate, label-free and automated analysis of pollen grains, in order to reduce the cost, workload and possible errors associated to traditional approaches. Methods: We propose a new multimodal approach that combines electrical sensing and optical imaging to classify pollen grains flowing in a microfluidic chip at a throughput of 150 grains per second. Electrical signals and synchronized optical images are processed by two independent machine learning-based classifiers, whose predictions are then combined to provide the final classification outcome. Results: The applicability of the method is demonstrated in a proof-of-concept classification experiment involving eight pollen classes from different taxa. The average balanced accuracy is 78.7 % for the electrical classifier, 76.7 % for the optical classifier and 84.2 % for the multimodal classifier. The accuracy is 82.8 % for the electrical classifier, 84.1 % for the optical classifier and 88.3 % for the multimodal classifier. Conclusion: The multimodal approach provides better classification results with respect to the analysis based on electrical or optical features alone. Significance: The proposed methodology paves the way for automated multimodal palynology. Moreover, it can be extended to other fields, such as diagnostics and cell therapy, where it could be used for label-free identification of cell populations in heterogeneous samples.
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21
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Early prediction of neoadjuvant chemotherapy response by exploiting a transfer learning approach on breast DCE-MRIs. Sci Rep 2021; 11:14123. [PMID: 34238968 PMCID: PMC8266861 DOI: 10.1038/s41598-021-93592-z] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
The dynamic contrast-enhanced MR imaging plays a crucial role in evaluating the effectiveness of neoadjuvant chemotherapy (NAC) even since its early stage through the prediction of the final pathological complete response (pCR). In this study, we proposed a transfer learning approach to predict if a patient achieved pCR (pCR) or did not (non-pCR) by exploiting, separately or in combination, pre-treatment and early-treatment exams from I-SPY1 TRIAL public database. First, low-level features, i.e., related to local structure of the image, were automatically extracted by a pre-trained convolutional neural network (CNN) overcoming manual feature extraction. Next, an optimal set of most stable features was detected and then used to design an SVM classifier. A first subset of patients, called fine-tuning dataset (30 pCR; 78 non-pCR), was used to perform the optimal choice of features. A second subset not involved in the feature selection process was employed as an independent test (7 pCR; 19 non-pCR) to validate the model. By combining the optimal features extracted from both pre-treatment and early-treatment exams with some clinical features, i.e., ER, PgR, HER2 and molecular subtype, an accuracy of 91.4% and 92.3%, and an AUC value of 0.93 and 0.90, were returned on the fine-tuning dataset and the independent test, respectively. Overall, the low-level CNN features have an important role in the early evaluation of the NAC efficacy by predicting pCR. The proposed model represents a first effort towards the development of a clinical support tool for an early prediction of pCR to NAC.
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22
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Comes MC, La Forgia D, Didonna V, Fanizzi A, Giotta F, Latorre A, Martinelli E, Mencattini A, Paradiso AV, Tamborra P, Terenzio A, Zito A, Lorusso V, Massafra R. Early Prediction of Breast Cancer Recurrence for Patients Treated with Neoadjuvant Chemotherapy: A Transfer Learning Approach on DCE-MRIs. Cancers (Basel) 2021; 13:2298. [PMID: 34064923 PMCID: PMC8151784 DOI: 10.3390/cancers13102298] [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] [Received: 03/01/2021] [Revised: 05/05/2021] [Accepted: 05/08/2021] [Indexed: 12/12/2022] Open
Abstract
Cancer treatment planning benefits from an accurate early prediction of the treatment efficacy. The goal of this study is to give an early prediction of three-year Breast Cancer Recurrence (BCR) for patients who underwent neoadjuvant chemotherapy. We addressed the task from a new perspective based on transfer learning applied to pre-treatment and early-treatment DCE-MRI scans. Firstly, low-level features were automatically extracted from MR images using a pre-trained Convolutional Neural Network (CNN) architecture without human intervention. Subsequently, the prediction model was built with an optimal subset of CNN features and evaluated on two sets of patients from I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot public databases: a fine-tuning dataset (70 not recurrent and 26 recurrent cases), which was primarily used to find the optimal subset of CNN features, and an independent test (45 not recurrent and 17 recurrent cases), whose patients had not been involved in the feature selection process. The best results were achieved when the optimal CNN features were augmented by four clinical variables (age, ER, PgR, HER2+), reaching an accuracy of 91.7% and 85.2%, a sensitivity of 80.8% and 84.6%, a specificity of 95.7% and 85.4%, and an AUC value of 0.93 and 0.83 on the fine-tuning dataset and the independent test, respectively. Finally, the CNN features extracted from pre-treatment and early-treatment exams were revealed to be strong predictors of BCR.
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Affiliation(s)
- Maria Colomba Comes
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Daniele La Forgia
- Struttura Semplice Dipartimentale di Radiologia Senologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Vittorio Didonna
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Annarita Fanizzi
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Francesco Giotta
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Agnese Latorre
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Eugenio Martinelli
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy; (E.M.); (A.M.)
- Dipartimento di Ingegneria Elettronica, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Roma, Italy
| | - Arianna Mencattini
- Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, 00133 Rome, Italy; (E.M.); (A.M.)
- Dipartimento di Ingegneria Elettronica, Università di Roma Tor Vergata, Via del Politecnico 1, 00133 Roma, Italy
| | - Angelo Virgilio Paradiso
- Oncologia Medica Sperimentale, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Pasquale Tamborra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
| | - Antonella Terenzio
- Unità di Oncologia Medica, Università Campus Bio-Medico, 00128 Roma, Italy;
| | - Alfredo Zito
- Unità Operativa Complessa di Anatomia Patologica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy;
| | - Vito Lorusso
- Unità Operativa Complessa di Oncologia Medica, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (F.G.); (A.L.); (V.L.)
| | - Raffaella Massafra
- Struttura Semplice Dipartimentale di Fisica Sanitaria, I.R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Viale Orazio Flacco 65, 70124 Bari, Italy; (M.C.C.); (V.D.); (P.T.); (R.M.)
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23
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Veith I, Mencattini A, Picant V, Serra M, Leclerc M, Comes MC, Mami-Chouaib F, Camonis J, Descroix S, Shirvani H, Mechta-Grigoriou F, Zalcman G, Parrini MC, Martinelli E. Apoptosis mapping in space and time of 3D tumor ecosystems reveals transmissibility of cytotoxic cancer death. PLoS Comput Biol 2021; 17:e1008870. [PMID: 33784299 PMCID: PMC8034728 DOI: 10.1371/journal.pcbi.1008870] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 04/09/2021] [Accepted: 03/12/2021] [Indexed: 01/26/2023] Open
Abstract
The emerging tumor-on-chip (ToC) approaches allow to address biomedical questions out of reach with classical cell culture techniques: in biomimetic 3D hydrogels they partially reconstitute ex vivo the complexity of the tumor microenvironment and the cellular dynamics involving multiple cell types (cancer cells, immune cells, fibroblasts, etc.). However, a clear bottleneck is the extraction and interpretation of the rich biological information contained, sometime hidden, in the cell co-culture videos. In this work, we develop and apply novel video analysis algorithms to automatically measure the cytotoxic effects on human cancer cells (lung and breast) induced either by doxorubicin chemotherapy drug or by autologous tumor-infiltrating cytotoxic T lymphocytes (CTL). A live fluorescent dye (red) is used to selectively pre-stain the cancer cells before co-cultures and a live fluorescent reporter for caspase activity (green) is used to monitor apoptotic cell death. The here described open-source computational method, named STAMP (spatiotemporal apoptosis mapper), extracts the temporal kinetics and the spatial maps of cancer death, by localizing and tracking cancer cells in the red channel, and by counting the red to green transition signals, over 2-3 days. The robustness and versatility of the method is demonstrated by its application to different cell models and co-culture combinations. Noteworthy, this approach reveals the strong contribution of primary cancer-associated fibroblasts (CAFs) to breast cancer chemo-resistance, proving to be a powerful strategy to investigate intercellular cross-talks and drug resistance mechanisms. Moreover, we defined a new parameter, the 'potential of death induction', which is computed in time and in space to quantify the impact of dying cells on neighbor cells. We found that, contrary to natural death, cancer death induced by chemotherapy or by CTL is transmissible, in that it promotes the death of nearby cancer cells, suggesting the release of diffusible factors which amplify the initial cytotoxic stimulus.
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Affiliation(s)
- Irina Veith
- Institut Roche, 4 cours de l’Ile Seguin, Boulogne-Billancourt, France
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, Paris, France
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Valentin Picant
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, Paris, France
| | - Marco Serra
- Institut Curie, CNRS UMR168, Laboratoire Physico Chimie Curie, Institut Pierre-Gilles de Gennes, PSL Research University, Paris, France
| | - Marine Leclerc
- INSERM UMR 1186, Integrative Tumor Immunology and Immunotherapy, Gustave Roussy, Fac. de Médecine—Univ. Paris-Sud, Université Paris-Saclay, Villejuif, France
| | - Maria Colomba Comes
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
| | - Fathia Mami-Chouaib
- INSERM UMR 1186, Integrative Tumor Immunology and Immunotherapy, Gustave Roussy, Fac. de Médecine—Univ. Paris-Sud, Université Paris-Saclay, Villejuif, France
| | - Jacques Camonis
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, Paris, France
| | - Stéphanie Descroix
- Institut Curie, CNRS UMR168, Laboratoire Physico Chimie Curie, Institut Pierre-Gilles de Gennes, PSL Research University, Paris, France
| | - Hamasseh Shirvani
- Institut Roche, 4 cours de l’Ile Seguin, Boulogne-Billancourt, France
| | - Fatima Mechta-Grigoriou
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, Paris, France
| | - Gérard Zalcman
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, Paris, France
- CIC INSERM 1425, Thoracic Oncology Department, University Hospital Bichat-Claude Bernard, Université de Paris, Paris, France
| | - Maria Carla Parrini
- Institut Curie, INSERM U830, Stress and Cancer Laboratory, PSL Research University, Paris, France
- * E-mail: (EM); (MCP)
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy
- * E-mail: (EM); (MCP)
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24
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Mattei F, Andreone S, Mencattini A, De Ninno A, Businaro L, Martinelli E, Schiavoni G. Oncoimmunology Meets Organs-on-Chip. Front Mol Biosci 2021; 8:627454. [PMID: 33842539 PMCID: PMC8032996 DOI: 10.3389/fmolb.2021.627454] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/04/2021] [Indexed: 01/04/2023] Open
Abstract
Oncoimmunology represents a biomedical research discipline coined to study the roles of immune system in cancer progression with the aim of discovering novel strategies to arm it against the malignancy. Infiltration of immune cells within the tumor microenvironment is an early event that results in the establishment of a dynamic cross-talk. Here, immune cells sense antigenic cues to mount a specific anti-tumor response while cancer cells emanate inhibitory signals to dampen it. Animals models have led to giant steps in this research context, and several tools to investigate the effect of immune infiltration in the tumor microenvironment are currently available. However, the use of animals represents a challenge due to ethical issues and long duration of experiments. Organs-on-chip are innovative tools not only to study how cells derived from different organs interact with each other, but also to investigate on the crosstalk between immune cells and different types of cancer cells. In this review, we describe the state-of-the-art of microfluidics and the impact of OOC in the field of oncoimmunology underlining the importance of this system in the advancements on the complexity of tumor microenvironment.
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Affiliation(s)
- Fabrizio Mattei
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | - Sara Andreone
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
| | - Arianna Mencattini
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.,Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, Rome, Italy
| | - Adele De Ninno
- Institute for Photonics and Nanotechnologies, Italian National Research Council, Rome, Italy
| | - Luca Businaro
- Institute for Photonics and Nanotechnologies, Italian National Research Council, Rome, Italy
| | - Eugenio Martinelli
- Department of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.,Interdisciplinary Center for Advanced Studies on Lab-on-Chip and Organ-on-Chip Applications (ICLOC), University of Rome Tor Vergata, Rome, Italy
| | - Giovanna Schiavoni
- Department of Oncology and Molecular Medicine, Istituto Superiore di Sanità, Rome, Italy
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25
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Artificial intelligence applications in medical imaging: A review of the medical physics research in Italy. Phys Med 2021; 83:221-241. [DOI: 10.1016/j.ejmp.2021.04.010] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 03/31/2021] [Accepted: 04/03/2021] [Indexed: 02/06/2023] Open
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26
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Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020; 25:E5277. [PMID: 33198233 PMCID: PMC7696134 DOI: 10.3390/molecules25225277] [Citation(s) in RCA: 118] [Impact Index Per Article: 29.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 12/30/2022] Open
Abstract
The advancements of information technology and related processing techniques have created a fertile base for progress in many scientific fields and industries. In the fields of drug discovery and development, machine learning techniques have been used for the development of novel drug candidates. The methods for designing drug targets and novel drug discovery now routinely combine machine learning and deep learning algorithms to enhance the efficiency, efficacy, and quality of developed outputs. The generation and incorporation of big data, through technologies such as high-throughput screening and high through-put computational analysis of databases used for both lead and target discovery, has increased the reliability of the machine learning and deep learning incorporated techniques. The use of these virtual screening and encompassing online information has also been highlighted in developing lead synthesis pathways. In this review, machine learning and deep learning algorithms utilized in drug discovery and associated techniques will be discussed. The applications that produce promising results and methods will be reviewed.
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Affiliation(s)
- Lauv Patel
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Tripti Shukla
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
| | - Xiuzhen Huang
- Department of Computer Science, Arkansas State University, Jonesboro, AR 72467, USA;
| | - David W. Ussery
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA;
| | - Shanzhi Wang
- Chemistry Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA; (L.P.); (T.S.)
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