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Tang Q, Ratnayake R, Seabra G, Jiang Z, Fang R, Cui L, Ding Y, Kahveci T, Bian J, Li C, Luesch H, Li Y. Morphological profiling for drug discovery in the era of deep learning. Brief Bioinform 2024; 25:bbae284. [PMID: 38886164 PMCID: PMC11182685 DOI: 10.1093/bib/bbae284] [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: 03/09/2024] [Revised: 05/13/2024] [Accepted: 06/03/2024] [Indexed: 06/20/2024] Open
Abstract
Morphological profiling is a valuable tool in phenotypic drug discovery. The advent of high-throughput automated imaging has enabled the capturing of a wide range of morphological features of cells or organisms in response to perturbations at the single-cell resolution. Concurrently, significant advances in machine learning and deep learning, especially in computer vision, have led to substantial improvements in analyzing large-scale high-content images at high throughput. These efforts have facilitated understanding of compound mechanism of action, drug repurposing, characterization of cell morphodynamics under perturbation, and ultimately contributing to the development of novel therapeutics. In this review, we provide a comprehensive overview of the recent advances in the field of morphological profiling. We summarize the image profiling analysis workflow, survey a broad spectrum of analysis strategies encompassing feature engineering- and deep learning-based approaches, and introduce publicly available benchmark datasets. We place a particular emphasis on the application of deep learning in this pipeline, covering cell segmentation, image representation learning, and multimodal learning. Additionally, we illuminate the application of morphological profiling in phenotypic drug discovery and highlight potential challenges and opportunities in this field.
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Affiliation(s)
- Qiaosi Tang
- Calico Life Sciences, South San Francisco, CA 94080, United States
| | - Ranjala Ratnayake
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Gustavo Seabra
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Zhe Jiang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Ruogu Fang
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Lina Cui
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yousong Ding
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Tamer Kahveci
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL 32611, United States
| | - Chenglong Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Hendrik Luesch
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
| | - Yanjun Li
- Department of Medicinal Chemistry, Center for Natural Products, Drug Discovery and Development, University of Florida, Gainesville, FL 32610, United States
- Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, United States
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2
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Ko J, Hyung S, Cheong S, Chung Y, Li Jeon N. Revealing the clinical potential of high-resolution organoids. Adv Drug Deliv Rev 2024; 207:115202. [PMID: 38336091 DOI: 10.1016/j.addr.2024.115202] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 01/01/2024] [Accepted: 02/02/2024] [Indexed: 02/12/2024]
Abstract
The symbiotic interplay of organoid technology and advanced imaging strategies yields innovative breakthroughs in research and clinical applications. Organoids, intricate three-dimensional cell cultures derived from pluripotent or adult stem/progenitor cells, have emerged as potent tools for in vitro modeling, reflecting in vivo organs and advancing our grasp of tissue physiology and disease. Concurrently, advanced imaging technologies such as confocal, light-sheet, and two-photon microscopy ignite fresh explorations, uncovering rich organoid information. Combined with advanced imaging technologies and the power of artificial intelligence, organoids provide new insights that bridge experimental models and real-world clinical scenarios. This review explores exemplary research that embodies this technological synergy and how organoids reshape personalized medicine and therapeutics.
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Affiliation(s)
- Jihoon Ko
- Department of BioNano Technology, Gachon University, Gyeonggi 13120, Republic of Korea
| | - Sujin Hyung
- Precision Medicine Research Institute, Samsung Medical Center, Seoul 08826, Republic of Korea; Division of Hematology-Oncology, Department of Medicine, Sungkyunkwan University, Samsung Medical Center, Seoul 08826, Republic of Korea
| | - Sunghun Cheong
- Interdisciplinary Program in Bioengineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea
| | - Yoojin Chung
- Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea
| | - Noo Li Jeon
- Interdisciplinary Program in Bioengineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Department of Mechanical Engineering, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Institute of Advanced Machines and Design, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea; Qureator, Inc., San Diego, CA, USA.
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3
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Maramraju S, Kowalczewski A, Kaza A, Liu X, Singaraju JP, Albert MV, Ma Z, Yang H. AI-organoid integrated systems for biomedical studies and applications. Bioeng Transl Med 2024; 9:e10641. [PMID: 38435826 PMCID: PMC10905559 DOI: 10.1002/btm2.10641] [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] [Received: 06/29/2023] [Revised: 12/07/2023] [Accepted: 12/13/2023] [Indexed: 03/05/2024] Open
Abstract
In this review, we explore the growing role of artificial intelligence (AI) in advancing the biomedical applications of human pluripotent stem cell (hPSC)-derived organoids. Stem cell-derived organoids, these miniature organ replicas, have become essential tools for disease modeling, drug discovery, and regenerative medicine. However, analyzing the vast and intricate datasets generated from these organoids can be inefficient and error-prone. AI techniques offer a promising solution to efficiently extract insights and make predictions from diverse data types generated from microscopy images, transcriptomics, metabolomics, and proteomics. This review offers a brief overview of organoid characterization and fundamental concepts in AI while focusing on a comprehensive exploration of AI applications in organoid-based disease modeling and drug evaluation. It provides insights into the future possibilities of AI in enhancing the quality control of organoid fabrication, label-free organoid recognition, and three-dimensional image reconstruction of complex organoid structures. This review presents the challenges and potential solutions in AI-organoid integration, focusing on the establishment of reliable AI model decision-making processes and the standardization of organoid research.
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Affiliation(s)
- Sudhiksha Maramraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Andrew Kowalczewski
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Anirudh Kaza
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Xiyuan Liu
- Department of Mechanical & Aerospace EngineeringSyracuse UniversitySyracuseNew YorkUSA
| | - Jathin Pranav Singaraju
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Texas Academy of Mathematics and ScienceUniversity of North TexasDentonTexasUSA
| | - Mark V. Albert
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
- Department of Computer Science and EngineeringUniversity of North TexasDentonTexasUSA
| | - Zhen Ma
- Department of Biomedical & Chemical EngineeringSyracuse UniversitySyracuseNew YorkUSA
- BioInspired Institute for Material and Living SystemsSyracuse UniversitySyracuseNew YorkUSA
| | - Huaxiao Yang
- Department of Biomedical EngineeringUniversity of North TexasDentonTexasUSA
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4
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Bai L, Wu Y, Li G, Zhang W, Zhang H, Su J. AI-enabled organoids: Construction, analysis, and application. Bioact Mater 2024; 31:525-548. [PMID: 37746662 PMCID: PMC10511344 DOI: 10.1016/j.bioactmat.2023.09.005] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 09/09/2023] [Accepted: 09/09/2023] [Indexed: 09/26/2023] Open
Abstract
Organoids, miniature and simplified in vitro model systems that mimic the structure and function of organs, have attracted considerable interest due to their promising applications in disease modeling, drug screening, personalized medicine, and tissue engineering. Despite the substantial success in cultivating physiologically relevant organoids, challenges remain concerning the complexities of their assembly and the difficulties associated with data analysis. The advent of AI-Enabled Organoids, which interfaces with artificial intelligence (AI), holds the potential to revolutionize the field by offering novel insights and methodologies that can expedite the development and clinical application of organoids. This review succinctly delineates the fundamental concepts and mechanisms underlying AI-Enabled Organoids, summarizing the prospective applications on rapid screening of construction strategies, cost-effective extraction of multiscale image features, streamlined analysis of multi-omics data, and precise preclinical evaluation and application. We also explore the challenges and limitations of interfacing organoids with AI, and discuss the future direction of the field. Taken together, the AI-Enabled Organoids hold significant promise for advancing our understanding of organ development and disease progression, ultimately laying the groundwork for clinical application.
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Affiliation(s)
- Long Bai
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Wenzhou Institute of Shanghai University, Wenzhou, 325000, China
| | - Yan Wu
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Guangfeng Li
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
- Department of Orthopedics, Shanghai Zhongye Hospital, Shanghai, 201941, China
| | - Wencai Zhang
- Department of Orthopedics, First Affiliated Hospital, Jinan University, Guangzhou, 510632, China
| | - Hao Zhang
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
| | - Jiacan Su
- Department of Orthopedics, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China
- Organoid Research Center, Institute of Translational Medicine, Shanghai University, Shanghai, 200444, China
- National Center for Translational Medicine (Shanghai) SHU Branch, Shanghai University, Shanghai, 200444, China
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5
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Sockell A, Wong W, Longwell S, Vu T, Karlsson K, Mokhtari D, Schaepe J, Lo YH, Cornelius V, Kuo C, Van Valen D, Curtis C, Fordyce PM. A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids. Cell Syst 2023; 14:764-776.e6. [PMID: 37734323 DOI: 10.1016/j.cels.2023.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 02/24/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023]
Abstract
Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Alexandra Sockell
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Wing Wong
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Scott Longwell
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Thy Vu
- Department of Biochemistry, UT Austin, Austin, TX 78712, USA
| | - Kasper Karlsson
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Daniel Mokhtari
- Department of Biochemistry, Stanford University, Stanford, CA 94305, USA
| | - Julia Schaepe
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Yuan-Hung Lo
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - Vincent Cornelius
- Department of Bioengineering, Stanford University, Stanford, CA 94305, USA
| | - Calvin Kuo
- Department of Medicine, Stanford University, Stanford, CA 94305, USA
| | - David Van Valen
- Division of Biology and Bioengineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Christina Curtis
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Medicine, Stanford University, Stanford, CA 94305, USA; Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA; Stanford Cancer Institute, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA.
| | - Polly M Fordyce
- Department of Genetics, Stanford University, Stanford, CA 94305, USA; Department of Bioengineering, Stanford University, Stanford, CA 94305, USA; Chan Zuckerberg Biohub, San Francisco, CA 94110, USA; ChEM-H Institute, Stanford University, Stanford, CA 94305, USA.
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6
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Debruyne AC, Okkelman IA, Dmitriev RI. Balance between the cell viability and death in 3D. Semin Cell Dev Biol 2023; 144:55-66. [PMID: 36117019 DOI: 10.1016/j.semcdb.2022.09.005] [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: 04/19/2022] [Revised: 09/08/2022] [Accepted: 09/08/2022] [Indexed: 11/25/2022]
Abstract
Cell death is a phenomenon, frequently perceived as an absolute event for cell, tissue and the organ. However, the rising popularity and complexity of such 3D multicellular 'tissue building blocks' as heterocellular spheroids, organoids, and 'assembloids' prompts to revise the definition and quantification of cell viability and death. It raises several questions on the overall viability of all the cells within 3D volume and on choosing the appropriate, continuous, and non-destructive viability assay enabling for a single-cell analysis. In this review, we look at cell viability and cell death modalities with attention to the intrinsic features of such 3D models as spheroids, organoids, and bioprints. Furthermore, we look at emerging and promising methodologies, which can help define and understand the balance between cell viability and death in dynamic and complex 3D environments. We conclude that the recent innovations in biofabrication, biosensor probe development, and fluorescence microscopy can help answer these questions.
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Affiliation(s)
- Angela C Debruyne
- Tissue Engineering and Biomaterials Group, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Irina A Okkelman
- Tissue Engineering and Biomaterials Group, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium
| | - Ruslan I Dmitriev
- Tissue Engineering and Biomaterials Group, Department of Human Structure and Repair, Faculty of Medicine and Health Sciences, Ghent University, Ghent 9000, Belgium.
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7
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Thalheim T, Aust G, Galle J. Organoid Cultures In Silico: Tools or Toys? BIOENGINEERING (BASEL, SWITZERLAND) 2022; 10:bioengineering10010050. [PMID: 36671623 PMCID: PMC9854934 DOI: 10.3390/bioengineering10010050] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 12/28/2022] [Accepted: 12/29/2022] [Indexed: 01/03/2023]
Abstract
The implementation of stem-cell-based organoid culture more than ten years ago started a development that created new avenues for diagnostic analyses and regenerative medicine. In parallel, computational modelling groups realized the potential of this culture system to support their theoretical approaches to study tissues in silico. These groups developed computational organoid models (COMs) that enabled testing consistency between cell biological data and developing theories of tissue self-organization. The models supported a mechanistic understanding of organoid growth and maturation and helped linking cell mechanics and tissue shape in general. What comes next? Can we use COMs as tools to complement the equipment of our biological and medical research? While these models already support experimental design, can they also quantitatively predict tissue behavior? Here, we review the current state of the art of COMs and discuss perspectives for their application.
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Affiliation(s)
- Torsten Thalheim
- Interdisciplinary Institute for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
- Correspondence:
| | - Gabriela Aust
- Department of Surgery, Research Laboratories, Leipzig University, Liebigstraße 20, 04103 Leipzig, Germany
| | - Joerg Galle
- Interdisciplinary Institute for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16–18, 04107 Leipzig, Germany
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Zhao Z, Chen X, Dowbaj AM, Sljukic A, Bratlie K, Lin L, Fong ELS, Balachander GM, Chen Z, Soragni A, Huch M, Zeng YA, Wang Q, Yu H. Organoids. NATURE REVIEWS. METHODS PRIMERS 2022; 2:94. [PMID: 37325195 PMCID: PMC10270325 DOI: 10.1038/s43586-022-00174-y] [Citation(s) in RCA: 189] [Impact Index Per Article: 94.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/28/2022] [Indexed: 06/17/2023]
Abstract
Organoids have attracted increasing attention because they are simple tissue-engineered cell-based in vitro models that recapitulate many aspects of the complex structure and function of the corresponding in vivo tissue. They can be dissected and interrogated for fundamental mechanistic studies on development, regeneration, and repair in human tissues. Organoids can also be used in diagnostics, disease modeling, drug discovery, and personalized medicine. Organoids are derived from either pluripotent or tissue-resident stem (embryonic or adult) or progenitor or differentiated cells from healthy or diseased tissues, such as tumors. To date, numerous organoid engineering strategies that support organoid culture and growth, proliferation, differentiation and maturation have been reported. This Primer serves to highlight the rationale underlying the selection and development of these materials and methods to control the cellular/tissue niche; and therefore, structure and function of the engineered organoid. We also discuss key considerations for generating robust organoids, such as those related to cell isolation and seeding, matrix and soluble factor selection, physical cues and integration. The general standards for data quality, reproducibility and deposition within the organoid community is also outlined. Lastly, we conclude by elaborating on the limitations of organoids in different applications, and key priorities in organoid engineering for the coming years.
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Affiliation(s)
- Zixuan Zhao
- Mechanobiology Institute, National University of Singapore, Singapore
| | - Xinyi Chen
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Anna M. Dowbaj
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Aleksandra Sljukic
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Kaitlin Bratlie
- Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA
| | - Luda Lin
- Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, California, USA
- Molecular Biology Institute, University of California Los Angeles, California, USA
| | - Eliza Li Shan Fong
- Translational Tumor Engineering Laboratory, Department of Biomedical Engineering, National University of Singapore, Singapore
- The N.1 Institute for Health, National University of Singapore, Singapore
| | - Gowri Manohari Balachander
- Department of Physiology, Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, Singapore
| | - Zhaowei Chen
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Alice Soragni
- Department of Orthopaedic Surgery, David Geffen School of Medicine, University of California Los Angeles, California, USA
- Molecular Biology Institute, University of California Los Angeles, California, USA
- Jonsson Comprehensive Cancer Center, University of California Los Angeles, California, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, California, USA
- California NanoSystems Institute, University of California Los Angeles, California, USA
| | - Meritxell Huch
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
| | - Yi Arial Zeng
- State Key Laboratory of Cell Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
- School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, China
| | - Qun Wang
- Department of Chemical and Biological Engineering, Iowa State University, Ames, Iowa, USA
| | - Hanry Yu
- Mechanobiology Institute, National University of Singapore, Singapore
- Department of Physiology, Institute for Digital Medicine (WisDM), Yong Loo Lin School of Medicine, Singapore
- Institute of Bioengineering and Bioimaging, A*STAR, Singapore
- CAMP, Singapore-MIT Alliance for Research and Technology, Singapore
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Berker Y, ElHarouni D, Peterziel H, Fiesel P, Witt O, Oehme I, Schlesner M, Oppermann S. Patient-by-Patient Deep Transfer Learning for Drug-Response Profiling Using Confocal Fluorescence Microscopy of Pediatric Patient-Derived Tumor-Cell Spheroids. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3981-3999. [PMID: 36099221 DOI: 10.1109/tmi.2022.3205554] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Image-based phenotypic drug profiling is receiving increasing attention in drug discovery and precision medicine. Compared to classical end-point measurements quantifying drug response, image-based profiling enables both the quantification of drug response and characterization of disease entities and drug-induced cell-death phenotypes. Here, we aim to quantify image-based drug responses in patient-derived 3D spheroid tumor cell cultures, tackling the challenges of a lack of single-cell-segmentation methods and limited patient-derived material. Therefore, we investigate deep transfer learning with patient-by-patient fine-tuning for cell-viability quantification. We fine-tune a convolutional neural network (pre-trained on ImageNet) with 210 control images specific to a single training cell line and 54 additional screen -specific assay control images. This method of image-based drug profiling is validated on 6 cell lines with known drug sensitivities, and further tested with primary patient-derived samples in a medium-throughput setting. Network outputs at different drug concentrations are used for drug-sensitivity scoring, and dense-layer activations are used in t-distributed stochastic neighbor embeddings of drugs to visualize groups of drugs with similar cell-death phenotypes. Image-based cell-line experiments show strong correlation to metabolic results ( R ≈ 0.7 ) and confirm expected hits, indicating the predictive power of deep learning to identify drug-hit candidates for individual patients. In patient-derived samples, combining drug sensitivity scoring with phenotypic analysis may provide opportunities for complementary combination treatments. Deep transfer learning with patient-by-patient fine-tuning is a promising, segmentation-free image-analysis approach for precision medicine and drug discovery.
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Munck S, Cawthorne C, Escamilla‐Ayala A, Kerstens A, Gabarre S, Wesencraft K, Battistella E, Craig R, Reynaud EG, Swoger J, McConnell G. Challenges and advances in optical 3D mesoscale imaging. J Microsc 2022; 286:201-219. [PMID: 35460574 PMCID: PMC9325079 DOI: 10.1111/jmi.13109] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 03/02/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022]
Abstract
Optical mesoscale imaging is a rapidly developing field that allows the visualisation of larger samples than is possible with standard light microscopy, and fills a gap between cell and organism resolution. It spans from advanced fluorescence imaging of micrometric cell clusters to centimetre-size complete organisms. However, with larger volume specimens, new problems arise. Imaging deeper into tissues at high resolution poses challenges ranging from optical distortions to shadowing from opaque structures. This manuscript discusses the latest developments in mesoscale imaging and highlights limitations, namely labelling, clearing, absorption, scattering, and also sample handling. We then focus on approaches that seek to turn mesoscale imaging into a more quantitative technique, analogous to quantitative tomography in medical imaging, highlighting a future role for digital and physical phantoms as well as artificial intelligence.
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Affiliation(s)
- Sebastian Munck
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | | | - Abril Escamilla‐Ayala
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | - Axelle Kerstens
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | - Sergio Gabarre
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | | | | | - Rebecca Craig
- Department of Physics, SUPAUniversity of StrathclydeGlasgowUK
| | - Emmanuel G. Reynaud
- School of Biomolecular and Biomedical ScienceUniversity College DublinDublinBelfieldIreland
| | - Jim Swoger
- European Molecular Biology Laboratory (EMBL) BarcelonaBarcelonaSpain
| | - Gail McConnell
- Department of Physics, SUPAUniversity of StrathclydeGlasgowUK
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11
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Wang Y, Jeon H. 3D cell cultures toward quantitative high-throughput drug screening. Trends Pharmacol Sci 2022; 43:569-581. [DOI: 10.1016/j.tips.2022.03.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 01/16/2023]
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Newmaster KT, Kronman FA, Wu YT, Kim Y. Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain. Front Neuroanat 2022; 15:787601. [PMID: 35095432 PMCID: PMC8794814 DOI: 10.3389/fnana.2021.787601] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.
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Affiliation(s)
- Kyra T Newmaster
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Fae A Kronman
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yuan-Ting Wu
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
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Jacques M, Dobrzyński M, Gagliardi PA, Sznitman R, Pertz O. CODEX, a neural network approach to explore signaling dynamics landscapes. Mol Syst Biol 2021; 17:e10026. [PMID: 33835701 PMCID: PMC8034356 DOI: 10.15252/msb.202010026] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/01/2021] [Accepted: 03/03/2021] [Indexed: 12/19/2022] Open
Abstract
Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human-interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data-driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs' predictions. CODEX provides several views of the data: visualization of all the single-cell trajectories in a low-dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ-SMAD2 signaling.
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Affiliation(s)
| | | | | | - Raphael Sznitman
- ARTORG Center for Biomedical Engineering ResearchUniversity of BernBernSwitzerland
| | - Olivier Pertz
- Institute of Cell BiologyUniversity of BernBernSwitzerland
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